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This manual is for GNU PSPP version 1.6.2-geeb758, software for statistical analysis.
Copyright © 1997, 1998, 2004, 2005, 2009, 2012, 2013, 2014, 2016, 2019, 2020 Free Software Foundation, Inc.
Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license is included in the section entitled "GNU Free Documentation License".
pspp
psppire
COMPUTE
CommandCALL
CommandPRINT
CommandDO IF
CommandLOOP
and BREAK
Commands
READ
and WRITE
Commands
GET
CommandSAVE
CommandMGET
CommandMSAVE
CommandDISPLAY
CommandRELEASE
Commandpspp-convert
pspp-output
pspp-dump-sav
Next: Your rights and obligations, Previous: GNU PSPP, Up: GNU PSPP [Contents][Index]
PSPP is a tool for statistical analysis of sampled data. It reads the data, analyzes the data according to commands provided, and writes the results to a listing file, to the standard output or to a window of the graphical display.
The language accepted by PSPP is similar to those accepted by SPSS statistical products. The details of PSPP’s language are given later in this manual.
PSPP produces tables and charts as output, which it can produce in several formats; currently, ASCII, PostScript, PDF, HTML, DocBook and TeX are supported.
The current version of PSPP, 1.6.2-geeb758, is incomplete in terms of its statistical procedure support. PSPP is a work in progress. The authors hope to fully support all features in the products that PSPP replaces, eventually. The authors welcome questions, comments, donations, and code submissions. See Submitting Bug Reports, for instructions on contacting the authors.
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PSPP is not in the public domain. It is copyrighted and there are restrictions on its distribution, but these restrictions are designed to permit everything that a good cooperating citizen would want to do. What is not allowed is to try to prevent others from further sharing any version of this program that they might get from you.
Specifically, we want to make sure that you have the right to give away copies of PSPP, that you receive source code or else can get it if you want it, that you can change these programs or use pieces of them in new free programs, and that you know you can do these things.
To make sure that everyone has such rights, we have to forbid you to deprive anyone else of these rights. For example, if you distribute copies of PSPP, you must give the recipients all the rights that you have. You must make sure that they, too, receive or can get the source code. And you must tell them their rights.
Also, for our own protection, we must make certain that everyone finds out that there is no warranty for PSPP. If these programs are modified by someone else and passed on, we want their recipients to know that what they have is not what we distributed, so that any problems introduced by others will not reflect on our reputation.
Finally, any free program is threatened constantly by software patents. We wish to avoid the danger that redistributors of a free program will individually obtain patent licenses, in effect making the program proprietary. To prevent this, we have made it clear that any patent must be licensed for everyone’s free use or not licensed at all.
The precise conditions of the license for PSPP are found in the GNU General Public License. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. This manual specifically is covered by the GNU Free Documentation License (see GNU Free Documentation License).
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pspp
PSPP has two separate user interfaces. This chapter describes
pspp
, PSPP’s command-line driven text-based user interface.
The following chapter briefly describes PSPPIRE, the graphical user
interface to PSPP.
The sections below describe the pspp
program’s command-line
interface.
Here is a summary of all the options, grouped by type, followed by explanations in the same order.
In the table, arguments to long options also apply to any corresponding short options.
syntax-file
-o, --output=output-file -O option=value -O format=format -O device={terminal|listing} --no-output --table-look=file -e, --error-file=error-file
-I, --include=dir -I-, --no-include -b, --batch -i, --interactive -r, --no-statrc -a, --algorithm={compatible|enhanced} -x, --syntax={compatible|enhanced} --syntax-encoding=encoding
-h, --help -V, --version
-s, --safer --testing-mode
Read and execute the named syntax file. If no syntax files are specified, PSPP prompts for commands. If any syntax files are specified, PSPP by default exits after it runs them, but you may make it prompt for commands by specifying ‘-’ as an additional syntax file.
Write output to output-file. PSPP has several different output drivers that support output in various formats (use --help to list the available formats). Specify this option more than once to produce multiple output files, presumably in different formats.
Use ‘-’ as output-file to write output to standard output.
If no -o option is used, then PSPP writes text and CSV output to standard output and other kinds of output to whose name is based on the format, e.g. pspp.pdf for PDF output.
Sets an option for the output file configured by a preceding -o. Most options are specific to particular output formats. A few options that apply generically are listed below.
PSPP uses the extension of the file name given on -o to select an output format. Use this option to override this choice by specifying an alternate format, e.g. -o pspp.out -O format=html to write HTML to a file named pspp.out. Use --help to list the available formats.
Sets whether PSPP considers the output device configured by the preceding -o to be a terminal or a listing device. This affects what output will be sent to the device, as configured by the SET command’s output routing subcommands (see SET). By default, output written to standard output is considered a terminal device and other output is considered a listing device.
Disables output entirely, if neither -o nor -O is also used. If one of those options is used, --no-output has no effect.
Reads a table style from file and applies it to all PSPP table output. The file should be a TableLook .stt or .tlo file. PSPP searches for file in the current directory, then in .pspp/looks in the user’s home directory, then in a looks subdirectory inside PSPP’s data directory (usually /usr/local/share/pspp). If PSPP cannot find file under the given name, it also tries adding a .stt extension.
When this option is not specified, PSPP looks for default.stt using the algorithm above, and otherwise it falls back to a default built-in style.
Using SET TLOOK
in PSPP syntax overrides the style set on
the command line (see SET).
Configures a file to receive PSPP error, warning, and note messages in plain text format. Use ‘-’ as error-file to write messages to standard output. The default error file is standard output in the absence of these options, but this is suppressed if an output device writes to standard output (or another terminal), to avoid printing every message twice. Use ‘none’ as error-file to explicitly suppress the default.
Appends dir to the set of directories searched by the INCLUDE
(see INCLUDE) and INSERT
(see INSERT) commands.
Clears all directories from the include path, including directories inserted in the include path by default. The default include path is . (the current directory), followed by .pspp in the user’s home directory, followed by PSPP’s system configuration directory (usually /etc/pspp or /usr/local/etc/pspp).
These options forces syntax files to be interpreted in batch mode or interactive mode, respectively, rather than the default “auto” mode. See Syntax Variants, for a description of the differences.
By default, at startup PSPP searches for a file named rc in the include path (described above) and, if it finds one, runs the commands in it. This option disables this behavior.
With enhanced
, the default, PSPP uses the best implemented
algorithms for statistical procedures. With compatible
,
however, PSPP will in some cases use inferior algorithms to produce
the same results as the proprietary program SPSS.
Some commands have subcommands that override this setting on a per command basis.
With enhanced
, the default, PSPP accepts its own extensions
beyond those compatible with the proprietary program SPSS. With
compatible
, PSPP rejects syntax that uses these extensions.
Specifies encoding as the encoding for syntax files named on the
command line. The encoding also becomes the default encoding
for other syntax files read during the PSPP session by the
INCLUDE
and INSERT
commands. See INSERT, for the
accepted forms of encoding.
Prints a message describing PSPP command-line syntax and the available device formats, then exits.
Prints a brief message listing PSPP’s version, warranties you don’t have, copying conditions and copyright, and e-mail address for bug reports, then exits.
Disables certain unsafe operations. This includes the ERASE
and
HOST
commands, as well as use of pipes as input and output files.
Invoke heuristics to assist with testing PSPP. For use
by make check
and similar scripts.
Next: Plain Text Output Options, Previous: Main Options, Up: Invoking pspp
[Contents][Index]
To produce output in PDF, PostScript, SVG, or PNG format, specify -o file on the PSPP command line, optionally followed by any of the options shown in the table below to customize the output format.
PDF, PostScript, and SVG use real units: each dimension among the options listed below may have a suffix ‘mm’ for millimeters, ‘in’ for inches, or ‘pt’ for points. Lacking a suffix, numbers below 50 are assumed to be in inches and those above 50 are assumed to be in millimeters.
PNG files are pixel-based, so dimensions in PNG output must ultimately be measured in pixels. For output to these files, PSPP translates the specified dimensions to pixels at 72 pixels per inch. For PNG output only, fonts are by default rendered larger than this, at 96 pixels per inch.
An SVG or PNG file can only hold a single page. When PSPP outputs
more than one page to SVG or PNG, it creates multiple files. It
outputs the second page to a file named with a -2
suffix, the
third with a -3
suffix, and so on.
Specify the output format. This is only necessary if the file name given on -o does not end in .pdf, .ps, .svg, or .png.
Paper size, as a name (e.g. a4
, letter
) or
measurements (e.g. 210x297
, 8.5x11in
).
The default paper size is taken from the PAPERSIZE
environment
variable or the file indicated by the PAPERCONF
environment
variable, if either variable is set. If not, and your system supports
the LC_PAPER
locale category, then the default paper size is
taken from the locale. Otherwise, if /etc/papersize exists,
the default paper size is read from it. As a last resort, A4 paper is
assumed.
Sets color as the default color for lines and text. Use a CSS
color format (e.g. #rrggbb
) or name (e.g.
black
) as color.
Either portrait
or landscape
. Default: portrait
.
Sets the margins around the page. See
below for the allowed forms of dimension. Default: 0.5in
.
Sets the amount of vertical space between objects (such as headings or tables).
Sets the default font used for ordinary text. Most systems support CSS-like font names such as “Sans Serif”, but a wide range of system-specific fonts are likely to be supported as well.
Default: proportional font Sans Serif
.
Sets the size of the default fonts, in thousandths of a point. Default: 10000 (10 point).
This option makes PSPP trim empty space around each page of output, before adding the margins. This can make the output easier to include in other documents.
For PDF output only, this option controls whether PSPP includes an outline in the output file. PDF viewers usually display the outline as a side bar that allows for easy navigation of the file. The default is true unless -O trim=true is also specified. (The Cairo graphics library that PSPP uses to produce PDF output has a bug that can cause a crash when outlines and trimming are used together.)
Sets the resolution for font rendering, in dots per inch. For PDF, PostScript, and SVG output, the default is 72 dpi, so that a 10-point font is rendered with a height of 10 points. For PNG output, the default is 96 dpi, so that a 10-point font is rendered with a height of 10 / 72 * 96 = 13.3 pixels. Use a larger dpi to enlarge text output, or a smaller dpi to shrink it.
Next: SPV Output Options, Previous: PDF, PostScript, SVG, and PNG Output Options, Up: Invoking pspp
[Contents][Index]
PSPP can produce plain text output, drawing boxes using ASCII or Unicode line drawing characters. To produce plain text output, specify -o file on the PSPP command line, optionally followed by options from the table below to customize the output format.
Plain text output is encoded in UTF-8.
Specify the output format. This is only necessary if the file name given on -o does not end in .txt or .list.
Name for chart files included in output. The value should be a file name that includes a single ‘#’ and ends in png. When a chart is output, the ‘#’ is replaced by the chart number. The default is the file name specified on -o with the extension stripped off and replaced by -#.png.
Specify none
to disable chart output.
Sets color as the color to be used for the background or foreground to
be used for charts.
Color should be given in the format #RRRRGGGGBBBB
,
where RRRR, GGGG and BBBB are 4 character hexadecimal
representations of the red, green and blue components respectively.
If charts are disabled, this option has no effect.
Width of a page, in columns. If unspecified or given as auto
,
the default is the width of the terminal, for interactive output, or
the WIDTH setting (see SET), for output to a file.
Sets the characters used for lines in tables.
If set to
ascii
, output uses use the characters ‘-’, ‘|’, and ‘+’ for single-width
lines and ‘=’ and ‘#’ for double-width lines.
If set to unicode
then, output uses Unicode box drawing characters.
The default is unicode
if the locale’s character encoding is "UTF-8"
or ascii
otherwise.
How to emphasize text. Bold and underline emphasis are achieved with
overstriking, which may not be supported by all the software to which
you might pass the output. Default: none
.
Next: TeX Output Options, Previous: Plain Text Output Options, Up: Invoking pspp
[Contents][Index]
SPSS 16 and later write .spv files to represent the contents of its output editor. To produce output in .spv format, specify -o file on the PSPP command line, optionally followed by any of the options shown in the table below to customize the output format.
Specify the output format. This is only necessary if the file name given on -o does not end in .spv.
These have the same syntax and meaning as for PDF output. See PDF, PostScript, SVG, and PNG Output Options, for details.
Next: HTML Output Options, Previous: SPV Output Options, Up: Invoking pspp
[Contents][Index]
If you want to publish statistical results in professional or academic journals, you will probably want to provide results in TeX format. To do this, specify -o file on the PSPP command line where file is a file name ending in .tex, or you can specify -O format=tex.
The resulting file can be directly processed using TeX or you can manually edit the file to add commentary text. Alternatively, you can cut and paste desired sections to another TeX file.
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[Contents][Index]
To produce output in HTML format, specify -o file on the PSPP command line, optionally followed by any of the options shown in the table below to customize the output format.
Specify the output format. This is only necessary if the file name given on -o does not end in .html.
Sets the name used for chart files. See Plain Text Output Options, for details.
Decorate the tables with borders. If set to false, the tables produced will have no borders. The default value is true.
The HTML output driver ordinarily outputs a complete HTML document.
If set to true, the driver instead outputs only what would normally be
the contents of the body
element. The default value is false.
Use cascading style sheets. Cascading style sheets give an improved appearance and can be used to produce pages which fit a certain web site’s style. The default value is true.
Next: Comma-Separated Value Output Options, Previous: HTML Output Options, Up: Invoking pspp
[Contents][Index]
To produce output as an OpenDocument text (ODT) document, specify -o file on the PSPP command line. If file does not end in .odt, you must also specify -O format=odt.
ODT support is only available if your installation of PSPP was compiled with the libxml2 library.
The OpenDocument output format does not have any configurable options.
Previous: OpenDocument Output Options, Up: Invoking pspp
[Contents][Index]
To produce output in comma-separated value (CSV) format, specify -o file on the PSPP command line, optionally followed by any of the options shown in the table below to customize the output format.
Specify the output format. This is only necessary if the file name given on -o does not end in .csv.
Sets the character used to separate fields. Default: a comma (‘,’).
Sets qualifier as the character used to quote fields that contain white space, the separator (or any of the characters in the separator, if it contains more than one character), or the quote character itself. If qualifier is longer than one character, only the first character is used; if qualifier is the empty string, then fields are never quoted.
Whether table titles (brief descriptions) should be printed. Default:
on
.
Whether table captions (more extensive descriptions) should be printed. Default: on.
The CSV format used is an extension to that specified in RFC 4180:
Each table row is output on a separate line, and each column is output as a field. The contents of a cell that spans multiple rows or columns is output only for the top-left row and column; the rest are output as empty fields.
When a table has a title and titles are enabled, the title is output just above the table as a single field prefixed by ‘Table:’.
When a table has a caption and captions are enabled, the caption is output just below the table as a single field prefixed by ‘Caption:’.
Within a table, footnote markers are output as bracketed letters following the cell’s contents, e.g. ‘[a]’, ‘[b]’, ... The footnotes themselves are output following the body of the table, as a separate two-column table introduced with a line that says ‘Footnotes:’. Each row in the table represent one footnote: the first column is the marker, the second column is the text.
Text in output is printed as a field on a line by itself. The TITLE and SUBTITLE produce similar output, prefixed by ‘Title:’ or ‘Subtitle:’, respectively.
Errors, warnings, and notes are printed the same way as text.
Charts are not included in CSV output.
Successive output items are separated by a blank line.
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, Up: GNU PSPP [Contents][Index]
psppire
The PSPPIRE graphic user interface for PSPP can perform all functionality of the command line interface. In addition it gives an instantaneous view of the data, variables and statistical output.
The graphic user interface can be started by typing psppire
at a
command prompt.
Alternatively many systems have a system of interactive menus or buttons
from which psppire
can be started by a series of mouse clicks.
Once the principles of the PSPP system are understood, the graphic user interface is designed to be largely intuitive, and for this reason is covered only very briefly by this manual.
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, Up: GNU PSPP [Contents][Index]
PSPP is a tool for the statistical analysis of sampled data. You can use it to discover patterns in the data, to explain differences in one subset of data in terms of another subset and to find out whether certain beliefs about the data are justified. This chapter does not attempt to introduce the theory behind the statistical analysis, but it shows how such analysis can be performed using PSPP.
For the purposes of this tutorial, it is assumed that you are using PSPP in its interactive mode from the command line. However, the example commands can also be typed into a file and executed in a post-hoc mode by typing ‘pspp file-name’ at a shell prompt, where file-name is the name of the file containing the commands. Alternatively, from the graphical interface, you can select File → New → Syntax to open a new syntax window and use the Run menu when a syntax fragment is ready to be executed. Whichever method you choose, the syntax is identical.
When using the interactive method, PSPP tells you that it’s waiting for your data with a string like PSPP> or data>. In the examples of this chapter, whenever you see text like this, it indicates the prompt displayed by PSPP, not something that you should type.
Throughout this chapter reference is made to a number of sample data files. So that you can try the examples for yourself, you should have received these files along with your copy of PSPP.1
Please note: Normally these files are installed in the directory /usr/local/share/pspp/examples. If however your system administrator or operating system vendor has chosen to install them in a different location, you will have to adjust the examples accordingly.
Next: Data Screening and Transformation, Up: Using PSPP [Contents][Index]
Before analysis can commence, the data must be loaded into PSPP and arranged such that both PSPP and humans can understand what the data represents. There are two aspects of data:
For example, a data set which has the variables height, weight, and name, might have the observations:
1881 89.2 Ahmed 1192 107.01 Frank 1230 67 Julie
The following sections explain how to define a dataset.
Next: Listing the data, Up: Preparation of Data Files [Contents][Index]
Variables come in two basic types, viz: numeric and string. Variables such as age, height and satisfaction are numeric, whereas name is a string variable. String variables are best reserved for commentary data to assist the human observer. However they can also be used for nominal or categorical data.
The following example defines two variables forename and height, and reads data into them by manual input:
PSPP> data list list /forename (A12) height. PSPP> begin data. data> Ahmed 188 data> Bertram 167 data> Catherine 134.231 data> David 109.1 data> end data PSPP>
There are several things to note about this example.
DATA LIST
command. See DATA LIST.
It tells PSPP to prepare for reading data.
The word ‘list’ intentionally appears twice.
The first occurrence is part of the DATA LIST
call,
whilst the second
tells PSPP that the data is to be read as free format data with
one record per line.
Next: Reading data from a text file, Previous: Defining Variables, Up: Preparation of Data Files [Contents][Index]
Once the data has been entered, you could type
PSPP> list /format=numbered.
to list the data. The optional text ‘/format=numbered’ requests the case numbers to be shown along with the data. It should show the following output:
|
Note that the numeric variable height is displayed to 2 decimal
places, because the format for that variable is ‘F8.2’.
For a complete description of the LIST
command, see LIST.
Next: Reading data from a pre-prepared PSPP file, Previous: Listing the data, Up: Preparation of Data Files [Contents][Index]
The previous example showed how to define a set of variables and to manually enter the data for those variables. Manual entering of data is tedious work, and often a file containing the data will be have been previously prepared. Let us assume that you have a file called mydata.dat containing the ascii encoded data:
Ahmed 188.00 Bertram 167.00 Catherine 134.23 David 109.10 . . . Zachariah 113.02
You can can tell the DATA LIST
command to read the data directly from
this file instead of by manual entry, with a command like:
PSPP> data list file='mydata.dat' list /forename (A12) height.
Notice however, that it is still necessary to specify the names of the variables and their formats, since this information is not contained in the file. It is also possible to specify the file’s character encoding and other parameters. For full details refer to see DATA LIST.
Next: Saving data to a PSPP file., Previous: Reading data from a text file, Up: Preparation of Data Files [Contents][Index]
When working with other PSPP users, or users of other software which uses the PSPP data format, you may be given the data in a pre-prepared PSPP file. Such files contain not only the data, but the variable definitions, along with their formats, labels and other meta-data. Conventionally, these files (sometimes called “system” files) have the suffix .sav, but that is not mandatory. The following syntax loads a file called my-file.sav.
PSPP> get file='my-file.sav'.
You will encounter several instances of this in future examples.
Next: Reading data from other sources, Previous: Reading data from a pre-prepared PSPP file, Up: Preparation of Data Files [Contents][Index]
If you want to save your data, along with the variable definitions so
that you or other PSPP users can use it later, you can do this with
the SAVE
command.
The following syntax will save the existing data and variables to a file called my-new-file.sav.
PSPP> save outfile='my-new-file.sav'.
If my-new-file.sav already exists, then it will be overwritten. Otherwise it will be created.
Next: Exiting PSPP, Previous: Saving data to a PSPP file., Up: Preparation of Data Files [Contents][Index]
Sometimes it’s useful to be able to read data from comma
separated text, from spreadsheets, databases or other sources.
In these instances you should
use the GET DATA
command (see GET DATA).
Previous: Reading data from other sources, Up: Preparation of Data Files [Contents][Index]
Use the FINISH
command to exit PSPP:
PSPP> finish.
Next: Hypothesis Testing, Previous: Preparation of Data Files, Up: Using PSPP [Contents][Index]
Once data has been entered, it is often desirable, or even necessary, to transform it in some way before performing analysis upon it. At the very least, it’s good practice to check for errors.
Data from real sources is rarely error free. PSPP has a number of procedures which can be used to help identify data which might be incorrect.
The DESCRIPTIVES
command (see DESCRIPTIVES) is used to generate
simple linear statistics for a dataset. It is also useful for
identifying potential problems in the data.
The example file physiology.sav contains a number of physiological
measurements of a sample of healthy adults selected at random.
However, the data entry clerk made a number of mistakes when entering
the data.
The following example illustrates the use of DESCRIPTIVES
to screen this
data and identify the erroneous values:
PSPP> get file='/usr/local/share/pspp/examples/physiology.sav'. PSPP> descriptives sex, weight, height.
For this example, PSPP produces the following output:
|
The most interesting column in the output is the minimum value. The weight variable has a minimum value of less than zero, which is clearly erroneous. Similarly, the height variable’s minimum value seems to be very low. In fact, it is more than 5 standard deviations from the mean, and is a seemingly bizarre height for an adult person.
We can look deeper into these discrepancies by issuing an additional
EXAMINE
command:
PSPP> examine height, weight /statistics=extreme(3).
This command produces the following additional output (in part):
|
From this new output, you can see that the lowest value of height is
179 (which we suspect to be erroneous), but the second lowest is 1598
which
we know from DESCRIPTIVES
is within 1 standard deviation from the mean.
Similarly, the lowest value of weight is
negative, but its second lowest value is plausible.
This suggests that the two extreme values are outliers and probably
represent data entry errors.
The output also identifies the case numbers for each extreme value, so we can see that cases 30 and 38 are the ones with the erroneous values.
Next: Inverting negatively coded variables, Previous: Identifying incorrect data, Up: Data Screening and Transformation [Contents][Index]
If possible, suspect data should be checked and re-measured.
However, this may not always be feasible, in which case the researcher may
decide to disregard these values.
PSPP has a feature whereby data can assume the special value ‘SYSMIS’, and
will be disregarded in future analysis. See Handling missing observations.
You can set the two suspect values to the ‘SYSMIS’ value using the RECODE
command.
PSPP> recode height (179 = SYSMIS). PSPP> recode weight (LOWEST THRU 0 = SYSMIS).
The first command says that for any observation which has a
height value of 179, that value should be changed to the SYSMIS
value.
The second command says that any weight values of zero or less
should be changed to SYSMIS.
From now on, they will be ignored in analysis.
For detailed information about the RECODE
command see RECODE.
If you now re-run the DESCRIPTIVES
or EXAMINE
commands from
the previous section,
you will see a data summary with more plausible parameters.
You will also notice that the data summaries indicate the two missing values.
Next: Testing data consistency, Previous: Dealing with suspicious data, Up: Data Screening and Transformation [Contents][Index]
Data entry errors are not the only reason for wanting to recode data. The sample file hotel.sav comprises data gathered from a customer satisfaction survey of clients at a particular hotel. The following commands load the file and display its variables and associated data:
PSPP> get file='/usr/local/share/pspp/examples/hotel.sav'. PSPP> display dictionary.
It yields the following output:
|
The output shows that all of the variables v1 through v5 are measured on a 5 point Likert scale,
with 1 meaning “Strongly disagree” and 5 meaning “Strongly agree”.
However, some of the questions are positively worded (v1, v2, v4) and others are negatively worded (v3, v5).
To perform meaningful analysis, we need to recode the variables so
that they all measure in the same direction.
We could use the RECODE
command, with syntax such as:
recode v3 (1 = 5) (2 = 4) (4 = 2) (5 = 1).
However an easier and more elegant way uses the COMPUTE
command (see COMPUTE).
Since the variables are Likert variables in the range (1 … 5),
subtracting their value from 6 has the effect of inverting them:
compute var = 6 - var.
The following section uses this technique to recode the variables
v3 and v5.
After applying COMPUTE
for both variables,
all subsequent commands will use the inverted values.
Next: Testing for normality, Previous: Inverting negatively coded variables, Up: Data Screening and Transformation [Contents][Index]
A sensible check to perform on survey data is the calculation of
reliability.
This gives the statistician some confidence that the questionnaires have been
completed thoughtfully.
If you examine the labels of variables v1, v3 and v4,
you will notice that they ask very similar questions.
One would therefore expect the values of these variables (after recoding)
to closely follow one another, and we can test that with the RELIABILITY
command (see RELIABILITY).
The following example shows a PSPP session where the user recodes
negatively scaled variables and then requests reliability statistics for
v1, v3, and v4.
PSPP> get file='/usr/local/share/pspp/examples/hotel.sav'. PSPP> compute v3 = 6 - v3. PSPP> compute v5 = 6 - v5. PSPP> reliability v1, v3, v4.
This yields the following output:
Scale: ANY
|
As a rule of thumb, many statisticians consider a value of Cronbach’s Alpha of 0.7 or higher to indicate reliable data.
Here, the value is 0.81, which suggests a high degree of reliability among variables v1, v3 and v4, so the data and the recoding that we performed are vindicated.
Previous: Testing data consistency, Up: Data Screening and Transformation [Contents][Index]
Many statistical tests rely upon certain properties of the data.
One common property, upon which many linear tests depend, is that of
normality — the data must have been drawn from a normal distribution.
It is necessary then to ensure normality before deciding upon the
test procedure to use. One way to do this uses the EXAMINE
command.
In the following example, a researcher was examining the failure rates of equipment produced by an engineering company. The file repairs.sav contains the mean time between failures (mtbf) of some items of equipment subject to the study. Before performing linear analysis on the data, the researcher wanted to ascertain that the data is normally distributed.
PSPP> get file='/usr/local/share/pspp/examples/repairs.sav'. PSPP> examine mtbf /statistics=descriptives.
This produces the following output:
|
A normal distribution has a skewness and kurtosis of zero. The skewness of mtbf in the output above makes it clear that the mtbf figures have a lot of positive skew and are therefore not drawn from a normally distributed variable. Positive skew can often be compensated for by applying a logarithmic transformation, as in the following continuation of the example:
PSPP> compute mtbf_ln = ln (mtbf). PSPP> examine mtbf_ln /statistics=descriptives.
which produces the following additional output:
|
The COMPUTE
command in the first line above performs the
logarithmic transformation:
compute mtbf_ln = ln (mtbf).
Rather than redefining the existing variable, this use of COMPUTE
defines a new variable mtbf_ln which is
the natural logarithm of mtbf.
The final command in this example calls EXAMINE
on this new variable.
The results show that both the skewness and
kurtosis for mtbf_ln are very close to zero.
This provides some confidence that the mtbf_ln variable is
normally distributed and thus safe for linear analysis.
In the event that no suitable transformation can be found,
then it would be worth considering
an appropriate non-parametric test instead of a linear one.
See NPAR TESTS, for information about non-parametric tests.
Previous: Data Screening and Transformation, Up: Using PSPP [Contents][Index]
One of the most fundamental purposes of statistical analysis is hypothesis testing. Researchers commonly need to test hypotheses about a set of data. For example, she might want to test whether one set of data comes from the same distribution as another, or whether the mean of a dataset significantly differs from a particular value. This section presents just some of the possible tests that PSPP offers.
The researcher starts by making a null hypothesis. Often this is a hypothesis which he suspects to be false. For example, if he suspects that A is greater than B he will state the null hypothesis as A = B.2
The p-value is a recurring concept in hypothesis testing. It is the highest acceptable probability that the evidence implying a null hypothesis is false, could have been obtained when the null hypothesis is in fact true. Note that this is not the same as “the probability of making an error” nor is it the same as “the probability of rejecting a hypothesis when it is true”.
Next: Linear Regression, Up: Hypothesis Testing [Contents][Index]
A common statistical test involves hypotheses about means.
The T-TEST
command is used to find out whether or not two separate
subsets have the same mean.
A researcher suspected that the heights and core body temperature of persons might be different depending upon their sex. To investigate this, he posed two null hypotheses based on the data from physiology.sav previously encountered:
For the purposes of the investigation the researcher decided to use a p-value of 0.05.
In addition to the T-test, the T-TEST
command also performs the
Levene test for equal variances.
If the variances are equal, then a more powerful form of the T-test can be used.
However if it is unsafe to assume equal variances,
then an alternative calculation is necessary.
PSPP performs both calculations.
For the height variable, the output shows the significance of the Levene test to be 0.33 which means there is a 33% probability that the Levene test produces this outcome when the variances are equal. Had the significance been less than 0.05, then it would have been unsafe to assume that the variances were equal. However, because the value is higher than 0.05 the homogeneity of variances assumption is safe and the “Equal Variances” row (the more powerful test) can be used. Examining this row, the two tailed significance for the height t-test is less than 0.05, so it is safe to reject the null hypothesis and conclude that the mean heights of males and females are unequal.
For the temperature variable, the significance of the Levene test is 0.58 so again, it is safe to use the row for equal variances. The equal variances row indicates that the two tailed significance for temperature is 0.20. Since this is greater than 0.05 we must reject the null hypothesis and conclude that there is insufficient evidence to suggest that the body temperature of male and female persons are different.
The syntax for this analysis is:
PSPP> get file='/usr/local/share/pspp/examples/physiology.sav'. PSPP> recode height (179 = SYSMIS). PSPP> t-test group=sex(0,1) /variables = height temperature.
PSPP produces the following output for this syntax:
|
The T-TEST
command tests for differences of means.
Here, the height variable’s two tailed significance is less than
0.05, so the null hypothesis can be rejected.
Thus, the evidence suggests there is a difference between the heights of
male and female persons.
However the significance of the test for the temperature
variable is greater than 0.05 so the null hypothesis cannot be
rejected, and there is insufficient evidence to suggest a difference
in body temperature.
Previous: Testing for differences of means, Up: Hypothesis Testing [Contents][Index]
Linear regression is a technique used to investigate if and how a variable is linearly related to others. If a variable is found to be linearly related, then this can be used to predict future values of that variable.
In the following example, the service department of the company wanted to
be able to predict the time to repair equipment, in order to improve
the accuracy of their quotations.
It was suggested that the time to repair might be related to the time
between failures and the duty cycle of the equipment.
The p-value of 0.1 was chosen for this investigation.
In order to investigate this hypothesis, the REGRESSION
command
was used.
This command not only tests if the variables are related, but also
identifies the potential linear relationship. See REGRESSION.
A first attempt includes duty_cycle:
PSPP> get file='/usr/local/share/pspp/examples/repairs.sav'. PSPP> regression /variables = mtbf duty_cycle /dependent = mttr.
This attempt yields the following output (in part):
|
The coefficients in the above table suggest that the formula mttr = 9.81 + 3.1 \times mtbf + 1.09 \times duty_cycle can be used to predict the time to repair. However, the significance value for the duty_cycle coefficient is very high, which would make this an unsafe predictor. For this reason, the test was repeated, but omitting the duty_cycle variable:
PSPP> regression /variables = mtbf /dependent = mttr.
This second try produces the following output (in part):
|
This time, the significance of all coefficients is no higher than 0.06, suggesting that at the 0.06 level, the formula mttr = 10.5 + 3.11 \times mtbf is a reliable predictor of the time to repair.
Next: Mathematical Expressions, Previous: Using PSPP, Up: GNU PSPP [Contents][Index]
This chapter discusses elements common to many PSPP commands. Later chapters describe individual commands in detail.
Next: Forming commands of tokens, Up: The PSPP language [Contents][Index]
PSPP divides most syntax file lines into series of short chunks called tokens. Tokens are then grouped to form commands, each of which tells PSPP to take some action—read in data, write out data, perform a statistical procedure, etc. Each type of token is described below.
Identifiers are names that typically specify variables, commands, or subcommands. The first character in an identifier must be a letter, ‘#’, or ‘@’. The remaining characters in the identifier must be letters, digits, or one of the following special characters:
Identifiers may be any length, but only the first 64 bytes are
significant. Identifiers are not case-sensitive: foobar
,
Foobar
, FooBar
, FOOBAR
, and FoObaR
are
different representations of the same identifier.
Some identifiers are reserved. Reserved identifiers may not be used in any context besides those explicitly described in this manual. The reserved identifiers are:
Keywords are a subclass of identifiers that form a fixed part of command syntax. For example, command and subcommand names are keywords. Keywords may be abbreviated to their first 3 characters if this abbreviation is unambiguous. (Unique abbreviations of 3 or more characters are also accepted: ‘FRE’, ‘FREQ’, and ‘FREQUENCIES’ are equivalent when the last is a keyword.)
Reserved identifiers are always used as keywords. Other identifiers may be used both as keywords and as user-defined identifiers, such as variable names.
Numbers are expressed in decimal. A decimal point is optional. Numbers may be expressed in scientific notation by adding ‘e’ and a base-10 exponent, so that ‘1.234e3’ has the value 1234. Here are some more examples of valid numbers:
-5 3.14159265359 1e100 -.707 8945.
Negative numbers are expressed with a ‘-’ prefix. However, in situations where a literal ‘-’ token is expected, what appears to be a negative number is treated as ‘-’ followed by a positive number.
No white space is allowed within a number token, except for horizontal white space between ‘-’ and the rest of the number.
The last example above, ‘8945.’ is interpreted as two tokens, ‘8945’ and ‘.’, if it is the last token on a line. See Forming commands of tokens.
Strings are literal sequences of characters enclosed in pairs of single quotes (‘'’) or double quotes (‘"’). To include the character used for quoting in the string, double it, e.g. ‘'it''s an apostrophe'’. White space and case of letters are significant inside strings.
Strings can be concatenated using ‘+’, so that ‘"a" + 'b' + 'c'’ is equivalent to ‘'abc'’. So that a long string may be broken across lines, a line break may precede or follow, or both precede and follow, the ‘+’. (However, an entirely blank line preceding or following the ‘+’ is interpreted as ending the current command.)
Strings may also be expressed as hexadecimal character values by prefixing the initial quote character by ‘x’ or ‘X’. Regardless of the syntax file or active dataset’s encoding, the hexadecimal digits in the string are interpreted as Unicode characters in UTF-8 encoding.
Individual Unicode code points may also be expressed by specifying the
hexadecimal code point number in single or double quotes preceded by
‘u’ or ‘U’. For example, Unicode code point U+1D11E, the
musical G clef character, could be expressed as U'1D11E'
.
Invalid Unicode code points (above U+10FFFF or in between U+D800 and
U+DFFF) are not allowed.
When strings are concatenated with ‘+’, each segment’s prefix is
considered individually. For example, 'The G clef symbol is:' +
u"1d11e" + "."
inserts a G clef symbol in the middle of an otherwise
plain text string.
These tokens are the punctuators and operators:
Most of these appear within the syntax of commands, but the period (‘.’) punctuator is used only at the end of a command. It is a punctuator only as the last character on a line (except white space). When it is the last non-space character on a line, a period is not treated as part of another token, even if it would otherwise be part of, e.g., an identifier or a floating-point number.
Next: Syntax Variants, Previous: Tokens, Up: The PSPP language [Contents][Index]
Most PSPP commands share a common structure. A command begins with a
command name, such as FREQUENCIES
, DATA LIST
, or N OF
CASES
. The command name may be abbreviated to its first word, and
each word in the command name may be abbreviated to its first three
or more characters, where these abbreviations are unambiguous.
The command name may be followed by one or more subcommands. Each subcommand begins with a subcommand name, which may be abbreviated to its first three letters. Some subcommands accept a series of one or more specifications, which follow the subcommand name, optionally separated from it by an equals sign (‘=’). Specifications may be separated from each other by commas or spaces. Each subcommand must be separated from the next (if any) by a forward slash (‘/’).
There are multiple ways to mark the end of a command. The most common way is to end the last line of the command with a period (‘.’) as described in the previous section (see Tokens). A blank line, or one that consists only of white space or comments, also ends a command.
Next: Types of Commands, Previous: Forming commands of tokens, Up: The PSPP language [Contents][Index]
There are three variants of command syntax, which vary only in how they detect the end of one command and the start of the next.
In interactive mode, which is the default for syntax typed at a command prompt, a period as the last non-blank character on a line ends a command. A blank line also ends a command.
In batch mode, an end-of-line period or a blank line also ends a command. Additionally, it treats any line that has a non-blank character in the leftmost column as beginning a new command. Thus, in batch mode the second and subsequent lines in a command must be indented.
Regardless of the syntax mode, a plus sign, minus sign, or period in the leftmost column of a line is ignored and causes that line to begin a new command. This is most useful in batch mode, in which the first line of a new command could not otherwise be indented, but it is accepted regardless of syntax mode.
The default mode for reading commands from a file is auto mode. It is the same as batch mode, except that a line with a non-blank in the leftmost column only starts a new command if that line begins with the name of a PSPP command. This correctly interprets most valid PSPP syntax files regardless of the syntax mode for which they are intended.
The --interactive (or -i) or --batch (or -b) options set the syntax mode for files listed on the PSPP command line. See Main Options, for more details.
Next: Order of Commands, Previous: Syntax Variants, Up: The PSPP language [Contents][Index]
Commands in PSPP are divided roughly into six categories:
Set or display various global options that affect PSPP operations. May appear anywhere in a syntax file. See Utility commands.
Give instructions for reading data from text files or from special binary “system files”. Most of these commands replace any previous data or variables with new data or variables. At least one file definition command must appear before the first command in any of the categories below. See Data Input and Output.
Though rarely used, these provide tools for reading data files in arbitrary textual or binary formats. See INPUT PROGRAM.
Perform operations on data and write data to output files. Transformations are not carried out until a procedure is executed.
Transformations that cannot appear in certain contexts. See Order of Commands, for details.
Analyze data, writing results of analyses to the listing file. Cause transformations specified earlier in the file to be performed. In a more general sense, a procedure is any command that causes the active dataset (the data) to be read.
Next: Handling missing observations, Previous: Types of Commands, Up: The PSPP language [Contents][Index]
PSPP does not place many restrictions on ordering of commands. The main restriction is that variables must be defined before they are otherwise referenced. This section describes the details of command ordering, but most users will have no need to refer to them.
PSPP possesses five internal states, called initial, input-program
file-type, transformation, and procedure states. (Please note the
distinction between the INPUT PROGRAM
and FILE TYPE
commands and the input-program and file-type states.)
PSPP starts in the initial state. Each successful completion of a command may cause a state transition. Each type of command has its own rules for state transitions:
N OF CASES
is executed in the procedure state, it causes a transition to the
transformation state.
DATA LIST
INPUT PROGRAM
FILE TYPE
ADD FILES
, MATCH FILES
,
and UPDATE
.
Next: Datasets, Previous: Order of Commands, Up: The PSPP language [Contents][Index]
PSPP includes special support for unknown numeric data values. Missing observations are assigned a special value, called the system-missing value. This “value” actually indicates the absence of a value; it means that the actual value is unknown. Procedures automatically exclude from analyses those observations or cases that have missing values. Details of missing value exclusion depend on the procedure and can often be controlled by the user; refer to descriptions of individual procedures for details.
The system-missing value exists only for numeric variables. String variables always have a defined value, even if it is only a string of spaces.
Variables, whether numeric or string, can have designated user-missing values. Every user-missing value is an actual value for that variable. However, most of the time user-missing values are treated in the same way as the system-missing value.
For more information on missing values, see the following sections: Datasets, MISSING VALUES, Mathematical Expressions. See also the documentation on individual procedures for information on how they handle missing values.
Next: Files Used by PSPP, Previous: Handling missing observations, Up: The PSPP language [Contents][Index]
PSPP works with data organized into datasets. A dataset consists of a set of variables, which taken together are said to form a dictionary, and one or more cases, each of which has one value for each variable.
At any given time PSPP has exactly one distinguished dataset, called
the active dataset. Most PSPP commands work only with the
active dataset. In addition to the active dataset, PSPP also supports
any number of additional open datasets. The DATASET
commands
can choose a new active dataset from among those that are open, as
well as create and destroy datasets (see DATASET commands).
The sections below describe variables in more detail.
Next: Variables Automatically Defined by PSPP, Up: Datasets [Contents][Index]
Each variable has a number of attributes, including:
An identifier, up to 64 bytes long. Each variable must have a different name. See Tokens.
Some system variable names begin with ‘$’, but user-defined variables’ names may not begin with ‘$’.
The final character in a variable name should not be ‘.’, because
such an identifier will be misinterpreted when it is the final token
on a line: FOO.
is divided into two separate tokens,
‘FOO’ and ‘.’, indicating end-of-command. See Tokens.
The final character in a variable name should not be ‘_’, because some such identifiers are used for special purposes by PSPP procedures.
As with all PSPP identifiers, variable names are not case-sensitive. PSPP capitalizes variable names on output the same way they were capitalized at their point of definition in the input.
Numeric or string.
(string variables only) String variables with a width of 8 characters or fewer are called short string variables. Short string variables may be used in a few contexts where long string variables (those with widths greater than 8) are not allowed.
Variables in the dictionary are arranged in a specific order.
DISPLAY
can be used to show this order: see DISPLAY.
Either reinitialized to 0 or spaces for each case, or left at its existing value. See LEAVE.
Optionally, up to three values, or a range of values, or a specific value plus a range, can be specified as user-missing values. There is also a system-missing value that is assigned to an observation when there is no other obvious value for that observation. Observations with missing values are automatically excluded from analyses. User-missing values are actual data values, while the system-missing value is not a value at all. See Handling missing observations.
A string that describes the variable. See VARIABLE LABELS.
Optionally, these associate each possible value of the variable with a string. See VALUE LABELS.
Display width, format, and (for numeric variables) number of decimal places. This attribute does not affect how data are stored, just how they are displayed. Example: a width of 8, with 2 decimal places. See Input and Output Formats.
Similar to print format, but used by the WRITE
command
(see WRITE).
One of the following:
Each value of a nominal variable represents a distinct category. The possible categories are finite and often have value labels. The order of categories is not significant. Political parties, US states, and yes/no choices are nominal. Numeric and string variables can be nominal.
Ordinal variables also represent distinct categories, but their values
are arranged according to some natural order. Likert scales, e.g.
from strongly disagree to strongly agree, are ordinal. Data grouped
into ranges, e.g. age groups or income groups, are ordinal. Both
numeric and string variables can be ordinal. String values are
ordered alphabetically, so letter grades from A to F will work as
expected, but poor
, satisfactory
, excellent
will
not.
Scale variables are ones for which differences and ratios are meaningful. These are often values which have a natural unit attached, such as age in years, income in dollars, or distance in miles. Only numeric variables are scalar.
Variables created by COMPUTE
and similar transformations,
obtained from external sources, etc., initially have an unknown
measurement level. Any procedure that reads the data will then assign
a default measurement level. PSPP can assign some defaults without
reading the data:
Otherwise, PSPP reads the data and decides based on its distribution:
Finally, if none of the above is true, PSPP assigns the variable a nominal measurement level.
User-defined associations between names and values. See VARIABLE ATTRIBUTE.
The intended role of a variable for use in dialog boxes in graphical user interfaces. See VARIABLE ROLE.
Next: Lists of variable names, Previous: Attributes of Variables, Up: Datasets [Contents][Index]
There are seven system variables. These are not like ordinary variables because system variables are not always stored. They can be used only in expressions. These system variables, whose values and output formats cannot be modified, are described below.
$CASENUM
Case number of the case at the moment. This changes as cases are shuffled around.
$DATE
Date the PSPP process was started, in format A9, following the
pattern DD-MMM-YY
.
$DATE11
Date the PSPP process was started, in format A11, following the
pattern DD-MMM-YYYY
.
$JDATE
Number of days between 15 Oct 1582 and the time the PSPP process was started.
$LENGTH
Page length, in lines, in format F11.
$SYSMIS
System missing value, in format F1.
$TIME
Number of seconds between midnight 14 Oct 1582 and the time the active dataset was read, in format F20.
$WIDTH
Page width, in characters, in format F3.
Next: Input and Output Formats, Previous: Variables Automatically Defined by PSPP, Up: Datasets [Contents][Index]
To refer to a set of variables, list their names one after another.
Optionally, their names may be separated by commas. To include a
range of variables from the dictionary in the list, write the name of
the first and last variable in the range, separated by TO
. For
instance, if the dictionary contains six variables with the names
ID
, X1
, X2
, GOAL
, MET
, and
NEXTGOAL
, in that order, then X2 TO MET
would include
variables X2
, GOAL
, and MET
.
Commands that define variables, such as DATA LIST
, give
TO
an alternate meaning. With these commands, TO
define
sequences of variables whose names end in consecutive integers. The
syntax is two identifiers that begin with the same root and end with
numbers, separated by TO
. The syntax X1 TO X5
defines 5
variables, named X1
, X2
, X3
, X4
, and
X5
. The syntax ITEM0008 TO ITEM0013
defines 6
variables, named ITEM0008
, ITEM0009
, ITEM0010
,
ITEM0011
, ITEM0012
, and ITEM00013
. The syntaxes
QUES001 TO QUES9
and QUES6 TO QUES3
are invalid.
After a set of variables has been defined with DATA LIST
or
another command with this method, the same set can be referenced on
later commands using the same syntax.
Next: Scratch Variables, Previous: Lists of variable names, Up: Datasets [Contents][Index]
An input format describes how to interpret the contents of an
input field as a number or a string. It might specify that the field
contains an ordinary decimal number, a time or date, a number in binary
or hexadecimal notation, or one of several other notations. Input
formats are used by commands such as DATA LIST
that read data or
syntax files into the PSPP active dataset.
Every input format corresponds to a default output format that specifies the formatting used when the value is output later. It is always possible to explicitly specify an output format that resembles the input format. Usually, this is the default, but in cases where the input format is unfriendly to human readability, such as binary or hexadecimal formats, the default output format is an easier-to-read decimal format.
Every variable has two output formats, called its print format and
write format. Print formats are used in most output contexts;
write formats are used only by WRITE
(see WRITE). Newly
created variables have identical print and write formats, and
FORMATS
, the most commonly used command for changing formats
(see FORMATS), sets both of them to the same value as well. Thus,
most of the time, the distinction between print and write formats is
unimportant.
Input and output formats are specified to PSPP with
a format specification of the
form TYPEw
or TYPEw.d
, where
TYPE is one of the format types described later, w is a
field width measured in columns, and d is an optional number of
decimal places. If d is omitted, a value of 0 is assumed. Some
formats do not allow a nonzero d to be specified.
The following sections describe the input and output formats supported by PSPP.
Next: Custom Currency Formats, Up: Input and Output Formats [Contents][Index]
The basic numeric formats are used for input and output of real numbers in standard or scientific notation. The following table shows an example of how each format displays positive and negative numbers with the default decimal point setting:
Format | 3141.59 | -3141.59 |
---|---|---|
F8.2 | 3141.59 | -3141.59 |
COMMA9.2 | 3,141.59 | -3,141.59 |
DOT9.2 | 3.141,59 | -3.141,59 |
DOLLAR10.2 | $3,141.59 | -$3,141.59 |
PCT9.2 | 3141.59% | -3141.59% |
E8.1 | 3.1E+003 | -3.1E+003 |
On output, numbers in F format are expressed in standard decimal notation with the requested number of decimal places. The other formats output some variation on this style:
On input, the basic numeric formats accept positive and numbers in standard decimal notation or scientific notation. Leading and trailing spaces are allowed. An empty or all-spaces field, or one that contains only a single period, is treated as the system missing value.
In scientific notation, the exponent may be introduced by a sign (‘+’ or ‘-’), or by one of the letters ‘e’ or ‘d’ (in uppercase or lowercase), or by a letter followed by a sign. A single space may follow the letter or the sign or both.
On fixed-format DATA LIST
(see DATA LIST FIXED) and in a few
other contexts, decimals are implied when the field does not contain a
decimal point. In F6.5 format, for example, the field 314159
is
taken as the value 3.14159 with implied decimals. Decimals are never
implied if an explicit decimal point is present or if scientific
notation is used.
E and F formats accept the basic syntax already described. The other formats allow some additional variations:
All of the basic number formats have a maximum field width of 40 and accept no more than 16 decimal places, on both input and output. Some additional restrictions apply:
More details of basic numeric output formatting are given below:
3
in F1.0 format, and -1.125 as -1.13
in F5.1
format.
F
,
COMMA
, or DOT
formats, PSPP keeps the zero if
SET LEADZERO
is set to ON
(see SET LEADZERO).
In scientific notation, the number always includes a decimal point, even if it is not followed by a digit.
-$9.99
.
+Infinity
or -Infinity
for infinities, from
NaN
for “not a number,” or from Unknown
for other values
(if any are supported by the system). In fields under 3 columns wide,
special values are output as asterisks.
Next: Legacy Numeric Formats, Previous: Basic Numeric Formats, Up: Input and Output Formats [Contents][Index]
The custom currency formats are closely related to the basic numeric formats, but they allow users to customize the output format. The SET command configures custom currency formats, using the syntax
SET CCx="string".
where x is A, B, C, D, or E, and string is no more than 16 characters long.
string must contain exactly three commas or exactly three periods (but not both), except that a single quote character may be used to “escape” a following comma, period, or single quote. If three commas are used, commas are used for grouping in output, and a period is used as the decimal point. Uses of periods reverses these roles.
The commas or periods divide string into four fields, called the negative prefix, prefix, suffix, and negative suffix, respectively. The prefix and suffix are added to output whenever space is available. The negative prefix and negative suffix are always added to a negative number when the output includes a nonzero digit.
The following syntax shows how custom currency formats could be used to reproduce basic numeric formats:
SET CCA="-,,,". /* Same as COMMA. SET CCB="-...". /* Same as DOT. SET CCC="-,$,,". /* Same as DOLLAR. SET CCD="-,,%,". /* Like PCT, but groups with commas.
Here are some more examples of custom currency formats. The final example shows how to use a single quote to escape a delimiter:
SET CCA=",EUR,,-". /* Euro. SET CCB="(,USD ,,)". /* US dollar. SET CCC="-.R$..". /* Brazilian real. SET CCD="-,, NIS,". /* Israel shekel. SET CCE="-.Rp'. ..". /* Indonesia Rupiah.
These formats would yield the following output:
Format | 3145.59 | -3145.59 |
---|---|---|
CCA12.2 | EUR3,145.59 | EUR3,145.59- |
CCB14.2 | USD 3,145.59 | (USD 3,145.59) |
CCC11.2 | R$3.145,59 | -R$3.145,59 |
CCD13.2 | 3,145.59 NIS | -3,145.59 NIS |
CCE10.0 | Rp. 3.146 | -Rp. 3.146 |
The default for all the custom currency formats is ‘-,,,’, equivalent to COMMA format.
Next: Binary and Hexadecimal Numeric Formats, Previous: Custom Currency Formats, Up: Input and Output Formats [Contents][Index]
The N and Z numeric formats provide compatibility with legacy file formats. They have much in common:
The N format supports input and output of fields that contain only
digits. On input, leading or trailing spaces, a decimal point, or any
other non-digit character causes the field to be read as the
system-missing value. As a special exception, an N format used on
DATA LIST FREE
or DATA LIST LIST
is treated as the
equivalent F format.
On output, N pads the field on the left with zeros. Negative numbers are output like the system-missing value.
The Z format is a “zoned decimal” format used on IBM mainframes. Z format encodes the sign as part of the final digit, which must be one of the following:
0123456789 {ABCDEFGHI }JKLMNOPQR
where the characters in each row represent digits 0 through 9 in order. Characters in the first two rows indicate a positive sign; those in the third indicate a negative sign.
On output, Z fields are padded on the left with spaces. On input, leading and trailing spaces are ignored. Any character in an input field other than spaces, the digit characters above, and ‘.’ causes the field to be read as system-missing.
The decimal point character for input and output is always ‘.’, even if the decimal point character is a comma (see SET DECIMAL).
Nonzero, negative values output in Z format are marked as negative even when no nonzero digits are output. For example, -0.2 is output in Z1.0 format as ‘J’. The “negative zero” value supported by most machines is output as positive.
Next: Time and Date Formats, Previous: Legacy Numeric Formats, Up: Input and Output Formats [Contents][Index]
The binary and hexadecimal formats are primarily designed for compatibility with existing machine formats, not for human readability. All of them therefore have a F format as default output format. Some of these formats are only portable between machines with compatible byte ordering (endianness) or floating-point format.
Binary formats use byte values that in text files are interpreted as special control functions, such as carriage return and line feed. Thus, data in binary formats should not be included in syntax files or read from data files with variable-length records, such as ordinary text files. They may be read from or written to data files with fixed-length records. See FILE HANDLE, for information on working with fixed-length records.
These are binary-coded decimal formats, in which every byte (except the last, in P format) represents two decimal digits. The most-significant 4 bits of the first byte is the most-significant decimal digit, the least-significant 4 bits of the first byte is the next decimal digit, and so on.
In P format, the most-significant 4 bits of the last byte are the least-significant decimal digit. The least-significant 4 bits represent the sign: decimal 15 indicates a negative value, decimal 13 indicates a positive value.
Numbers are rounded downward on output. The system-missing value and numbers outside representable range are output as zero.
The maximum field width is 16. Decimal places may range from 0 up to the number of decimal digits represented by the field.
The default output format is an F format with twice the input field width, plus one column for a decimal point (if decimal places were requested).
These are integer binary formats. IB reads and writes 2’s complement binary integers, and PIB reads and writes unsigned binary integers. The byte ordering is by default the host machine’s, but SET RIB may be used to select a specific byte ordering for reading (see SET RIB) and SET WIB, similarly, for writing (see SET WIB).
The maximum field width is 8. Decimal places may range from 0 up to the number of decimal digits in the largest value representable in the field width.
The default output format is an F format whose width is the number of decimal digits in the largest value representable in the field width, plus 1 if the format has decimal places.
This is a binary format for real numbers. By default it reads and writes the host machine’s floating-point format, but SET RRB may be used to select an alternate floating-point format for reading (see SET RRB) and SET WRB, similarly, for writing (see SET WRB).
The recommended field width depends on the floating-point format. NATIVE (the default format), IDL, IDB, VD, VG, and ZL formats should use a field width of 8. ISL, ISB, VF, and ZS formats should use a field width of 4. Other field widths do not produce useful results. The maximum field width is 8. No decimal places may be specified.
The default output format is F8.2.
These are hexadecimal formats, for reading and writing binary formats where each byte has been recoded as a pair of hexadecimal digits.
A hexadecimal field consists solely of hexadecimal digits ‘0’…‘9’ and ‘A’…‘F’. Uppercase and lowercase are accepted on input; output is in uppercase.
Other than the hexadecimal representation, these formats are equivalent to PIB and RB formats, respectively. However, bytes in PIBHEX format are always ordered with the most-significant byte first (big-endian order), regardless of the host machine’s native byte order or PSPP settings.
Field widths must be even and between 2 and 16. RBHEX format allows no decimal places; PIBHEX allows as many decimal places as a PIB format with half the given width.
Next: Date Component Formats, Previous: Binary and Hexadecimal Numeric Formats, Up: Input and Output Formats [Contents][Index]
In PSPP, a time is an interval. The time formats translate between human-friendly descriptions of time intervals and PSPP’s internal representation of time intervals, which is simply the number of seconds in the interval. PSPP has three time formats:
Time Format | Template | Example |
---|---|---|
MTIME | MM:SS.ss | 91:17.01 |
TIME | hh:MM:SS.ss | 01:31:17.01 |
DTIME | DD HH:MM:SS.ss | 00 04:31:17.01 |
A date is a moment in the past or the future. Internally, PSPP represents a date as the number of seconds since the epoch, midnight, Oct. 14, 1582. The date formats translate between human-readable dates and PSPP’s numeric representation of dates and times. PSPP has several date formats:
Date Format | Template | Example |
---|---|---|
DATE | dd-mmm-yyyy | 01-OCT-1978 |
ADATE | mm/dd/yyyy | 10/01/1978 |
EDATE | dd.mm.yyyy | 01.10.1978 |
JDATE | yyyyjjj | 1978274 |
SDATE | yyyy/mm/dd | 1978/10/01 |
QYR | q Q yyyy | 3 Q 1978 |
MOYR | mmm yyyy | OCT 1978 |
WKYR | ww WK yyyy | 40 WK 1978 |
DATETIME | dd-mmm-yyyy HH:MM:SS.ss | 01-OCT-1978 04:31:17.01 |
YMDHMS | yyyy-mm-dd HH:MM:SS.ss | 1978-01-OCT 04:31:17.01 |
The templates in the preceding tables describe how the time and date formats are input and output:
dd
Day of month, from 1 to 31. Always output as two digits.
mm
mmm
Month. In output, mm
is output as two digits, mmm
as the
first three letters of an English month name (January, February,
…). In input, both of these formats, plus Roman numerals, are
accepted.
yyyy
Year. In output, DATETIME and YMDHMS always produce 4-digit years; other formats can produce a 2- or 4-digit year. The century assumed for 2-digit years depends on the EPOCH setting (see SET EPOCH). In output, a year outside the epoch causes the whole field to be filled with asterisks (‘*’).
jjj
Day of year (Julian day), from 1 to 366. This is exactly three digits giving the count of days from the start of the year. January 1 is considered day 1.
q
Quarter of year, from 1 to 4. Quarters start on January 1, April 1, July 1, and October 1.
ww
Week of year, from 1 to 53. Output as exactly two digits. January 1 is the first day of week 1.
DD
Count of days, which may be positive or negative. Output as at least two digits.
hh
Count of hours, which may be positive or negative. Output as at least two digits.
HH
Hour of day, from 0 to 23. Output as exactly two digits.
MM
In MTIME, count of minutes, which may be positive or negative. Output as at least two digits.
In other formats, minute of hour, from 0 to 59. Output as exactly two digits.
SS.ss
Seconds within minute, from 0 to 59. The integer part is output as exactly two digits. On output, seconds and fractional seconds may or may not be included, depending on field width and decimal places. On input, seconds and fractional seconds are optional. The DECIMAL setting controls the character accepted and displayed as the decimal point (see SET DECIMAL).
For output, the date and time formats use the delimiters indicated in the table. For input, date components may be separated by spaces or by one of the characters ‘-’, ‘/’, ‘.’, or ‘,’, and time components may be separated by spaces or ‘:’. On input, the ‘Q’ separating quarter from year and the ‘WK’ separating week from year may be uppercase or lowercase, and the spaces around them are optional.
On input, all time and date formats accept any amount of leading and trailing white space.
The maximum width for time and date formats is 40 columns. Minimum input and output width for each of the time and date formats is shown below:
Format | Min. Input Width | Min. Output Width | Option |
---|---|---|---|
DATE | 8 | 9 | 4-digit year |
ADATE | 8 | 8 | 4-digit year |
EDATE | 8 | 8 | 4-digit year |
JDATE | 5 | 5 | 4-digit year |
SDATE | 8 | 8 | 4-digit year |
QYR | 4 | 6 | 4-digit year |
MOYR | 6 | 6 | 4-digit year |
WKYR | 6 | 8 | 4-digit year |
DATETIME | 17 | 17 | seconds |
YMDHMS | 12 | 16 | seconds |
MTIME | 4 | 5 | |
TIME | 5 | 5 | seconds |
DTIME | 8 | 8 | seconds |
In the table, “Option” describes what increased output width enables:
A field 2 columns wider than the minimum includes a 4-digit year. (DATETIME and YMDHMS formats always include a 4-digit year.)
A field 3 columns wider than the minimum includes seconds as well as minutes. A field 5 columns wider than minimum, or more, can also include a decimal point and fractional seconds (but no more than allowed by the format’s decimal places).
For the time and date formats, the default output format is the same as the input format, except that PSPP increases the field width, if necessary, to the minimum allowed for output.
Time or dates narrower than the field width are right-justified within the field.
When a time or date exceeds the field width, characters are trimmed from the end until it fits. This can occur in an unusual situation, e.g. with a year greater than 9999 (which adds an extra digit), or for a negative value on MTIME, TIME, or DTIME (which adds a leading minus sign).
The system-missing value is output as a period at the right end of the field.
Next: String Formats, Previous: Time and Date Formats, Up: Input and Output Formats [Contents][Index]
The WKDAY and MONTH formats provide input and output for the names of weekdays and months, respectively.
On output, these formats convert a number between 1 and 7, for WKDAY, or between 1 and 12, for MONTH, into the English name of a day or month, respectively. If the name is longer than the field, it is trimmed to fit. If the name is shorter than the field, it is padded on the right with spaces. Values outside the valid range, and the system-missing value, are output as all spaces.
On input, English weekday or month names (in uppercase or lowercase) are converted back to their corresponding numbers. Weekday and month names may be abbreviated to their first 2 or 3 letters, respectively.
The field width may range from 2 to 40, for WKDAY, or from 3 to 40, for MONTH. No decimal places are allowed.
The default output format is the same as the input format.
Previous: Date Component Formats, Up: Input and Output Formats [Contents][Index]
The A and AHEX formats are the only ones that may be assigned to string variables. Neither format allows any decimal places.
In A format, the entire field is treated as a string value. The field width may range from 1 to 32,767, the maximum string width. The default output format is the same as the input format.
In AHEX format, the field is composed of characters in a string encoded as hex digit pairs. On output, hex digits are output in uppercase; on input, uppercase and lowercase are both accepted. The default output format is A format with half the input width.
Previous: Input and Output Formats, Up: Datasets [Contents][Index]
Most of the time, variables don’t retain their values between cases.
Instead, either they’re being read from a data file or the active dataset,
in which case they assume the value read, or, if created with
COMPUTE
or
another transformation, they’re initialized to the system-missing value
or to blanks, depending on type.
However, sometimes it’s useful to have a variable that keeps its value
between cases. You can do this with LEAVE
(see LEAVE), or you can
use a scratch variable. Scratch variables are variables whose
names begin with an octothorpe (‘#’).
Scratch variables have the same properties as variables left with
LEAVE
: they retain their values between cases, and for the first
case they are initialized to 0 or blanks. They have the additional
property that they are deleted before the execution of any procedure.
For this reason, scratch variables can’t be used for analysis. To use
a scratch variable in an analysis, use COMPUTE
(see COMPUTE)
to copy its value into an ordinary variable, then use that ordinary
variable in the analysis.
Next: File Handles, Previous: Datasets, Up: The PSPP language [Contents][Index]
PSPP makes use of many files each time it runs. Some of these it reads, some it writes, some it creates. Here is a table listing the most important of these files:
These names (synonyms) refer to the file that contains instructions
that tell PSPP what to do. The syntax file’s name is specified on
the PSPP command line. Syntax files can also be read with
INCLUDE
(see INCLUDE).
Data files contain raw data in text or binary format. Data can also
be embedded in a syntax file with BEGIN DATA
and END DATA
.
One or more output files are created by PSPP each time it is run. The output files receive the tables and charts produced by statistical procedures. The output files may be in any number of formats, depending on how PSPP is configured.
System files are binary files that store a dictionary and a set of
cases. GET
and SAVE
read and write system files.
Portable files are files in a text-based format that store a dictionary
and a set of cases. IMPORT
and EXPORT
read and write
portable files.
Next: Backus-Naur Form, Previous: Files Used by PSPP, Up: The PSPP language [Contents][Index]
A file handle is a reference to a data file, system file, or portable file. Most often, a file handle is specified as the name of a file as a string, that is, enclosed within ‘'’ or ‘"’.
A file name string that begins or ends with ‘|’ is treated as the
name of a command to pipe data to or from. You can use this feature
to read data over the network using a program such as ‘curl’
(e.g. GET '|curl -s -S http://example.com/mydata.sav'
), to
read compressed data from a file using a program such as ‘zcat’
(e.g. GET '|zcat mydata.sav.gz'
), and for many other
purposes.
PSPP also supports declaring named file handles with the FILE
HANDLE
command. This command associates an identifier of your choice
(the file handle’s name) with a file. Later, the file handle name can
be substituted for the name of the file. When PSPP syntax accesses a
file multiple times, declaring a named file handle simplifies updating
the syntax later to use a different file. Use of FILE HANDLE
is
also required to read data files in binary formats. See FILE HANDLE,
for more information.
In some circumstances, PSPP must distinguish whether a file handle
refers to a system file or a portable file. When this is necessary to
read a file, e.g. as an input file for GET
or MATCH FILES
,
PSPP uses the file’s contents to decide. In the context of writing a
file, e.g. as an output file for SAVE
or AGGREGATE
, PSPP
decides based on the file’s name: if it ends in ‘.por’ (with any
capitalization), then PSPP writes a portable file; otherwise, PSPP
writes a system file.
INLINE is reserved as a file handle name. It refers to the “data
file” embedded into the syntax file between BEGIN DATA
and
END DATA
. See BEGIN DATA, for more information.
The file to which a file handle refers may be reassigned on a later
FILE HANDLE
command if it is first closed using CLOSE FILE
HANDLE
. See CLOSE FILE HANDLE, for
more information.
Previous: File Handles, Up: The PSPP language [Contents][Index]
The syntax of some parts of the PSPP language is presented in this manual using the formalism known as Backus-Naur Form, or BNF. The following table describes BNF:
number
A real number.
integer
An integer number.
string
A string.
var-name
A single variable name.
=
, /
, +
, -
, etc.Operators and punctuators.
.
The end of the command. This is not necessarily an actual dot in the syntax file (see Forming commands of tokens).
var-list
A list of one or more variable names or the keyword ALL
.
expression
An expression. See Mathematical Expressions, for details.
Next: Data Input and Output, Previous: The PSPP language, Up: GNU PSPP [Contents][Index]
Expressions share a common syntax each place they appear in PSPP commands. Expressions are made up of operands, which can be numbers, strings, or variable names, separated by operators. There are five types of operators: grouping, arithmetic, logical, relational, and functions.
Every operator takes one or more operands as input and yields exactly one result as output. Depending on the operator, operands accept strings or numbers as operands. With few exceptions, operands may be full-fledged expressions in themselves.
Next: Missing Values in Expressions, Up: Mathematical Expressions [Contents][Index]
Some PSPP operators and expressions work with Boolean values, which represent true/false conditions. Booleans have only three possible values: 0 (false), 1 (true), and system-missing (unknown). System-missing is neither true nor false and indicates that the true value is unknown.
Boolean-typed operands or function arguments must take on one of these three values. Other values are considered false, but provoke a warning when the expression is evaluated.
Strings and Booleans are not compatible, and neither may be used in place of the other.
Next: Grouping Operators, Previous: Boolean Values, Up: Mathematical Expressions [Contents][Index]
Most numeric operators yield system-missing when given any system-missing operand. A string operator given any system-missing operand typically results in the empty string. Exceptions are listed under particular operator descriptions.
String user-missing values are not treated specially in expressions.
User-missing values for numeric variables are always transformed into
the system-missing value, except inside the arguments to the
VALUE
and SYSMIS
functions.
The missing-value functions can be used to precisely control how missing values are treated in expressions. See Missing-Value Functions, for more details.
Next: Arithmetic Operators, Previous: Missing Values in Expressions, Up: Mathematical Expressions [Contents][Index]
Parentheses (‘()’) are the grouping operators. Surround an expression with parentheses to force early evaluation.
Parentheses also surround the arguments to functions, but in that situation they act as punctuators, not as operators.
Next: Logical Operators, Previous: Grouping Operators, Up: Mathematical Expressions [Contents][Index]
The arithmetic operators take numeric operands and produce numeric results.
a + b
Yields the sum of a and b.
a - b
Subtracts b from a and yields the difference.
a * b
Yields the product of a and b. If either a or b is 0, then the result is 0, even if the other operand is missing.
a / b
Divides a by b and yields the quotient. If a is 0, then the result is 0, even if b is missing. If b is zero, the result is system-missing.
a ** b
Yields the result of raising a to the power b. If
a is negative and b is not an integer, the result is
system-missing. The result of 0**0
is system-missing as well.
- a
Reverses the sign of a.
Next: Relational Operators, Previous: Arithmetic Operators, Up: Mathematical Expressions [Contents][Index]
The logical operators take logical operands and produce logical results, meaning “true or false.” Logical operators are not true Boolean operators because they may also result in a system-missing value. See Boolean Values, for more information.
a AND b
a & b
True if both a and b are true, false otherwise. If one operand is false, the result is false even if the other is missing. If both operands are missing, the result is missing.
a OR b
a | b
True if at least one of a and b is true. If one operand is true, the result is true even if the other operand is missing. If both operands are missing, the result is missing.
NOT a
~ a
True if a is false. If the operand is missing, then the result is missing.
Next: Functions, Previous: Logical Operators, Up: Mathematical Expressions [Contents][Index]
The relational operators take numeric or string operands and produce Boolean results.
Strings cannot be compared to numbers. When strings of different lengths are compared, the shorter string is right-padded with spaces to match the length of the longer string.
The results of string comparisons, other than tests for equality or inequality, depend on the character set in use. String comparisons are case-sensitive.
a EQ b
a = b
True if a is equal to b.
a LE b
a <= b
True if a is less than or equal to b.
a LT b
a < b
True if a is less than b.
a GE b
a >= b
True if a is greater than or equal to b.
a GT b
a > b
True if a is greater than b.
a NE b
a ~= b
a <> b
True if a is not equal to b.
Next: Operator Precedence, Previous: Relational Operators, Up: Mathematical Expressions [Contents][Index]
PSPP functions provide mathematical abilities above and beyond those possible using simple operators. Functions have a common syntax: each is composed of a function name followed by a left parenthesis, one or more arguments, and a right parenthesis.
Function names are not reserved. Their names are specially treated
only when followed by a left parenthesis, so that ‘EXP(10)’
refers to the constant value e raised to the 10th power, but
‘EXP’ by itself refers to the value of a variable called EXP
.
The sections below describe each function in detail.
Next: Miscellaneous Mathematical Functions, Up: Functions [Contents][Index]
Advanced mathematical functions take numeric arguments and produce numeric results.
Returns e (approximately 2.71828) raised to power exponent.
Takes the base-10 logarithm of number. If number is not positive, the result is system-missing.
Takes the base-e logarithm of number. If number is not positive, the result is system-missing.
Yields the base-e logarithm of the complete gamma of number. If number is a negative integer, the result is system-missing.
Takes the square root of number. If number is negative, the result is system-missing.
Next: Trigonometric Functions, Previous: Mathematical Functions, Up: Functions [Contents][Index]
Miscellaneous mathematical functions take numeric arguments and produce numeric results.
Results in the absolute value of number.
Returns the remainder (modulus) of numerator divided by denominator. If numerator is 0, then the result is 0, even if denominator is missing. If denominator is 0, the result is system-missing.
Returns the remainder when number is divided by 10. If number is negative, MOD10(number) is negative or zero.
Rounds number and rounds it to a multiple of mult (by default 1). Halves are rounded away from zero, as are values that fall short of halves by less than fuzzbits of errors in the least-significant bits of number. If fuzzbits is not specified then the default is taken from SET FUZZBITS (see SET FUZZBITS), which is 6 unless overridden.
Rounds number to a multiple of mult, toward zero. For the default mult of 1, this is equivalent to discarding the fractional part of number. Values that fall short of a multiple of mult by less than fuzzbits of errors in the least-significant bits of number are rounded away from zero. If fuzzbits is not specified then the default is taken from SET FUZZBITS (see SET FUZZBITS), which is 6 unless overridden.
Next: Missing-Value Functions, Previous: Miscellaneous Mathematical Functions, Up: Functions [Contents][Index]
Trigonometric functions take numeric arguments and produce numeric results.
Takes the arccosine, in radians, of number. Results in system-missing if number is not between -1 and 1 inclusive. This function is a PSPP extension.
Takes the arcsine, in radians, of number. Results in system-missing if number is not between -1 and 1 inclusive.
Takes the cosine of angle which should be in radians.
Takes the sine of angle which should be in radians.
Takes the tangent of angle which should be in radians. Results in system-missing at values of angle that are too close to odd multiples of \pi/2. Portability: none.
Next: Set-Membership Functions, Previous: Trigonometric Functions, Up: Functions [Contents][Index]
Missing-value functions take various numeric arguments and yield various types of results. Except where otherwise stated below, the normal rules of evaluation apply within expression arguments to these functions. In particular, user-missing values for numeric variables are converted to system-missing values.
When expr is simply the name of a numeric variable, returns 1 if the variable has the system-missing value or if it is user-missing. For any other value 0 is returned. If expr takes another form, the function returns 1 if the value is system-missing, 0 otherwise.
Each argument must be a numeric expression. Returns the number of
system-missing values in the list, which may include variable ranges
using the var1 TO var2
syntax.
Each argument must be a numeric expression. Returns the number of
values in the list that are not system-missing. The list may include
variable ranges using the var1 TO var2
syntax.
Returns 1 if expr has the system-missing value, 0 otherwise.
Prevents the user-missing values of the variable or vector element from being transformed into system-missing values, and always results in its actual value, whether it is valid, user-missing, or system-missing.
Next: Statistical Functions, Previous: Missing-Value Functions, Up: Functions [Contents][Index]
Set membership functions determine whether a value is a member of a set. They take a set of numeric arguments or a set of string arguments, and produce Boolean results.
String comparisons are performed according to the rules given in Relational Operators. User-missing string values are treated as valid values.
Returns true if value is equal to any of the set values, and false otherwise. For numeric arguments, returns system-missing if value is system-missing or if all the values in set are system-missing. If value
Returns true if value is in any of the intervals bounded by low and high inclusive, and false otherwise. low and high must be given in pairs. Returns system-missing if any high is less than its low or, for numeric arguments, if value is system-missing or if all the low-high pairs contain one (or two) system-missing values. A pair does not match value if either low or high is missing, even if value equals the non-missing endpoint.
Next: String Functions, Previous: Set-Membership Functions, Up: Functions [Contents][Index]
Statistical functions compute descriptive statistics on a list of values. Some statistics can be computed on numeric or string values; other can only be computed on numeric values. Their results have the same type as their arguments. The current case’s weighting factor (see WEIGHT) has no effect on statistical functions.
These functions’ argument lists may include entire ranges of variables
using the var1 TO var2
syntax.
Unlike most functions, statistical functions can return non-missing
values even when some of their arguments are missing. Most
statistical functions, by default, require only 1 non-missing value to
have a non-missing return, but CFVAR
, SD
, and VARIANCE
require 2.
These defaults can be increased (but not decreased) by appending a dot
and the minimum number of valid arguments to the function name. For
example, MEAN.3(X, Y, Z)
would only return non-missing if all
of ‘X’, ‘Y’, and ‘Z’ were valid.
Results in the coefficient of variation of the values of number. (The coefficient of variation is the standard deviation divided by the mean.)
Results in the value of the greatest value. The values may be numeric or string.
Results in the mean of the values of number.
Results in the median of the values of number. Given an even number of nonmissing arguments, yields the mean of the two middle values.
Results in the value of the least value. The values may be numeric or string.
Results in the standard deviation of the values of number.
Results in the sum of the values of number.
Results in the variance of the values of number.
Next: Time & Date Functions, Previous: Statistical Functions, Up: Functions [Contents][Index]
String functions take various arguments and return various results.
Returns a string consisting of each string in sequence.
CONCAT("abc", "def", "ghi")
has a value of "abcdefghi"
.
The resultant string is truncated to a maximum of 32767 bytes.
Returns a positive integer indicating the position of the first (for
INDEX
) or last (for RINDEX
) occurrence of needle
in haystack. Returns 0 if haystack does not contain
needle. Returns 1 if needle is the empty string.
Divides needles into multiple needles, each with length
needle_len, which must be a positive integer that evenly divides
the length of needles. Searches haystack for the
occurrences of each needle and returns a positive integer indicating
the byte index of the beginning of the first (for INDEX
) or
last (for RINDEX
) needle it finds. Returns 0 if haystack
does not contain any of the needles, or if needles is the empty
string.
Returns the number of bytes in string.
Returns a string identical to string except that all uppercase letters are changed to lowercase letters. The definitions of “uppercase” and “lowercase” are system-dependent.
If string is at least length bytes long, these functions
return string unchanged. Otherwise, they return string
padded with padding on the left side (for LPAD
) or right
side (for RPAD
) to length bytes. These functions report
an error and return string unchanged if length is missing
or bigger than 32767.
The padding argument must not be an empty string and defaults to a space if not specified. If its length does not evenly fit the amount of space needed for padding, the returned string will be shorter than length.
These functions return string, after removing leading (for
LTRIM
) or trailing (for RTRIM
) copies of padding.
If padding is omitted, these functions remove spaces (but not
tabs or other white space). These functions return string
unchanged if padding is the empty string.
Returns the number produced when string is interpreted according
to format specifier format. If the format width w is less
than the length of string, then only the first w bytes in
string are used, e.g. NUMBER("123", F3.0)
and
NUMBER("1234", F3.0)
both have value 123. If w is
greater than string’s length, then it is treated as if it were
right-padded with spaces. If string is not in the correct
format for format, system-missing is returned.
Returns string haystack with instances of needle replaced by replacement. If nonnegative integer n is specified, it limits the maximum number of replacements; otherwise, all instances of needle are replaced.
Returns a string corresponding to number in the format given by
format specifier format. For example, STRING(123.56, F5.1)
has the value "123.6"
.
Returns string, first trimming it to at most n bytes, then removing trailing spaces (but not tabs or other white space). Returns an empty string if n is zero or negative, or string unchanged if n is missing.
Returns a string consisting of the value of string from position start onward. Returns an empty string if start is system-missing, less than 1, or greater than the length of string.
Returns a string consisting of the first count bytes from
string beginning at position start. Returns an empty
string if start or count is system-missing, if start
is less than 1 or greater than the number of bytes in string, or
if count is less than 1. Returns a string shorter than
count bytes if start + count - 1 is greater than the
number of bytes in string. Examples: SUBSTR("abcdefg", 3,
2)
has value "cd"
; SUBSTR("nonsense", 4, 10)
has the
value "sense"
.
Returns string, changing lowercase letters to uppercase letters.
Next: Miscellaneous Functions, Previous: String Functions, Up: Functions [Contents][Index]
For compatibility, PSPP considers dates before 15 Oct 1582 invalid. Most time and date functions will not accept earlier dates.
Next: Functions that Produce Times, Up: Time & Date Functions [Contents][Index]
Times and dates are handled by PSPP as single numbers. A time is an interval. PSPP measures times in seconds. Thus, the following intervals correspond with the numeric values given:
10 minutes 600 1 hour 3,600 1 day, 3 hours, 10 seconds 97,210 40 days 3,456,000
A date, on the other hand, is a particular instant in the past or the future. PSPP represents a date as a number of seconds since midnight preceding 14 Oct 1582. Because midnight preceding the dates given below correspond with the numeric PSPP dates given:
15 Oct 1582 86,400 4 Jul 1776 6,113,318,400 1 Jan 1900 10,010,390,400 1 Oct 1978 12,495,427,200 24 Aug 1995 13,028,601,600
Next: Functions that Examine Times, Previous: How times & dates are defined and represented, Up: Time & Date Functions [Contents][Index]
These functions take numeric arguments and return numeric values that represent times.
Returns a time corresponding to ndays days.
Returns a time corresponding to nhours hours, nmins minutes, and nsecs seconds. The arguments may not have mixed signs: if any of them are positive, then none may be negative, and vice versa.
Next: Functions that Produce Dates, Previous: Functions that Produce Times, Up: Time & Date Functions [Contents][Index]
These functions take numeric arguments in PSPP time format and give numeric results.
Results in the number of days and fractional days in time.
Results in the number of hours and fractional hours in time.
Results in the number of minutes and fractional minutes in time.
Results in the number of seconds and fractional seconds in time.
(CTIME.SECONDS
does nothing; CTIME.SECONDS(x)
is
equivalent to x
.)
Next: Functions that Examine Dates, Previous: Functions that Examine Times, Up: Time & Date Functions [Contents][Index]
These functions take numeric arguments and give numeric results that represent dates. Arguments taken by these functions are:
Refers to a day of the month between 1 and 31. Day 0 is also accepted and refers to the final day of the previous month. Days 29, 30, and 31 are accepted even in months that have fewer days and refer to a day near the beginning of the following month.
Refers to a month of the year between 1 and 12. Months 0 and 13 are also accepted and refer to the last month of the preceding year and the first month of the following year, respectively.
Refers to a quarter of the year between 1 and 4. The quarters of the year begin on the first day of months 1, 4, 7, and 10.
Refers to a week of the year between 1 and 53.
Refers to a day of the year between 1 and 366.
Refers to a year, 1582 or greater. Years between 0 and 99 are treated according to the epoch set on SET EPOCH, by default beginning 69 years before the current date (see SET EPOCH).
If these functions’ arguments are out-of-range, they are correctly normalized before conversion to date format. Non-integers are rounded toward zero.
Results in a date value corresponding to the midnight before day day of month month of year year.
Results in a date value corresponding to the midnight before the first day of month month of year year.
Results in a date value corresponding to the midnight before the first day of quarter quarter of year year.
Results in a date value corresponding to the midnight before the first day of week week of year year.
Results in a date value corresponding to the day yday of year year.
Next: Time and Date Arithmetic, Previous: Functions that Produce Dates, Up: Time & Date Functions [Contents][Index]
These functions take numeric arguments in PSPP date or time format and give numeric results. These names are used for arguments:
A numeric value in PSPP date format.
A numeric value in PSPP time format.
A numeric value in PSPP time or date format.
For a time, results in the time corresponding to the number of whole days date-or-time includes. For a date, results in the date corresponding to the latest midnight at or before date-or-time; that is, gives the date that date-or-time is in.
For a time, results in the number of whole hours beyond the number of whole days represented by date-or-time. For a date, results in the hour (as an integer between 0 and 23) corresponding to date-or-time.
Results in the day of the year (as an integer between 1 and 366) corresponding to date.
Results in the day of the month (as an integer between 1 and 31) corresponding to date.
Results in the number of minutes (as an integer between 0 and 59) after the last hour in time-or-date.
Results in the month of the year (as an integer between 1 and 12) corresponding to date.
Results in the quarter of the year (as an integer between 1 and 4) corresponding to date.
Results in the number of whole seconds after the last whole minute (as an integer between 0 and 59) in time-or-date.
Results in the number of whole days from 14 Oct 1582 to date.
Results in the time of day at the instant corresponding to date, as a time value. This is the number of seconds since midnight on the day corresponding to date.
Results in the week of the year (as an integer between 1 and 53) corresponding to date.
Results in the day of week (as an integer between 1 and 7) corresponding to date, where 1 represents Sunday.
Returns the year (as an integer 1582 or greater) corresponding to date.
Previous: Functions that Examine Dates, Up: Time & Date Functions [Contents][Index]
Ordinary arithmetic operations on dates and times often produce sensible results. Adding a time to, or subtracting one from, a date produces a new date that much earlier or later. The difference of two dates yields the time between those dates. Adding two times produces the combined time. Multiplying a time by a scalar produces a time that many times longer. Since times and dates are just numbers, the ordinary addition and subtraction operators are employed for these purposes.
Adding two dates does not produce a useful result.
Dates and times may have very large values. Thus, it is not a good idea to take powers of these values; also, the accuracy of some procedures may be affected. If necessary, convert times or dates in seconds to some other unit, like days or years, before performing analysis.
PSPP supplies a few functions for date arithmetic:
Returns the span of time from date1 to date2 in terms of unit, which must be a quoted string, one of ‘years’, ‘quarters’, ‘months’, ‘weeks’, ‘days’, ‘hours’, ‘minutes’, and ‘seconds’. The result is an integer, truncated toward zero.
One year is considered to span from a given date to the same month, day, and time of day the next year. Thus, from Jan. 1 of one year to Jan. 1 the next year is considered to be a full year, but Feb. 29 of a leap year to the following Feb. 28 is not. Similarly, one month spans from a given day of the month to the same day of the following month. Thus, there is never a full month from Jan. 31 of a given year to any day in the following February.
Returns date advanced by the given quantity of the specified unit, which must be one of the strings ‘years’, ‘quarters’, ‘months’, ‘weeks’, ‘days’, ‘hours’, ‘minutes’, and ‘seconds’.
When unit is ‘years’, ‘quarters’, or ‘months’, only the integer part of quantity is considered. Adding one of these units can cause the day of the month to exceed the number of days in the month. In this case, the method comes into play: if it is omitted or specified as ‘closest’ (as a quoted string), then the resulting day is the last day of the month; otherwise, if it is specified as ‘rollover’, then the extra days roll over into the following month.
When unit is ‘weeks’, ‘days’, ‘hours’, ‘minutes’, or ‘seconds’, the quantity is not rounded to an integer and method, if specified, is ignored.
Next: Statistical Distribution Functions, Previous: Time & Date Functions, Up: Functions [Contents][Index]
variable must be a numeric or string variable name. LAG
yields the value of that variable for the case n before the
current one. Results in system-missing (for numeric variables) or
blanks (for string variables) for the first n cases.
LAG
obtains values from the cases that become the new active
dataset
after a procedure executes. Thus, LAG
will not return values
from cases dropped by transformations such as SELECT IF
, and
transformations like COMPUTE
that modify data will change the
values returned by LAG
. These are both the case whether these
transformations precede or follow the use of LAG
.
If LAG
is used before TEMPORARY
, then the values it returns
are those in cases just before TEMPORARY
. LAG
may not be
used after TEMPORARY
.
If omitted, ncases defaults to 1. Otherwise, ncases must be a small positive constant integer. There is no explicit limit, but use of a large value will increase memory consumption.
year is a year, either between 0 and 99 or at least 1582. Unlike other PSPP date functions, years between 0 and 99 always correspond to 1900 through 1999. month is a month between 1 and 13. day is a day between 0 and 31. A day of 0 refers to the last day of the previous month, and a month of 13 refers to the first month of the next year. year must be in range. year, month, and day must all be integers.
YRMODA
results in the number of days between 15 Oct 1582 and
the date specified, plus one. The date passed to YRMODA
must be
on or after 15 Oct 1582. 15 Oct 1582 has a value of 1.
Returns a string matching the label associated with the current value of variable. If the current value of variable has no associated label, then this function returns the empty string. variable may be a numeric or string variable.
Previous: Miscellaneous Functions, Up: Functions [Contents][Index]
PSPP can calculate several functions of standard statistical distributions. These functions are named systematically based on the function and the distribution. The table below describes the statistical distribution functions in general:
Probability density function for dist. The domain of x depends on dist. For continuous distributions, the result is the density of the probability function at x, and the range is nonnegative real numbers. For discrete distributions, the result is the probability of x.
Cumulative distribution function for dist, that is, the probability that a random variate drawn from the distribution is less than x. The domain of x depends dist. The result is a probability.
Tail probability function for dist, that is, the probability
that a random variate drawn from the distribution is greater than
x. The domain of x depends dist. The result is a
probability. Only a few distributions include an SIG
function.
Inverse distribution function for dist, the value of x for which the CDF would yield p. The value of p is a probability. The range depends on dist and is identical to the domain for the corresponding CDF.
Random variate function for dist. The range depends on the distribution.
Noncentral probability density function. The result is the density of
the given noncentral distribution at x. The domain of x
depends on dist. The range is nonnegative real numbers. Only a
few distributions include an NPDF
function.
Noncentral cumulative distribution function for dist, that is, the probability that a random variate drawn from the given noncentral distribution is less than x. The domain of x depends dist. The result is a probability. Only a few distributions include an NCDF function.
The individual distributions are described individually below.
The following continuous distributions are available:
Beta distribution with shape parameters a and b. The noncentral distribution takes an additional parameter lambda. Constraints: a > 0, b > 0, lambda >= 0, 0 <= x <= 1, 0 <= p <= 1.
Bivariate normal distribution of two standard normal variables with correlation coefficient rho. Two variates x0 and x1 must be provided. Constraints: 0 <= rho <= 1, 0 <= p <= 1.
Cauchy distribution with location parameter a and scale parameter b. Constraints: b > 0, 0 < p < 1.
Chi-squared distribution with df degrees of freedom. The noncentral distribution takes an additional parameter lambda. Constraints: df > 0, lambda > 0, x >= 0, 0 <= p < 1.
Exponential distribution with scale parameter a. The inverse of a represents the rate of decay. Constraints: a > 0, x >= 0, 0 <= p < 1.
Exponential power distribution with positive scale parameter a and nonnegative power parameter b. Constraints: a > 0, b >= 0, x >= 0, 0 <= p <= 1. This distribution is a PSPP extension.
F-distribution of two chi-squared deviates with df1 and df2 degrees of freedom. The noncentral distribution takes an additional parameter lambda. Constraints: df1 > 0, df2 > 0, lambda >= 0, x >= 0, 0 <= p < 1.
Gamma distribution with shape parameter a and scale parameter b. Constraints: a > 0, b > 0, x >= 0, 0 <= p < 1.
Laplace distribution with location parameter a and scale parameter b. Constraints: b > 0, 0 < p < 1.
Levy symmetric alpha-stable distribution with scale c and exponent alpha. Constraints: 0 < alpha <= 2.
Levy skew alpha-stable distribution with scale c, exponent alpha, and skewness parameter beta. Constraints: 0 < alpha <= 2, -1 <= beta <= 1.
Logistic distribution with location parameter a and scale parameter b. Constraints: b > 0, 0 < p < 1.
Lognormal distribution with parameters a and b. Constraints: a > 0, b > 0, x >= 0, 0 <= p < 1.
Normal distribution with mean mu and standard deviation sigma. Constraints: b > 0, 0 < p < 1. Three additional functions are available as shorthand:
Equivalent to CDF.NORMAL(x, 0, 1).
Equivalent to IDF.NORMAL(p, 0, 1).
Equivalent to RV.NORMAL(0, sigma).
Normal tail distribution with lower limit a and standard deviation sigma. This distribution is a PSPP extension. Constraints: a > 0, x > a, 0 < p < 1.
Pareto distribution with threshold parameter a and shape parameter b. Constraints: a > 0, b > 0, x >= a, 0 <= p < 1.
Rayleigh distribution with scale parameter sigma. This distribution is a PSPP extension. Constraints: sigma > 0, x > 0.
Rayleigh tail distribution with lower limit a and scale parameter sigma. This distribution is a PSPP extension. Constraints: a > 0, sigma > 0, x > a.
T-distribution with df degrees of freedom. The noncentral distribution takes an additional parameter lambda. Constraints: df > 0, 0 < p < 1.
Type-1 Gumbel distribution with parameters a and b. This distribution is a PSPP extension. Constraints: 0 < p < 1.
Type-2 Gumbel distribution with parameters a and b. This distribution is a PSPP extension. Constraints: x > 0, 0 < p < 1.
Uniform distribution with parameters a and b. Constraints: a <= x <= b, 0 <= p <= 1. An additional function is available as shorthand:
Equivalent to RV.UNIFORM(0, b).
Weibull distribution with parameters a and b. Constraints: a > 0, b > 0, x >= 0, 0 <= p < 1.
Previous: Continuous Distributions, Up: Statistical Distribution Functions [Contents][Index]
The following discrete distributions are available:
Bernoulli distribution with probability of success p. Constraints: x = 0 or 1, 0 <= p <= 1.
Binomial distribution with n trials and probability of success p. Constraints: integer n > 0, 0 <= p <= 1, integer x <= n.
Geometric distribution with probability of success p. Constraints: 0 <= p <= 1, integer x > 0.
Hypergeometric distribution when b objects out of a are drawn and c of the available objects are distinctive. Constraints: integer a > 0, integer b <= a, integer c <= a, integer x >= 0.
Logarithmic distribution with probability parameter p. Constraints: 0 <= p < 1, x >= 1.
Negative binomial distribution with number of successes parameter n and probability of success parameter p. Constraints: integer n >= 0, 0 < p <= 1, integer x >= 1.
Poisson distribution with mean mu. Constraints: mu > 0, integer x >= 0.
Previous: Functions, Up: Mathematical Expressions [Contents][Index]
The following table describes operator precedence. Smaller-numbered levels in the table have higher precedence. Within a level, operations are always performed from left to right. The first occurrence of ‘-’ represents unary negation, the second binary subtraction.
()
**
-
* /
+ -
= >= > <= < <>
NOT
AND
OR
Next: System and Portable File I/O, Previous: Mathematical Expressions, Up: GNU PSPP [Contents][Index]
Data are the focus of the PSPP language. Each datum belongs to a case (also called an observation). Each case represents an individual or “experimental unit”. For example, in the results of a survey, the names of the respondents, their sex, age, etc. and their responses are all data and the data pertaining to single respondent is a case. This chapter examines the PSPP commands for defining variables and reading and writing data. There are alternative commands to read data from predefined sources such as system files or databases (See GET DATA.)
Note: These commands tell PSPP how to read data, but the data will not actually be read until a procedure is executed.
Next: CLOSE FILE HANDLE, Up: Data Input and Output [Contents][Index]
BEGIN DATA. … END DATA.
BEGIN DATA
and END DATA
can be used to embed raw ASCII
data in a PSPP syntax file. DATA LIST
or another input
procedure must be used before BEGIN DATA
(see DATA LIST).
BEGIN DATA
and END DATA
must be used together. END
DATA
must appear by itself on a single line, with no leading
white space and exactly one space between the words END
and
DATA
, like this:
END DATA.
Next: DATAFILE ATTRIBUTE, Previous: BEGIN DATA, Up: Data Input and Output [Contents][Index]
CLOSE FILE HANDLE handle_name.
CLOSE FILE HANDLE
disassociates the name of a file handle with a
given file. The only specification is the name of the handle to close.
Afterward
FILE HANDLE
.
The file named INLINE, which represents data entered between BEGIN
DATA
and END DATA
, cannot be closed. Attempts to close it with
CLOSE FILE HANDLE
have no effect.
CLOSE FILE HANDLE
is a PSPP extension.
Next: DATASET commands, Previous: CLOSE FILE HANDLE, Up: Data Input and Output [Contents][Index]
DATAFILE ATTRIBUTE ATTRIBUTE=name(’value’) [name(’value’)]… ATTRIBUTE=name[index](’value’) [name[index](’value’)]… DELETE=name [name]… DELETE=name[index] [name[index]]…
DATAFILE ATTRIBUTE
adds, modifies, or removes user-defined
attributes associated with the active dataset. Custom data file
attributes are not interpreted by PSPP, but they are saved as part of
system files and may be used by other software that reads them.
Use the ATTRIBUTE
subcommand to add or modify a custom data file
attribute. Specify the name of the attribute as an identifier
(see Tokens), followed by the desired value, in parentheses, as a
quoted string. Attribute names that begin with $
are reserved
for PSPP’s internal use, and attribute names that begin with @
or $@
are not displayed by most PSPP commands that display
other attributes. Other attribute names are not treated specially.
Attributes may also be organized into arrays. To assign to an array
element, add an integer array index enclosed in square brackets
([
and ]
) between the attribute name and value. Array
indexes start at 1, not 0. An attribute array that has a single
element (number 1) is not distinguished from a non-array attribute.
Use the DELETE
subcommand to delete an attribute. Specify an
attribute name by itself to delete an entire attribute, including all
array elements for attribute arrays. Specify an attribute name
followed by an array index in square brackets to delete a single
element of an attribute array. In the latter case, all the array
elements numbered higher than the deleted element are shifted down,
filling the vacated position.
To associate custom attributes with particular variables, instead of
with the entire active dataset, use VARIABLE ATTRIBUTE
(see VARIABLE ATTRIBUTE) instead.
DATAFILE ATTRIBUTE
takes effect immediately. It is not affected
by conditional and looping structures such as DO IF
or
LOOP
.
Next: DATA LIST, Previous: DATAFILE ATTRIBUTE, Up: Data Input and Output [Contents][Index]
DATASET NAME name [WINDOW={ASIS,FRONT}]. DATASET ACTIVATE name [WINDOW={ASIS,FRONT}]. DATASET COPY name [WINDOW={MINIMIZED,HIDDEN,FRONT}]. DATASET DECLARE name [WINDOW={MINIMIZED,HIDDEN,FRONT}]. DATASET CLOSE {name,*,ALL}. DATASET DISPLAY.
The DATASET
commands simplify use of multiple datasets within a
PSPP session. They allow datasets to be created and destroyed. At
any given time, most PSPP commands work with a single dataset, called
the active dataset.
The DATASET NAME command gives the active dataset the specified name, or if it already had a name, it renames it. If another dataset already had the given name, that dataset is deleted.
The DATASET ACTIVATE command selects the named dataset, which must
already exist, as the active dataset. Before switching the active
dataset, any pending transformations are executed, as if EXECUTE
had been specified. If the active dataset is unnamed before
switching, then it is deleted and becomes unavailable after switching.
The DATASET COPY command creates a new dataset with the specified
name, whose contents are a copy of the active dataset. Any pending
transformations are executed, as if EXECUTE
had been specified,
before making the copy. If a dataset with the given name already
exists, it is replaced. If the name is the name of the active
dataset, then the active dataset becomes unnamed.
The DATASET DECLARE command creates a new dataset that is initially “empty,” that is, it has no dictionary or data. If a dataset with the given name already exists, this has no effect. The new dataset can be used with commands that support output to a dataset, e.g. AGGREGATE (see AGGREGATE).
The DATASET CLOSE command deletes a dataset. If the active dataset is specified by name, or if ‘*’ is specified, then the active dataset becomes unnamed. If a different dataset is specified by name, then it is deleted and becomes unavailable. Specifying ALL deletes all datasets except for the active dataset, which becomes unnamed.
The DATASET DISPLAY command lists all the currently defined datasets.
Many DATASET commands accept an optional WINDOW
subcommand. In the
PSPPIRE GUI, the value given for this subcommand influences how the
dataset’s window is displayed. Outside the GUI, the WINDOW
subcommand
has no effect. The valid values are:
Do not change how the window is displayed. This is the default for DATASET NAME and DATASET ACTIVATE.
Raise the dataset’s window to the top. Make it the default dataset for running syntax.
Display the window “minimized” to an icon. Prefer other datasets for running syntax. This is the default for DATASET COPY and DATASET DECLARE.
Hide the dataset’s window. Prefer other datasets for running syntax.
Next: END CASE, Previous: DATASET commands, Up: Data Input and Output [Contents][Index]
Used to read text or binary data, DATA LIST
is the most
fundamental data-reading command. Even the more sophisticated input
methods use DATA LIST
commands as a building block.
Understanding DATA LIST
is important to understanding how to use
PSPP to read your data files.
There are two major variants of DATA LIST
, which are fixed
format and free format. In addition, free format has a minor variant,
list format, which is discussed in terms of its differences from vanilla
free format.
Each form of DATA LIST
is described in detail below.
See GET DATA, for a command that offers a few enhancements over DATA LIST and that may be substituted for DATA LIST in many situations.
Next: DATA LIST FREE, Up: DATA LIST [Contents][Index]
DATA LIST [FIXED] {TABLE,NOTABLE} [FILE=’file_name’ [ENCODING=’encoding’]] [RECORDS=record_count] [END=end_var] [SKIP=record_count] /[line_no] var_spec… where each var_spec takes one of the forms var_list start-end [type_spec] var_list (fortran_spec)
DATA LIST FIXED
is used to read data files that have values at fixed
positions on each line of single-line or multiline records. The
keyword FIXED is optional.
The FILE
subcommand must be used if input is to be taken from an
external file. It may be used to specify a file name as a string or a
file handle (see File Handles). If the FILE
subcommand is not used,
then input is assumed to be specified within the command file using
BEGIN DATA
…END DATA
(see BEGIN DATA).
The ENCODING
subcommand may only be used if the FILE
subcommand is also used. It specifies the character encoding of the
file. See INSERT, for information on supported encodings.
The optional RECORDS
subcommand, which takes a single integer as an
argument, is used to specify the number of lines per record.
If RECORDS
is not specified, then the number of lines per record is calculated from
the list of variable specifications later in DATA LIST
.
The END
subcommand is only useful in conjunction with INPUT
PROGRAM
. See INPUT PROGRAM, for details.
The optional SKIP
subcommand specifies a number of records to skip at
the beginning of an input file. It can be used to skip over a row
that contains variable names, for example.
DATA LIST
can optionally output a table describing how the data file
is read. The TABLE
subcommand enables this output, and
NOTABLE
disables it. The default is to output the table.
The list of variables to be read from the data list must come last. Each line in the data record is introduced by a slash (‘/’). Optionally, a line number may follow the slash. Following, any number of variable specifications may be present.
Each variable specification consists of a list of variable names
followed by a description of their location on the input line. Sets of
variables may be specified using the DATA LIST
TO
convention
(see Lists of variable names). There are two ways to specify the location of the variable
on the line: columnar style and FORTRAN style.
In columnar style, the starting column and ending column for the field are specified after the variable name, separated by a dash (‘-’). For instance, the third through fifth columns on a line would be specified ‘3-5’. By default, variables are considered to be in ‘F’ format (see Input and Output Formats). (This default can be changed; see SET for more information.)
In columnar style, to use a variable format other than the default, specify the format type in parentheses after the column numbers. For instance, for alphanumeric ‘A’ format, use ‘(A)’.
In addition, implied decimal places can be specified in parentheses after the column numbers. As an example, suppose that a data file has a field in which the characters ‘1234’ should be interpreted as having the value 12.34. Then this field has two implied decimal places, and the corresponding specification would be ‘(2)’. If a field that has implied decimal places contains a decimal point, then the implied decimal places are not applied.
Changing the variable format and adding implied decimal places can be done together; for instance, ‘(N,5)’.
When using columnar style, the input and output width of each variable is computed from the field width. The field width must be evenly divisible into the number of variables specified.
FORTRAN style is an altogether different approach to specifying field locations. With this approach, a list of variable input format specifications, separated by commas, are placed after the variable names inside parentheses. Each format specifier advances as many characters into the input line as it uses.
Implied decimal places also exist in FORTRAN style. A format specification with d decimal places also has d implied decimal places.
In addition to the standard format specifiers (see Input and Output Formats), FORTRAN style defines some extensions:
X
Advance the current column on this line by one character position.
T
xSet the current column on this line to column x, with column numbers considered to begin with 1 at the left margin.
NEWREC
xSkip forward x lines in the current record, resetting the active column to the left margin.
Any format specifier may be preceded by a number. This causes the action of that format specifier to be repeated the specified number of times.
Group the given specifiers together. This is most useful when preceded by a repeat count. Groups may be nested arbitrarily.
FORTRAN and columnar styles may be freely intermixed. Columnar style
leaves the active column immediately after the ending column
specified. Record motion using NEWREC
in FORTRAN style also
applies to later FORTRAN and columnar specifiers.
Up: DATA LIST FIXED [Contents][Index]
DATA LIST TABLE /NAME 1-10 (A) INFO1 TO INFO3 12-17 (1). BEGIN DATA. John Smith 102311 Bob Arnold 122015 Bill Yates 918 6 END DATA.
NAME
, a 10-character-wide string variable, in columns 1
through 10.
INFO1
, a numeric variable, in columns 12 through 13.
INFO2
, a numeric variable, in columns 14 through 15.
INFO3
, a numeric variable, in columns 16 through 17.
The BEGIN DATA
/END DATA
commands cause three cases to be
defined:
Case NAME INFO1 INFO2 INFO3 1 John Smith 10 23 11 2 Bob Arnold 12 20 15 3 Bill Yates 9 18 6
The TABLE
keyword causes PSPP to print out a table
describing the four variables defined.
DATA LIST FILE="survey.dat" /ID 1-5 NAME 7-36 (A) SURNAME 38-67 (A) MINITIAL 69 (A) /Q01 TO Q50 7-56 /.
ID
, a numeric variable, in columns 1-5 of the first record.
NAME
, a 30-character string variable, in columns 7-36 of the
first record.
SURNAME
, a 30-character string variable, in columns 38-67 of
the first record.
MINITIAL
, a 1-character string variable, in column 69 of
the first record.
Q01
, Q02
, Q03
, …, Q49
,
Q50
, all numeric, Q01
in column 7, Q02
in column 8,
…, Q49
in column 55, Q50
in column 56, all in the second
record.
Cases are separated by a blank record.
Data is read from file survey.dat in the current directory.
Next: DATA LIST LIST, Previous: DATA LIST FIXED, Up: DATA LIST [Contents][Index]
DATA LIST FREE [({TAB,’c’}, …)] [{NOTABLE,TABLE}] [FILE=’file_name’ [ENCODING=’encoding’]] [SKIP=n_records] /var_spec… where each var_spec takes one of the forms var_list [(type_spec)] var_list *
In free format, the input data is, by default, structured as a series
of fields separated by spaces, tabs, or line breaks.
If the current DECIMAL
separator is DOT
(see SET),
then commas are also treated as field separators.
Each
field’s content may be unquoted, or it may be quoted with a pairs of
apostrophes (‘'’) or double quotes (‘"’). Unquoted white
space separates fields but is not part of any field. Any mix of
spaces, tabs, and line breaks is equivalent to a single space for the
purpose of separating fields, but consecutive commas will skip a
field.
Alternatively, delimiters can be specified explicitly, as a parenthesized, comma-separated list of single-character strings immediately following FREE. The word TAB may also be used to specify a tab character as a delimiter. When delimiters are specified explicitly, only the given characters, plus line breaks, separate fields. Furthermore, leading spaces at the beginnings of fields are not trimmed, consecutive delimiters define empty fields, and no form of quoting is allowed.
The NOTABLE
and TABLE
subcommands are as in DATA LIST FIXED
above.
NOTABLE
is the default.
The FILE
, SKIP
, and ENCODING
subcommands
are as in DATA LIST FIXED
above.
The variables to be parsed are given as a single list of variable names. This list must be introduced by a single slash (‘/’). The set of variable names may contain format specifications in parentheses (see Input and Output Formats). Format specifications apply to all variables back to the previous parenthesized format specification.
In addition, an asterisk may be used to indicate that all variables preceding it are to have input/output format ‘F8.0’.
Specified field widths are ignored on input, although all normal limits on field width apply, but they are honored on output.
Previous: DATA LIST FREE, Up: DATA LIST [Contents][Index]
DATA LIST LIST [({TAB,’c’}, …)] [{NOTABLE,TABLE}] [FILE=’file_name’ [ENCODING=’encoding’]] [SKIP=record_count] /var_spec… where each var_spec takes one of the forms var_list [(type_spec)] var_list *
With one exception, DATA LIST LIST
is syntactically and
semantically equivalent to DATA LIST FREE
. The exception is
that each input line is expected to correspond to exactly one input
record. If more or fewer fields are found on an input line than
expected, an appropriate diagnostic is issued.
Next: END FILE, Previous: DATA LIST, Up: Data Input and Output [Contents][Index]
END CASE.
END CASE
is used only within INPUT PROGRAM
to output the
current case. See INPUT PROGRAM, for details.
Next: FILE HANDLE, Previous: END CASE, Up: Data Input and Output [Contents][Index]
END FILE.
END FILE
is used only within INPUT PROGRAM
to terminate
the current input program. See INPUT PROGRAM.
Next: INPUT PROGRAM, Previous: END FILE, Up: Data Input and Output [Contents][Index]
For text files: FILE HANDLE handle_name /NAME=’file_name [/MODE=CHARACTER] [/ENDS={CR,CRLF}] /TABWIDTH=tab_width [ENCODING=’encoding’] For binary files in native encoding with fixed-length records: FILE HANDLE handle_name /NAME=’file_name’ /MODE=IMAGE [/LRECL=rec_len] [ENCODING=’encoding’] For binary files in native encoding with variable-length records: FILE HANDLE handle_name /NAME=’file_name’ /MODE=BINARY [/LRECL=rec_len] [ENCODING=’encoding’] For binary files encoded in EBCDIC: FILE HANDLE handle_name /NAME=’file_name’ /MODE=360 /RECFORM={FIXED,VARIABLE,SPANNED} [/LRECL=rec_len] [ENCODING=’encoding’]
Use FILE HANDLE
to associate a file handle name with a file and
its attributes, so that later commands can refer to the file by its
handle name. Names of text files can be specified directly on
commands that access files, so that FILE HANDLE
is only needed when a
file is not an ordinary file containing lines of text. However,
FILE HANDLE
may be used even for text files, and it may be
easier to specify a file’s name once and later refer to it by an
abstract handle.
Specify the file handle name as the identifier immediately following the
FILE HANDLE
command name. The identifier INLINE is reserved for
representing data embedded in the syntax file (see BEGIN DATA) The
file handle name must not already have been used in a previous
invocation of FILE HANDLE
, unless it has been closed by an
intervening command (see CLOSE FILE HANDLE).
The effect and syntax of FILE HANDLE
depends on the selected MODE:
In CHARACTER mode only, tabs are expanded to spaces by input programs,
except by DATA LIST FREE
with explicitly specified delimiters.
Each tab is 4 characters wide by default, but TABWIDTH (a PSPP
extension) may be used to specify an alternate width. Use a TABWIDTH
of 0 to suppress tab expansion.
A file written in CHARACTER mode by default uses the line ends of the system on which PSPP is running, that is, on Windows, the default is CR LF line ends, and on other systems the default is LF only. Specify ENDS as CR or CRLF to override the default. PSPP reads files using either convention on any kind of system, regardless of ENDS.
Alphanumeric data in mode 360 files are encoded in EBCDIC. PSPP translates EBCDIC to or from the host’s native format as necessary on input or output, using an ASCII/EBCDIC translation that is one-to-one, so that a “round trip” from ASCII to EBCDIC back to ASCII, or vice versa, always yields exactly the original data.
The RECFORM
subcommand is required in mode 360. The precise file
format depends on its setting:
This record format is equivalent to IMAGE mode, except for EBCDIC translation.
IBM documentation calls this *F
(fixed-length, deblocked)
format.
The file comprises a sequence of zero or more variable-length blocks. Each block begins with a 4-byte block descriptor word (BDW). The first two bytes of the BDW are an unsigned integer in big-endian byte order that specifies the length of the block, including the BDW itself. The other two bytes of the BDW are ignored on input and written as zeros on output.
Following the BDW, the remainder of each block is a sequence of one or more variable-length records, each of which in turn begins with a 4-byte record descriptor word (RDW) that has the same format as the BDW. Following the RDW, the remainder of each record is the record data.
The maximum length of a record in VARIABLE mode is 65,527 bytes: 65,535 bytes (the maximum value of a 16-bit unsigned integer), minus 4 bytes for the BDW, minus 4 bytes for the RDW.
In mode VARIABLE, LRECL specifies a maximum, not a fixed, record length, in bytes. The default is 8,192.
IBM documentation calls this *VB
(variable-length, blocked,
unspanned) format.
The file format is like that of VARIABLE mode, except that logical records may be split among multiple physical records (called segments) or blocks. In SPANNED mode, the third byte of each RDW is called the segment control character (SCC). Odd SCC values cause the segment to be appended to a record buffer maintained in memory; even values also append the segment and then flush its contents to the input procedure. Canonically, SCC value 0 designates a record not spanned among multiple segments, and values 1 through 3 designate the first segment, the last segment, or an intermediate segment, respectively, within a multi-segment record. The record buffer is also flushed at end of file regardless of the final record’s SCC.
The maximum length of a logical record in VARIABLE mode is limited only by memory available to PSPP. Segments are limited to 65,527 bytes, as in VARIABLE mode.
This format is similar to what IBM documentation call *VS
(variable-length, deblocked, spanned) format.
In mode 360, fields of type A that extend beyond the end of a record
read from disk are padded with spaces in the host’s native character
set, which are then translated from EBCDIC to the native character
set. Thus, when the host’s native character set is based on ASCII,
these fields are effectively padded with character X'80'
. This
wart is implemented for compatibility.
The NAME
subcommand specifies the name of the file associated with the
handle. It is required in all modes but SCRATCH mode, in which its
use is forbidden.
The ENCODING subcommand specifies the encoding of text in the file. For reading text files in CHARACTER mode, all of the forms described for ENCODING on the INSERT command are supported (see INSERT). For reading in other file-based modes, encoding autodetection is not supported; if the specified encoding requests autodetection then the default encoding is used. This is also true when a file handle is used for writing a file in any mode.
Next: LIST, Previous: FILE HANDLE, Up: Data Input and Output [Contents][Index]
INPUT PROGRAM. … input commands … END INPUT PROGRAM.
INPUT PROGRAM
…END INPUT PROGRAM
specifies a
complex input program. By placing data input commands within INPUT
PROGRAM
, PSPP programs can take advantage of more complex file
structures than available with only DATA LIST
.
The first sort of extended input program is to simply put multiple DATA
LIST
commands within the INPUT PROGRAM
. This will cause all of
the data
files to be read in parallel. Input will stop when end of file is
reached on any of the data files.
Transformations, such as conditional and looping constructs, can also be
included within INPUT PROGRAM
. These can be used to combine input
from several data files in more complex ways. However, input will still
stop when end of file is reached on any of the data files.
To prevent INPUT PROGRAM
from terminating at the first end of
file, use
the END
subcommand on DATA LIST
. This subcommand takes a
variable name,
which should be a numeric scratch variable (see Scratch Variables).
(It need not be a scratch variable but otherwise the results can be
surprising.) The value of this variable is set to 0 when reading the
data file, or 1 when end of file is encountered.
Two additional commands are useful in conjunction with INPUT PROGRAM
.
END CASE
is the first. Normally each loop through the
INPUT PROGRAM
structure produces one case. END CASE
controls exactly
when cases are output. When END CASE
is used, looping from the end of
INPUT PROGRAM
to the beginning does not cause a case to be output.
END FILE
is the second. When the END
subcommand is used on DATA
LIST
, there is no way for the INPUT PROGRAM
construct to stop
looping,
so an infinite loop results. END FILE
, when executed,
stops the flow of input data and passes out of the INPUT PROGRAM
structure.
INPUT PROGRAM
must contain at least one DATA LIST
or
END FILE
command.
The following example reads variable X from file a.txt and variable Y from file b.txt. If one file is shorter than the other then the extra data in the longer file is ignored.
INPUT PROGRAM. DATA LIST NOTABLE FILE='a.txt'/X 1-10. DATA LIST NOTABLE FILE='b.txt'/Y 1-10. END INPUT PROGRAM. LIST.
The following example also reads variable X from a.txt and variable Y from b.txt. If one file is shorter than the other then it continues reading the longer to its end, setting the other variable to system-missing.
INPUT PROGRAM. NUMERIC #A #B. DO IF NOT #A. DATA LIST NOTABLE END=#A FILE='a.txt'/X 1-10. END IF. DO IF NOT #B. DATA LIST NOTABLE END=#B FILE='b.txt'/Y 1-10. END IF. DO IF #A AND #B. END FILE. END IF. END CASE. END INPUT PROGRAM. LIST.
The following example reads data from file a.txt, then from b.txt, and concatenates them into a single active dataset.
INPUT PROGRAM. NUMERIC #A #B. DO IF #A. DATA LIST NOTABLE END=#B FILE='b.txt'/X 1-10. DO IF #B. END FILE. ELSE. END CASE. END IF. ELSE. DATA LIST NOTABLE END=#A FILE='a.txt'/X 1-10. DO IF NOT #A. END CASE. END IF. END IF. END INPUT PROGRAM. LIST.
This is another way to do the same thing as Example 3.
INPUT PROGRAM. NUMERIC #EOF. LOOP IF NOT #EOF. DATA LIST NOTABLE END=#EOF FILE='a.txt'/X 1-10. DO IF NOT #EOF. END CASE. END IF. END LOOP. COMPUTE #EOF = 0. LOOP IF NOT #EOF. DATA LIST NOTABLE END=#EOF FILE='b.txt'/X 1-10. DO IF NOT #EOF. END CASE. END IF. END LOOP. END FILE. END INPUT PROGRAM. LIST.
The follows example creates a dataset that consists of 50 random variates between 0 and 10.
INPUT PROGRAM. LOOP #I=1 TO 50. COMPUTE X=UNIFORM(10). END CASE. END LOOP. END FILE. END INPUT PROGRAM. LIST /FORMAT=NUMBERED.
Next: NEW FILE, Previous: INPUT PROGRAM, Up: Data Input and Output [Contents][Index]
LIST /VARIABLES=var_list /CASES=FROM start_index TO end_index BY incr_index /FORMAT={UNNUMBERED,NUMBERED} {WRAP,SINGLE}
The LIST
procedure prints the values of specified variables to the
listing file.
The VARIABLES
subcommand specifies the variables whose values are to be
printed. Keyword VARIABLES is optional. If VARIABLES
subcommand is not
specified then all variables in the active dataset are printed.
The CASES
subcommand can be used to specify a subset of cases to be
printed. Specify FROM
and the case number of the first case to print,
TO
and the case number of the last case to print, and BY
and the number
of cases to advance between printing cases, or any subset of those
settings. If CASES
is not specified then all cases are printed.
The FORMAT
subcommand can be used to change the output format. NUMBERED
will print case numbers along with each case; UNNUMBERED
, the default,
causes the case numbers to be omitted. The WRAP
and SINGLE
settings are
currently not used.
Case numbers start from 1. They are counted after all transformations have been considered.
LIST
is a procedure. It causes the data to be read.
Next: PRINT, Previous: LIST, Up: Data Input and Output [Contents][Index]
NEW FILE.
NEW FILE
command clears the dictionary and data from the current
active dataset.
Next: PRINT EJECT, Previous: NEW FILE, Up: Data Input and Output [Contents][Index]
PRINT [OUTFILE=’file_name’] [RECORDS=n_lines] [{NOTABLE,TABLE}] [ENCODING=’encoding’] [/[line_no] arg…] arg takes one of the following forms: ’string’ [start] var_list start-end [type_spec] var_list (fortran_spec) var_list *
The PRINT
transformation writes variable data to the listing
file or an output file. PRINT
is executed when a procedure
causes the data to be read. Follow PRINT
by EXECUTE
to
print variable data without invoking a procedure (see EXECUTE).
All PRINT
subcommands are optional. If no strings or variables
are specified, PRINT
outputs a single blank line.
The OUTFILE
subcommand specifies the file to receive the output. The
file may be a file name as a string or a file handle (see File Handles). If OUTFILE
is not present then output is sent to
PSPP’s output listing file. When OUTFILE
is present, the
output is written to file_name in a plain text format, with a
space inserted at beginning of each output line, even lines that
otherwise would be blank.
The ENCODING
subcommand may only be used if the
OUTFILE
subcommand is also used. It specifies the character
encoding of the file. See INSERT, for information on supported
encodings.
The RECORDS
subcommand specifies the number of lines to be output. The
number of lines may optionally be surrounded by parentheses.
TABLE
will cause the PRINT
command to output a table to the listing file
that describes what it will print to the output file. NOTABLE
, the
default, suppresses this output table.
Introduce the strings and variables to be printed with a slash (‘/’). Optionally, the slash may be followed by a number indicating which output line is specified. In the absence of this line number, the next line number is specified. Multiple lines may be specified using multiple slashes with the intended output for a line following its respective slash.
Literal strings may be printed. Specify the string itself. Optionally the string may be followed by a column number, specifying the column on the line where the string should start. Otherwise, the string is printed at the current position on the line.
Variables to be printed can be specified in the same ways as available
for DATA LIST FIXED
(see DATA LIST FIXED). In addition, a
variable
list may be followed by an asterisk (‘*’), which indicates that the
variables should be printed in their dictionary print formats, separated
by spaces. A variable list followed by a slash or the end of command
is interpreted in the same way.
If a FORTRAN type specification is used to move backwards on the current line, then text is written at that point on the line, the line is truncated to that length, although additional text being added will again extend the line to that length.
Next: PRINT SPACE, Previous: PRINT, Up: Data Input and Output [Contents][Index]
PRINT EJECT OUTFILE=’file_name’ RECORDS=n_lines {NOTABLE,TABLE} /[line_no] arg… arg takes one of the following forms: ’string’ [start-end] var_list start-end [type_spec] var_list (fortran_spec) var_list *
PRINT EJECT
advances to the beginning of a new output page in
the listing file or output file. It can also output data in the same
way as PRINT
.
All PRINT EJECT
subcommands are optional.
Without OUTFILE
, PRINT EJECT
ejects the current page in
the listing file, then it produces other output, if any is specified.
With OUTFILE
, PRINT EJECT
writes its output to the specified file.
The first line of output is written with ‘1’ inserted in the
first column. Commonly, this is the only line of output. If
additional lines of output are specified, these additional lines are
written with a space inserted in the first column, as with PRINT
.
See PRINT, for more information on syntax and usage.
Next: REREAD, Previous: PRINT EJECT, Up: Data Input and Output [Contents][Index]
PRINT SPACE [OUTFILE=’file_name’] [ENCODING=’encoding’] [n_lines].
PRINT SPACE
prints one or more blank lines to an output file.
The OUTFILE
subcommand is optional. It may be used to direct output to
a file specified by file name as a string or file handle (see File Handles). If OUTFILE is not specified then output is directed to
the listing file.
The ENCODING
subcommand may only be used if OUTFILE
is also used. It specifies the character encoding of the file.
See INSERT, for information on supported encodings.
n_lines is also optional. If present, it is an expression (see Mathematical Expressions) specifying the number of blank lines to be printed. The expression must evaluate to a nonnegative value.
Next: REPEATING DATA, Previous: PRINT SPACE, Up: Data Input and Output [Contents][Index]
REREAD [FILE=handle] [COLUMN=column] [ENCODING=’encoding’].
The REREAD
transformation allows the previous input line in a
data file
already processed by DATA LIST
or another input command to be re-read
for further processing.
The FILE
subcommand, which is optional, is used to specify the file to
have its line re-read. The file must be specified as the name of a file
handle (see File Handles). If FILE is not specified then the last
file specified on DATA LIST
is assumed (last file specified
lexically, not in terms of flow-of-control).
By default, the line re-read is re-read in its entirety. With the
COLUMN
subcommand, a prefix of the line can be exempted from
re-reading. Specify an expression (see Mathematical Expressions) evaluating to
the first column that should be included in the re-read line. Columns
are numbered from 1 at the left margin.
The ENCODING
subcommand may only be used if the FILE
subcommand is also used. It specifies the character encoding of the
file. See INSERT, for information on supported encodings.
Issuing REREAD
multiple times will not back up in the data
file. Instead, it will re-read the same line multiple times.
Next: WRITE, Previous: REREAD, Up: Data Input and Output [Contents][Index]
REPEATING DATA /STARTS=start-end /OCCURS=n_occurs /FILE=’file_name’ /LENGTH=length /CONTINUED[=cont_start-cont_end] /ID=id_start-id_end=id_var /{TABLE,NOTABLE} /DATA=var_spec… where each var_spec takes one of the forms var_list start-end [type_spec] var_list (fortran_spec)
REPEATING DATA
parses groups of data repeating in
a uniform format, possibly with several groups on a single line. Each
group of data corresponds with one case. REPEATING DATA
may only be
used within an INPUT PROGRAM
structure (see INPUT PROGRAM).
When used with DATA LIST
, it
can be used to parse groups of cases that share a subset of variables
but differ in their other data.
The STARTS
subcommand is required. Specify a range of columns, using
literal numbers or numeric variable names. This range specifies the
columns on the first line that are used to contain groups of data. The
ending column is optional. If it is not specified, then the record
width of the input file is used. For the inline file (see BEGIN DATA) this is 80 columns; for a file with fixed record widths it is the
record width; for other files it is 1024 characters by default.
The OCCURS
subcommand is required. It must be a number or the name of a
numeric variable. Its value is the number of groups present in the
current record.
The DATA
subcommand is required. It must be the last subcommand
specified. It is used to specify the data present within each repeating
group. Column numbers are specified relative to the beginning of a
group at column 1. Data is specified in the same way as with DATA LIST
FIXED
(see DATA LIST FIXED).
All other subcommands are optional.
FILE specifies the file to read, either a file name as a string or a
file handle (see File Handles). If FILE is not present then the
default is the last file handle used on DATA LIST
(lexically, not in
terms of flow of control).
By default REPEATING DATA
will output a table describing how it will
parse the input data. Specifying NOTABLE
will disable this behavior;
specifying TABLE will explicitly enable it.
The LENGTH
subcommand specifies the length in characters of each group.
If it is not present then length is inferred from the DATA
subcommand.
LENGTH can be a number or a variable name.
Normally all the data groups are expected to be present on a single
line. Use the CONTINUED
command to indicate that data can be continued
onto additional lines. If data on continuation lines starts at the left
margin and continues through the entire field width, no column
specifications are necessary on CONTINUED
. Otherwise, specify the
possible range of columns in the same way as on STARTS.
When data groups are continued from line to line, it is easy
for cases to get out of sync through careless hand editing. The
ID
subcommand allows a case identifier to be present on each line of
repeating data groups. REPEATING DATA
will check for the same
identifier on each line and report mismatches. Specify the range of
columns that the identifier will occupy, followed by an equals sign
(‘=’) and the identifier variable name. The variable must already
have been declared with NUMERIC
or another command.
REPEATING DATA
should be the last command given within an
INPUT PROGRAM
. It should not be enclosed within a LOOP
structure (see LOOP). Use DATA LIST
before, not after,
REPEATING DATA
.
Previous: REPEATING DATA, Up: Data Input and Output [Contents][Index]
WRITE OUTFILE=’file_name’ RECORDS=n_lines {NOTABLE,TABLE} /[line_no] arg… arg takes one of the following forms: ’string’ [start-end] var_list start-end [type_spec] var_list (fortran_spec) var_list *
WRITE
writes text or binary data to an output file.
See PRINT, for more information on syntax and usage. PRINT
and WRITE
differ in only a few ways:
WRITE
uses write formats by default, whereas PRINT
uses
print formats.
PRINT
inserts a space between variables unless a format is
explicitly specified, but WRITE
never inserts space between
variables in output.
PRINT
inserts a space at the beginning of each line that it
writes to an output file (and PRINT EJECT
inserts ‘1’ at
the beginning of each line that should begin a new page), but
WRITE
does not.
PRINT
outputs the system-missing value according to its
specified output format, whereas WRITE
outputs the
system-missing value as a field filled with spaces. Binary formats
are an exception.
Next: Combining Data Files, Previous: Data Input and Output, Up: GNU PSPP [Contents][Index]
The commands in this chapter read, write, and examine system files and portable files.
Next: EXPORT, Up: System and Portable File I/O [Contents][Index]
APPLY DICTIONARY FROM={’file_name’,file_handle}.
APPLY DICTIONARY
applies the variable labels, value labels,
and missing values taken from a file to corresponding
variables in the active dataset. In some cases it also updates the
weighting variable.
The FROM
clause is mandatory. Use it to specify a system
file or portable file’s name in single quotes, a data set name
(see Datasets), or a file handle name (see File Handles).
The dictionary in the file is be read, but it does not replace the active
dataset’s dictionary. The file’s data is not read.
Only variables with names that exist in both the active dataset and the system file are considered. Variables with the same name but different types (numeric, string) cause an error message. Otherwise, the system file variables’ attributes replace those in their matching active dataset variables:
In addition to properties of variables, some properties of the active file dictionary as a whole are updated:
APPLY DICTIONARY
takes effect immediately. It does not read the
active dataset. The system file is not modified.
Next: GET, Previous: APPLY DICTIONARY, Up: System and Portable File I/O [Contents][Index]
EXPORT /OUTFILE=’file_name’ /UNSELECTED={RETAIN,DELETE} /DIGITS=n /DROP=var_list /KEEP=var_list /RENAME=(src_names=target_names)… /TYPE={COMM,TAPE} /MAP
The EXPORT
procedure writes the active dataset’s dictionary and
data to a specified portable file.
By default, cases excluded with FILTER are written to the
file. These can be excluded by specifying DELETE on the UNSELECTED
subcommand. Specifying RETAIN makes the default explicit.
Portable files express real numbers in base 30. Integers are always
expressed to the maximum precision needed to make them exact.
Non-integers are, by default, expressed to the machine’s maximum
natural precision (approximately 15 decimal digits on many machines).
If many numbers require this many digits, the portable file may
significantly increase in size. As an alternative, the DIGITS
subcommand may be used to specify the number of decimal digits of
precision to write. DIGITS
applies only to non-integers.
The OUTFILE
subcommand, which is the only required subcommand, specifies
the portable file to be written as a file name string or
a file handle (see File Handles).
DROP
, KEEP
, and RENAME
follow the same format as the
SAVE
procedure (see SAVE).
The TYPE
subcommand specifies the character set for use in the
portable file. Its value is currently not used.
The MAP
subcommand is currently ignored.
EXPORT
is a procedure. It causes the active dataset to be read.
Next: GET DATA, Previous: EXPORT, Up: System and Portable File I/O [Contents][Index]
GET /FILE={’file_name’,file_handle} /DROP=var_list /KEEP=var_list /RENAME=(src_names=target_names)… /ENCODING=’encoding’
GET
clears the current dictionary and active dataset and
replaces them with the dictionary and data from a specified file.
The FILE
subcommand is the only required subcommand. Specify
the SPSS system file, SPSS/PC+ system file, or SPSS portable file to
be read as a string file name or a file handle (see File Handles).
By default, all the variables in a file are read. The DROP
subcommand can be used to specify a list of variables that are not to be
read. By contrast, the KEEP
subcommand can be used to specify
variable that are to be read, with all other variables not read.
Normally variables in a file retain the names that they were
saved under. Use the RENAME
subcommand to change these names.
Specify,
within parentheses, a list of variable names followed by an equals sign
(‘=’) and the names that they should be renamed to. Multiple
parenthesized groups of variable names can be included on a single
RENAME
subcommand.
Variables’ names may be swapped using a RENAME
subcommand of the form /RENAME=(A B=B A)
.
Alternate syntax for the RENAME
subcommand allows the parentheses to be
eliminated. When this is done, only a single variable may be renamed at
once. For instance, /RENAME=A=B
. This alternate syntax is
deprecated.
DROP
, KEEP
, and RENAME
are executed in left-to-right order.
Each may be present any number of times. GET
never modifies a
file on disk. Only the active dataset read from the file
is affected by these subcommands.
PSPP automatically detects the encoding of string data in the file,
when possible. The character encoding of old SPSS system files cannot
always be guessed correctly, and SPSS/PC+ system files do not include
any indication of their encoding. Specify the ENCODING
subcommand with an IANA character set name as its string
argument to override the default. Use SYSFILE INFO
to analyze
the encodings that might be valid for a system file. The
ENCODING
subcommand is a PSPP extension.
GET
does not cause the data to be read, only the dictionary. The data
is read later, when a procedure is executed.
Use of GET
to read a portable file is a PSPP extension.
Next: IMPORT, Previous: GET, Up: System and Portable File I/O [Contents][Index]
GET DATA /TYPE={GNM,ODS,PSQL,TXT} …additional subcommands depending on TYPE…
The GET DATA
command is used to read files and other data
sources created by other applications. When this command is executed,
the current dictionary and active dataset are replaced with variables
and data read from the specified source.
The TYPE
subcommand is mandatory and must be the first subcommand
specified. It determines the type of the file or source to read.
PSPP currently supports the following file types:
Spreadsheet files created by Gnumeric (http://gnumeric.org).
Spreadsheet files in OpenDocument format (http://opendocumentformat.org).
Relations from PostgreSQL databases (http://postgresql.org).
Textual data files in columnar and delimited formats.
Each supported file type has additional subcommands, explained in separate sections below.
Next: Postgres Database Queries, Up: GET DATA [Contents][Index]
GET DATA /TYPE={GNM, ODS} /FILE={’file_name’} /SHEET={NAME ’sheet_name’, INDEX n} /CELLRANGE={RANGE ’range’, FULL} /READNAMES={ON, OFF} /ASSUMEDSTRWIDTH=n.
Gnumeric spreadsheets (http://gnumeric.org), and spreadsheets
in OpenDocument format
(http://libreplanet.org/wiki/Group:OpenDocument/Software)
can be read using the GET DATA
command.
Use the TYPE
subcommand to indicate the file’s format.
/TYPE=GNM indicates Gnumeric files,
/TYPE=ODS indicates OpenDocument.
The FILE
subcommand is mandatory.
Use it to specify the name file to be read.
All other subcommands are optional.
The format of each variable is determined by the format of the spreadsheet
cell containing the first datum for the variable.
If this cell is of string (text) format, then the width of the variable is
determined from the length of the string it contains, unless the
ASSUMEDSTRWIDTH
subcommand is given.
The SHEET
subcommand specifies the sheet within the spreadsheet file to read.
There are two forms of the SHEET
subcommand.
In the first form,
/SHEET=name sheet_name
, the string sheet_name is the
name of the sheet to read.
In the second form, /SHEET=index idx
, idx is a
integer which is the index of the sheet to read.
The first sheet has the index 1.
If the SHEET
subcommand is omitted, then the command reads the
first sheet in the file.
The CELLRANGE
subcommand specifies the range of cells within the sheet to read.
If the subcommand is given as /CELLRANGE=FULL
, then the entire
sheet is read.
To read only part of a sheet, use the form
/CELLRANGE=range 'top_left_cell:bottom_right_cell'
.
For example, the subcommand /CELLRANGE=range 'C3:P19'
reads
columns C–P, and rows 3–19 inclusive.
If no CELLRANGE
subcommand is given, then the entire sheet is read.
If /READNAMES=ON
is specified, then the contents of cells of
the first row are used as the names of the variables in which to store
the data from subsequent rows. This is the default.
If /READNAMES=OFF
is
used, then the variables receive automatically assigned names.
The ASSUMEDSTRWIDTH
subcommand specifies the maximum width of string
variables read from the file.
If omitted, the default value is determined from the length of the
string in the first spreadsheet cell for each variable.
Next: Textual Data Files, Previous: Spreadsheet Files, Up: GET DATA [Contents][Index]
GET DATA /TYPE=PSQL /CONNECT={connection info} /SQL={query} [/ASSUMEDSTRWIDTH=w] [/UNENCRYPTED] [/BSIZE=n].
GET DATA /TYPE=PSQL
imports data from a local or remote
Postgres database server.
It automatically creates variables based on the table column names
or the names specified in the SQL query.
PSPP cannot support the full precision of some Postgres data types,
so data of those types will lose some precision when PSPP imports them.
PSPP does not support all Postgres data types.
If PSPP cannot support a datum, GET DATA
issues a warning
and substitutes the system-missing value.
The CONNECT
subcommand is mandatory.
It is a string specifying the parameters of the database server from
which the data should be fetched.
The format of the string is given in the postgres manual
http://www.postgresql.org/docs/8.0/static/libpq.html#LIBPQ-CONNECT.
The SQL
subcommand is mandatory.
It must be a valid SQL string to retrieve data from the database.
The ASSUMEDSTRWIDTH
subcommand specifies the maximum width of string
variables read from the database.
If omitted, the default value is determined from the length of the
string in the first value read for each variable.
The UNENCRYPTED
subcommand allows data to be retrieved over an insecure
connection.
If the connection is not encrypted, and the UNENCRYPTED
subcommand is
not given, then an error occurs.
Whether or not the connection is
encrypted depends upon the underlying psql library and the
capabilities of the database server.
The BSIZE
subcommand serves only to optimise the speed of data transfer.
It specifies an upper limit on
number of cases to fetch from the database at once.
The default value is 4096.
If your SQL statement fetches a large number of cases but only a small number of
variables, then the data transfer may be faster if you increase this value.
Conversely, if the number of variables is large, or if the machine on which
PSPP is running has only a
small amount of memory, then a smaller value is probably better.
The following syntax is an example:
GET DATA /TYPE=PSQL /CONNECT='host=example.com port=5432 dbname=product user=fred passwd=xxxx' /SQL='select * from manufacturer'.
Previous: Postgres Database Queries, Up: GET DATA [Contents][Index]
GET DATA /TYPE=TXT /FILE={’file_name’,file_handle} [ENCODING=’encoding’] [/ARRANGEMENT={DELIMITED,FIXED}] [/FIRSTCASE={first_case}] [/IMPORTCASES=...] …additional subcommands depending on ARRANGEMENT…
When TYPE=TXT is specified, GET DATA reads data in a delimited or fixed columnar format, much like DATA LIST (see DATA LIST).
The FILE
subcommand is mandatory. Specify the file to be read as
a string file name or (for textual data only) a
file handle (see File Handles).
The ENCODING
subcommand specifies the character encoding of
the file to be read. See INSERT, for information on supported
encodings.
The ARRANGEMENT
subcommand determines the file’s basic format.
DELIMITED, the default setting, specifies that fields in the input
data are separated by spaces, tabs, or other user-specified
delimiters. FIXED specifies that fields in the input data appear at
particular fixed column positions within records of a case.
By default, cases are read from the input file starting from the first
line. To skip lines at the beginning of an input file, set FIRSTCASE
to the number of the first line to read: 2 to skip the first line, 3
to skip the first two lines, and so on.
IMPORTCASES
is ignored, for compatibility. Use N OF
CASES
to limit the number of cases read from a file (see N OF CASES), or SAMPLE
to obtain a random sample of cases
(see SAMPLE).
The remaining subcommands apply only to one of the two file arrangements, described below.
Next: Reading Fixed Columnar Data, Up: Textual Data Files [Contents][Index]
GET DATA /TYPE=TXT /FILE={’file_name’,file_handle} [/ARRANGEMENT={DELIMITED,FIXED}] [/FIRSTCASE={first_case}] [/IMPORTCASE={ALL,FIRST max_cases,PERCENT percent}] /DELIMITERS="delimiters" [/QUALIFIER="quotes" [/DELCASE={LINE,VARIABLES n_variables}] /VARIABLES=del_var1 [del_var2]… where each del_var takes the form: variable format
The GET DATA command with TYPE=TXT and ARRANGEMENT=DELIMITED reads input data from text files in delimited format, where fields are separated by a set of user-specified delimiters. Its capabilities are similar to those of DATA LIST FREE (see DATA LIST FREE), with a few enhancements.
The required FILE
subcommand and optional FIRSTCASE
and IMPORTCASE
subcommands are described above (see Textual Data Files).
DELIMITERS
, which is required, specifies the set of characters that
may separate fields. Each character in the string specified on
DELIMITERS
separates one field from the next. The end of a line also
separates fields, regardless of DELIMITERS
. Two consecutive
delimiters in the input yield an empty field, as does a delimiter at
the end of a line. A space character as a delimiter is an exception:
consecutive spaces do not yield an empty field and neither does any
number of spaces at the end of a line.
To use a tab as a delimiter, specify ‘\t’ at the beginning of the
DELIMITERS
string. To use a backslash as a delimiter, specify
‘\\’ as the first delimiter or, if a tab should also be a
delimiter, immediately following ‘\t’. To read a data file in
which each field appears on a separate line, specify the empty string
for DELIMITERS
.
The optional QUALIFIER
subcommand names one or more characters that
can be used to quote values within fields in the input. A field that
begins with one of the specified quote characters ends at the next
matching quote. Intervening delimiters become part of the field,
instead of terminating it. The ability to specify more than one quote
character is a PSPP extension.
The character specified on QUALIFIER
can be embedded within a
field that it quotes by doubling the qualifier. For example, if
‘'’ is specified on QUALIFIER
, then 'a''b'
specifies a field that contains ‘a'b’.
The DELCASE
subcommand controls how data may be broken across lines in
the data file. With LINE, the default setting, each line must contain
all the data for exactly one case. For additional flexibility, to
allow a single case to be split among lines or multiple cases to be
contained on a single line, specify VARIABLES n_variables, where
n_variables is the number of variables per case.
The VARIABLES
subcommand is required and must be the last subcommand.
Specify the name of each variable and its input format (see Input and Output Formats) in the order they should be read from the input
file.
On a Unix-like system, the ‘/etc/passwd’ file has a format similar to this:
root:$1$nyeSP5gD$pDq/:0:0:,,,:/root:/bin/bash blp:$1$BrP/pFg4$g7OG:1000:1000:Ben Pfaff,,,:/home/blp:/bin/bash john:$1$JBuq/Fioq$g4A:1001:1001:John Darrington,,,:/home/john:/bin/bash jhs:$1$D3li4hPL$88X1:1002:1002:Jason Stover,,,:/home/jhs:/bin/csh
The following syntax reads a file in the format used by ‘/etc/passwd’:
GET DATA /TYPE=TXT /FILE='/etc/passwd' /DELIMITERS=':' /VARIABLES=username A20 password A40 uid F10 gid F10 gecos A40 home A40 shell A40.
Consider the following data on used cars:
model year mileage price type age Civic 2002 29883 15900 Si 2 Civic 2003 13415 15900 EX 1 Civic 1992 107000 3800 n/a 12 Accord 2002 26613 17900 EX 1
The following syntax can be used to read the used car data:
GET DATA /TYPE=TXT /FILE='cars.data' /DELIMITERS=' ' /FIRSTCASE=2 /VARIABLES=model A8 year F4 mileage F6 price F5 type A4 age F2.
Consider the following information on animals in a pet store:
'Pet''s Name', "Age", "Color", "Date Received", "Price", "Height", "Type" , (Years), , , (Dollars), , "Rover", 4.5, Brown, "12 Feb 2004", 80, '1''4"', "Dog" "Charlie", , Gold, "5 Apr 2007", 12.3, "3""", "Fish" "Molly", 2, Black, "12 Dec 2006", 25, '5"', "Cat" "Gilly", , White, "10 Apr 2007", 10, "3""", "Guinea Pig"
The following syntax can be used to read the pet store data:
GET DATA /TYPE=TXT /FILE='pets.data' /DELIMITERS=', ' /QUALIFIER='''"' /ESCAPE /FIRSTCASE=3 /VARIABLES=name A10 age F3.1 color A5 received EDATE10 price F5.2 height a5 type a10.
Previous: Reading Delimited Data, Up: Textual Data Files [Contents][Index]
GET DATA /TYPE=TXT /FILE={’file_name’,file_handle} [/ARRANGEMENT={DELIMITED,FIXED}] [/FIRSTCASE={first_case}] [/IMPORTCASE={ALL,FIRST max_cases,PERCENT percent}] [/FIXCASE=n] /VARIABLES fixed_var [fixed_var]… [/rec# fixed_var [fixed_var]…]… where each fixed_var takes the form: variable start-end format
The GET DATA
command with TYPE=TXT and ARRANGEMENT=FIXED reads input
data from text files in fixed format, where each field is located in
particular fixed column positions within records of a case. Its
capabilities are similar to those of DATA LIST FIXED (see DATA LIST FIXED), with a few enhancements.
The required FILE
subcommand and optional FIRSTCASE
and IMPORTCASE
subcommands are described above (see Textual Data Files).
The optional FIXCASE
subcommand may be used to specify the positive
integer number of input lines that make up each case. The default
value is 1.
The VARIABLES
subcommand, which is required, specifies the positions
at which each variable can be found. For each variable, specify its
name, followed by its start and end column separated by ‘-’
(e.g. ‘0-9’), followed by an input format type (e.g.
‘F’) or a full format specification (e.g. ‘DOLLAR12.2’).
For this command, columns are numbered starting from 0 at
the left column. Introduce the variables in the second and later
lines of a case by a slash followed by the number of the line within
the case, e.g. ‘/2’ for the second line.
Consider the following data on used cars:
model year mileage price type age Civic 2002 29883 15900 Si 2 Civic 2003 13415 15900 EX 1 Civic 1992 107000 3800 n/a 12 Accord 2002 26613 17900 EX 1
The following syntax can be used to read the used car data:
GET DATA /TYPE=TXT /FILE='cars.data' /ARRANGEMENT=FIXED /FIRSTCASE=2 /VARIABLES=model 0-7 A year 8-15 F mileage 16-23 F price 24-31 F type 32-40 A age 40-47 F.
Next: SAVE, Previous: GET DATA, Up: System and Portable File I/O [Contents][Index]
IMPORT /FILE=’file_name’ /TYPE={COMM,TAPE} /DROP=var_list /KEEP=var_list /RENAME=(src_names=target_names)…
The IMPORT
transformation clears the active dataset dictionary and
data and
replaces them with a dictionary and data from a system file or
portable file.
The FILE
subcommand, which is the only required subcommand, specifies
the portable file to be read as a file name string or a file handle
(see File Handles).
The TYPE
subcommand is currently not used.
DROP
, KEEP
, and RENAME
follow the syntax used by GET
(see GET).
IMPORT
does not cause the data to be read; only the dictionary. The
data is read later, when a procedure is executed.
Use of IMPORT
to read a system file is a PSPP extension.
Next: SAVE DATA COLLECTION, Previous: IMPORT, Up: System and Portable File I/O [Contents][Index]
SAVE /OUTFILE={’file_name’,file_handle} /UNSELECTED={RETAIN,DELETE} /{UNCOMPRESSED,COMPRESSED,ZCOMPRESSED} /PERMISSIONS={WRITEABLE,READONLY} /DROP=var_list /KEEP=var_list /VERSION=version /RENAME=(src_names=target_names)… /NAMES /MAP
The SAVE
procedure causes the dictionary and data in the active
dataset to
be written to a system file.
OUTFILE is the only required subcommand. Specify the system file to be written as a string file name or a file handle (see File Handles).
By default, cases excluded with FILTER are written to the system file.
These can be excluded by specifying DELETE
on the UNSELECTED
subcommand. Specifying RETAIN
makes the default explicit.
The UNCOMPRESSED
, COMPRESSED
, and
ZCOMPRESSED
subcommand determine the system file’s
compression level:
UNCOMPRESSED
Data is not compressed. Each numeric value uses 8 bytes of disk space. Each string value uses one byte per column width, rounded up to a multiple of 8 bytes.
COMPRESSED
Data is compressed with a simple algorithm. Each integer numeric value between -99 and 151, inclusive, or system missing value uses one byte of disk space. Each 8-byte segment of a string that consists only of spaces uses 1 byte. Any other numeric value or 8-byte string segment uses 9 bytes of disk space.
ZCOMPRESSED
Data is compressed with the “deflate” compression algorithm
specified in RFC 1951 (the same algorithm used by
gzip
). Files written with this compression level cannot be
read by PSPP 0.8.1 or earlier or by SPSS 20 or earlier.
COMPRESSED
is the default compression level. The SET command
(see SET) can change this default.
The PERMISSIONS
subcommand specifies permissions for the new system
file. WRITEABLE, the default, creates the file with read and write
permission. READONLY creates the file for read-only access.
By default, all the variables in the active dataset dictionary are written
to the system file. The DROP
subcommand can be used to specify a list
of variables not to be written. In contrast, KEEP specifies variables
to be written, with all variables not specified not written.
Normally variables are saved to a system file under the same names they
have in the active dataset. Use the RENAME
subcommand to change these names.
Specify, within parentheses, a list of variable names followed by an
equals sign (‘=’) and the names that they should be renamed to.
Multiple parenthesized groups of variable names can be included on a
single RENAME
subcommand. Variables’ names may be swapped using a
RENAME
subcommand of the
form /RENAME=(A B=B A)
.
Alternate syntax for the RENAME
subcommand allows the parentheses to be
eliminated. When this is done, only a single variable may be renamed at
once. For instance, /RENAME=A=B
. This alternate syntax is
deprecated.
DROP
, KEEP
, and RENAME
are performed in
left-to-right order. They
each may be present any number of times. SAVE
never modifies
the active dataset. DROP
, KEEP
, and RENAME
only
affect the system file written to disk.
The VERSION
subcommand specifies the version of the file format. Valid
versions are 2 and 3. The default version is 3. In version 2 system
files, variable names longer than 8 bytes are truncated. The two
versions are otherwise identical.
The NAMES
and MAP
subcommands are currently ignored.
SAVE
causes the data to be read. It is a procedure.
Next: SAVE TRANSLATE, Previous: SAVE, Up: System and Portable File I/O [Contents][Index]
SAVE DATA COLLECTION /OUTFILE={’file_name’,file_handle} /METADATA={’file_name’,file_handle} /{UNCOMPRESSED,COMPRESSED,ZCOMPRESSED} /PERMISSIONS={WRITEABLE,READONLY} /DROP=var_list /KEEP=var_list /VERSION=version /RENAME=(src_names=target_names)… /NAMES /MAP
Like SAVE
, SAVE DATA COLLECTION
writes the dictionary and
data in the active dataset to a system file. In addition, it writes
metadata to an additional XML metadata file.
OUTFILE is required. Specify the system file to be written as a string file name or a file handle (see File Handles).
METADATA is also required. Specify the metadata file to be written as a string file name or a file handle. Metadata files customarily use a .mdd extension.
The current implementation of this command is experimental. It only outputs an approximation of the metadata file format. Please report bugs.
Other subcommands are optional. They have the same meanings as in the
SAVE
command.
SAVE DATA COLLECTION
causes the data to be read. It is a
procedure.
Next: SYSFILE INFO, Previous: SAVE DATA COLLECTION, Up: System and Portable File I/O [Contents][Index]
SAVE TRANSLATE /OUTFILE={’file_name’,file_handle} /TYPE={CSV,TAB} [/REPLACE] [/MISSING={IGNORE,RECODE}] [/DROP=var_list] [/KEEP=var_list] [/RENAME=(src_names=target_names)…] [/UNSELECTED={RETAIN,DELETE}] [/MAP] …additional subcommands depending on TYPE…
The SAVE TRANSLATE
command is used to save data into various
formats understood by other applications.
The OUTFILE
and TYPE
subcommands are mandatory.
OUTFILE
specifies the file to be written, as a string file name or a file handle
(see File Handles). TYPE
determines the type of the file or
source to read. It must be one of the following:
Comma-separated value format,
Tab-delimited format.
By default, SAVE TRANSLATE
does not overwrite an existing file. Use
REPLACE
to force an existing file to be overwritten.
With MISSING=IGNORE, the default, SAVE TRANSLATE
treats user-missing
values as if they were not missing. Specify MISSING=RECODE to output
numeric user-missing values like system-missing values and string
user-missing values as all spaces.
By default, all the variables in the active dataset dictionary are
saved to the system file, but DROP
or KEEP
can
select a subset of variable to save. The RENAME
subcommand
can also be used to change the names under which variables are saved;
because they are used only in the output, these names do not have to
conform to the usual PSPP variable naming rules. UNSELECTED
determines whether cases filtered out by the FILTER
command are
written to the output file. These subcommands have the same syntax
and meaning as on the SAVE
command (see SAVE).
Each supported file type has additional subcommands, explained in separate sections below.
SAVE TRANSLATE
causes the data to be read. It is a procedure.
Up: SAVE TRANSLATE [Contents][Index]
SAVE TRANSLATE /OUTFILE={’file_name’,file_handle} /TYPE=CSV [/REPLACE] [/MISSING={IGNORE,RECODE}] [/DROP=var_list] [/KEEP=var_list] [/RENAME=(src_names=target_names)…] [/UNSELECTED={RETAIN,DELETE}] [/FIELDNAMES] [/CELLS={VALUES,LABELS}] [/TEXTOPTIONS DELIMITER=’delimiter’] [/TEXTOPTIONS QUALIFIER=’qualifier’] [/TEXTOPTIONS DECIMAL={DOT,COMMA}] [/TEXTOPTIONS FORMAT={PLAIN,VARIABLE}]
The SAVE TRANSLATE command with TYPE=CSV or TYPE=TAB writes data in a comma- or tab-separated value format similar to that described by RFC 4180. Each variable becomes one output column, and each case becomes one line of output. If FIELDNAMES is specified, an additional line at the top of the output file lists variable names.
The CELLS and TEXTOPTIONS FORMAT settings determine how values are written to the output file:
Writes variables to the output in “plain” formats that ignore the details of variable formats. Numeric values are written as plain decimal numbers with enough digits to indicate their exact values in machine representation. Numeric values include ‘e’ followed by an exponent if the exponent value would be less than -4 or greater than 16. Dates are written in MM/DD/YYYY format and times in HH:MM:SS format. WKDAY and MONTH values are written as decimal numbers.
Numeric values use, by default, the decimal point character set with SET DECIMAL (see SET DECIMAL). Use DECIMAL=DOT or DECIMAL=COMMA to force a particular decimal point character.
Writes variables using their print formats. Leading and trailing spaces are removed from numeric values, and trailing spaces are removed from string values.
Writes value labels where they exist, and otherwise writes the values themselves as described above.
Regardless of CELLS and TEXTOPTIONS FORMAT, numeric system-missing values are output as a single space.
For TYPE=TAB, tab characters delimit values. For TYPE=CSV, the TEXTOPTIONS DELIMITER and DECIMAL settings determine the character that separate values within a line. If DELIMITER is specified, then the specified string separate values. If DELIMITER is not specified, then the default is a comma with DECIMAL=DOT or a semicolon with DECIMAL=COMMA. If DECIMAL is not given either, it is implied by the decimal point character set with SET DECIMAL (see SET DECIMAL).
The TEXTOPTIONS QUALIFIER setting specifies a character that is output before and after a value that contains the delimiter character or the qualifier character. The default is a double quote (‘"’). A qualifier character that appears within a value is doubled.
Next: XEXPORT, Previous: SAVE TRANSLATE, Up: System and Portable File I/O [Contents][Index]
SYSFILE INFO FILE=’file_name’ [ENCODING=’encoding’].
SYSFILE INFO
reads the dictionary in an SPSS system file,
SPSS/PC+ system file, or SPSS portable file, and displays the
information in its dictionary.
Specify a file name or file handle. SYSFILE INFO
reads that
file and displays information on its dictionary.
PSPP automatically detects the encoding of string data in the file,
when possible. The character encoding of old SPSS system files cannot
always be guessed correctly, and SPSS/PC+ system files do not include
any indication of their encoding. Specify the ENCODING
subcommand with an IANA character set name as its string
argument to override the default, or specify ENCODING='DETECT'
to analyze and report possibly valid encodings for the system file.
The ENCODING
subcommand is a PSPP extension.
SYSFILE INFO
does not affect the current active dataset.
Next: XSAVE, Previous: SYSFILE INFO, Up: System and Portable File I/O [Contents][Index]
XEXPORT /OUTFILE=’file_name’ /DIGITS=n /DROP=var_list /KEEP=var_list /RENAME=(src_names=target_names)… /TYPE={COMM,TAPE} /MAP
The XEXPORT
transformation writes the active dataset dictionary and
data to a specified portable file.
This transformation is a PSPP extension.
It is similar to the EXPORT
procedure, with two differences:
XEXPORT
is a transformation, not a procedure. It is executed when
the data is read by a procedure or procedure-like command.
XEXPORT
does not support the UNSELECTED
subcommand.
See EXPORT, for more information.
Previous: XEXPORT, Up: System and Portable File I/O [Contents][Index]
XSAVE /OUTFILE=’file_name’ /{UNCOMPRESSED,COMPRESSED,ZCOMPRESSED} /PERMISSIONS={WRITEABLE,READONLY} /DROP=var_list /KEEP=var_list /VERSION=version /RENAME=(src_names=target_names)… /NAMES /MAP
The XSAVE
transformation writes the active dataset’s dictionary and
data to a system file. It is similar to the SAVE
procedure, with two differences:
XSAVE
is a transformation, not a procedure. It is executed when
the data is read by a procedure or procedure-like command.
XSAVE
does not support the UNSELECTED
subcommand.
See SAVE, for more information.
Next: Manipulating Variables, Previous: System and Portable File I/O, Up: GNU PSPP [Contents][Index]
This chapter describes commands that allow data from system files, portable files, and open datasets to be combined to form a new active dataset. These commands can combine data files in the following ways:
ADD FILES
interleaves or appends the cases from each input file.
It is used with input files that have variables in common, but
distinct sets of cases.
MATCH FILES
adds the data together in cases that match across
multiple input files. It is used with input files that have cases in
common, but different information about each case.
UPDATE
updates a master data file from data in a set of
transaction files. Each case in a transaction data file modifies a
matching case in the primary data file, or it adds a new case if no
matching case can be found.
These commands share the majority of their syntax, which is described in the following section, followed by one section for each command that describes its specific syntax and semantics.
Next: ADD FILES, Up: Combining Data Files [Contents][Index]
Per input file: /FILE={*,’file_name’} [/RENAME=(src_names=target_names)…] [/IN=var_name] [/SORT] Once per command: /BY var_list[({D|A})] [var_list[({D|A}]]… [/DROP=var_list] [/KEEP=var_list] [/FIRST=var_name] [/LAST=var_name] [/MAP]
This section describes the syntactical features in common among the
ADD FILES
, MATCH FILES
, and UPDATE
commands. The
following sections describe details specific to each command.
Each of these commands reads two or more input files and combines them. The command’s output becomes the new active dataset. None of the commands actually change the input files. Therefore, if you want the changes to become permanent, you must explicitly save them using an appropriate procedure or transformation (see System and Portable File I/O).
The syntax of each command begins with a specification of the files to
be read as input. For each input file, specify FILE with a system
file or portable file’s name as a string, a dataset (see Datasets)
or file handle name, (see File Handles), or an asterisk (‘*’)
to use the active dataset as input. Use of portable files on FILE
is a
PSPP extension.
At least two FILE
subcommands must be specified. If the active dataset
is used as an input source, then TEMPORARY
must not be in
effect.
Each FILE
subcommand may be followed by any number of RENAME
subcommands that specify a parenthesized group or groups of variable
names as they appear in the input file, followed by those variables’
new names, separated by an equals sign (=
),
e.g. /RENAME=(OLD1=NEW1)(OLD2=NEW2)
. To rename a single
variable, the parentheses may be omitted: /RENAME=old=new
.
Within a parenthesized group, variables are renamed simultaneously, so
that /RENAME=(A B=B A)
exchanges the
names of variables A and B.
Otherwise, renaming occurs in left-to-right order.
Each FILE
subcommand may optionally be followed by a single IN
subcommand, which creates a numeric variable with the specified name
and format F1.0. The IN variable takes value 1 in an output case if
the given input file contributed to that output case, and 0 otherwise.
The DROP
, KEEP
, and RENAME
subcommands have no effect on IN variables.
If BY
is used (see below), the SORT
keyword must be specified after a
FILE
if that input file is not already sorted on the BY
variables.
When SORT
is specified, PSPP sorts the input file’s data on the BY
variables before it applies it to the command. When SORT
is used, BY
is required. SORT
is a PSPP extension.
PSPP merges the dictionaries of all of the input files to form the dictionary of the new active dataset, like so:
RENAME
subcommand.
Thus, RENAME
can be used to resolve conflicts.
FILE
that has a file label.
FILE
subcommands are specified.
The remaining subcommands apply to the output file as a whole, rather
than to individual input files. They must be specified at the end of
the command specification, following all of the FILE
and related
subcommands. The most important of these subcommands is BY
, which
specifies a set of one or more variables that may be used to find
corresponding cases in each of the input files. The variables
specified on BY
must be present in all of the input files.
Furthermore, if any of the input files are not sorted on the BY
variables, then SORT
must be specified for those input files.
The variables listed on BY
may include (A) or (D) annotations to
specify ascending or descending sort order. See SORT CASES, for
more details on this notation. Adding (A) or (D) to the BY
subcommand
specification is a PSPP extension.
The DROP
subcommand can be used to specify a list of variables to
exclude from the output. By contrast, the KEEP
subcommand can be used
to specify variables to include in the output; all variables not
listed are dropped. DROP
and KEEP
are executed in left-to-right order
and may be repeated any number of times. DROP
and KEEP
do not affect
variables created by the IN
, FIRST
, and LAST
subcommands, which are
always included in the new active dataset, but they can be used to drop
BY
variables.
The FIRST
and LAST
subcommands are optional. They may only be
specified on MATCH FILES
and ADD FILES
, and only when BY
is used. FIRST
and LIST
each adds a numeric variable to the new
active dataset, with the name given as the subcommand’s argument and F1.0
print and write formats. The value of the FIRST
variable is 1 in the
first output case with a given set of values for the BY
variables, and
0 in other cases. Similarly, the LAST
variable is 1 in the last case
with a given of BY
values, and 0 in other cases.
When any of these commands creates an output case, variables that are only in files that are not present for the current case are set to the system-missing value for numeric variables or spaces for string variables.
These commands may combine any number of files, limited only by the machine’s memory.
Next: MATCH FILES, Previous: Common Syntax, Up: Combining Data Files [Contents][Index]
ADD FILES Per input file: /FILE={*,’file_name’} [/RENAME=(src_names=target_names)…] [/IN=var_name] [/SORT] Once per command: [/BY var_list[({D|A})] [var_list[({D|A})]…]] [/DROP=var_list] [/KEEP=var_list] [/FIRST=var_name] [/LAST=var_name] [/MAP]
ADD FILES
adds cases from multiple input files. The output,
which replaces the active dataset, consists all of the cases in all of
the input files.
ADD FILES
shares the bulk of its syntax with other PSPP commands for
combining multiple data files. See Common Syntax,
above, for an explanation of this common syntax.
When BY
is not used, the output of ADD FILES
consists of all the cases
from the first input file specified, followed by all the cases from
the second file specified, and so on. When BY
is used, the output is
additionally sorted on the BY
variables.
When ADD FILES
creates an output case, variables that are not part of
the input file from which the case was drawn are set to the
system-missing value for numeric variables or spaces for string
variables.
Next: UPDATE, Previous: ADD FILES, Up: Combining Data Files [Contents][Index]
MATCH FILES Per input file: /{FILE,TABLE}={*,’file_name’} [/RENAME=(src_names=target_names)…] [/IN=var_name] [/SORT] Once per command: /BY var_list[({D|A}] [var_list[({D|A})]…] [/DROP=var_list] [/KEEP=var_list] [/FIRST=var_name] [/LAST=var_name] [/MAP]
MATCH FILES
merges sets of corresponding cases in multiple
input files into single cases in the output, combining their data.
MATCH FILES
shares the bulk of its syntax with other PSPP commands for
combining multiple data files. See Common Syntax,
above, for an explanation of this common syntax.
How MATCH FILES
matches up cases from the input files depends on
whether BY
is specified:
BY
is not used, MATCH FILES
combines the first case from each input
file to produce the first output case, then the second case from each
input file for the second output case, and so on. If some input files
have fewer cases than others, then the shorter files do not contribute
to cases output after their input has been exhausted.
BY
is used, MATCH FILES
combines cases from each input file that
have identical values for the BY
variables.
When BY
is used, TABLE
subcommands may be used to introduce table
lookup file. TABLE
has same syntax as FILE
, and the RENAME
, IN
, and
SORT
subcommands may follow a TABLE
in the same way as FILE
.
Regardless of the number of TABLE
s, at least one FILE
must specified.
Table lookup files are treated in the same way as other input files
for most purposes and, in particular, table lookup files must be
sorted on the BY
variables or the SORT
subcommand must be specified
for that TABLE
.
Cases in table lookup files are not consumed after they have been used
once. This means that data in table lookup files can correspond to
any number of cases in FILE
input files. Table lookup files are
analogous to lookup tables in traditional relational database systems.
If a table lookup file contains more than one case with a given set of
BY
variables, only the first case is used.
When MATCH FILES
creates an output case, variables that are only in
files that are not present for the current case are set to the
system-missing value for numeric variables or spaces for string
variables.
Previous: MATCH FILES, Up: Combining Data Files [Contents][Index]
UPDATE Per input file: /FILE={*,’file_name’} [/RENAME=(src_names=target_names)…] [/IN=var_name] [/SORT] Once per command: /BY var_list[({D|A})] [var_list[({D|A})]]… [/DROP=var_list] [/KEEP=var_list] [/MAP]
UPDATE
updates a master file by applying modifications
from one or more transaction files.
UPDATE
shares the bulk of its syntax with other PSPP commands for
combining multiple data files. See Common Syntax,
above, for an explanation of this common syntax.
At least two FILE
subcommands must be specified. The first FILE
subcommand names the master file, and the rest name transaction files.
Every input file must either be sorted on the variables named on the
BY
subcommand, or the SORT
subcommand must be used just after the FILE
subcommand for that input file.
UPDATE
uses the variables specified on the BY
subcommand, which is
required, to attempt to match each case in a transaction file with a
case in the master file:
BY
values, then those are
applied in order to the master file.
When a variable in a transaction file has a missing value or when a string variable’s value is all blanks, that value is never used to update the master file.
Next: Data transformations, Previous: Combining Data Files, Up: GNU PSPP [Contents][Index]
Every value in a dataset is associated with a variable. Variables describe what the values represent and properties of those values, such as the format in which they should be displayed, whether they are numeric or alphabetic and how missing values should be represented. There are several utility commands for examining and adjusting variables.
Next: NUMERIC, Up: Manipulating Variables [Contents][Index]
The DISPLAY
command displays information about the variables in the active dataset.
A variety of different forms of information can be requested.
By default, all variables in the active dataset are displayed. However you can select
variables of interest using the /VARIABLES
subcommand.
DISPLAY [SORTED] NAMES [[/VARIABLES=]var_list]. DISPLAY [SORTED] INDEX [[/VARIABLES=]var_list]. DISPLAY [SORTED] LABELS [[/VARIABLES=]var_list]. DISPLAY [SORTED] VARIABLES [[/VARIABLES=]var_list]. DISPLAY [SORTED] DICTIONARY [[/VARIABLES=]var_list]. DISPLAY [SORTED] SCRATCH [[/VARIABLES=]var_list]. DISPLAY [SORTED] ATTRIBUTES [[/VARIABLES=]var_list]. DISPLAY [SORTED] @ATTRIBUTES [[/VARIABLES=]var_list]. DISPLAY [SORTED] VECTORS.
The following keywords primarily cause information about variables to
be displayed. With these keywords, by default information is
displayed about all variable in the active dataset, in the order that
variables occur in the active dataset dictionary. The SORTED
keyword
causes output to be sorted alphabetically by variable name.
The variables’ names are displayed.
The variables’ names are displayed along with a value describing their position within the active dataset dictionary.
Variable names, positions, and variable labels are displayed.
Variable names, positions, print and write formats, and missing values are displayed.
Variable names, positions, print and write formats, missing values, variable labels, and value labels are displayed.
Variable names are displayed, for scratch variables only (see Scratch Variables).
Datafile and variable attributes are displayed.
The first form of the command omits those attributes
whose names begin with @
or $@
.
In the second for, all datafile and variable attributes are displayed.
With the VECTOR
keyword, DISPLAY
lists all the currently
declared vectors. If the SORTED
keyword is given, the vectors are
listed in alphabetical order; otherwise, they are listed in textual
order of definition within the PSPP syntax file.
For related commands, see DISPLAY DOCUMENTS and DISPLAY FILE LABEL.
Next: STRING, Previous: DISPLAY, Up: Manipulating Variables [Contents][Index]
NUMERIC
explicitly declares new numeric variables, optionally
setting their output formats.
NUMERIC var_list [(fmt_spec)] [/var_list [(fmt_spec)]]…
Specify the names of the new numeric variables as var_list. If you wish to set the variables’ output formats, follow their names by an output format specification in parentheses (see Input and Output Formats); otherwise, the default is F8.2.
Variables created with NUMERIC
are initialized to the
system-missing value.
Next: RENAME VARIABLES, Previous: NUMERIC, Up: Manipulating Variables [Contents][Index]
STRING
creates new string variables.
STRING var_list (fmt_spec) [/var_list (fmt_spec)] […].
Specify a list of names for the variable you want to create, followed by the desired output format specification in parentheses (see Input and Output Formats). Variable widths are implicitly derived from the specified output formats. The created variables will be initialized to spaces.
If you want to create several variables with distinct
output formats, you can either use two or more separate STRING
commands,
or you can specify further variable list and format specification pairs, each separated
from the previous by a slash (‘/’).
The following example is one way to create three string variables; Two of the variables have format A24 and the other A80:
STRING firstname lastname (A24) / address (A80).
Here is another way to achieve the same result:
STRING firstname lastname (A24). STRING address (A80).
… and here is yet another way:
STRING firstname (A24). STRING lastname (A24). STRING address (A80).
Next: SORT VARIABLES, Previous: STRING, Up: Manipulating Variables [Contents][Index]
RENAME VARIABLES
changes the names of variables in the active
dataset.
RENAME VARIABLES (old_names=new_names)… .
Specify lists of the old variable names and new variable names, separated by an equals sign (‘=’), within parentheses. There must be the same number of old and new variable names. Each old variable is renamed to the corresponding new variable name. Multiple parenthesized groups of variables may be specified. When the old and new variable names contain only a single variable name, the parentheses are optional.
RENAME VARIABLES
takes effect immediately. It does not cause the data
to be read.
RENAME VARIABLES
may not be specified following TEMPORARY
(see TEMPORARY).
Next: DELETE VARIABLES, Previous: RENAME VARIABLES, Up: Manipulating Variables [Contents][Index]
SORT VARIABLES
reorders the variables in the active dataset’s dictionary
according to a chosen sort key.
SORT VARIABLES [BY] (NAME | TYPE | FORMAT | LABEL | VALUES | MISSING | MEASURE | ROLE | COLUMNS | ALIGNMENT | ATTRIBUTE name) [(D)].
The main specification is one of the following identifiers, which determines how the variables are sorted:
Sorts the variables according to their names, in a case-insensitive
fashion. However, when variable names differ only in a number at the
end, they are sorted numerically. For example, VAR5
is sorted
before VAR400
even though ‘4’ precedes ‘5’.
Sorts numeric variables before string variables, and shorter string variables before longer ones.
Groups variables by print format; within a format, sorts narrower formats before wider ones; with the same format and width, sorts fewer decimal places before more decimal places. See FORMATS.
Sorts variables without a variable label before those with one. See VARIABLE LABELS.
Sorts variables without value labels before those with some. See VALUE LABELS.
Sorts variables without missing values before those with some. See MISSING VALUES.
Sorts nominal variables first, followed by ordinal variables, followed by scale variables. See VARIABLE LEVEL.
Groups variables according to their role. See VARIABLE ROLE.
Sorts variables in ascending display width. See VARIABLE WIDTH.
Sorts variables according to their alignment, first left-aligned, then right-aligned, then centered. See VARIABLE ALIGNMENT.
Sorts variables according to the first value of their name attribute. Variables without attribute are sorted first. See VARIABLE ATTRIBUTE.
Only one sort criterion can be specified. The sort is “stable,” so to sort on multiple criteria one may perform multiple sorts. For example, the following will sort primarily based on alignment, with variables that have the same alignment ordered based on display width:
SORT VARIABLES BY COLUMNS. SORT VARIABLES BY ALIGNMENT.
Specify (D)
to reverse the sort order.
Next: VARIABLE LABELS, Previous: SORT VARIABLES, Up: Manipulating Variables [Contents][Index]
DELETE VARIABLES
deletes the specified variables from the dictionary.
DELETE VARIABLES var_list.
DELETE VARIABLES
should not be used after defining transformations
but before executing a procedure. If it is used in such a context, it
causes the data to be read. If it is used while TEMPORARY
is in
effect, it causes the temporary transformations to become permanent.
DELETE VARIABLES
may not be used to delete all variables from the
dictionary; use NEW FILE
to do that (see NEW FILE).
Next: PRINT FORMATS, Previous: DELETE VARIABLES, Up: Manipulating Variables [Contents][Index]
In addition to a variable’s name, each variable can have a label. Whereas a variable name is a concise, easy-to-type mnemonic for the variable, a label may be longer and more descriptive.
VARIABLE LABELS variable ’label’ [variable ’label’]…
VARIABLE LABELS
associates explanatory names
with variables. This name, called a variable label, is displayed by
statistical procedures.
Specify each variable followed by its label as a quoted string. Variable-label pairs may be separated by an optional slash ‘/’.
If a listed variable already has a label, the new one replaces it. Specifying an empty string as the label, e.g.‘''’, removes a label.
Next: WRITE FORMATS, Previous: VARIABLE LABELS, Up: Manipulating Variables [Contents][Index]
PRINT FORMATS var_list (fmt_spec) [var_list (fmt_spec)]….
PRINT FORMATS
sets the print formats for the specified
variables to the specified format specification.
Its syntax is identical to that of FORMATS
(see FORMATS),
but PRINT FORMATS
sets only print formats, not write formats.
Next: FORMATS, Previous: PRINT FORMATS, Up: Manipulating Variables [Contents][Index]
WRITE FORMATS var_list (fmt_spec) [var_list (fmt_spec)]….
WRITE FORMATS
sets the write formats for the specified variables
to the specified format specification. Its syntax is identical to
that of FORMATS
(see FORMATS), but WRITE FORMATS
sets only
write formats, not print formats.
Next: VALUE LABELS, Previous: WRITE FORMATS, Up: Manipulating Variables [Contents][Index]
FORMATS var_list (fmt_spec) [var_list (fmt_spec)]….
FORMATS
set both print and write formats for the specified
variables to the specified format specification.
See Input and Output Formats.
Specify a list of variables followed by a format specification in parentheses. The print and write formats of the specified variables will be changed. All of the variables listed together must have the same type and, for string variables, the same width.
Additional lists of variables and formats may be included following the first one.
FORMATS
takes effect immediately. It is not affected by
conditional and looping structures such as DO IF
or LOOP
.
Next: ADD VALUE LABELS, Previous: FORMATS, Up: Manipulating Variables [Contents][Index]
The values of a variable can be associated with an arbitrary text string. In this way, a short value can stand for a longer, more descriptive label.
Both numeric and string variables can be given labels. For string variables, the values are case-sensitive, so that, for example, a capitalized value and its lowercase variant would have to be labeled separately if both are present in the data.
VALUE LABELS /var_list value ’label’ [value ’label’]…
VALUE LABELS
allows values of variables to be associated with labels.
To set up value labels for one or more variables, specify the variable names after a slash (‘/’), followed by a list of values and their associated labels, separated by spaces.
Value labels in output are normally broken into lines automatically. Put ‘\n’ in a label string to force a line break at that point. The label may still be broken into lines at additional points.
Before VALUE LABELS
is executed, any existing value labels
are cleared from the variables specified. Use ADD VALUE LABELS
(see ADD VALUE LABELS) to add value labels without clearing those
already present.
Next: MISSING VALUES, Previous: VALUE LABELS, Up: Manipulating Variables [Contents][Index]
ADD VALUE LABELS
has the same syntax and purpose as VALUE
LABELS
(see VALUE LABELS), but it does not clear value
labels from the variables before adding the ones specified.
ADD VALUE LABELS /var_list value ’label’ [value ’label’]…
Next: VARIABLE ATTRIBUTE, Previous: ADD VALUE LABELS, Up: Manipulating Variables [Contents][Index]
In many situations the data available for analysis is incomplete and a placeholder must be used in place of a value to indicate that the value is unknown. One way that missing values are represented is through the $SYSMIS variable (see Variables Automatically Defined by PSPP). Another, more flexible way is through user-missing values which are determined on a per variable basis.
The MISSING VALUES
command sets user-missing values for variables.
MISSING VALUES var_list (missing_values). where missing_values takes one of the following forms: num1 num1, num2 num1, num2, num3 num1 THRU num2 num1 THRU num2, num3 string1 string1, string2 string1, string2, string3 As part of a range,LO
orLOWEST
may take the place of num1;HI
orHIGHEST
may take the place of num2.
MISSING VALUES
sets user-missing values for numeric and string
variables. Long string variables may have missing values, but
characters after the first 8 bytes of the missing value must be
spaces.
Specify a list of variables, followed by a list of their user-missing
values in parentheses. Up to three discrete values may be given, or,
for numeric variables only, a range of values optionally accompanied by
a single discrete value. Ranges may be open-ended on one end, indicated
through the use of the
keyword LO
or LOWEST
or HI
or HIGHEST
.
The MISSING VALUES
command takes effect immediately. It is not
affected by conditional and looping constructs such as DO IF
or
LOOP
.
Next: VARIABLE ALIGNMENT, Previous: MISSING VALUES, Up: Manipulating Variables [Contents][Index]
VARIABLE ATTRIBUTE
adds, modifies, or removes user-defined
attributes associated with variables in the active dataset. Custom
variable attributes are not interpreted by PSPP, but they are saved as
part of system files and may be used by other software that reads
them.
VARIABLE ATTRIBUTE VARIABLES=var_list ATTRIBUTE=name(’value’) [name(’value’)]… ATTRIBUTE=name[index](’value’) [name[index](’value’)]… DELETE=name [name]… DELETE=name[index] [name[index]]…
The required VARIABLES
subcommand must come first. Specify the
variables to which the following ATTRIBUTE
or DELETE
subcommand
should apply.
Use the ATTRIBUTE
subcommand to add or modify custom variable
attributes. Specify the name of the attribute as an identifier
(see Tokens), followed by the desired value, in parentheses, as a
quoted string. The specified attributes are then added or modified in
the variables specified on VARIABLES
. Attribute names that begin with
$
are reserved for PSPP’s internal use, and attribute names
that begin with @
or $@
are not displayed by most PSPP
commands that display other attributes. Other attribute names are not
treated specially.
Attributes may also be organized into arrays. To assign to an array
element, add an integer array index enclosed in square brackets
([
and ]
) between the attribute name and value. Array
indexes start at 1, not 0. An attribute array that has a single
element (number 1) is not distinguished from a non-array attribute.
Use the DELETE
subcommand to delete an attribute from the variable
specified on VARIABLES
. Specify an attribute name by itself to delete
an entire attribute, including all array elements for attribute
arrays. Specify an attribute name followed by an array index in
square brackets to delete a single element of an attribute array. In
the latter case, all the array elements numbered higher than the
deleted element are shifted down, filling the vacated position.
To associate custom attributes with the entire active dataset, instead of
with particular variables, use DATAFILE ATTRIBUTE
(see DATAFILE ATTRIBUTE) instead.
VARIABLE ATTRIBUTE
takes effect immediately. It is not affected
by conditional and looping structures such as DO IF
or
LOOP
.
Next: VARIABLE WIDTH, Previous: VARIABLE ATTRIBUTE, Up: Manipulating Variables [Contents][Index]
VARIABLE ALIGNMENT
sets the alignment of variables for display editing
purposes. It does not affect the display of variables in the PSPP output.
VARIABLE ALIGNMENT var_list ( LEFT | RIGHT | CENTER ) [ /var_list ( LEFT | RIGHT | CENTER ) ] . . . [ /var_list ( LEFT | RIGHT | CENTER ) ]
Next: VARIABLE LEVEL, Previous: VARIABLE ALIGNMENT, Up: Manipulating Variables [Contents][Index]
VARIABLE WIDTH var_list (width) [ /var_list (width) ] . . . [ /var_list (width) ]
VARIABLE WIDTH
sets the column width of variables for display editing
purposes. It does not affect the display of variables in the PSPP output.
Next: VARIABLE ROLE, Previous: VARIABLE WIDTH, Up: Manipulating Variables [Contents][Index]
VARIABLE LEVEL variables ({SCALE | NOMINAL | ORDINAL})…
VARIABLE LEVEL
sets the measurement level of variables as
specified. See Attributes of Variables, for the definitions of the available
measurement levels.
Next: VECTOR, Previous: VARIABLE LEVEL, Up: Manipulating Variables [Contents][Index]
VARIABLE ROLE /role var_list [/role var_list]…
VARIABLE ROLE
sets the intended role of a variable for use in
dialog boxes in graphical user interfaces. Each role specifies
one of the following roles for the variables that follow it:
INPUT
An input variable, such as an independent variable.
TARGET
An output variable, such as an dependent variable.
BOTH
A variable used for input and output.
NONE
No role assigned. (This is a variable’s default role.)
PARTITION
Used to break the data into groups for testing.
SPLIT
No meaning except for certain third party software. (This role’s
meaning is unrelated to SPLIT FILE
.)
The PSPPIRE GUI does not yet use variable roles as intended.
Next: MRSETS, Previous: VARIABLE ROLE, Up: Manipulating Variables [Contents][Index]
Two possible syntaxes: VECTOR vec_name=var_list. VECTOR vec_name_list(count [format]).
VECTOR
allows a group of variables to be accessed as if they
were consecutive members of an array with a vector(index) notation.
To make a vector out of a set of existing variables, specify a name for the vector followed by an equals sign (‘=’) and the variables to put in the vector. The variables must be all numeric or all string, and string variables must have the same width.
To make a vector and create variables at the same time, specify one or
more vector names followed by a count in parentheses. This will
create variables named vec1
through
veccount
. By default, the new variables are
numeric with format F8.2, but an alternate format may be specified
inside the parentheses before or after the count and separated from it
by white space or a comma. With a string format such as A8, the
variables will be string variables; with a numeric format, they will
be numeric. Variable names including the suffixes may not exceed 64
characters in length, and none of the variables may exist prior to
VECTOR
.
Vectors created with VECTOR
disappear after any procedure or
procedure-like command is executed. The variables contained in the
vectors remain, unless they are scratch variables (see Scratch Variables).
Variables within a vector may be referenced in expressions using
vector(index)
syntax.
Next: LEAVE, Previous: VECTOR, Up: Manipulating Variables [Contents][Index]
MRSETS
creates, modifies, deletes, and displays multiple
response sets. A multiple response set is a set of variables that
represent multiple responses to a survey question.
Multiple responses are represented in one of the two following ways:
MRSETS /MDGROUP NAME=name VARIABLES=var_list VALUE=value [CATEGORYLABELS={VARLABELS,COUNTEDVALUES}] [{LABEL=’label’,LABELSOURCE=VARLABEL}] /MCGROUP NAME=name VARIABLES=var_list [LABEL=’label’] /DELETE NAME={[names],ALL} /DISPLAY NAME={[names],ALL}
Any number of subcommands may be specified in any order.
The MDGROUP
subcommand creates a new multiple dichotomy set or
replaces an existing multiple response set. The NAME
,
VARIABLES
, and
VALUE
specifications are required. The others are optional:
VARIABLES
specifies the variables that belong to the set. At least
two variables must be specified. The variables must be all string or
all numeric.
VALUE
specifies the counted value. If the variables are numeric, the
value must be an integer. If the variables are strings, then the
value must be a string that is no longer than the shortest of the
variables in the set (ignoring trailing spaces).
CATEGORYLABELS
optionally specifies the source of the labels for each
category in the set:
VARLABELS
, the default, uses variable labels or, for variables without
variable labels, variable names. PSPP warns if two variables have the
same variable label, since these categories cannot be distinguished in
output.
COUNTEDVALUES
instead uses each variable’s value label for the counted
value. PSPP warns if two variables have the same value label for the
counted value or if one of the variables lacks a value label, since
such categories cannot be distinguished in output.
LABEL
optionally specifies a label for the multiple response set. If
neither LABEL
nor LABELSOURCE=VARLABEL
is specified, the set is
unlabeled.
LABELSOURCE=VARLABEL
draws the multiple response set’s label from the
first variable label among the variables in the set; if none of the
variables has a label, the name of the first variable is used.
LABELSOURCE=VARLABEL
must be used with CATEGORYLABELS=COUNTEDVALUES
.
It is mutually exclusive with LABEL
.
The MCGROUP
subcommand creates a new multiple category set or
replaces an existing multiple response set. The NAME
and VARIABLES
specifications are required, and LABEL
is optional. Their meanings
are as described above in MDGROUP
. PSPP warns if two variables in the
set have different value labels for a single value, since each of the
variables in the set should have the same possible categories.
The DELETE
subcommand deletes multiple response groups. A list of
groups may be named within a set of required square brackets, or ALL
may be used to delete all groups.
The DISPLAY
subcommand displays information about defined multiple
response sets. Its syntax is the same as the DELETE
subcommand.
Multiple response sets are saved to and read from system files by,
e.g., the SAVE
and GET
command. Otherwise, multiple
response sets are currently used only by third party software.
Previous: MRSETS, Up: Manipulating Variables [Contents][Index]
LEAVE
prevents the specified variables from being
reinitialized whenever a new case is processed.
LEAVE var_list.
Normally, when a data file is processed, every variable in the active
dataset is initialized to the system-missing value or spaces at the
beginning of processing for each case. When a variable has been
specified on LEAVE
, this is not the case. Instead, that variable is
initialized to 0 (not system-missing) or spaces for the first case.
After that, it retains its value between cases.
This becomes useful for counters. For instance, in the example below
the variable SUM
maintains a running total of the values in the ITEM
variable.
DATA LIST /ITEM 1-3. COMPUTE SUM=SUM+ITEM. PRINT /ITEM SUM. LEAVE SUM BEGIN DATA. 123 404 555 999 END DATA.
Partial output from this example:
123 123.00 404 527.00 555 1082.00 999 2081.00
It is best to use LEAVE
command immediately before invoking a
procedure command, because the left status of variables is reset by
certain transformations—for instance, COMPUTE
and IF
.
Left status is also reset by all procedure invocations.
Next: Selecting data for analysis, Previous: Manipulating Variables, Up: GNU PSPP [Contents][Index]
The PSPP procedures examined in this chapter manipulate data and prepare the active dataset for later analyses. They do not produce output, as a rule.
Next: AUTORECODE, Up: Data transformations [Contents][Index]
AGGREGATE [OUTFILE={*,’file_name’,file_handle} [MODE={REPLACE,ADDVARIABLES}]] [/MISSING=COLUMNWISE] [/PRESORTED] [/DOCUMENT] [/BREAK=var_list] /dest_var[’label’]…=agr_func(src_vars[, args]…)…
AGGREGATE
summarizes groups of cases into single cases.
It divides cases into groups that have the same values for one or more
variables called break variables. Several functions are available
for summarizing case contents.
The AGGREGATE
syntax consists of subcommands to control its
behavior, all of which are optional, followed by one or more
destination variable assigments, each of which uses an aggregation
function to define how it is calculated.
The OUTFILE
subcommand, which must be first, names the
destination for AGGREGATE
output. It may name a system file by
file name or file handle (see File Handles), a dataset by its name
(see Datasets), or ‘*’ to replace the active dataset.
AGGREGATE
writes its output to this file.
With OUTFILE=*
only, MODE
may be specified immediately
afterward with the value ADDVARIABLES
or REPLACE
:
REPLACE
, the default, the active dataset is replaced by a new dataset
which contains just the break variables and the destination varibles.
The new file contains as many cases as there are
unique combinations of the break variables.
ADDVARIABLES
, the destination variables are added to those in
the existing active dataset.
Cases that have the same combination of values in their break
variables receive identical values for the destination variables.
The number of cases in the active dataset remains unchanged.
The data must be
sorted on the break variables, that is, ADDVARIABLES
implies PRESORTED
If OUTFILE
is omitted, AGGREGATE
acts as if
OUTFILE=* MODE=ADDVARIABLES
were specified.
By default, AGGREGATE
first sorts the data on the break variables.
If the active dataset is already sorted
or grouped by the break variables, specify
PRESORTED
to save time.
With MODE=ADDVARIABLES
, the data must be pre-sorted.
Specify DOCUMENT
to copy the documents from the active dataset into the
aggregate file (see DOCUMENT). Otherwise, the aggregate file does
not contain any documents, even if the aggregate file replaces the
active dataset.
Normally, AGGREGATE
produces a non-missing value whenever there
is enough non-missing data for the aggregation function in use, that
is, just one non-missing value or, for the SD
and SD.
aggregation functions, two non-missing values. Specify
/MISSING=COLUMNWISE
to make AGGREGATE
output a missing
value when one or more of the input values are missing.
The BREAK
subcommand is optionally but usually present. On
BREAK
, list the variables used to divide the active dataset
into groups to be summarized.
AGGREGATE
is particular about the order of subcommands.
OUTFILE
must be first, followed by MISSING
.
PRESORTED
and DOCUMENT
follow MISSING
, in
either order, followed by BREAK
, then followed by aggregation
variable specifications.
At least one set of aggregation variables is required. Each set comprises a list of aggregation variables, an equals sign (‘=’), the name of an aggregation function (see the list below), and a list of source variables in parentheses. A few aggregation functions do not accept source variables, and some aggregation functions expect additional arguments after the source variable names.
AGGREGATE
typically creates aggregation variables with no
variable label, value labels, or missing values. Their default print
and write formats depend on the aggregation function used, with
details given in the table below. A variable label for an aggregation
variable may be specified just after the variable’s name in the
aggregation variable list.
Each set must have exactly as many source variables as aggregation variables. Each aggregation variable receives the results of applying the specified aggregation function to the corresponding source variable.
The following aggregation functions may be applied only to numeric variables:
MEAN(var_name…)
Arithmetic mean. Limited to numeric values. The default format is F8.2.
MEDIAN(var_name…)
The median value. Limited to numeric values. The default format is F8.2.
SD(var_name…)
Standard deviation of the mean. Limited to numeric values. The default format is F8.2.
SUM(var_name…)
Sum. Limited to numeric values. The default format is F8.2.
These aggregation functions may be applied to numeric and string variables:
CGT(var_name…, value)
CLT(var_name…, value)
CIN(var_name…, low, high)
COUT(var_name…, low, high)
Total weight of cases greater than or less than value or inside or outside the closed range [low,high], respectively. The default format is F5.3.
FGT(var_name…, value)
FLT(var_name…, value)
FIN(var_name…, low, high)
FOUT(var_name…, low, high)
Fraction of values greater than or less than value or inside or outside the closed range [low,high], respectively. The default format is F5.3.
FIRST(var_name…)
LAST(var_name…)
First or last non-missing value, respectively, in break group. The
aggregation variable
receives the complete dictionary information from the source variable.
The sort performed by AGGREGATE
(and by SORT CASES
) is stable.
This means that
the first (or last) case with particular values for the break variables before
sorting is also the first (or last) case in that break group after sorting.
MIN(var_name…)
MAX(var_name…)
Minimum or maximum value, respectively. The aggregation variable receives the complete dictionary information from the source variable.
N(var_name…)
NMISS(var_name…)
Total weight of non-missing or missing values, respectively. The default format is F7.0 if weighting is not enabled, F8.2 if it is (see WEIGHT).
NU(var_name…)
NUMISS(var_name…)
Count of non-missing or missing values, respectively, ignoring case weights. The default format is F7.0.
PGT(var_name…, value)
PLT(var_name…, value)
PIN(var_name…, low, high)
POUT(var_name…, low, high)
Percentage between 0 and 100 of values greater than or less than VALUE or inside or outside the closed range [low,high], respectively. The default format is F5.1.
These aggregation functions do not accept source variables:
N
Total weight of cases aggregated to form this group. The default format is F7.0 if weighting is not enabled, F8.2 if it is (see WEIGHT).
NU
Count of cases aggregated to form this group, ignoring case weights. The default format is F7.0.
Aggregation functions compare string values in terms of internal character codes. On most modern computers, this is ASCII or a superset thereof.
The aggregation functions listed above exclude all user-missing values from calculations. To include user-missing values, insert a period (‘.’) at the end of the function name. (e.g. ‘SUM.’). (Be aware that specifying such a function as the last token on a line causes the period to be interpreted as the end of the command.)
AGGREGATE
both ignores and cancels the current SPLIT FILE
settings (see SPLIT FILE).
The personnel.sav dataset provides the occupations and salaries of
many individuals. For many purposes however such detailed information is
not interesting, but often the aggregated statistics of each occupation are
of interest. In Example 12.1 the AGGREGATE
command is used
to calculate the mean, the median and the standard deviation of each
occupation.
GET FILE="personnel.sav". AGGREGATE OUTFILE=* MODE=REPLACE /BREAK=occupation /occ_mean_salary=MEAN(salary) /occ_median_salary=MEDIAN(salary) /occ_std_dev_salary=SD(salary). LIST. |
Example 12.1: Calculating aggregated statistics from the personnel.sav file.
Since we chose the ‘MODE=REPLACE’ option, in Results 12.1 cases for the individual persons are no longer present. They have each been replaced by a single case per aggregated value.
|
Results 12.1: Aggregated mean, median and standard deviation per occupation.
Note that some values for the standard deviation are blank. This is because there is only one case with the respective occupation.
Next: COMPUTE, Previous: AGGREGATE, Up: Data transformations [Contents][Index]
AUTORECODE VARIABLES=src_vars INTO dest_vars [ /DESCENDING ] [ /PRINT ] [ /GROUP ] [ /BLANK = {VALID, MISSING} ]
The AUTORECODE
procedure considers the n values that a variable
takes on and maps them onto values 1…n on a new numeric
variable.
Subcommand VARIABLES
is the only required subcommand and must come
first. Specify VARIABLES
, an equals sign (‘=’), a list of source
variables, INTO
, and a list of target variables. There must the same
number of source and target variables. The target variables must not
already exist.
AUTORECODE
ordinarily assigns each increasing non-missing value
of a source variable (for a string, this is based on character code
comparisons) to consecutive values of its target variable. For
example, the smallest non-missing value of the source variable is
recoded to value 1, the next smallest to 2, and so on. If the source
variable has user-missing values, they are recoded to
consecutive values just above the non-missing values. For example, if
a source variables has seven distinct non-missing values, then the
smallest missing value would be recoded to 8, the next smallest to 9,
and so on.
Use DESCENDING
to reverse the sort order for non-missing
values, so that the largest non-missing value is recoded to 1, the
second-largest to 2, and so on. Even with DESCENDING
,
user-missing values are still recoded in ascending order just above
the non-missing values.
The system-missing value is always recoded into the system-missing variable in target variables.
If a source value has a value label, then that value label is retained for the new value in the target variable. Otherwise, the source value itself becomes each new value’s label.
Variable labels are copied from the source to target variables.
PRINT
is currently ignored.
The GROUP
subcommand is relevant only if more than one variable is to be
recoded. It causes a single mapping between source and target values to
be used, instead of one map per variable. With GROUP
,
user-missing values are taken from the first source variable that has
any user-missing values.
If /BLANK=MISSING
is given, then string variables which contain only
whitespace are recoded as SYSMIS. If /BLANK=VALID
is specified then they
are allocated a value like any other. /BLANK
is not relevant
to numeric values. /BLANK=VALID
is the default.
AUTORECODE
is a procedure. It causes the data to be read.
In the file personnel.sav, the variable occupation is a string
variable. Except for data of a purely commentary nature, string variables
are generally a bad idea. One reason is that data entry errors are easily
overlooked. This has happened in personnel.sav; one entry which should
read “Scientist” has been mistyped as “Scrientist”. In Example 12.2
first, this error is corrected by the DO IF
clause,
3
then we use AUTORECODE
to
create a new numeric variable which takes recoded values of occupation.
Finally, we remove the old variable and rename the new variable to
the name of the old variable.
get file='personnel.sav'. * Correct a typing error in the original file. do if occupation = "Scrientist". compute occupation = "Scientist". end if. autorecode variables = occupation into occ /blank = missing. * Delete the old variable. delete variables occupation. * Rename the new variable to the old variable's name. rename variables (occ = occupation). * Inspect the new variable. display dictionary /variables=occupation. |
Example 12.2: Changing a string variable to a numeric variable using AUTORECODE
after correcting a data entry error
![]() |
Screenshot 12.1: Autorecode dialog box set to recode occupation to occ
Notice in Result 12.1, how the new variable has been automatically allocated value labels which correspond to the strings of the old variable. This means that in future analyses the descriptive strings are reported instead of the numeric values.
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Result 12.1: The properties of the occupation variable following AUTORECODE
Next: COUNT, Previous: AUTORECODE, Up: Data transformations [Contents][Index]
COMPUTE variable = expression.
or
COMPUTE vector(index) = expression.
COMPUTE
assigns the value of an expression to a target
variable. For each case, the expression is evaluated and its value
assigned to the target variable. Numeric and string
variables may be assigned. When a string expression’s width differs
from the target variable’s width, the string result of the expression
is truncated or padded with spaces on the right as necessary. The
expression and variable types must match.
For numeric variables only, the target variable need not already
exist. Numeric variables created by COMPUTE
are assigned an
F8.2
output format. String variables must be declared before
they can be used as targets for COMPUTE
.
The target variable may be specified as an element of a vector (see VECTOR). In this case, an expression index must be specified in parentheses following the vector name. The expression index must evaluate to a numeric value that, after rounding down to the nearest integer, is a valid index for the named vector.
Using COMPUTE
to assign to a variable specified on LEAVE
(see LEAVE) resets the variable’s left state. Therefore,
LEAVE
should be specified following COMPUTE
, not before.
COMPUTE
is a transformation. It does not cause the active dataset to be
read.
When COMPUTE
is specified following TEMPORARY
(see TEMPORARY), the LAG
function may not be used
(see LAG).
The dataset physiology.sav contains the height and weight of persons. For some purposes, neither height nor weight alone is of interest. Epidemiologists are often more interested in the body mass index which can sometimes be used as a predictor for clinical conditions. The body mass index is defined as the weight of the person in kilograms divided by the square of the person’s height in metres. 4
get file='physiology.sav'. * height is in mm so we must divide by 1000 to get metres. compute bmi = weight / (height/1000)**2. variable label bmi "Body Mass Index". descriptives /weight height bmi. |
Example 12.3: Computing the body mass index from weight and height
Example 12.3 shows how you can use COMPUTE
to generate a new variable called
bmi and have every case’s value calculated from the existing values of
weight and height.
It also shows how you can add a label to this new variable (see VARIABLE LABELS),
so that a more descriptive label appears in subsequent analyses, and this can be seen
in the ouput from the DESCRIPTIVES
command in Results 12.2.
![]() |
Screenshot 12.2: Using the dialog box to generate a new variable and compute its values
The expression which follows the ‘=’ sign can be as complicated as necessary. See Mathematical Expressions for a precise description of the language accepted. Normally it is easiest to enter the code directly, however there is a dialog box available if desired. This is illustrated in Screenshot 12.2. One advantage is that it offers a list of mathematical functions which can be selected and pasted into the expression.
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Results 12.2: An analysis which includes bmi in its results
Next: FLIP, Previous: COMPUTE, Up: Data transformations [Contents][Index]
COUNT var_name = var… (value…) [/var_name = var… (value…)]… Each value takes one of the following forms: number string num1 THRU num2 MISSING SYSMIS where num1 is a numeric expression or the wordsLO
orLOWEST
and num2 is a numeric expression orHI
orHIGHEST
.
COUNT
creates or replaces a numeric target variable that
counts the occurrence of a criterion value or set of values over
one or more test variables for each case.
The target variable values are always nonnegative integers. They are never missing. The target variable is assigned an F8.2 output format. See Input and Output Formats. Any variables, including string variables, may be test variables.
User-missing values of test variables are treated just like any other
values. They are not treated as system-missing values.
User-missing values that are criterion values or inside ranges of
criterion values are counted as any other values. However (for numeric
variables), keyword MISSING
may be used to refer to all system-
and user-missing values.
COUNT
target variables are assigned values in the order
specified. In the command COUNT A=A B(1) /B=A B(2).
, the
following actions occur:
Despite this ordering, all COUNT
criterion variables must exist
before the procedure is executed—they may not be created as target
variables earlier in the command! Break such a command into two
separate commands.
In the survey results in dataset hotel.sav a manager wishes to know how many respondents answered with low valued answers to questions v1, v2 and v3. This can be found using the code in Example 12.4. Specifically, this code creates a new variable, and populates it with the number of values in v1–v2 which are 2 or lower.
get file="hotel.sav". count low_counts = v1 v2 v3 (low thru 2). list /variables v1 v2 v3 low_counts. |
Example 12.4: Counting low values to responses v1, v2 and v3
In Example 12.4 the COUNT
transformation creates a new variable, low_counts and
its values are shown using the LIST
command.
If using the graphic user interface, a two step process must be used to set
up the COUNT
transformation. The first dialog box (Screenshot 12.3) provides for the
variables to be chosen.
Then, one must click on the button marked “Define Values...” to reveal
the dialog box for selecting the values to count.
![]() |
Screenshot 12.3: The variables v1, v2 and v3 selected, ready to define values to count
In this dialog box, you must select the values you wish to count — in this case all values up to and including 2 — as shown in Screenshot 12.4 and click “Add”. As many ranges or may be added as you desire. When all desired ranges have been added click “Continue”.
![]() |
Screenshot 12.4: Count “Define Values” dialog with ‘lowest thru 2’ selected
In Result 12.2 we can see the values of low_counts after the COUNT
transformation has completed. The first value is 1, because there is only one
variable amoung v1, v2 and 3 which has a value of 2 or less.
The second value is 2, because both v1 and v2 are 2 or less.
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Result 12.2: The values of v1, v2, v3 and low_counts after
the COUNT
transformation has run
Next: IF, Previous: COUNT, Up: Data transformations [Contents][Index]
FLIP /VARIABLES=var_list /NEWNAMES=var_name.
FLIP
transposes rows and columns in the active dataset. It
causes cases to be swapped with variables, and vice versa.
All variables in the transposed active dataset are numeric. String variables take on the system-missing value in the transposed file.
N
subcommands are required. If specified, the VARIABLES
subcommand
selects variables to be transformed into cases, and variables not
specified are discarded. If the VARIABLES
subcommand is omitted, all
variables are selected for transposition.
The variables specified by NEWNAMES
, which must be a
string variable, is
used to give names to the variables created by FLIP
. Only the
first 8 characters of the variable are used. If
NEWNAMES
is not
specified then the default is a variable named CASE_LBL, if it exists.
If it does not then the variables created by FLIP
are named VAR000
through VAR999, then VAR1000, VAR1001, and so on.
When a NEWNAMES
variable is available, the names must be canonicalized
before becoming variable names. Invalid characters are replaced by
letter ‘V’ in the first position, or by ‘_’ in subsequent
positions. If the name thus generated is not unique, then numeric
extensions are added, starting with 1, until a unique name is found or
there are no remaining possibilities. If the latter occurs then the
FLIP
operation aborts.
The resultant dictionary contains a CASE_LBL variable, a string
variable of width 8, which stores the names of the variables in the
dictionary before the transposition. Variables names longer than 8
characters are truncated. If FLIP
is called again on
this dataset, the CASE_LBL variable can be passed to the NEWNAMES
subcommand to recreate the original variable names.
FLIP
honors N OF CASES
(see N OF CASES). It ignores
TEMPORARY
(see TEMPORARY), so that “temporary”
transformations become permanent.
In Example 12.5, data has been entered using DATA LIST
(see DATA LIST)
such that the first variable in the dataset is a string variable containing
a description of the other data for the case.
Clearly this is not a convenient arrangement for performing statistical analyses,
so it would have been better to think a little more carefully about how the data
should have been arranged.
However often the data is provided by some third party source, and you have
no control over the form.
Fortunately, we can use FLIP
to exchange the variables
and cases in the active dataset.
data list notable list /heading (a16) v1 v2 v3 v4 v5 v6 begin data. date-of-birth 1970 1989 2001 1966 1976 1982 sex 1 0 0 1 0 1 score 10 10 9 3 8 9 end data. echo 'Before FLIP:'. display variables. list. flip /variables = all /newnames = heading. echo 'After FLIP:'. display variables. list. |
Example 12.5: Using FLIP
to exchange variables and cases in a dataset
As you can see in Results 12.3 before the FLIP
command has run there
are seven variables (six containing data and one for the heading) and three cases.
Afterwards there are four variables (one per case, plus the CASE_LBL variable)
and six cases.
You can delete the CASE_LBL variable (see DELETE VARIABLES) if you don’t need it.
Before FLIP:
After FLIP:
|
Results 12.3: The results of using FLIP
to exchange variables and cases in a dataset
Next: RECODE, Previous: FLIP, Up: Data transformations [Contents][Index]
IF condition variable=expression.
or
IF condition vector(index)=expression.
The IF
transformation conditionally assigns the value of a target
expression to a target variable, based on the truth of a test
expression.
Specify a boolean-valued expression (see Mathematical Expressions) to be tested
following the IF
keyword. This expression is evaluated for each case.
If the value is true, then the value of the expression is computed and
assigned to the specified variable. If the value is false or missing,
nothing is done. Numeric and string variables may be
assigned. When a string expression’s width differs from the target
variable’s width, the string result of the expression is truncated or
padded with spaces on the right as necessary. The expression and
variable types must match.
The target variable may be specified as an element of a vector (see VECTOR). In this case, a vector index expression must be specified in parentheses following the vector name. The index expression must evaluate to a numeric value that, after rounding down to the nearest integer, is a valid index for the named vector.
Using IF
to assign to a variable specified on LEAVE
(see LEAVE) resets the variable’s left state. Therefore,
LEAVE
should be specified following IF
, not before.
When IF
is specified following TEMPORARY
(see TEMPORARY), the LAG
function may not be used
(see LAG).
Next: SORT CASES, Previous: IF, Up: Data transformations [Contents][Index]
The RECODE
command is used to transform existing values into other,
user specified values.
The general form is:
RECODE src_vars (src_value src_value … = dest_value) (src_value src_value … = dest_value) (src_value src_value … = dest_value) … [INTO dest_vars].
Following the RECODE
keyword itself comes src_vars which is a list
of variables whose values are to be transformed.
These variables may be string variables or they may be numeric.
However the list must be homogeneous; you may not mix string variables and
numeric variables in the same recoding.
After the list of source variables, there should be one or more mappings. Each mapping is enclosed in parentheses, and contains the source values and a destination value separated by a single ‘=’. The source values are used to specify the values in the dataset which need to change, and the destination value specifies the new value to which they should be changed. Each src_value may take one of the following forms:
If the source variables are numeric then src_value may be a literal number.
If the source variables are string variables then src_value may be a literal string (like all strings, enclosed in single or double quotes).
This form is valid only when the source variables are numeric. It specifies all values in the range between num1 and num2, including both endpoints of the range. By convention, num1 should be less than num2. Open-ended ranges may be specified using ‘LO’ or ‘LOWEST’ for num1 or ‘HI’ or ‘HIGHEST’ for num2.
The literal keyword ‘MISSING’ matches both system missing and user missing values. It is valid for both numeric and string variables.
The literal keyword ‘SYSMIS’ matches system missing values. It is valid for both numeric variables only.
The ‘ELSE’ keyword may be used to match any values which are not matched by any other src_value appearing in the command. If this keyword appears, it should be used in the last mapping of the command.
After the source variables comes an ‘=’ and then the dest_value. The dest_value may take any of the following forms:
A literal numeric value to which the source values should be changed. This implies the destination variable must be numeric.
A literal string value (enclosed in quotation marks) to which the source values should be changed. This implies the destination variable must be a string variable.
The keyword ‘SYSMIS’ changes the value to the system missing value. This implies the destination variable must be numeric.
The special keyword ‘COPY’ means that the source value should not be modified, but copied directly to the destination value. This is meaningful only if ‘INTO dest_vars’ is specified.
Mappings are considered from left to right. Therefore, if a value is matched by a src_value from more than one mapping, the first (leftmost) mapping which matches is considered. Any subsequent matches are ignored.
The clause ‘INTO dest_vars’ is optional. The behaviour of the command is slightly different depending on whether it appears or not.
If ‘INTO dest_vars’ does not appear, then values are recoded “in place”. This means that the recoded values are written back to the source variables from whence the original values came. In this case, the dest_value for every mapping must imply a value which has the same type as the src_value. For example, if the source value is a string value, it is not permissible for dest_value to be ‘SYSMIS’ or another forms which implies a numeric result. It is also not permissible for dest_value to be longer than the width of the source variable.
The following example two numeric variables x and y are recoded in place. Zero is recoded to 99, the values 1 to 10 inclusive are unchanged, values 1000 and higher are recoded to the system-missing value and all other values are changed to 999:
recode x y (0 = 99) (1 THRU 10 = COPY) (1000 THRU HIGHEST = SYSMIS) (ELSE = 999).
If ‘INTO dest_vars’ is given, then recoded values are written
into the variables specified in dest_vars, which must therefore
contain a list of valid variable names.
The number of variables in dest_vars must be the same as the number
of variables in src_vars
and the respective order of the variables in dest_vars corresponds to
the order of src_vars.
That is to say, the recoded value whose
original value came from the nth variable in src_vars is
placed into the nth variable in dest_vars.
The source variables are unchanged.
If any mapping implies a string as its destination value, then the respective
destination variable must already exist, or
have been declared using STRING
or another transformation.
Numeric variables however are automatically created if they don’t already
exist.
The following example deals with two source variables, a and b
which contain string values. Hence there are two destination variables
v1 and v2.
Any cases where a or b contain the values ‘apple’,
‘pear’ or ‘pomegranate’ result in v1 or v2 being
filled with the string ‘fruit’ whilst cases with
‘tomato’, ‘lettuce’ or ‘carrot’ result in ‘vegetable’.
Any other values produce the result ‘unknown’:
string v1 (a20). string v2 (a20). recode a b ("apple" "pear" "pomegranate" = "fruit") ("tomato" "lettuce" "carrot" = "vegetable") (ELSE = "unknown") into v1 v2.
There is one very special mapping, not mentioned above. If the source variable is a string variable then a mapping may be specified as ‘(CONVERT)’. This mapping, if it appears must be the last mapping given and the ‘INTO dest_vars’ clause must also be given and must not refer to a string variable. ‘CONVERT’ causes a number specified as a string to be converted to a numeric value. For example it converts the string ‘"3"’ into the numeric value 3 (note that it does not convert ‘three’ into 3). If the string cannot be parsed as a number, then the system-missing value is assigned instead. In the following example, cases where the value of x (a string variable) is the empty string, are recoded to 999 and all others are converted to the numeric equivalent of the input value. The results are placed into the numeric variable y:
recode x ("" = 999) (convert) into y.
It is possible to specify multiple recodings on a single command. Introduce additional recodings with a slash (‘/’) to separate them from the previous recodings:
recode a (2 = 22) (else = 99) /b (1 = 3) into z .
Here we have two recodings. The first affects the source variable a and recodes in-place the value 2 into 22 and all other values to 99. The second recoding copies the values of b into the variable z, changing any instances of 1 into 3.
Previous: RECODE, Up: Data transformations [Contents][Index]
SORT CASES BY var_list[({D|A}] [ var_list[({D|A}] ] ...
SORT CASES
sorts the active dataset by the values of one or more
variables.
Specify BY
and a list of variables to sort by. By default, variables
are sorted in ascending order. To override sort order, specify (D)
or
(DOWN)
after a list of variables to get descending order, or (A)
or (UP)
for ascending order. These apply to all the listed variables
up until the preceding (A)
, (D)
, (UP)
or (DOWN)
.
The sort algorithms used by SORT CASES
are stable. This means
records which have equal values of the sort variables have the
same relative order before and after sorting. Thus,
re-sorting an already sorted file does not affect the ordering of
cases.
SORT CASES
is a procedure. It causes the data to be read.
SORT CASES
attempts to sort the entire active dataset in main memory.
If workspace is exhausted, it falls back to a merge sort algorithm which
creates numerous temporary files.
SORT CASES
may not be specified following TEMPORARY
.
In Example 12.6 the data from the file physiology.sav is sorted by two variables, viz sex in descending order and temperature in ascending order.
get file='physiology.sav'. sort cases by sex (D) temperature(A). list. |
Example 12.6: Sorting cases by two variables.
In Results 12.4 you can see that all the cases with a sex of ‘1’ (female) appear before those with a sex of ‘0’ (male). This is because they have been sorted in descending order. Within each sex, the data is sorted on the temperature variable, this time in ascending order.
|
Results 12.4: The physiology.sav file after sorting.
Note that SORT CASES
, like all other transformations, affects only the active file.
It does not have any effect upon the physiology.sav file itself. For that, you
would have to rewrite the file using the SAVE
command (see SAVE).
When using the graphic user interface, it is often simpler to perform a sort directly from the data view. To do this, switch to the data view. Select the column corresponding to the variable by which you want to sort and click button 1 and then click button 3. A popup menu will appear like that shown in Screenshot 12.5. Select either “Sort Ascending” or “Sort Descending” from this menu.
![]() |
Screenshot 12.5: Sorting the data on a single variable height
However, sometimes you will want to sort on two or more variables, and that is not possible using this method. In this case, you must either use some code or the “Sort Cases” dialog from the Data menu. Screenshot 12.6 shows the dialog box set up to perform a sort on both sex and height. Note that the order in which you enter the variables is important. In this case, the data will be first sorted on sex, and then all cases for which sex is the same will then be sorted by height.
![]() |
Screenshot 12.6: Sorting the data on two variables sex and height
Next: Conditional and Looping Constructs, Previous: Data transformations, Up: GNU PSPP [Contents][Index]
This chapter documents PSPP commands that temporarily or permanently select data records from the active dataset for analysis.
Next: N OF CASES, Up: Selecting data for analysis [Contents][Index]
FILTER BY var_name. FILTER OFF.
FILTER
allows a boolean-valued variable to be used to select
cases from the data stream for processing.
To set up filtering, specify BY
and a variable name. Keyword
BY is optional but recommended. Cases which have a zero or system- or
user-missing value are excluded from analysis, but not deleted from the
data stream. Cases with other values are analyzed.
To filter based on a different condition, use
transformations such as COMPUTE
or RECODE
to compute a
filter variable of the required form, then specify that variable on
FILTER
.
FILTER OFF
turns off case filtering.
Filtering takes place immediately before cases pass to a procedure for
analysis. Only one filter variable may be active at a time. Normally,
case filtering continues until it is explicitly turned off with FILTER
OFF
. However, if FILTER
is placed after TEMPORARY
, it filters only
the next procedure or procedure-like command.
Next: SAMPLE, Previous: FILTER, Up: Selecting data for analysis [Contents][Index]
N [OF CASES] num_of_cases [ESTIMATED].
N OF CASES
limits the number of cases processed by any
procedures that follow it in the command stream. N OF CASES
100
, for example, tells PSPP to disregard all cases after the first
100.
When N OF CASES
is specified after TEMPORARY
, it affects
only the next procedure (see TEMPORARY). Otherwise, cases beyond
the limit specified are not processed by any later procedure.
If the limit specified on N OF CASES
is greater than the number
of cases in the active dataset, it has no effect.
When N OF CASES
is used along with SAMPLE
or SELECT
IF
, the case limit is applied to the cases obtained after sampling or
case selection, regardless of how N OF CASES
is placed relative
to SAMPLE
or SELECT IF
in the command file. Thus, the
commands N OF CASES 100
and SAMPLE .5
both randomly
sample approximately half of the active dataset’s cases, then select the
first 100 of those sampled, regardless of their order in the command
file.
N OF CASES
with the ESTIMATED
keyword gives an estimated
number of cases before DATA LIST
or another command to read in
data. ESTIMATED
never limits the number of cases processed by
procedures. PSPP currently does not make use of case count estimates.
Next: SELECT IF, Previous: N OF CASES, Up: Selecting data for analysis [Contents][Index]
SAMPLE num1 [FROM num2].
SAMPLE
randomly samples a proportion of the cases in the active
file. Unless it follows TEMPORARY
, it operates as a
transformation, permanently removing cases from the active dataset.
The proportion to sample can be expressed as a single number between 0
and 1. If k is the number specified, and N is the number
of currently-selected cases in the active dataset, then after
SAMPLE k.
, approximately k*N cases are
selected.
The proportion to sample can also be specified in the style SAMPLE
m FROM N
. With this style, cases are selected as follows:
SAMPLE
and SELECT IF
are performed in
the order specified by the syntax file.
SAMPLE
is always performed before N OF CASES
, regardless
of ordering in the syntax file (see N OF CASES).
The same values for SAMPLE
may result in different samples. To
obtain the same sample, use the SET
command to set the random
number seed to the same value before each SAMPLE
. Different
samples may still result when the file is processed on systems with
differing endianness or floating-point formats. By default, the
random number seed is based on the system time.
Next: SPLIT FILE, Previous: SAMPLE, Up: Selecting data for analysis [Contents][Index]
SELECT IF expression.
SELECT IF
selects cases for analysis based on the value of
expression. Cases not selected are permanently eliminated
from the active dataset, unless TEMPORARY
is in effect
(see TEMPORARY).
Specify a boolean expression (see Mathematical Expressions). If the value of the expression is true for a particular case, the case is analyzed. If the expression has a false or missing value, then the case is deleted from the data stream.
Place SELECT IF
as early in the command file as
possible. Cases that are deleted early can be processed more
efficiently in time and space.
Once cases have been deleted from the active dataset using SELECT IF
they
cannot be re-instated.
If you want to be able to re-instate cases, then use FILTER
(see FILTER)
instead.
When SELECT IF
is specified following TEMPORARY
(see TEMPORARY), the LAG
function may not be used
(see LAG).
A shop steward is interested in the salaries of younger personnel in a firm.
The file personnel.sav provides the salaries of all the workers and their
dates of birth. The syntax in Example 13.1 shows how SELECT IF
can
be used to limit analysis only to those persons born after December 31, 1999.
get file = 'personnel.sav'. echo 'Salaries of all personnel'. descriptives salary. echo 'Salaries of personnel born after December 31 1999'. select if dob > date.dmy (31,12,1999). descriptives salary. |
Example 13.1: Using SELECT IF
to select persons born on or after a certain date.
From Result 13.1 one can see that there are 56 persons listed in the dataset, and 17 of them were born after December 31, 1999.
Salaries of all personnel
Salaries of personnel born after December 31 1999
|
Result 13.1: Salary descriptives before and after the SELECT IF
transformation.
Note that the personnel.sav file from which the data were read is unaffected. The transformation affects only the active file.
Next: TEMPORARY, Previous: SELECT IF, Up: Selecting data for analysis [Contents][Index]
SPLIT FILE [{LAYERED, SEPARATE}] BY var_list. SPLIT FILE OFF.
SPLIT FILE
allows multiple sets of data present in one data
file to be analyzed separately using single statistical procedure
commands.
Specify a list of variable names to analyze multiple sets of data separately. Groups of adjacent cases having the same values for these variables are analyzed by statistical procedure commands as one group. An independent analysis is carried out for each group of cases, and the variable values for the group are printed along with the analysis.
When a list of variable names is specified, one of the keywords
LAYERED
or SEPARATE
may also be specified. With
LAYERED
, which is the default, the separate analyses for each
group are presented together in a single table. With
SEPARATE
, each analysis is presented in a separate table.
Not all procedures honor the distinction.
Groups are formed only by adjacent cases. To create a split using a variable where like values are not adjacent in the working file, first sort the data by that variable (see SORT CASES).
Specify OFF
to disable SPLIT FILE
and resume analysis of the
entire active dataset as a single group of data.
When SPLIT FILE
is specified after TEMPORARY
, it affects only
the next procedure (see TEMPORARY).
The file horticulture.sav contains data describing the yield
of a number of horticultural specimens which have been subjected to
various treatments. If we wanted to investigate linear statistics
of the yeild, one way to do this is using the DESCRIPTIVES
(see DESCRIPTIVES).
However, it is reasonable to expect the mean to be different depending
on the treatment. So we might want to perform three separate
procedures — one for each treatment.
5
Example 13.2 shows how this can be done automatically using
the SPLIT FILE
command.
get file='horticulture.sav'. * Ensure cases are sorted before splitting. sort cases by treatment. split file by treatment. * Run descriptives on the yield variable descriptives /variable = yield. |
Example 13.2: Running DESCRIPTIVES
on each value of treatment
In Example 13.3 you can see that the table of descriptive statistics appears 3 times — once for each value of treatment. In this example ‘N’, the number of observations are identical in all splits. This is because that experiment was deliberately designed that way. However in general one can expect a different ‘N’ for each split.
|
Example 13.3: The results of running DESCRIPTIVES
with an active split
Unless TEMPORARY
was used, after a split has been defined for
a dataset it remains active until explicitly disabled.
In the graphical user interface, the active split variable (if any) is
displayed in the status bar (see Screenshot 13.1.
If a dataset is saved to a system file (see SAVE) whilst a split
is active, the split stastus is stored in the file and will be
automatically loaded when that file is loaded.
![]() |
Screenshot 13.1: The status bar indicating that the data set is split using the treatment variable
Next: WEIGHT, Previous: SPLIT FILE, Up: Selecting data for analysis [Contents][Index]
TEMPORARY.
TEMPORARY
is used to make the effects of transformations
following its execution temporary. These transformations
affect only the execution of the next procedure or procedure-like
command. Their effects are not be saved to the active dataset.
The only specification on TEMPORARY
is the command name.
TEMPORARY
may not appear within a DO IF
or LOOP
construct. It may appear only once between procedures and
procedure-like commands.
Scratch variables cannot be used following TEMPORARY
.
In Example 13.4 there are two COMPUTE
transformation. One
of them immediatly follows a TEMPORARY
command, and therefore has
effect only for the next procedure, which in this case is the first
DESCRIPTIVES
command.
data list notable /x 1-2. begin data. 2 4 10 15 20 24 end data. compute x=x/2. temporary. compute x=x+3. descriptives x. descriptives x. |
Example 13.4: Running a COMPUTE
transformation after TEMPORARY
The data read by the first DESCRIPTIVES
procedure are 4, 5, 8,
10.5, 13, 15. The data read by the second DESCRIPTIVES
procedure are 1, 2,
5, 7.5, 10, 12. This is because the second COMPUTE
transformation
has no effect on the second DESCRIPTIVES
procedure. You can check these
figures in Result 13.2.
|
Result 13.2: The results of running two consecutive DESCRIPTIVES
commands after
a temporary transformation
Previous: TEMPORARY, Up: Selecting data for analysis [Contents][Index]
WEIGHT BY var_name. WEIGHT OFF.
WEIGHT
assigns cases varying weights,
changing the frequency distribution of the active dataset. Execution of
WEIGHT
is delayed until data have been read.
If a variable name is specified, WEIGHT
causes the values of that
variable to be used as weighting factors for subsequent statistical
procedures. Use of keyword BY
is optional but recommended. Weighting
variables must be numeric. Scratch variables may not be used for
weighting (see Scratch Variables).
When OFF
is specified, subsequent statistical procedures weight all
cases equally.
A positive integer weighting factor w on a case yields the same statistical output as would replicating the case w times. A weighting factor of 0 is treated for statistical purposes as if the case did not exist in the input. Weighting values need not be integers, but negative and system-missing values for the weighting variable are interpreted as weighting factors of 0. User-missing values are not treated specially.
When WEIGHT
is specified after TEMPORARY
, it affects only
the next procedure (see TEMPORARY).
WEIGHT
does not cause cases in the active dataset to be
replicated in memory.
One could define a dataset containing an inventory of stock items. It would be reasonable to use a string variable for a description of the item, and a numeric variable for the number in stock, like in Example 13.5.
data list notable list /item (a16) quantity (f8.0). begin data nuts 345 screws 10034 washers 32012 bolts 876 end data. echo 'Unweighted frequency table'. frequencies /variables = item /format=dfreq. weight by quantity. echo 'Weighted frequency table'. frequencies /variables = item /format=dfreq. |
Example 13.5: Setting the weight on the variable quantity
One analysis which most surely would be of interest is
the relative amounts or each item in stock.
However without setting a weight variable, FREQUENCIES
(see FREQUENCIES) does not tell us what we want to know, since
there is only one case for each stock item. Example 13.6 shows the
difference between the weighted and unweighted frequency tables.
Unweighted frequency table
Weighted frequency table
|
Example 13.6: Weighted and unweighted frequency tables of items
Next: Statistics, Previous: Selecting data for analysis, Up: GNU PSPP [Contents][Index]
This chapter documents PSPP commands used for conditional execution, looping, and flow of control.
Next: DEFINE, Up: Conditional and Looping Constructs [Contents][Index]
BREAK.
BREAK
terminates execution of the innermost currently executing
LOOP
construct.
BREAK
is allowed only inside LOOP
…END LOOP
.
See LOOP, for more details.
Next: DO IF, Previous: BREAK, Up: Conditional and Looping Constructs [Contents][Index]
Next: Introduction, Up: DEFINE [Contents][Index]
DEFINE macro_name([argument[/argument]…]) …body… !ENDDEFINE.
Each argument takes the following form:
{!arg_name= | !POSITIONAL} [!DEFAULT(default)] [!NOEXPAND] {!TOKENS(count) | !CHAREND('token') | !ENCLOSE('start' | 'end') | !CMDEND}
The following directives may be used within body:
!OFFEXPAND !ONEXPAND
The following functions may be used within the body:
!BLANKS(count) !CONCAT(arg…) !EVAL(arg) !HEAD(arg) !INDEX(haystack, needle) !LENGTH(arg) !NULL !QUOTE(arg) !SUBSTR(arg, start[, count]) !TAIL(arg) !UNQUOTE(arg) !UPCASE(arg)
The body may also include the following constructs:
!IF (condition) !THEN true-expansion !ENDIF !IF (condition) !THEN true-expansion !ELSE false-expansion !ENDIF !DO !var = start !TO end [!BY step] body !DOEND !DO !var !IN (expression) body !DOEND !LET !var = expression
Next: Macro Bodies, Previous: Overview, Up: DEFINE [Contents][Index]
The DEFINE command creates a macro, which is a name for a fragment of PSPP syntax called the macro’s body. Following the DEFINE command, syntax may call the macro by name any number of times. Each call substitutes, or expands, the macro’s body in place of the call, as if the body had been written in its place.
The following syntax defines a macro named !vars
that expands
to the variable names v1 v2 v3
. The macro’s name begins with
‘!’, which is optional for macro names. The ()
following
the macro name are required:
DEFINE !vars() v1 v2 v3 !ENDDEFINE.
Here are two ways that !vars
might be called given the
preceding definition:
DESCRIPTIVES !vars. FREQUENCIES /VARIABLES=!vars.
With macro expansion, the above calls are equivalent to the following:
DESCRIPTIVES v1 v2 v3. FREQUENCIES /VARIABLES=v1 v2 v3.
The !vars
macro expands to a fixed body. Macros may have more
sophisticated contents:
!IF
constructs, for conditional expansion. See Macro Conditional Expansion.
!DO
construct, for looping over a numerical range
or a collection of tokens. See Macro Loops.
!LET
constructs, for assigning to macro variables. See Macro Variable Assignment.
Many identifiers associated with macros begin with ‘!’, a character not normally allowed in identifiers. These identifiers are reserved only for use with macros, which helps keep them from being confused with other kinds of identifiers.
The following sections provide more details on macro syntax and semantics.
Next: Macro Arguments, Previous: Introduction, Up: DEFINE [Contents][Index]
As previously shown, a macro body may contain a fragment of a PSPP command (such as a variable name). A macro body may also contain full PSPP commands. In the latter case, the macro body should also contain the command terminators.
Most PSPP commands may occur within a macro. The DEFINE
command itself is one exception, because the inner !ENDDEFINE
ends the outer macro definition. For compatibility, BEGIN
DATA
…END DATA.
should not be used within a macro.
The body of a macro may call another macro. The following shows one way that could work:
DEFINE !commands() DESCRIPTIVES !vars. FREQUENCIES /VARIABLES=!vars. !ENDDEFINE. * Initially define the 'vars' macro to analyze v1...v3. DEFINE !vars() v1 v2 v3 !ENDDEFINE. !commands * Redefine 'vars' macro to analyze different variables. DEFINE !vars() v4 v5 !ENDDEFINE. !commands
The !commands
macro would be easier to use if it took the
variables to analyze as an argument rather than through another macro.
The following section shows how to do that.
Next: Controlling Macro Expansion, Previous: Macro Bodies, Up: DEFINE [Contents][Index]
This section explains how to use macro arguments. As an initial
example, the following syntax defines a macro named !analyze
that takes all the syntax up to the first command terminator as an
argument:
DEFINE !analyze(!POSITIONAL !CMDEND) DESCRIPTIVES !1. FREQUENCIES /VARIABLES=!1. !ENDDEFINE.
When !analyze
is called, it expands to a pair of analysis
commands with each !1
in the body replaced by the argument.
That is, these calls:
!analyze v1 v2 v3. !analyze v4 v5.
act like the following:
DESCRIPTIVES v1 v2 v3. FREQUENCIES /VARIABLES=v1 v2 v3. DESCRIPTIVES v4 v5. FREQUENCIES /VARIABLES=v4 v5.
Macros may take any number of arguments, described within the parentheses in the DEFINE command. Arguments come in two varieties based on how their values are specified when the macro is called:
!POSITIONAL
keyword to declare a
positional argument.
When a macro is called, the positional argument values appear in the same order as their definitions, before any keyword argument values.
References to a positional argument in a macro body are numbered:
!1
is the first positional argument, !2
the second, and
so on. In addition, !*
expands to all of the positional
arguments’ values, separated by spaces.
The following example uses a positional argument:
DEFINE !analyze(!POSITIONAL !CMDEND) DESCRIPTIVES !1. FREQUENCIES /VARIABLES=!1. !ENDDEFINE. !analyze v1 v2 v3. !analyze v4 v5.
name=value
. The names allow
keyword argument values to take any order in the call.
In declaration and calls, a keyword argument’s name may not begin with ‘!’, but references to it in the macro body do start with a leading ‘!’.
The following example uses a keyword argument that defaults to ALL if the argument is not assigned a value:
DEFINE !analyze_kw(vars=!DEFAULT(ALL) !CMDEND) DESCRIPTIVES !vars. FREQUENCIES /VARIABLES=!vars. !ENDDEFINE. !analyze_kw vars=v1 v2 v3. /* Analyze specified variables. !analyze_kw. /* Analyze all variables.
If a macro has both positional and keyword arguments, then the
positional arguments must come first in the DEFINE command, and their
values also come first in macro calls. A keyword argument may be
omitted by leaving its keyword out of the call, and a positional
argument may be omitted by putting a command terminator where it would
appear. (The latter case also omits any following positional
arguments and all keyword arguments, if there are any.) When an
argument is omitted, a default value is used: either the value
specified in !DEFAULT(value)
, or an empty value otherwise.
Each argument declaration specifies the form of its value:
!TOKENS(count)
Exactly count tokens, e.g. !TOKENS(1)
for a single
token. Each identifier, number, quoted string, operator, or
punctuator is a token. See Tokens, for a complete definition.
The following variant of !analyze_kw
accepts only a single
variable name (or ALL
) as its argument:
DEFINE !analyze_one_var(!POSITIONAL !TOKENS(1)) DESCRIPTIVES !1. FREQUENCIES /VARIABLES=!1. !ENDDEFINE. !analyze_one_var v1.
!CHAREND('token')
Any number of tokens up to token, which should be an operator or punctuator token such as ‘/’ or ‘+’. The token does not become part of the value.
With the following variant of !analyze_kw
, the variables must
be following by ‘/’:
DEFINE !analyze_parens(vars=!CHARNED('/')) DESCRIPTIVES !vars. FREQUENCIES /VARIABLES=!vars. !ENDDEFINE. !analyze_parens vars=v1 v2 v3/.
!ENCLOSE('start','end')
Any number of tokens enclosed between start and end, which
should each be operator or punctuator tokens. For example, use
!ENCLOSE('(',')')
for a value enclosed within parentheses.
(Such a value could never have right parentheses inside it, even
paired with left parentheses.) The start and end tokens are not part
of the value.
With the following variant of !analyze_kw
, the variables must
be specified within parentheses:
DEFINE !analyze_parens(vars=!ENCLOSE('(',')')) DESCRIPTIVES !vars. FREQUENCIES /VARIABLES=!vars. !ENDDEFINE. !analyze_parens vars=(v1 v2 v3).
!CMDEND
Any number of tokens up to the end of the command. This should be used only for the last positional parameter, since it consumes all of the tokens in the command calling the macro.
The following variant of !analyze_kw
takes all the variable
names up to the end of the command as its argument:
DEFINE !analyze_kw(vars=!CMDEND) DESCRIPTIVES !vars. FREQUENCIES /VARIABLES=!vars. !ENDDEFINE. !analyze_kw vars=v1 v2 v3.
By default, when an argument’s value contains a macro call, the call
is expanded each time the argument appears in the macro’s body. The
!NOEXPAND
keyword in an argument declaration suppresses this
expansion. See Controlling Macro Expansion.
Next: Macro Functions, Previous: Macro Arguments, Up: DEFINE [Contents][Index]
Multiple factors control whether macro calls are expanded in different
situations. At the highest level, SET MEXPAND
controls whether
macro calls are expanded. By default, it is enabled. See SET MEXPAND, for details.
A macro body may contain macro calls. By default, these are expanded.
If a macro body contains !OFFEXPAND
or !ONEXPAND
directives, then !OFFEXPAND
disables expansion of macro calls
until the following !ONEXPAND
.
A macro argument’s value may contain a macro call. These macro calls
are expanded, unless the argument was declared with the
!NOEXPAND
keyword.
The argument to a macro function is a special context that does not
expand macro calls. For example, if !vars
is the name of a
macro, then !LENGTH(!vars)
expands to 5, as does
!LENGTH(!1)
if positional argument 1 has value !vars
.
To expand macros in these cases, use the !EVAL
macro function,
e.g. !LENGTH(!EVAL(!vars))
or !LENGTH(!EVAL(!1))
.
See Macro Functions, for details.
These rules apply to macro calls, not to uses within a macro body of
macro functions, macro arguments, and macro variables created by
!DO
or !LET
, which are always expanded.
SET MEXPAND
may appear within the body of a macro, but it will
not affect expansion of the macro that it appears in. Use
!OFFEXPAND
and !ONEXPAND
instead.
Next: Macro Expressions, Previous: Controlling Macro Expansion, Up: DEFINE [Contents][Index]
Macro bodies may manipulate syntax using macro functions. Macro functions accept tokens as arguments and expand to sequences of characters.
The arguments to macro functions have a restricted form. They may only be a single token (such as an identifier or a string), a macro argument, or a call to a macro function. Thus, the following are valid macro arguments:
x 5.0 x !1 "5 + 6" !CONCAT(x,y)
and the following are not:
x y 5+6
Macro functions expand to sequences of characters. When these
character strings are processed further as character strings, e.g.
with !LENGTH
, any character string is valid. When they are
interpreted as PSPP syntax, e.g. when the expansion becomes part of
a command, they need to be valid for that purpose. For example,
!UNQUOTE("It's")
will yield an error if the expansion
It's
becomes part of a PSPP command, because it contains
unbalanced single quotes, but !LENGTH(!UNQUOTE("It's"))
expands
to 4.
The following macro functions are available. Each function’s
documentation includes examples in the form call
→ expansion
.
Expands to count unquoted spaces, where count is a
nonnegative integer. Outside quotes, any positive number of spaces
are equivalent; for a quoted string of spaces, use
!QUOTE(!BLANKS(count))
.
In the examples below, ‘_’ stands in for a space to make the results visible.
!BLANKS(0) → empty
!BLANKS(1) → _
!BLANKS(2) → __
!QUOTE(!BLANKS(5)) → '_____'
Expands to the concatenation of all of the arguments. Before
concatenation, each quoted string argument is unquoted, as if
!UNQUOTE
were applied. This allows for “token pasting”,
combining two (or more) tokens into a single one:
!CONCAT(x, y) → xy !CONCAT('x', 'y') → xy !CONCAT(12, 34) → 1234 !CONCAT(!NULL, 123) → 123
!CONCAT
is often used for constructing a series of similar
variable names from a prefix followed by a number and perhaps a
suffix. For example:
!CONCAT(x, 0) → x0 !CONCAT(x, 0, y) → x0y
An identifier token must begin with a letter (or ‘#’ or ‘@’), which means that attempting to use a number as the first part of an identifier will produce a pair of distinct tokens rather than a single one. For example:
!CONCAT(0, x) → 0 x !CONCAT(0, x, y) → 0 xy
Expands macro calls in arg. This is especially useful if arg is the name of a macro or a macro argument that expands to one, because arguments to macro functions are not expanded by default (see Controlling Macro Expansion).
The following examples assume that !vars
is a macro that
expands to a b c
:
!vars → a b c !QUOTE(!vars) → '!vars' !EVAL(!vars) → a b c !QUOTE(!EVAL(!vars)) → 'a b c'
These examples additionally assume that argument !1
has value
!vars
:
!1 → a b c !QUOTE(!1) → '!vars' !EVAL(!1) → a b c !QUOTE(!EVAL(!1)) → 'a b c'
!HEAD
expands to just the first token in an unquoted version of
arg, and !TAIL
to all the tokens after the first.
!HEAD('a b c') → a !HEAD('a') → a !HEAD(!NULL) → empty !HEAD('') → empty !TAIL('a b c') → b c !TAIL('a') → empty !TAIL(!NULL) → empty !TAIL('') → empty
Looks for needle in haystack. If it is present, expands to the 1-based index of its first occurrence; if not, expands to 0.
!INDEX(banana, an) → 2 !INDEX(banana, nan) → 3 !INDEX(banana, apple) → 0 !INDEX("banana", nan) → 4 !INDEX("banana", "nan") → 0 !INDEX(!UNQUOTE("banana"), !UNQUOTE("nan")) → 3
Expands to a number token representing the number of characters in arg.
!LENGTH(123) → 3 !LENGTH(123.00) → 6 !LENGTH( 123 ) → 3 !LENGTH("123") → 5 !LENGTH(xyzzy) → 5 !LENGTH("xyzzy") → 7 !LENGTH("xy""zzy") → 9 !LENGTH(!UNQUOTE("xyzzy")) → 5 !LENGTH(!UNQUOTE("xy""zzy")) → 6 !LENGTH(!1) → 5 if !1 is a b c !LENGTH(!1) → 0 if !1 is empty !LENGTH(!NULL) → 0
Expands to an empty character sequence.
!NULL → empty
!QUOTE(!NULL) → ''
The !QUOTE
function expands to its argument surrounded by
apostrophes, doubling any apostrophes inside the argument to make sure
that it is valid PSPP syntax for a string. If the argument was
already a quoted string, !QUOTE
expands to it unchanged.
Given a quoted string argument, the !UNQUOTED
function expands
to the string’s contents, with the quotes removed and any doubled
quote marks reduced to singletons. If the argument was not a quoted
string, !UNQUOTE
expands to the argument unchanged.
!QUOTE(123.0) → '123.0' !QUOTE( 123 ) → '123' !QUOTE('a b c') → 'a b c' !QUOTE("a b c") → "a b c" !QUOTE(!1) → 'a ''b'' c' if !1 is a 'b' c !UNQUOTE(123.0) → 123.0 !UNQUOTE( 123 ) → 123 !UNQUOTE('a b c') → a b c !UNQUOTE("a b c") → a b c !UNQUOTE(!1) → a 'b' c if !1 is a 'b' c !QUOTE(!UNQUOTE(123.0)) → '123.0' !QUOTE(!UNQUOTE( 123 )) → '123' !QUOTE(!UNQUOTE('a b c')) → 'a b c' !QUOTE(!UNQUOTE("a b c")) → 'a b c' !QUOTE(!UNQUOTE(!1)) → 'a ''b'' c' if !1 is a 'b' c
Expands to a substring of arg starting from 1-based position start. If count is given, it limits the number of characters in the expansion; if it is omitted, then the expansion extends to the end of arg.
!SUBSTR(banana, 3) → nana
!SUBSTR(banana, 3, 3) → nan
!SUBSTR("banana", 1, 3) → error ("ba
is not a valid token)
!SUBSTR(!UNQUOTE("banana"), 3) → nana
!SUBSTR("banana", 3, 3) → ana
!SUBSTR(banana, 3, 0) → empty
!SUBSTR(banana, 3, 10) → nana
!SUBSTR(banana, 10, 3) → empty
Expands to an unquoted version of arg with all letters converted to uppercase.
!UPCASE(freckle) → FRECKLE !UPCASE('freckle') → FRECKLE !UPCASE('a b c') → A B C !UPCASE('A B C') → A B C
Next: Macro Conditional Expansion, Previous: Macro Functions, Up: DEFINE [Contents][Index]
Macro expressions are used in conditional expansion and loops, which are described in the following sections. A macro expression may use the following operators, listed in descending order of operator precedence:
()
Parentheses override the default operator precedence.
!EQ !NE !GT !LT !GE !LE = ~= <> > < >= <=
Relational operators compare their operands and yield a Boolean result, either ‘0’ for false or ‘1’ for true.
These operators always compare their operands as strings. This can be
surprising when the strings are numbers because, e.g., 1 <
1.0
and 10 < 2
both evaluate to ‘1’ (true).
Comparisons are case sensitive, so that a = A
evaluates to
‘0’ (false).
!NOT ~
!AND &
!OR |
Logical operators interpret their operands as Boolean values, where quoted or unquoted ‘0’ is false and anything else is true, and yield a Boolean result, either ‘0’ for false or ‘1’ for true.
Macro expressions do not include any arithmetic operators.
An operand in an expression may be a single token (including a macro
argument name) or a macro function invocation. Either way, the
expression evaluator unquotes the operand, so that 1 = '1'
is
true.
Next: Macro Loops, Previous: Macro Expressions, Up: DEFINE [Contents][Index]
The !IF
construct may be used inside a macro body to allow for
conditional expansion. It takes the following forms:
!IF (expression) !THEN true-expansion !IFEND !IF (expression) !THEN true-expansion !ELSE false-expansion !IFEND
When expression evaluates to true, the macro processor expands true-expansion; otherwise, it expands false-expansion, if it is present. The macro processor considers quoted or unquoted ‘0’ to be false, and anything else to be true.
Next: Macro Variable Assignment, Previous: Macro Conditional Expansion, Up: DEFINE [Contents][Index]
The body of a macro may include two forms of loops: loops over numerical ranges and loops over tokens. Both forms expand a loop body multiple times, each time setting a named loop variable to a different value. The loop body typically expands the loop variable at least once.
The MITERATE setting (see SET MITERATE) limits the number of iterations in a loop. This is a safety measure to ensure that macro expansion terminates. PSPP issues a warning when the MITERATE limit is exceeded.
!DO !var = start !TO end [!BY step] body !DOEND
A loop over a numerical range has the form shown above. start, end, and step (if included) must be expressions with numeric values. The macro processor accepts both integers and real numbers. The macro processor expands body for each numeric value from start to end, inclusive.
The default value for step is 1. If step is positive and first > last, or if step is negative and first < last, then the macro processor doesn’t expand the body at all. step may not be zero.
!DO !var !IN (expression) body !DOEND
A loop over tokens takes the form shown above. The macro processor evaluates expression and expands body once per token in the result, substituting the token for !var each time it appears.
Next: Macro Settings, Previous: Macro Loops, Up: DEFINE [Contents][Index]
The !LET
construct evaluates an expression and assigns the
result to a macro variable. It may create a new macro variable or
change the value of one created by a previous !LET
or
!DO
, but it may not change the value of a macro argument.
!LET
has the following form:
!LET !var = expression
If expression is more than one token, it must be enclosed in parentheses.
Next: Additional Notes, Previous: Macro Variable Assignment, Up: DEFINE [Contents][Index]
Some macro behavior is controlled through the SET command (see SET). This section describes these settings.
Any SET command that changes these settings within a macro body only takes effect following the macro. This is because PSPP expands a macro’s entire body at once, so that the SET command inside the body only executes afterwards.
The MEXPAND setting (see SET MEXPAND) controls whether macros will
be expanded at all. By default, macro expansion is on. To avoid
expansion of macros called within a macro body, use !OFFEXPAND
and !ONEXPAND
(see Controlling Macro Expansion).
When MPRINT (see SET MPRINT) is turned on, PSPP outputs an expansion of each macro called. This feature can be useful for debugging macro definitions. For reading the expanded version, note that macro expansion removes comments and standardizes white space.
MNEST (see SET MNEST) limits the depth of expansion of macro calls, that is, the nesting level of macro expansion. The default is 50. This is mainly useful to avoid infinite expansion in the case of a macro that calls itself.
MITERATE (see SET MITERATE) limits the number of iterations in a
!DO
construct. The default is 1000.
Previous: Macro Settings, Up: DEFINE [Contents][Index]
If the body of macro A includes a call to macro B, the call can use
macro arguments (including !*
) and macro variables as part of
arguments to B. For !TOKENS
arguments, the argument or
variable name counts as one token regardless of the number that it
expands into; for !CHAREND
and !ENCLOSE
arguments, the
delimiters come only from the call, not the expansions; and
!CMDEND
ends at the calling command, not any end of command
within an argument or variable.
Macro functions are not supported as part of the arguments in a macro
call. To get the same effect, use !LET
to define a macro
variable, then pass the macro variable to the macro.
When macro A calls macro B, the order of their DEFINE
commands
doesn’t matter, as long as macro B has been defined when A is called.
Macros and command terminators require care. Macros honor the syntax differences between interactive and batch syntax (see Syntax Variants), which means that the interpretation of a macro can vary depending on the syntax mode in use. We assume here that interactive mode is in use, in which ‘.’ at the end of a line is the primary way to end a command.
The DEFINE
command needs to end with ‘.’ following the
!ENDDEFINE
. The macro body may contain ‘.’ if it is
intended to expand to whole commands, but using ‘.’ within a
macro body that expands to just syntax fragments (such as a list of
variables) will cause syntax errors.
Macro directives such as !IF
and !DO
do not end with
‘.’.
Macros do not expand within comments, whether introduced within a line
by /*
or as a separate COMMENT or ‘*’ commands
(see COMMENT). (SPSS does expand macros in COMMENT and ‘*’.)
Macros do not expand within quoted strings.
Macros are expanded in the TITLE
and SUBTITLE
commands
as long as their arguments are not quoted strings.
Some macro bodies might use the SET command to change certain settings. When this is the case, consider using the PRESERVE and RESTORE commands to save and then restore these settings. See PRESERVE and RESTORE.
Next: DO REPEAT, Previous: DEFINE, Up: Conditional and Looping Constructs [Contents][Index]
DO IF condition. … [ELSE IF condition. … ]… [ELSE. …] END IF.
DO IF
allows one of several sets of transformations to be
executed, depending on user-specified conditions.
If the specified boolean expression evaluates as true, then the block
of code following DO IF
is executed. If it evaluates as
missing, then
none of the code blocks is executed. If it is false, then
the boolean expression on the first ELSE IF
, if present, is tested in
turn, with the same rules applied. If all expressions evaluate to
false, then the ELSE
code block is executed, if it is present.
When DO IF
or ELSE IF
is specified following TEMPORARY
(see TEMPORARY), the LAG
function may not be used
(see LAG).
Next: LOOP, Previous: DO IF, Up: Conditional and Looping Constructs [Contents][Index]
DO REPEAT dummy_name=expansion…. … END REPEAT [PRINT]. expansion takes one of the following forms: var_list num_or_range… ’string’… ALL num_or_range takes one of the following forms: number num1 TO num2
DO REPEAT
repeats a block of code, textually substituting
different variables, numbers, or strings into the block with each
repetition.
Specify a dummy variable name followed by an equals sign (‘=’)
and the list of replacements. Replacements can be a list of existing
or new variables, numbers, strings, or ALL
to specify all
existing variables. When numbers are specified, runs of increasing
integers may be indicated as num1 TO num2
, so that
‘1 TO 5’ is short for ‘1 2 3 4 5’.
Multiple dummy variables can be specified. Each variable must have the same number of replacements.
The code within DO REPEAT
is repeated as many times as there are
replacements for each variable. The first time, the first value for
each dummy variable is substituted; the second time, the second value
for each dummy variable is substituted; and so on.
Dummy variable substitutions work like macros. They take place
anywhere in a line that the dummy variable name occurs. This includes
command and subcommand names, so command and subcommand names that
appear in the code block should not be used as dummy variable
identifiers. Dummy variable substitutions do not occur inside quoted
strings, comments, unquoted strings (such as the text on the
TITLE
or DOCUMENT
command), or inside BEGIN
DATA
…END DATA
.
Substitution occurs only on whole words, so that, for example, a dummy variable PRINT would not be substituted into the word PRINTOUT.
New variable names used as replacements are not automatically created
as variables, but only if used in the code block in a context that
would create them, e.g. on a NUMERIC
or STRING
command
or on the left side of a COMPUTE
assignment.
Any command may appear within DO REPEAT
, including nested DO REPEAT
commands. If INCLUDE
or INSERT
appears within DO REPEAT
,
the substitutions do not apply to the included file.
If PRINT
is specified on END REPEAT
, the commands after
substitutions are made should be printed to the listing file, prefixed
by a plus sign (‘+’). This feature is not yet implemented.
Previous: DO REPEAT, Up: Conditional and Looping Constructs [Contents][Index]
LOOP [index_var=start TO end [BY incr]] [IF condition]. … END LOOP [IF condition].
LOOP
iterates a group of commands. A number of
termination options are offered.
Specify index_var to make that variable count from one value to another by a particular increment. index_var must be a pre-existing numeric variable. start, end, and incr are numeric expressions (see Mathematical Expressions.)
During the first iteration, index_var is set to the value of start. During each successive iteration, index_var is increased by the value of incr. If end > start, then the loop terminates when index_var > end; otherwise it terminates when index_var < end. If incr is not specified then it defaults to +1 or -1 as appropriate.
If end > start and incr < 0, or if end < start and incr > 0, then the loop is never executed. index_var is nevertheless set to the value of start.
Modifying index_var within the loop is allowed, but it has no effect on the value of index_var in the next iteration.
Specify a boolean expression for the condition on LOOP
to
cause the loop to be executed only if the condition is true. If the
condition is false or missing before the loop contents are executed the
first time, the loop contents are not executed at all.
If index and condition clauses are both present on LOOP
, the
index variable is always set before the condition is evaluated. Thus,
a condition that makes use of the index variable will always see the
index value to be used in the next execution of the body.
Specify a boolean expression for the condition on END LOOP
to cause
the loop to terminate if the condition is true after the enclosed
code block is executed. The condition is evaluated at the end of the
loop, not at the beginning, so that the body of a loop with only a
condition on END LOOP
will always execute at least once.
If the index clause is not present, then the global MXLOOPS
setting, which defaults to 40, limits the number of iterations
(see SET MXLOOPS).
BREAK
also terminates LOOP
execution (see BREAK).
Loop index variables are by default reset to system-missing from one
case to another, not left, unless a scratch variable is used as index.
When loops are nested, this is usually undesired behavior, which can
be corrected with LEAVE
(see LEAVE) or by using a scratch
variable as the loop index.
When LOOP
or END LOOP
is specified following TEMPORARY
(see TEMPORARY), the LAG
function may not be used
(see LAG).
Next: Matrices, Previous: Conditional and Looping Constructs, Up: GNU PSPP [Contents][Index]
This chapter documents the statistical procedures that PSPP supports so far.
Next: FREQUENCIES, Up: Statistics [Contents][Index]
DESCRIPTIVES /VARIABLES=var_list /MISSING={VARIABLE,LISTWISE} {INCLUDE,NOINCLUDE} /FORMAT={LABELS,NOLABELS} {NOINDEX,INDEX} {LINE,SERIAL} /SAVE /STATISTICS={ALL,MEAN,SEMEAN,STDDEV,VARIANCE,KURTOSIS, SKEWNESS,RANGE,MINIMUM,MAXIMUM,SUM,DEFAULT, SESKEWNESS,SEKURTOSIS} /SORT={NONE,MEAN,SEMEAN,STDDEV,VARIANCE,KURTOSIS,SKEWNESS, RANGE,MINIMUM,MAXIMUM,SUM,SESKEWNESS,SEKURTOSIS,NAME} {A,D}
The DESCRIPTIVES
procedure reads the active dataset and outputs
linear descriptive statistics requested by the user. In addition, it can optionally
compute Z-scores.
The VARIABLES
subcommand, which is required, specifies the list of
variables to be analyzed. Keyword VARIABLES
is optional.
All other subcommands are optional:
The MISSING
subcommand determines the handling of missing variables. If
INCLUDE
is set, then user-missing values are included in the
calculations. If NOINCLUDE
is set, which is the default, user-missing
values are excluded. If VARIABLE
is set, then missing values are
excluded on a variable by variable basis; if LISTWISE
is set, then
the entire case is excluded whenever any value in that case has a
system-missing or, if INCLUDE
is set, user-missing value.
The FORMAT
subcommand has no effect. It is accepted for
backward compatibility.
The SAVE
subcommand causes DESCRIPTIVES
to calculate Z scores for all
the specified variables. The Z scores are saved to new variables.
Variable names are generated by trying first the original variable name
with Z prepended and truncated to a maximum of 8 characters, then the
names ZSC000 through ZSC999, STDZ00 through STDZ09, ZZZZ00 through
ZZZZ09, ZQZQ00 through ZQZQ09, in that sequence. In addition, Z score
variable names can be specified explicitly on VARIABLES
in the variable
list by enclosing them in parentheses after each variable.
When Z scores are calculated, PSPP ignores TEMPORARY
,
treating temporary transformations as permanent.
The STATISTICS
subcommand specifies the statistics to be displayed:
ALL
All of the statistics below.
MEAN
Arithmetic mean.
SEMEAN
Standard error of the mean.
STDDEV
Standard deviation.
VARIANCE
Variance.
KURTOSIS
Kurtosis and standard error of the kurtosis.
SKEWNESS
Skewness and standard error of the skewness.
RANGE
Range.
MINIMUM
Minimum value.
MAXIMUM
Maximum value.
SUM
Sum.
DEFAULT
Mean, standard deviation of the mean, minimum, maximum.
SEKURTOSIS
Standard error of the kurtosis.
SESKEWNESS
Standard error of the skewness.
The SORT
subcommand specifies how the statistics should be sorted. Most
of the possible values should be self-explanatory. NAME
causes the
statistics to be sorted by name. By default, the statistics are listed
in the order that they are specified on the VARIABLES
subcommand.
The A
and D
settings request an ascending or descending
sort order, respectively.
The physiology.sav file contains various physiological data for a sample
of persons. Running the DESCRIPTIVES
command on the variables height
and temperature with the default options allows one to see simple linear
statistics for these two variables. In Example 15.1, these variables
are specfied on the VARIABLES
subcommand and the SAVE
option
has been used, to request that Z scores be calculated.
After the command has completed, this example runs DESCRIPTIVES
again, this
time on the zheight and ztemperature variables,
which are the two normalized (Z-score) variables generated by the
first DESCRIPTIVES
command.
get file='physiology.sav'. descriptives /variables = height temperature /save. descriptives /variables = zheight ztemperature. |
Example 15.1: Running two DESCRIPTIVES
commands, one with the SAVE
subcommand
![]() |
Screenshot 15.1: The Descriptives dialog box with two variables and Z-Scores option selected
In Result 15.1, we can see that there are 40 valid data for each of the variables and no missing values. The mean average of the height and temperature is 16677.12 and 37.02 respectively. The descriptive statistics for temperature seem reasonable. However there is a very high standard deviation for height and a suspiciously low minimum. This is due to a data entry error in the data (see Identifying incorrect data).
In the second Descriptive Statistics command, one can see that the mean and standard deviation of both Z score variables is 0 and 1 respectively. All Z score statistics should have these properties since they are normalized versions of the original scores.
|
Result 15.1: Descriptives statistics including two normalized variables (Z-scores)
Next: EXAMINE, Previous: DESCRIPTIVES, Up: Statistics [Contents][Index]
FREQUENCIES /VARIABLES=var_list /FORMAT={TABLE,NOTABLE,LIMIT(limit)} {AVALUE,DVALUE,AFREQ,DFREQ} /MISSING={EXCLUDE,INCLUDE} /STATISTICS={DEFAULT,MEAN,SEMEAN,MEDIAN,MODE,STDDEV,VARIANCE, KURTOSIS,SKEWNESS,RANGE,MINIMUM,MAXIMUM,SUM, SESKEWNESS,SEKURTOSIS,ALL,NONE} /NTILES=ntiles /PERCENTILES=percent… /HISTOGRAM=[MINIMUM(x_min)] [MAXIMUM(x_max)] [{FREQ[(y_max)],PERCENT[(y_max)]}] [{NONORMAL,NORMAL}] /PIECHART=[MINIMUM(x_min)] [MAXIMUM(x_max)] [{FREQ,PERCENT}] [{NOMISSING,MISSING}] /BARCHART=[MINIMUM(x_min)] [MAXIMUM(x_max)] [{FREQ,PERCENT}] /ORDER={ANALYSIS,VARIABLE} (These options are not currently implemented.) /HBAR=… /GROUPED=…
The FREQUENCIES
procedure outputs frequency tables for specified
variables.
FREQUENCIES
can also calculate and display descriptive statistics
(including median and mode) and percentiles, and various graphical representations
of the frequency distribution.
The VARIABLES
subcommand is the only required subcommand. Specify the
variables to be analyzed.
The FORMAT
subcommand controls the output format. It has several
possible settings:
TABLE
, the default, causes a frequency table to be output for every
variable specified. NOTABLE
prevents them from being output. LIMIT
with a numeric argument causes them to be output except when there are
more than the specified number of values in the table.
Normally frequency tables are sorted in ascending order by value. This
is AVALUE
. DVALUE
tables are sorted in descending order by value.
AFREQ
and DFREQ
tables are sorted in ascending and descending order,
respectively, by frequency count.
The MISSING
subcommand controls the handling of user-missing values.
When EXCLUDE
, the default, is set, user-missing values are not included
in frequency tables or statistics. When INCLUDE
is set, user-missing
are included. System-missing values are never included in statistics,
but are listed in frequency tables.
The available STATISTICS
are the same as available
in DESCRIPTIVES
(see DESCRIPTIVES), with the addition
of MEDIAN
, the data’s median
value, and MODE, the mode. (If there are multiple modes, the smallest
value is reported.) By default, the mean, standard deviation of the
mean, minimum, and maximum are reported for each variable.
PERCENTILES
causes the specified percentiles to be reported.
The percentiles should be presented at a list of numbers between 0
and 100 inclusive.
The NTILES
subcommand causes the percentiles to be reported at the
boundaries of the data set divided into the specified number of ranges.
For instance, /NTILES=4
would cause quartiles to be reported.
The HISTOGRAM
subcommand causes the output to include a histogram for
each specified numeric variable. The X axis by default ranges from
the minimum to the maximum value observed in the data, but the MINIMUM
and MAXIMUM
keywords can set an explicit range.
6
Histograms are not created for string variables.
Specify NORMAL
to superimpose a normal curve on the
histogram.
The PIECHART
subcommand adds a pie chart for each variable to the data. Each
slice represents one value, with the size of the slice proportional to
the value’s frequency. By default, all non-missing values are given
slices.
The MINIMUM
and MAXIMUM
keywords can be used to limit the
displayed slices to a given range of values.
The keyword NOMISSING
causes missing values to be omitted from the
piechart. This is the default.
If instead, MISSING
is specified, then the pie chart includes
a single slice representing all system missing and user-missing cases.
The BARCHART
subcommand produces a bar chart for each variable.
The MINIMUM
and MAXIMUM
keywords can be used to omit
categories whose counts which lie outside the specified limits.
The FREQ
option (default) causes the ordinate to display the frequency
of each category, whereas the PERCENT
option displays relative
percentages.
The FREQ
and PERCENT
options on HISTOGRAM
and
PIECHART
are accepted but not currently honoured.
The ORDER
subcommand is accepted but ignored.
Example 15.2 runs a frequency analysis on the sex and occupation variables from the personnel.sav file. This is useful to get an general idea of the way in which these nominal variables are distributed.
get file='personnel.sav'. frequencies /variables = sex occupation /statistics = none. |
Example 15.2: Running frequencies on the sex and occupation variables
If you are using the graphic user interface, the dialog box is set up such that by default, several statistics are calculated. Some are not particularly useful for categorical variables, so you may want to disable those.
![]() |
Screenshot 15.2: The frequencies dialog box with the sex and occupation variables selected
From Result 15.2 it is evident that there are 33 males, 21 females and 2 persons for whom their sex has not been entered.
One can also see how many of each occupation there are in the data. When dealing with string variables used as nominal values, running a frequency analysis is useful to detect data input entries. Notice that one occupation value has been mistyped as “Scrientist”. This entry should be corrected, or marked as missing before using the data.
|
Result 15.2: The relative frequencies of sex and occupation
Next: GRAPH, Previous: FREQUENCIES, Up: Statistics [Contents][Index]
EXAMINE VARIABLES= var1 [var2] … [varN] [BY factor1 [BY subfactor1] [ factor2 [BY subfactor2]] … [ factor3 [BY subfactor3]] ] /STATISTICS={DESCRIPTIVES, EXTREME[(n)], ALL, NONE} /PLOT={BOXPLOT, NPPLOT, HISTOGRAM, SPREADLEVEL[(t)], ALL, NONE} /CINTERVAL p /COMPARE={GROUPS,VARIABLES} /ID=identity_variable /{TOTAL,NOTOTAL} /PERCENTILE=[percentiles]={HAVERAGE, WAVERAGE, ROUND, AEMPIRICAL, EMPIRICAL } /MISSING={LISTWISE, PAIRWISE} [{EXCLUDE, INCLUDE}] [{NOREPORT,REPORT}]
The EXAMINE
command is used to perform exploratory data analysis.
In particular, it is useful for testing how closely a distribution follows a
normal distribution, and for finding outliers and extreme values.
The VARIABLES
subcommand is mandatory.
It specifies the dependent variables and optionally variables to use as
factors for the analysis.
Variables listed before the first BY
keyword (if any) are the
dependent variables.
The dependent variables may optionally be followed by a list of
factors which tell PSPP how to break down the analysis for each
dependent variable.
Following the dependent variables, factors may be specified.
The factors (if desired) should be preceded by a single BY
keyword.
The format for each factor is
factorvar [BY subfactorvar].
Each unique combination of the values of factorvar and
subfactorvar divide the dataset into cells.
Statistics are calculated for each cell
and for the entire dataset (unless NOTOTAL
is given).
The STATISTICS
subcommand specifies which statistics to show.
DESCRIPTIVES
produces a table showing some parametric and
non-parametrics statistics.
EXTREME
produces a table showing the extremities of each cell.
A number in parentheses, n determines
how many upper and lower extremities to show.
The default number is 5.
The subcommands TOTAL
and NOTOTAL
are mutually exclusive.
If TOTAL
appears, then statistics for the entire dataset
as well as for each cell are produced.
If NOTOTAL
appears, then statistics are produced only for the cells
(unless no factor variables have been given).
These subcommands have no effect if there have been no factor variables
specified.
The PLOT
subcommand specifies which plots are to be produced if any.
Available plots are HISTOGRAM
, NPPLOT
, BOXPLOT
and
SPREADLEVEL
.
The first three can be used to visualise how closely each cell conforms to a
normal distribution, whilst the spread vs. level plot can be useful to visualise
how the variance differs between factors.
Boxplots show you the outliers and extreme values.
7
The SPREADLEVEL
plot displays the interquartile range versus the
median. It takes an optional parameter t, which specifies how the data
should be transformed prior to plotting.
The given value t is a power to which the data are raised. For example, if
t is given as 2, then the square of the data is used.
Zero, however is a special value. If t is 0 or
is omitted, then data are transformed by taking its natural logarithm instead of
raising to the power of t.
When one or more plots are requested, EXAMINE
also performs the
Shapiro-Wilk test for each category.
There are however a number of provisos:
The COMPARE
subcommand is only relevant if producing boxplots, and it is only
useful there is more than one dependent variable and at least one factor.
If
/COMPARE=GROUPS
is specified, then one plot per dependent variable is produced,
each of which contain boxplots for all the cells.
If /COMPARE=VARIABLES
is specified, then one plot per cell is produced,
each containing one boxplot per dependent variable.
If the /COMPARE
subcommand is omitted, then PSPP behaves as if
/COMPARE=GROUPS
were given.
The ID
subcommand is relevant only if /PLOT=BOXPLOT
or
/STATISTICS=EXTREME
has been given.
If given, it should provide the name of a variable which is to be used
to labels extreme values and outliers.
Numeric or string variables are permissible.
If the ID
subcommand is not given, then the case number is used for
labelling.
The CINTERVAL
subcommand specifies the confidence interval to use in
calculation of the descriptives command. The default is 95%.
The PERCENTILES
subcommand specifies which percentiles are to be calculated,
and which algorithm to use for calculating them. The default is to
calculate the 5, 10, 25, 50, 75, 90, 95 percentiles using the
HAVERAGE
algorithm.
The TOTAL
and NOTOTAL
subcommands are mutually exclusive. If NOTOTAL
is given and factors have been specified in the VARIABLES
subcommand,
then statistics for the unfactored dependent variables are
produced in addition to the factored variables. If there are no
factors specified then TOTAL
and NOTOTAL
have no effect.
The following example generates descriptive statistics and histograms for
two variables score1 and score2.
Two factors are given, viz: gender and gender BY culture.
Therefore, the descriptives and histograms are generated for each
distinct value
of gender and for each distinct combination of the values
of gender and race.
Since the NOTOTAL
keyword is given, statistics and histograms for
score1 and score2 covering the whole dataset are not produced.
EXAMINE score1 score2 BY gender gender BY culture /STATISTICS = DESCRIPTIVES /PLOT = HISTOGRAM /NOTOTAL.
Here is a second example showing how the examine
command can be used to find extremities.
EXAMINE height weight BY gender /STATISTICS = EXTREME (3) /PLOT = BOXPLOT /COMPARE = GROUPS /ID = name.
In this example, we look at the height and weight of a sample of individuals and
how they differ between male and female.
A table showing the 3 largest and the 3 smallest values of height and
weight for each gender, and for the whole dataset as are shown.
In addition, the /PLOT
subcommand requests boxplots.
Because /COMPARE = GROUPS
was specified, boxplots for male and female are
shown in juxtaposed in the same graphic, allowing us to easily see the difference between
the genders.
Since the variable name was specified on the ID
subcommand,
values of the name variable are used to label the extreme values.
Warning!
If you specify many dependent variables or factor variables
for which there are many distinct values, then EXAMINE
will produce a very
large quantity of output.
Next: CORRELATIONS, Previous: EXAMINE, Up: Statistics [Contents][Index]
GRAPH /HISTOGRAM [(NORMAL)]= var /SCATTERPLOT [(BIVARIATE)] = var1 WITH var2 [BY var3] /BAR = {summary-function(var1) | count-function} BY var2 [BY var3] [ /MISSING={LISTWISE, VARIABLE} [{EXCLUDE, INCLUDE}] ] [{NOREPORT,REPORT}]
The GRAPH
command produces graphical plots of data. Only one of the subcommands
HISTOGRAM
, BAR
or SCATTERPLOT
can be specified, i.e. only one plot
can be produced per call of GRAPH
. The MISSING
is optional.
The subcommand SCATTERPLOT
produces an xy plot of the
data.
GRAPH
uses the third variable var3, if specified, to determine
the colours and/or markers for the plot.
The following is an example for producing a scatterplot.
GRAPH /SCATTERPLOT = height WITH weight BY gender.
This example produces a scatterplot where height is plotted versus weight. Depending on the value of the gender variable, the colour of the datapoint is different. With this plot it is possible to analyze gender differences for height versus weight relation.
Next: Bar Chart, Previous: Scatterplot, Up: GRAPH [Contents][Index]
The subcommand HISTOGRAM
produces a histogram. Only one variable is allowed for
the histogram plot.
The keyword NORMAL
may be specified in parentheses, to indicate that the ideal normal curve
should be superimposed over the histogram.
For an alternative method to produce histograms see EXAMINE. The
following example produces a histogram plot for the variable weight.
GRAPH /HISTOGRAM = weight.
The subcommand BAR
produces a bar chart.
This subcommand requires that a count-function be specified (with no arguments) or a summary-function with a variable var1 in parentheses.
Following the summary or count function, the keyword BY
should be specified and then a catagorical variable, var2.
The values of the variable var2 determine the labels of the bars to be plotted.
Optionally a second categorical variable var3 may be specified in which case a clustered (grouped) bar chart is produced.
Valid count functions are
COUNT
The weighted counts of the cases in each category.
PCT
The weighted counts of the cases in each category expressed as a percentage of the total weights of the cases.
CUFREQ
The cumulative weighted counts of the cases in each category.
CUPCT
The cumulative weighted counts of the cases in each category expressed as a percentage of the total weights of the cases.
The summary function is applied to var1 across all cases in each category. The recognised summary functions are:
SUM
The sum.
MEAN
The arithmetic mean.
MAXIMUM
The maximum value.
MINIMUM
The minimum value.
The following examples assume a dataset which is the results of a survey. Each respondent has indicated annual income, their sex and city of residence. One could create a bar chart showing how the mean income varies between of residents of different cities, thus:
GRAPH /BAR = MEAN(income) BY city.
This can be extended to also indicate how income in each city differs between the sexes.
GRAPH /BAR = MEAN(income) BY city BY sex.
One might also want to see how many respondents there are from each city. This can be achieved as follows:
GRAPH /BAR = COUNT BY city.
Bar charts can also be produced using the FREQUENCIES and CROSSTABS commands.
Next: CROSSTABS, Previous: GRAPH, Up: Statistics [Contents][Index]
CORRELATIONS /VARIABLES = var_list [ WITH var_list ] [ . . . /VARIABLES = var_list [ WITH var_list ] /VARIABLES = var_list [ WITH var_list ] ] [ /PRINT={TWOTAIL, ONETAIL} {SIG, NOSIG} ] [ /STATISTICS=DESCRIPTIVES XPROD ALL] [ /MISSING={PAIRWISE, LISTWISE} {INCLUDE, EXCLUDE} ]
The CORRELATIONS
procedure produces tables of the Pearson correlation coefficient
for a set of variables. The significance of the coefficients are also given.
At least one VARIABLES
subcommand is required. If you specify the WITH
keyword, then a non-square correlation table is produced.
The variables preceding WITH
, are used as the rows of the table,
and the variables following WITH
are used as the columns of the table.
If no WITH
subcommand is specified, then CORRELATIONS
produces a
square, symmetrical table using all variables.
The MISSING
subcommand determines the handling of missing variables.
If INCLUDE
is set, then user-missing values are included in the
calculations, but system-missing values are not.
If EXCLUDE
is set, which is the default, user-missing
values are excluded as well as system-missing values.
If LISTWISE
is set, then the entire case is excluded from analysis
whenever any variable specified in any /VARIABLES
subcommand
contains a missing value.
If PAIRWISE
is set, then a case is considered missing only if either of the
values for the particular coefficient are missing.
The default is PAIRWISE
.
The PRINT
subcommand is used to control how the reported significance values are printed.
If the TWOTAIL
option is used, then a two-tailed test of significance is
printed. If the ONETAIL
option is given, then a one-tailed test is used.
The default is TWOTAIL
.
If the NOSIG
option is specified, then correlation coefficients with significance less than
0.05 are highlighted.
If SIG
is specified, then no highlighting is performed. This is the default.
The STATISTICS
subcommand requests additional statistics to be displayed. The keyword
DESCRIPTIVES
requests that the mean, number of non-missing cases, and the non-biased
estimator of the standard deviation are displayed.
These statistics are displayed in a separated table, for all the variables listed
in any /VARIABLES
subcommand.
The XPROD
keyword requests cross-product deviations and covariance estimators to
be displayed for each pair of variables.
The keyword ALL
is the union of DESCRIPTIVES
and XPROD
.
Next: CTABLES, Previous: CORRELATIONS, Up: Statistics [Contents][Index]
CROSSTABS /TABLES=var_list BY var_list [BY var_list]… /MISSING={TABLE,INCLUDE,REPORT} /FORMAT={TABLES,NOTABLES} {AVALUE,DVALUE} /CELLS={COUNT,ROW,COLUMN,TOTAL,EXPECTED,RESIDUAL,SRESIDUAL, ASRESIDUAL,ALL,NONE} /COUNT={ASIS,CASE,CELL} {ROUND,TRUNCATE} /STATISTICS={CHISQ,PHI,CC,LAMBDA,UC,BTAU,CTAU,RISK,GAMMA,D, KAPPA,ETA,CORR,ALL,NONE} /BARCHART (Integer mode.) /VARIABLES=var_list (low,high)…
The CROSSTABS
procedure displays crosstabulation
tables requested by the user. It can calculate several statistics for
each cell in the crosstabulation tables. In addition, a number of
statistics can be calculated for each table itself.
The TABLES
subcommand is used to specify the tables to be reported. Any
number of dimensions is permitted, and any number of variables per
dimension is allowed. The TABLES
subcommand may be repeated as many
times as needed. This is the only required subcommand in general
mode.
Occasionally, one may want to invoke a special mode called integer
mode. Normally, in general mode, PSPP automatically determines
what values occur in the data. In integer mode, the user specifies the
range of values that the data assumes. To invoke this mode, specify the
VARIABLES
subcommand, giving a range of data values in parentheses for
each variable to be used on the TABLES
subcommand. Data values inside
the range are truncated to the nearest integer, then assigned to that
value. If values occur outside this range, they are discarded. When it
is present, the VARIABLES
subcommand must precede the TABLES
subcommand.
In general mode, numeric and string variables may be specified on TABLES. In integer mode, only numeric variables are allowed.
The MISSING
subcommand determines the handling of user-missing values.
When set to TABLE
, the default, missing values are dropped on a table by
table basis. When set to INCLUDE
, user-missing values are included in
tables and statistics. When set to REPORT
, which is allowed only in
integer mode, user-missing values are included in tables but marked with
a footnote and excluded from statistical calculations.
The FORMAT
subcommand controls the characteristics of the
crosstabulation tables to be displayed. It has a number of possible
settings:
TABLES
, the default, causes crosstabulation tables to be output.
NOTABLES
, which is equivalent to CELLS=NONE
, suppresses them.
AVALUE
, the default, causes values to be sorted in ascending order.
DVALUE
asserts a descending sort order.
The CELLS
subcommand controls the contents of each cell in the displayed
crosstabulation table. The possible settings are:
Frequency count.
Row percent.
Column percent.
Table percent.
Expected value.
Residual.
Standardized residual.
Adjusted standardized residual.
All of the above.
Suppress cells entirely.
‘/CELLS’ without any settings specified requests COUNT
, ROW
,
COLUMN
, and TOTAL
.
If CELLS
is not specified at all then only COUNT
is selected.
By default, crosstabulation and statistics use raw case weights,
without rounding. Use the /COUNT
subcommand to perform
rounding: CASE rounds the weights of individual weights as cases are
read, CELL rounds the weights of cells within each crosstabulation
table after it has been constructed, and ASIS explicitly specifies the
default non-rounding behavior. When rounding is requested, ROUND, the
default, rounds to the nearest integer and TRUNCATE rounds toward
zero.
The STATISTICS
subcommand selects statistics for computation:
Pearson chi-square, likelihood ratio, Fisher’s exact test, continuity correction, linear-by-linear association.
Phi.
Contingency coefficient.
Lambda.
Uncertainty coefficient.
Tau-b.
Tau-c.
Risk estimate.
Gamma.
Somers’ D.
Cohen’s Kappa.
Eta.
Spearman correlation, Pearson’s r.
All of the above.
No statistics.
Selected statistics are only calculated when appropriate for the statistic. Certain statistics require tables of a particular size, and some statistics are calculated only in integer mode.
‘/STATISTICS’ without any settings selects CHISQ. If the
STATISTICS
subcommand is not given, no statistics are calculated.
The ‘/BARCHART’ subcommand produces a clustered bar chart for the first two variables on each table. If a table has more than two variables, the counts for the third and subsequent levels are aggregated and the chart is produced as if there were only two variables.
Please note: Currently the implementation of CROSSTABS
has the
following limitations:
Fixes for any of these deficiencies would be welcomed.
A researcher wishes to know if, in an industry, a person’s sex is related to the person’s occupation. To investigate this, she has determined that the personnel.sav is a representative, randomly selected sample of persons. The researcher’s null hypothesis is that a person’s sex has no relation to a person’s occupation. She uses a chi-squared test of independence to investigate the hypothesis.
get file="personnel.sav". crosstabs /tables= occupation by sex /cells = count expected /statistics=chisq. |
Example 15.3: Running crosstabs on the sex and occupation variables
The syntax in Example 15.3 conducts a chi-squared test of independence.
The line /tables = occupation by sex
indicates that occupation
and sex are the variables to be tabulated. To do this using the graphic user interface
you must place these variable names respectively in the ‘Row’ and
‘Column’ fields as shown in Screenshot 15.3.
![]() |
Screenshot 15.3: The Crosstabs dialog box with the sex and occupation variables selected
Similarly, the ‘Cells’ button shows a dialog box to select the count
and expected
options. All other cell options can be deselected for this
test.
You would use the ‘Format’ and ‘Statistics’ buttons to select options
for the FORMAT
and STATISTICS
subcommands. In this example,
the ‘Statistics’ requires only the ‘Chisq’ option to be checked. All
other options should be unchecked. No special settings are required from the
‘Format’ dialog.
As shown in Results 15.1 CROSSTABS
generates a contingency table
containing the observed count and the expected count of each sex and each
occupation. The expected count is the count which would be observed if the
null hypothesis were true.
The significance of the Pearson Chi-Square value is very much larger than the normally accepted value of 0.05 and so one cannot reject the null hypothesis. Thus the researcher must conclude that a person’s sex has no relation to the person’s occupation.
|
Results 15.1: The results of a test of independence between sex and occupation
Next: FACTOR, Previous: CROSSTABS, Up: Statistics [Contents][Index]
CTABLES
has the following overall syntax. At least one
TABLE
subcommand is required:
CTABLES …global subcommands… [/TABLE axis [BY axis [BY axis]] …per-table subcommands…]…
where each axis may be empty or take one of the following forms:
variable variable [{C | S}] axis + axis axis > axis (axis) axis [summary [string] [format]]
The following subcommands precede the first TABLE
subcommand
and apply to all of the output tables. All of these subcommands are
optional:
/FORMAT [MINCOLWIDTH={DEFAULT | width}] [MAXCOLWIDTH={DEFAULT | width}] [UNITS={POINTS | INCHES | CM}] [EMPTY={ZERO | BLANK | string}] [MISSING=string] /VLABELS VARIABLES=variables DISPLAY={DEFAULT | NAME | LABEL | BOTH | NONE} /SMISSING {VARIABLE | LISTWISE} /PCOMPUTE &postcompute=EXPR(expression) /PPROPERTIES &postcompute… [LABEL=string] [FORMAT=[summary format]…] [HIDESOURCECATS={NO | YES} /WEIGHT VARIABLE=variable /HIDESMALLCOUNTS COUNT=count
The following subcommands follow TABLE
and apply only to the
previous TABLE
. All of these subcommands are optional:
/SLABELS [POSITION={COLUMN | ROW | LAYER}] [VISIBLE={YES | NO}] /CLABELS {AUTO | {ROWLABELS|COLLABELS}={OPPOSITE|LAYER}} /CATEGORIES VARIABLES=variables {[value, value…] | [ORDER={A | D}] [KEY={VALUE | LABEL | summary(variable)}] [MISSING={EXCLUDE | INCLUDE}]} [TOTAL={NO | YES} [LABEL=string] [POSITION={AFTER | BEFORE}]] [EMPTY={INCLUDE | EXCLUDE}] /TITLES [TITLE=string…] [CAPTION=string…] [CORNER=string…]
The CTABLES
(aka “custom tables”) command produces
multi-dimensional tables from categorical and scale data. It offers
many options for data summarization and formatting.
This section’s examples use data from the 2008 (USA) National Survey of Drinking and Driving Attitudes and Behaviors, a public domain data set from the (USA) National Highway Traffic Administration and available at https://data.transportation.gov. PSPP includes this data set, with a modified dictionary, as examples/nhtsa.sav.
Next: Data Summarization, Up: CTABLES [Contents][Index]
The only required subcommand is TABLE
, which specifies the
variables to include along each axis:
/TABLE rows [BY columns [BY layers]]
In TABLE
, each of rows, columns, and layers
is either empty or an axis expression that specifies one or more
variables. At least one must specify an axis expression.
Next: Scalar Variables, Up: Basics [Contents][Index]
An axis expression that names a categorical variable divides the data
into cells according to the values of that variable. When all the
variables named on TABLE
are categorical, by default each cell
displays the number of cases that it contains, so specifying a single
variable yields a frequency table, much like the output of the
FREQUENCIES
command (see FREQUENCIES):
CTABLES /TABLE=ageGroup.
|
Specifying a row and a column categorical variable yields a
crosstabulation, much like the output of the CROSSTABS
command
(see CROSSTABS):
CTABLES /TABLE=ageGroup BY gender.
|
The ‘>’ “nesting” operator nests multiple variables on a single axis, e.g.:
CTABLES /TABLE likelihoodOfBeingStoppedByPolice BY ageGroup > gender.
|
The ‘+’ “stacking” operator allows a single output table to
include multiple data analyses. With ‘+’, CTABLES
divides
the output table into multiple sections, each of which includes
an analysis of the full data set. For example, the following command
separately tabulates age group and driving frequency by gender:
CTABLES /TABLE ageGroup + freqOfDriving BY gender.
|
When ‘+’ and ‘>’ are used together, ‘>’ binds more tightly. Use parentheses to override operator precedence. Thus:
CTABLES /TABLE hasConsideredReduction + hasBeenCriticized > gender. CTABLES /TABLE (hasConsideredReduction + hasBeenCriticized) > gender.
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Next: Overriding Measurement Level, Previous: Categorical Variables, Up: Basics [Contents][Index]
For a categorical variable, CTABLES
divides the table into a
cell per category. For a scalar variable, CTABLES
instead
calculates a summary measure, by default the mean, of the values that
fall into a cell. For example, if the only variable specified is a
scalar variable, then the output is a single cell that holds the mean
of all of the data:
CTABLES /TABLE age.
|
A scalar variable may nest with categorical variables. The following example shows the mean age of survey respondents across gender and language groups:
CTABLES /TABLE gender > age BY region.
|
The order of nesting of scalar and categorical variables affects table labeling, but it does not affect the data displayed in the table. The following example shows how the output changes when the nesting order of the scalar and categorical variable are interchanged:
CTABLES /TABLE age > gender BY region.
|
Only a single scalar variable may appear in each section; that is, a
scalar variable may not nest inside a scalar variable directly or
indirectly. Scalar variables may only appear on one axis within
TABLE
.
Previous: Scalar Variables, Up: Basics [Contents][Index]
By default, CTABLES
uses a variable’s measurement level to
decide whether to treat it as categorical or scalar. Variables
assigned the nominal or ordinal measurement level are treated as
categorical, and scalar variables are treated as scalar.
When PSPP reads data from a file in an external format, such as a
text file, variables’ measurement levels are often unknown. If
CTABLES
runs when a variable has an unknown measurement level,
it makes an initial pass through the data to guess measurement levels
using the rules described in an earlier section (see Measurement Level). Use the VARIABLE LEVEL
command to set or change a
variable’s measurement level (see VARIABLE LEVEL).
To treat a variable as categorical or scalar only for one use on
CTABLES
, add ‘[C]’ or ‘[S]’, respectively, after the
variable name. The following example shows the output when variable
monthDaysMin1drink
is analyzed as scalar (the default for its measurement
level) and as categorical:
CTABLES /TABLE monthDaysMin1drink BY gender /TABLE monthDaysMin1drink [C] BY gender.
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Next: Statistics Positions and Labels, Previous: Basics, Up: CTABLES [Contents][Index]
The CTABLES
command allows the user to control how the data are
summarized with summary specifications, syntax that lists one or
more summary function names, optionally separated by commas, and which
are enclosed in square brackets following a variable name on the
TABLE
subcommand. When all the variables are categorical,
summary specifications can be given for the innermost nested variables
on any one axis. When a scalar variable is present, only the scalar
variable may have summary specifications.
The following example includes a summary specification for column and row percentages for categorical variables, and mean and median for a scalar variable:
CTABLES /TABLE=age [MEAN, MEDIAN] BY gender /TABLE=ageGroup [COLPCT, ROWPCT] BY gender.
|
A summary specification may override the default label and format by appending a string or format specification or both (in that order) to the summary function name. For example:
CTABLES /TABLE=ageGroup [COLPCT 'Gender %' PCT5.0, ROWPCT 'Age Group %' PCT5.0] BY gender.
|
In addition to the standard formats, CTABLES
allows the user to
specify the following special formats:
NEGPARENw.d | Encloses negative numbers in parentheses. | 42.96 | (42.96) |
NEQUALw.d | Adds a N= prefix. | N=42.96 | N=-42.96 |
| Encloses all numbers in parentheses. | (42.96) | (-42.96) |
PCTPARENw.d | Encloses all numbers in parentheses with a ‘%’ suffix. | (42.96%) | (-42.96%) |
Parentheses provide a shorthand to apply summary specifications to multiple variables. For example, both of these commands:
CTABLES /TABLE=ageGroup[COLPCT] + membersOver16[COLPCT] BY gender. CTABLES /TABLE=(ageGroup + membersOver16)[COLPCT] BY gender.
produce the same output shown below:
|
The following sections list the available summary functions. After each function’s name is given its default label and format. If no format is listed, then the default format is the print format for the variable being summarized.
Next: Summary Functions for Groups of Cells, Up: Data Summarization [Contents][Index]
This section lists the summary functions that consider only an
individual cell in CTABLES
. Only one such summary function,
COUNT
, may be applied to both categorical and scale variables:
COUNT
(“Count”, F40.0)The sum of weights in a cell.
If CATEGORIES
for one or more of the variables in a table
include missing values (see Per-Variable Category Options), then some or all of the categories for a cell might be
missing values. COUNT
counts data included in a cell
regardless of whether its categories are missing.
The following summary functions apply only to scale variables or totals and subtotals for categorical variables. Be cautious about interpreting the summary value in the latter case, because it is not necessarily meaningful; however, the mean of a Likert scale, etc. may have a straightforward interpreation.
MAXIMUM
(“Maximum”)The largest value.
MEAN
(“Mean”)The mean.
MEDIAN
(“Median”)The median value.
MINIMUM
(“Minimum”)The smallest value.
MISSING
(“Missing”)Sum of weights of user- and system-missing values.
MODE
(“Mode”)The highest-frequency value. Ties are broken by taking the smallest mode.
PTILE
n (“Percentile n”)The nth percentile, where 0 ≤ n ≤ 100.
RANGE
(“Range”)The maximum minus the minimum.
SEMEAN
(“Std Error of Mean”)The standard error of the mean.
STDDEV
(“Std Deviation”)The standard deviation.
SUM
(“Sum”)The sum.
TOTALN
(“Total N”, F40.0)The sum of weights in a cell.
For scale data, COUNT
and TOTALN
are the same.
For categorical data, TOTALN
counts missing values in excluded
categories, that is, user-missing values not in an explicit category
list on CATEGORIES
(see Per-Variable Category Options), or user-missing values excluded because
MISSING=EXCLUDE
is in effect on CATEGORIES
, or
system-missing values. COUNT
does not count these.
See Missing Values for Summary Variables, for details of
how CTABLES
summarizes missing values.
VALIDN
(“Valid N”, F40.0)The sum of valid count weights in included categories.
For categorical variables, VALIDN
does not count missing values
regardless of whether they are in included categories via
CATEGORIES
. VALIDN
does not count valid values that are
in excluded categories. See Missing Values for Summary Variables, for details.
VARIANCE
(“Variance”)The variance.
Next: Summary Functions for Adjusted Weights, Previous: Summary Functions for Individual Cells, Up: Data Summarization [Contents][Index]
These summary functions summarize over multiple cells within an area of the output chosen by the user and specified as part of the function name. The following basic areas are supported, in decreasing order of size:
TABLE
A section. Stacked variables divide sections of the output from each other. sections may span multiple layers.
LAYER
A section within a single layer.
SUBTABLE
A subtable, whose contents are the cells that pair an innermost row variable and an innermost column variable within a single layer.
The following shows how the output for the table expression
hasBeenPassengerOfDesignatedDriver >
hasBeenPassengerOfDrunkDriver BY isLicensedDriver >
hasHostedEventWithAlcohol + hasBeenDesignatedDriver BY
gender
8 is divided up into
TABLE
, LAYER
, and SUBTABLE
areas. Each unique
value for Table ID is one section, and similarly for Layer ID and
Subtable ID. Thus, this output has two TABLE
areas (one for
isLicensedDriver
and one for hasBeenDesignatedDriver
),
four LAYER
areas (for those two variables, per layer), and 12
SUBTABLE
areas.
|
CTABLES
also supports the following areas that further
divide a subtable or a layer within a section:
LAYERROW
LAYERCOL
A row or column, respectively, in one layer of a section.
ROW
COL
A row or column, respectively, in a subtable.
The following summary functions for groups of cells are available for each area described above, for both categorical and scale variables:
areaPCT
or areaPCT.COUNT
(“Area %”, PCT40.1)A percentage of total counts within area.
areaPCT.VALIDN
(“Area Valid N %”, PCT40.1)A percentage of total counts for valid values within area.
areaPCT.TOTALN
(“Area Total N %”, PCT40.1)A percentage of total counts for all values within area.
Scale variables and totals and subtotals for categorical variables may use the following additional group cell summary function:
areaPCT.SUM
(“Area Sum %”, PCT40.1)Percentage of the sum of the values within area.
Next: Unweighted Summary Functions, Previous: Summary Functions for Groups of Cells, Up: Data Summarization [Contents][Index]
If the WEIGHT
subcommand specified an effective weight variable
(see Effective Weight), then the following summary functions
use its value instead of the dictionary weight variable. Otherwise,
they are equivalent to the summary function without the
‘E’-prefix:
ECOUNT
(“Adjusted Count”, F40.0)
ETOTALN
(“Adjusted Total N”, F40.0)
EVALIDN
(“Adjusted Valid N”, F40.0)
Previous: Summary Functions for Adjusted Weights, Up: Data Summarization [Contents][Index]
The following summary functions with a ‘U’-prefix are equivalent to the same ones without the prefix, except that they use unweighted counts:
UCOUNT
(“Unweighted Count”, F40.0)
UareaPCT
or UareaPCT.COUNT
(“Unweighted Area %”, PCT40.1)
UareaPCT.VALIDN
(“Unweighted Area Valid N %”, PCT40.1)
UareaPCT.TOTALN
(“Unweighted Area Total N %”, PCT40.1)
UMEAN
(“Unweighted Mean”)
UMEDIAN
(“Unweighted Median”)
UMISSING
(“Unweighted Missing”)
UMODE
(“Unweighted Mode”)
UareaPCT.SUM
(“Unweighted Area Sum %”, PCT40.1)
UPTILE
n (“Unweighted Percentile n”)
USEMEAN
(“Unweighted Std Error of Mean”)
USTDDEV
(“Unweighted Std Deviation”)
USUM
(“Unweighted Sum”)
UTOTALN
(“Unweighted Total N”, F40.0)
UVALIDN
(“Unweighted Valid N”, F40.0)
UVARIANCE
(“Unweighted Variance”, F40.0)
Next: Category Label Positions, Previous: Data Summarization, Up: CTABLES [Contents][Index]
/SLABELS [POSITION={COLUMN | ROW | LAYER}] [VISIBLE={YES | NO}]
The SLABELS
subcommand controls the position and visibility of
summary statistics for the TABLE
subcommand that it follows.
POSITION
sets the axis on which summary statistics appear.
With POSITION=COLUMN, which is the default, each summary statistic
appears in a column. For example:
CTABLES /TABLE=age [MEAN, MEDIAN] BY gender.
|
With POSITION=ROW, each summary statistic appears in a row, as shown below:
CTABLES /TABLE=age [MEAN, MEDIAN] BY gender /SLABELS POSITION=ROW.
|
POSITION=LAYER is also available to place each summary statistic in a separate layer.
Labels for summary statistics are shown by default. Use VISIBLE=NO to suppress them. Because unlabeled data can cause confusion, it should only be considered if the meaning of the data is evident, as in a simple case like this:
CTABLES /TABLE=ageGroup [TABLEPCT] /SLABELS VISIBLE=NO.
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Next: Per-Variable Category Options, Previous: Statistics Positions and Labels, Up: CTABLES [Contents][Index]
/CLABELS {AUTO | {ROWLABELS|COLLABELS}={OPPOSITE|LAYER}}
The CLABELS
subcommand controls the position of category labels
for the TABLE
subcommand that it follows. By default, or if
AUTO is specified, category labels for a given variable nest
inside the variable’s label on the same axis. For example, the
command below results in age categories nesting within the age group
variable on the rows axis and gender categories within the gender
variable on the columns axis:
CTABLES /TABLE ageGroup BY gender.
|
ROWLABELS=OPPOSITE or COLLABELS=OPPOSITE move row or column variable category labels, respectively, to the opposite axis. The setting affects only the innermost variable or variables, which must be categorical, on the given axis. For example:
CTABLES /TABLE ageGroup BY gender /CLABELS ROWLABELS=OPPOSITE. CTABLES /TABLE ageGroup BY gender /CLABELS COLLABELS=OPPOSITE.
|
ROWLABELS=LAYER or COLLABELS=LAYER move the innermost row or column variable category labels, respectively, to the layer axis.
Only one axis’s labels may be moved, whether to the opposite axis or to the layer axis.
CLABELS
primarily affects the appearance of tables, not the
data displayed in them. However, CTABLES
can affect the values
displayed for statistics that summarize areas of a table, since it can
change the definitions of these areas.
For example, consider the following syntax and output:
CTABLES /TABLE ageGroup BY gender [ROWPCT, COLPCT].
|
Using COLLABELS=OPPOSITE
changes the definitions of rows and
columns, so that column percentages display what were previously row
percentages and the new row percentages become meaningless (because
there is only one cell per row):
CTABLES /TABLE ageGroup BY gender [ROWPCT, COLPCT] /CLABELS COLLABELS=OPPOSITE.
|
If CLABELS
moves category labels from an axis with stacked
variables, the variables that are moved must have the same category
specifications (see Per-Variable Category Options) and the
same value labels.
The following shows both moving stacked category variables and adapting to the changing definitions of rows and columns:
CTABLES /TABLE (likelihoodOfBeingStoppedByPolice + likelihoodOfHavingAnAccident) [COLPCT]. CTABLES /TABLE (likelihoodOfBeingStoppedByPolice + likelihoodOfHavingAnAccident) [ROWPCT] /CLABELS ROW=OPPOSITE.
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Next: Titles, Previous: Category Label Positions, Up: CTABLES [Contents][Index]
/CATEGORIES VARIABLES=variables {[value, value…] | [ORDER={A | D}] [KEY={VALUE | LABEL | summary(variable)}] [MISSING={EXCLUDE | INCLUDE}]} [TOTAL={NO | YES} [LABEL=string] [POSITION={AFTER | BEFORE}]] [EMPTY={INCLUDE | EXCLUDE}]
The CATEGORIES
subcommand specifies, for one or more
categorical variables, the categories to include and exclude, the sort
order for included categories, and treatment of missing values. It
also controls the totals and subtotals to display. It may be
specified any number of times, each time for a different set of
variables. CATEGORIES
applies to the table produced by the
TABLE
subcommand that it follows.
CATEGORIES
does not apply to scalar variables.
VARIABLES is required and must list the variables for the subcommand to affect.
The syntax may specify the categories to include and their sort order
either explicitly or implicitly. The following sections give the
details of each form of syntax, followed by information on totals and
subtotals and the EMPTY
setting.
Next: Implicit Categories, Up: Per-Variable Category Options [Contents][Index]
To use CTABLES
to explicitly specify categories to include,
list the categories within square brackets in the desired sort order.
Use spaces or commas to separate values. Categories not covered by
the list are excluded from analysis.
Each element of the list takes one of the following forms:
A numeric or string category value, for variables that have the corresponding type.
A date or time category value, for variables that have a date or time print format.
A range of category values, where min and max each takes one of the forms above, in increasing order.
All user-missing values. (To match individual user-missing values, specify their category values.)
Any non-missing value not covered by any other element of the list (regardless of where OTHERNM is placed in the list).
A computed category name (see Computed Categories).
A subtotal (see Totals and Subtotals).
If multiple elements of the list cover a given category, the last one in the list takes precedence.
The following example syntax and output show how an explicit category can limit the displayed categories:
CTABLES /TABLE freqOfDriving. CTABLES /TABLE freqOfDriving /CATEGORIES VARIABLES=freqOfDriving [1, 2, 3].
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Next: Totals and Subtotals, Previous: Explicit Categories, Up: Per-Variable Category Options [Contents][Index]
In the absence of an explicit list of categories, CATEGORIES
allows KEY
, ORDER
, and MISSING
to specify how to
select and sort categories.
The KEY
setting specifies the sort key. By default, or with
KEY=VALUE
, categories are sorted by default. Categories may
also be sorted by value label, with KEY=LABEL
, or by the value
of a summary function, e.g. KEY=COUNT
.
By default, or with ORDER=A
, categories are sorted in ascending
order. Specify ORDER=D
to sort in descending order.
User-missing values are excluded by default, or with
MISSING=EXCLUDE
. Specify MISSING=INCLUDE
to include
user-missing values. The system-missing value is always excluded.
The following example syntax and output show how
MISSING=INCLUDE
causes missing values to be included in a
category list.
CTABLES /TABLE freqOfDriving. CTABLES /TABLE freqOfDriving /CATEGORIES VARIABLES=freqOfDriving MISSING=INCLUDE.
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Next: Categories Without Values, Previous: Implicit Categories, Up: Per-Variable Category Options [Contents][Index]
CATEGORIES
also controls display of totals and subtotals. By
default, or with TOTAL=NO
, totals are not displayed. Use
TOTAL=YES
to display a total. By default, the total is labeled
“Total”; use LABEL="label"
to override it.
Subtotals are also not displayed by default. To add one or more
subtotals, use an explicit category list and insert SUBTOTAL
or
HSUBTOTAL
in the position or positions where the subtotal
should appear. The subtotal becomes an extra row or column or layer.
HSUBTOTAL
additionally hides the categories that make up the
subtotal. Either way, the default label is “Subtotal”, use
SUBTOTAL="label"
or HSUBTOTAL="label"
to specify
a custom label.
The following example syntax and output show how to use
TOTAL=YES
and SUBTOTAL
:
CTABLES /TABLE freqOfDriving /CATEGORIES VARIABLES=freqOfDriving [OTHERNM, SUBTOTAL='Valid Total', MISSING, SUBTOTAL='Missing Total'] TOTAL=YES LABEL='Overall Total'.
|
By default, or with POSITION=AFTER
, totals are displayed in the
output after the last category and subtotals apply to categories that
precede them. With POSITION=BEFORE
, totals come before the
first category and subtotals apply to categories that follow them.
Only categorical variables may have totals and subtotals. Scalar
variables may be “totaled” indirectly by enabling totals and
subtotals on a categorical variable within which the scalar variable
is summarized. For example, the following syntax produces a mean,
count, and valid count across all data by adding a total on the
categorical region
variable, as shown:
CTABLES /TABLE=region > monthDaysMin1drink [MEAN, VALIDN] /CATEGORIES VARIABLES=region TOTAL=YES LABEL='All regions'.
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By default, PSPP uses the same summary functions for totals and
subtotals as other categories. To summarize totals and subtotals
differently, specify the summary functions for totals and subtotals
after the ordinary summary functions inside a nested set of []
following TOTALS
. For example, the following syntax displays
COUNT
for individual categories and totals and VALIDN
for totals, as shown:
CTABLES /TABLE isLicensedDriver [COUNT, TOTALS[COUNT, VALIDN]] /CATEGORIES VARIABLES=isLicensedDriver TOTAL=YES MISSING=INCLUDE.
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Previous: Totals and Subtotals, Up: Per-Variable Category Options [Contents][Index]
Some categories might not be included in the data set being analyzed.
For example, our example data set has no cases in the “15 or
younger” age group. By default, or with EMPTY=INCLUDE
,
PSPP includes these empty categories in output tables. To exclude
them, specify EMPTY=EXCLUDE
.
For implicit categories, empty categories potentially include all the
values with value labels for a given variable; for explicit
categories, they include all the values listed individually and all
values with value labels that are covered by ranges or MISSING
or OTHERNM
.
The following example syntax and output show the effect of
EMPTY=EXCLUDE
for the membersOver16
variable, in which 0
is labeled “None” but no cases exist with that value:
CTABLES /TABLE=membersOver16. CTABLES /TABLE=membersOver16 /CATEGORIES VARIABLES=membersOver16 EMPTY=EXCLUDE.
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Next: Table Formatting, Previous: Per-Variable Category Options, Up: CTABLES [Contents][Index]
/TITLES [TITLE=string…] [CAPTION=string…] [CORNER=string…]
The TITLES
subcommand sets the title, caption, and corner text
for the table output for the previous TABLE
subcommand. Any
number of strings may be specified for each kind of text, with each
string appearing on a separate line in the output. The title appears
above the table, the caption below the table, and the corner text
appears in the table’s upper left corner. By default, the title is
“Custom Tables” and the caption and corner text are empty. With
some table output styles, the corner text is not displayed.
The strings provided in this subcommand may contain the following macro-like keywords that PSPP substitutes at the time that it runs the command:
)DATE
The current date, e.g. MM/DD/YY. The format is locale-dependent.
)TIME
The current time, e.g. HH:MM:SS. The format is locale-dependent.
)TABLE
The expression specified on the TABLE
command. Summary
and measurement level specifications are omitted, and variable labels are used in place of variable names.
Next: Display of Variable Labels, Previous: Titles, Up: CTABLES [Contents][Index]
/FORMAT [MINCOLWIDTH={DEFAULT | width}] [MAXCOLWIDTH={DEFAULT | width}] [UNITS={POINTS | INCHES | CM}] [EMPTY={ZERO | BLANK | string}] [MISSING=string]
The FORMAT
subcommand, which must precede the first
TABLE
subcommand, controls formatting for all the output
tables. FORMAT
and all of its settings are optional.
Use MINCOLWIDTH
and MAXCOLWIDTH
to control the minimum
or maximum width of columns in output tables. By default, with
DEFAULT
, column width varies based on content. Otherwise,
specify a number for either or both of these settings. If both are
specified, MAXCOLWIDTH
must be greater than or equal to
MINCOLWIDTH
. The default unit, or with UNITS=POINTS
, is
points (1/72 inch), or specify UNITS=INCHES
to use inches or
UNITS=CM
for centimeters. PSPP does not currently honor any
of these settings.
By default, or with EMPTY=ZERO
, zero values are displayed in
their usual format. Use EMPTY=BLANK
to use an empty cell
instead, or EMPTY="string"
to use the specified string.
By default, missing values are displayed as ‘.’, the same as in
other tables. Specify MISSING="string"
to instead use a
custom string.
Next: Missing Value Treatment, Previous: Table Formatting, Up: CTABLES [Contents][Index]
/VLABELS VARIABLES=variables DISPLAY={DEFAULT | NAME | LABEL | BOTH | NONE}
The VLABELS
subcommand, which must precede the first
TABLE
subcommand, controls display of variable labels in all
the output tables. VLABELS
is optional. It may appear
multiple times to adjust settings for different variables.
VARIABLES
and DISPLAY
are required. The value of
DISPLAY
controls how variable labels are displayed for the
variables listed on VARIABLES
. The supported values are:
DEFAULT
Use the setting from SET TVARS
(see SET TVARS).
NAME
Show only a variable name.
LABEL
Show only a variable label.
BOTH
Show variable name and label.
NONE
Show nothing.
Next: Computed Categories, Previous: Display of Variable Labels, Up: CTABLES [Contents][Index]
The TABLE
subcommand on CTABLES
specifies two different
kinds of variables: variables that divide tables into cells (which are
always categorical) and variables being summarized (which may be
categorical or scale). PSPP treats missing values differently in
each kind of variable, as described in the sections below.
For variables that divide tables into cells, per-variable category options, as described in Per-Variable Category Options, determine which data is analyzed. If any of the categories for such a variable would exclude a case, then that case is not included.
As an example, consider the following entirely artificial dataset, in which ‘x’ and ‘y’ are categorical variables with missing value 9, and ‘z’ is scale:
|
Using ‘x’ and ‘y’ to define cells, and summarizing ‘z’, by default PSPP omits all the cases that have ‘x’ or ‘y’ (or both) missing:
CTABLES /TABLE x > y > z [SUM].
|
If, however, we add CATEGORIES
specifications to include
missing values for ‘y’ or for ‘x’ and ‘y’, the output
table includes them, like so:
CTABLES /TABLE x > y > z [SUM] /CATEGORIES VARIABLES=y MISSING=INCLUDE. CTABLES /TABLE x > y > z [SUM] /CATEGORIES VARIABLES=x y MISSING=INCLUDE.
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Next: Scale Missing Values, Previous: Missing Values for Cell-Defining Variables, Up: Missing Value Treatment [Contents][Index]
For summary variables, values that are valid and in included categories are analyzed, and values that are missing or in excluded categories are not analyzed, with the following exceptions:
VALIDN
, EVALIDN
,
UVALIDN
, areaPCT.VALIDN
, and
UareaPCT.VALIDN
) only count valid values in included
categories (not missing values in included categories).
TOTALN
, ETOTALN
,
UTOTALN
, areaPCT.TOTALN
), and
UareaPCT.TOTALN
count all values (valid and missing) in
included categories and missing (but not valid) values in excluded
categories.
For categorical variables, system-missing values are never in included categories. For scale variables, there is no notion of included and excluded categories, so all values are effectively included.
The following table provides another view of the above rules:
VALIDN | other | TOTALN | |
---|---|---|---|
Categorical variables: | |||
Valid values in included categories | yes | yes | yes |
Missing values in included categories | — | yes | yes |
Missing values in excluded categories | — | — | yes |
Valid values in excluded categories | — | — | — |
Scale variables: | |||
Valid values | yes | yes | yes |
User- or system-missing values | — | yes | yes |
Previous: Missing Values for Summary Variables, Up: Missing Value Treatment [Contents][Index]
/SMISSING {VARIABLE | LISTWISE}
The SMISSING
subcommand, which must precede the first
TABLE
subcommand, controls treatment of missing values for
scalar variables in producing all the output tables. SMISSING
is optional.
With SMISSING=VARIABLE
, which is the default, missing values
are excluded on a variable-by-variable basis. With
SMISSING=LISTWISE
, when stacked scalar variables are nested
together with a categorical variable, a missing value for any of the
scalar variables causes the case to be excluded for all of them.
As an example, consider the following dataset, in which ‘x’ is a categorical variable and ‘y’ and ‘z’ are scale:
|
With the default missing-value treatment, ‘x’’s mean is 20, based on the values 10, 20, and 30, and ‘y’’s mean is 50, based on 40, 50, and 60:
CTABLES /TABLE (y + z) > x.
|
By adding SMISSING=LISTWISE
, only cases where ‘y’ and
‘z’ are both non-missing are considered, so ‘x’’s mean
becomes 15, as the average of 10 and 20, and ‘y’’s mean becomes
55, the average of 50 and 60:
CTABLES /SMISSING LISTWISE /TABLE (y + z) > x.
|
Even with SMISSING=LISTWISE
, if ‘y’ and ‘z’ are
separately nested with ‘x’, instead of using a single ‘>’
operator, missing values revert to being considered on a
variable-by-variable basis:
CTABLES /SMISSING LISTWISE /TABLE (y > x) + (z > x).
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Next: Effective Weight, Previous: Missing Value Treatment, Up: CTABLES [Contents][Index]
/PCOMPUTE &postcompute=EXPR(expression) /PPROPERTIES &postcompute… [LABEL=string] [FORMAT=[summary format]…] [HIDESOURCECATS={NO | YES}
Computed categories, also called postcomputes, are
categories created using arithmetic on categories obtained from the
data. The PCOMPUTE
subcommand creates a postcompute, which may
then be used on CATEGORIES
within an explicit category list
(see CTABLES Explicit Category List). Optionally,
PPROPERTIES
refines how a postcompute is displayed. The
following sections provide the details.
Next: PPROPERTIES, Up: Computed Categories [Contents][Index]
/PCOMPUTE &postcompute=EXPR(expression)
The PCOMPUTE
subcommand, which must precede the first
TABLE
command, defines computed categories. It is optional and
may be used any number of times to define multiple postcomputes.
Each PCOMPUTE
defines one postcompute. Its syntax consists of
a name to identify the postcompute as a PSPP identifier prefixed by
‘&’, followed by ‘=’ and a postcompute expression enclosed
in EXPR(…)
. A postcompute expression consists of:
This form evaluates to the summary statistic for category, e.g.
[1]
evaluates to the value of the summary statistic associated
with category 1. The category may be a number, a quoted string,
or a quoted time or date value. All of the categories for a given
postcompute must have the same form. The category must appear in all
the CATEGORIES
list in which the postcompute is used.
These forms evaluate to the summary statistics for a category
specified with the same syntax, as described in previous section
(see CTABLES Explicit Category List). The category must appear in
all the CATEGORIES
list in which the postcompute is used.
The summary statistic for the subtotal category. This form is allowed
only if the CATEGORIES
lists that include this postcompute have
exactly one subtotal.
The summary statistic for subtotal category index, where 1 is the
first subtotal, 2 is the second, and so on. This form may be used for
CATEGORIES
lists with any number of subtotals.
The summary statistic for the total. The CATEGORIES
lsits that
include this postcompute must have a total enabled.
These forms perform arithmetic on the values of postcompute expressions a and b. The usual operator precedence rules apply.
Numeric constants may be used in postcompute expressions.
Parentheses override operator precedence.
A postcompute is not associated with any particular variable.
Instead, it may be referenced within CATEGORIES
for any
suitable variable (e.g. only a string variable is suitable for a
postcompute expression that refers to a string category, only a
variable with subtotals for an expression that refers to subtotals,
…).
Normally a named postcompute is defined only once, but if a later
PCOMPUTE
redefines a postcompute with the same name as an
earlier one, the later one take precedence.
The following syntax and output shows how PCOMPUTE
can compute
a total over subtotals, summing the “Frequent Drivers” and
“Infrequent Drivers” subtotals to form an “All Drivers”
postcompute. It also shows how to calculate and display a percentage,
in this case the percentage of valid responses that report never
driving. It uses PPROPERTIES
(see PPROPERTIES) to
display the latter in PCT
format.
CTABLES /PCOMPUTE &all_drivers=EXPR([1 THRU 2] + [3 THRU 4]) /PPROPERTIES &all_drivers LABEL='All Drivers' /PCOMPUTE &pct_never=EXPR([5] / ([1 THRU 2] + [3 THRU 4] + [5]) * 100) /PPROPERTIES &pct_never LABEL='% Not Drivers' FORMAT=COUNT PCT40.1 /TABLE=freqOfDriving BY gender /CATEGORIES VARIABLES=freqOfDriving [1 THRU 2, SUBTOTAL='Frequent Drivers', 3 THRU 4, SUBTOTAL='Infrequent Drivers', &all_drivers, 5, &pct_never, MISSING, SUBTOTAL='Not Drivers or Missing'].
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Previous: PCOMPUTE, Up: Computed Categories [Contents][Index]
/PPROPERTIES &postcompute… [LABEL=string] [FORMAT=[summary format]…] [HIDESOURCECATS={NO | YES}
The PPROPERTIES
subcommand, which must appear before
TABLE
, sets properties for one or more postcomputes defined on
prior PCOMPUTE
subcommands. The subcommand syntax begins with
the list of postcomputes, each prefixed with ‘&’ as specified on
PCOMPUTE
.
All of the settings on PPROPERTIES
are optional. Use
LABEL
to set the label shown for the postcomputes in table
output. The default label for a postcompute is the expression used to
define it.
A postcompute always uses same summary functions as the variable whose
categories contain it, but FORMAT
allows control over the
format used to display their values. It takes a list of summary
function names and format specifiers.
By default, or with HIDESOURCECATS=NO
, categories referred to
by computed categories are displayed like other categories. Use
HIDESOURCECATS=YES
to hide them.
The previous section provides an example for PPROPERTIES
.
Next: Hiding Small Counts, Previous: Computed Categories, Up: CTABLES [Contents][Index]
/WEIGHT VARIABLE=variable
The WEIGHT
subcommand is optional and must appear before
TABLE
. If it appears, it must name a numeric variable, known
as the effective weight or adjustment weight. The
effective weight variable stands in for the dictionary’s weight
variable (see WEIGHT), if any, in most calculations in
CTABLES
. The only exceptions are the COUNT
,
TOTALN
, and VALIDN
summary functions, which use the
dictionary weight instead.
Weights obtained from the PSPP dictionary are rounded to the nearest integer at the case level. Effective weights are not rounded. Regardless of the weighting source, PSPP does not analyze cases with zero, missing, or negative effective weights.
Previous: Effective Weight, Up: CTABLES [Contents][Index]
/HIDESMALLCOUNTS COUNT=count
The HIDESMALLCOUNTS
subcommand is optional. If it specified,
then COUNT
, ECOUNT
, and UCOUNT
values in output
tables less than the value of count are shown as <count
instead of their true values. The value of count must be an
integer and must be at least 2.
The following syntax and example shows how to use
HIDESMALLCOUNTS
:
CTABLES /HIDESMALLCOUNTS COUNT=10 /TABLE placeOfLastDrinkBeforeDrive.
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Next: GLM, Previous: CTABLES, Up: Statistics [Contents][Index]
FACTOR { VARIABLES=var_list, MATRIX IN ({CORR,COV}={*,file_spec}) } [ /METHOD = {CORRELATION, COVARIANCE} ] [ /ANALYSIS=var_list ] [ /EXTRACTION={PC, PAF}] [ /ROTATION={VARIMAX, EQUAMAX, QUARTIMAX, PROMAX[(k)], NOROTATE}] [ /PRINT=[INITIAL] [EXTRACTION] [ROTATION] [UNIVARIATE] [CORRELATION] [COVARIANCE] [DET] [KMO] [AIC] [SIG] [ALL] [DEFAULT] ] [ /PLOT=[EIGEN] ] [ /FORMAT=[SORT] [BLANK(n)] [DEFAULT] ] [ /CRITERIA=[FACTORS(n)] [MINEIGEN(l)] [ITERATE(m)] [ECONVERGE (delta)] [DEFAULT] ] [ /MISSING=[{LISTWISE, PAIRWISE}] [{INCLUDE, EXCLUDE}] ]
The FACTOR
command performs Factor Analysis or Principal Axis Factoring on a dataset. It may be used to find
common factors in the data or for data reduction purposes.
The VARIABLES
subcommand is required (unless the MATRIX IN
subcommand is used).
It lists the variables which are to partake in the analysis. (The ANALYSIS
subcommand may optionally further limit the variables that
participate; it is useful primarily in conjunction with MATRIX IN
.)
If MATRIX IN
instead of VARIABLES
is specified, then the analysis
is performed on a pre-prepared correlation or covariance matrix file instead of on
individual data cases. Typically the matrix file will have been generated by
MATRIX DATA
(see MATRIX DATA) or provided by a third party.
If specified, MATRIX IN
must be followed by ‘COV’ or ‘CORR’,
then by ‘=’ and file_spec all in parentheses.
file_spec may either be an asterisk, which indicates the currently loaded
dataset, or it may be a file name to be loaded. See MATRIX DATA, for the expected
format of the file.
The /EXTRACTION
subcommand is used to specify the way in which factors
(components) are extracted from the data.
If PC
is specified, then Principal Components Analysis is used.
If PAF
is specified, then Principal Axis Factoring is
used. By default Principal Components Analysis is used.
The /ROTATION
subcommand is used to specify the method by which the
extracted solution is rotated. Three orthogonal rotation methods are available:
VARIMAX
(which is the default), EQUAMAX
, and QUARTIMAX
.
There is one oblique rotation method, viz: PROMAX
.
Optionally you may enter the power of the promax rotation k, which must be enclosed in parentheses.
The default value of k is 5.
If you don’t want any rotation to be performed, the word NOROTATE
prevents the command from performing any rotation on the data.
The /METHOD
subcommand should be used to determine whether the
covariance matrix or the correlation matrix of the data is
to be analysed. By default, the correlation matrix is analysed.
The /PRINT
subcommand may be used to select which features of the analysis are reported:
UNIVARIATE
A table of mean values, standard deviations and total weights are printed.
INITIAL
Initial communalities and eigenvalues are printed.
EXTRACTION
Extracted communalities and eigenvalues are printed.
ROTATION
Rotated communalities and eigenvalues are printed.
CORRELATION
The correlation matrix is printed.
COVARIANCE
The covariance matrix is printed.
DET
The determinant of the correlation or covariance matrix is printed.
AIC
The anti-image covariance and anti-image correlation matrices are printed.
KMO
The Kaiser-Meyer-Olkin measure of sampling adequacy and the Bartlett test of sphericity is printed.
SIG
The significance of the elements of correlation matrix is printed.
ALL
All of the above are printed.
DEFAULT
Identical to INITIAL
and EXTRACTION
.
If /PLOT=EIGEN
is given, then a “Scree” plot of the eigenvalues is
printed. This can be useful for visualizing the factors and deciding
which factors (components) should be retained.
The /FORMAT
subcommand determined how data are to be
displayed in loading matrices. If SORT
is specified, then
the variables are sorted in descending order of significance. If
BLANK(n)
is specified, then coefficients whose absolute
value is less than n are not printed. If the keyword
DEFAULT
is specified, or if no /FORMAT
subcommand is
specified, then no sorting is performed, and all coefficients are printed.
You can use the /CRITERIA
subcommand to specify how the number of
extracted factors (components) are chosen. If FACTORS(n)
is
specified, where n is an integer, then n factors are
extracted. Otherwise, the MINEIGEN
setting is used.
MINEIGEN(l)
requests that all factors whose eigenvalues
are greater than or equal to l are extracted. The default value
of l is 1. The ECONVERGE
setting has effect only when
using iterative algorithms for factor extraction (such as Principal Axis
Factoring). ECONVERGE(delta)
specifies that
iteration should cease when the maximum absolute value of the
communality estimate between one iteration and the previous is less
than delta. The default value of delta is 0.001.
The ITERATE(m)
may appear any number of times and is
used for two different purposes. It is used to set the maximum number
of iterations (m) for convergence and also to set the maximum
number of iterations for rotation.
Whether it affects convergence or rotation depends upon which
subcommand follows the ITERATE
subcommand.
If EXTRACTION
follows, it affects convergence.
If ROTATION
follows, it affects rotation.
If neither ROTATION
nor EXTRACTION
follow a
ITERATE
subcommand, then the entire subcommand is ignored.
The default value of m is 25.
The MISSING
subcommand determines the handling of missing
variables. If INCLUDE
is set, then user-missing values are
included in the calculations, but system-missing values are not.
If EXCLUDE
is set, which is the default, user-missing
values are excluded as well as system-missing values. This is the
default. If LISTWISE
is set, then the entire case is excluded
from analysis whenever any variable specified in the VARIABLES
subcommand contains a missing value.
If PAIRWISE
is set, then a case is considered missing only if
either of the values for the particular coefficient are missing.
The default is LISTWISE
.
Next: LOGISTIC REGRESSION, Previous: FACTOR, Up: Statistics [Contents][Index]
GLM dependent_vars BY fixed_factors [/METHOD = SSTYPE(type)] [/DESIGN = interaction_0 [interaction_1 [... interaction_n]]] [/INTERCEPT = {INCLUDE|EXCLUDE}] [/MISSING = {INCLUDE|EXCLUDE}]
The GLM
procedure can be used for fixed effects factorial Anova.
The dependent_vars are the variables to be analysed.
You may analyse several variables in the same command in which case they should all
appear before the BY
keyword.
The fixed_factors list must be one or more categorical variables. Normally it does not make sense to enter a scalar variable in the fixed_factors and doing so may cause PSPP to do a lot of unnecessary processing.
The METHOD
subcommand is used to change the method for producing the sums of
squares. Available values of type are 1, 2 and 3. The default is type 3.
You may specify a custom design using the DESIGN
subcommand.
The design comprises a list of interactions where each interaction is a
list of variables separated by a ‘*’. For example the command
GLM subject BY sex age_group race /DESIGN = age_group sex group age_group*sex age_group*race
specifies the model subject = age_group + sex + race + age_group*sex + age_group*race.
If no DESIGN
subcommand is specified, then the default is all possible combinations
of the fixed factors. That is to say
GLM subject BY sex age_group race
implies the model subject = age_group + sex + race + age_group*sex + age_group*race + sex*race + age_group*sex*race.
The MISSING
subcommand determines the handling of missing
variables.
If INCLUDE
is set then, for the purposes of GLM analysis,
only system-missing values are considered
to be missing; user-missing values are not regarded as missing.
If EXCLUDE
is set, which is the default, then user-missing
values are considered to be missing as well as system-missing values.
A case for which any dependent variable or any factor
variable has a missing value is excluded from the analysis.
Next: MEANS, Previous: GLM, Up: Statistics [Contents][Index]
LOGISTIC REGRESSION [VARIABLES =] dependent_var WITH predictors [/CATEGORICAL = categorical_predictors] [{/NOCONST | /ORIGIN | /NOORIGIN }] [/PRINT = [SUMMARY] [DEFAULT] [CI(confidence)] [ALL]] [/CRITERIA = [BCON(min_delta)] [ITERATE(max_interations)] [LCON(min_likelihood_delta)] [EPS(min_epsilon)] [CUT(cut_point)]] [/MISSING = {INCLUDE|EXCLUDE}]
Bivariate Logistic Regression is used when you want to explain a dichotomous dependent variable in terms of one or more predictor variables.
The minimum command is
LOGISTIC REGRESSION y WITH x1 x2 … xn.
Here, y is the dependent variable, which must be dichotomous and x1 … xn are the predictor variables whose coefficients the procedure estimates.
By default, a constant term is included in the model. Hence, the full model is {\bf y} = b_0 + b_1 {\bf x_1} + b_2 {\bf x_2} + \dots + b_n {\bf x_n}
Predictor variables which are categorical in nature should be listed on the /CATEGORICAL
subcommand.
Simple variables as well as interactions between variables may be listed here.
If you want a model without the constant term b_0, use the keyword /ORIGIN
.
/NOCONST
is a synonym for /ORIGIN
.
An iterative Newton-Raphson procedure is used to fit the model.
The /CRITERIA
subcommand is used to specify the stopping criteria of the procedure,
and other parameters.
The value of cut_point is used in the classification table. It is the
threshold above which predicted values are considered to be 1. Values
of cut_point must lie in the range [0,1].
During iterations, if any one of the stopping criteria are satisfied, the procedure is
considered complete.
The stopping criteria are:
The PRINT
subcommand controls the display of optional statistics.
Currently there is one such option, CI
, which indicates that the
confidence interval of the odds ratio should be displayed as well as its value.
CI
should be followed by an integer in parentheses, to indicate the
confidence level of the desired confidence interval.
The MISSING
subcommand determines the handling of missing
variables.
If INCLUDE
is set, then user-missing values are included in the
calculations, but system-missing values are not.
If EXCLUDE
is set, which is the default, user-missing
values are excluded as well as system-missing values.
This is the default.
Next: NPAR TESTS, Previous: LOGISTIC REGRESSION, Up: Statistics [Contents][Index]
MEANS [TABLES =] {var_list} [ BY {var_list} [BY {var_list} [BY {var_list} … ]]] [ /{var_list} [ BY {var_list} [BY {var_list} [BY {var_list} … ]]] ] [/CELLS = [MEAN] [COUNT] [STDDEV] [SEMEAN] [SUM] [MIN] [MAX] [RANGE] [VARIANCE] [KURT] [SEKURT] [SKEW] [SESKEW] [FIRST] [LAST] [HARMONIC] [GEOMETRIC] [DEFAULT] [ALL] [NONE] ] [/MISSING = [INCLUDE] [DEPENDENT]]
You can use the MEANS
command to calculate the arithmetic mean and similar
statistics, either for the dataset as a whole or for categories of data.
The simplest form of the command is
MEANS v.
which calculates the mean, count and standard deviation for v. If you specify a grouping variable, for example
MEANS v BY g.
then the means, counts and standard deviations for v after having been grouped by g are calculated. Instead of the mean, count and standard deviation, you could specify the statistics in which you are interested:
MEANS x y BY g /CELLS = HARMONIC SUM MIN.
This example calculates the harmonic mean, the sum and the minimum values of x and y grouped by g.
The CELLS
subcommand specifies which statistics to calculate. The available statistics
are:
MEAN
The arithmetic mean.
COUNT
The count of the values.
STDDEV
The standard deviation.
SEMEAN
The standard error of the mean.
SUM
The sum of the values.
MIN
The minimum value.
MAX
The maximum value.
RANGE
The difference between the maximum and minimum values.
VARIANCE
The variance.
FIRST
The first value in the category.
LAST
The last value in the category.
SKEW
The skewness.
SESKEW
The standard error of the skewness.
KURT
The kurtosis
SEKURT
The standard error of the kurtosis.
HARMONIC
The harmonic mean.
GEOMETRIC
The geometric mean.
In addition, three special keywords are recognized:
DEFAULT
This is the same as MEAN
COUNT
STDDEV
.
ALL
All of the above statistics are calculated.
NONE
No statistics are calculated (only a summary is shown).
More than one table can be specified in a single command. Each table is separated by a ‘/’. For example
MEANS TABLES = c d e BY x /a b BY x y /f BY y BY z.
has three tables (the ‘TABLE =’ is optional). The first table has three dependent variables c, d and e and a single categorical variable x. The second table has two dependent variables a and b, and two categorical variables x and y. The third table has a single dependent variables f and a categorical variable formed by the combination of y and z.
By default values are omitted from the analysis only if missing values
(either system missing or user missing)
for any of the variables directly involved in their calculation are
encountered.
This behaviour can be modified with the /MISSING
subcommand.
Three options are possible: TABLE
, INCLUDE
and DEPENDENT
.
/MISSING = INCLUDE
says that user missing values, either in the dependent
variables or in the categorical variables should be taken at their face
value, and not excluded.
/MISSING = DEPENDENT
says that user missing values, in the dependent
variables should be taken at their face value, however cases which
have user missing values for the categorical variables should be omitted
from the calculation.
The dataset in repairs.sav contains the mean time between failures (mtbf) for a sample of artifacts produced by different factories and trialed under different operating conditions. Since there are four combinations of categorical variables, by simply looking at the list of data, it would be hard to how the scores vary for each category. Example 15.4 shows one way of tabulating the mtbf in a way which is easier to understand.
get file='repairs.sav'. means tables = mtbf by factory by environment. |
Example 15.4: Running MEANS
on the mtbf score with categories factory and environment
The results are shown in Result 15.3. The figures shown indicate the mean,
standard deviation and number of samples in each category.
These figures however do not indicate whether the results are statistically
significant. For that, you would need to use the procedures ONEWAY
, GLM
or
T-TEST
depending on the hypothesis being tested.
|
Result 15.3: The mtbf categorised by factory and environment
Note that there is no limit to the number of variables for which you can calculate
statistics, nor to the number of categorical variables per layer, nor the number
of layers.
However, running MEANS
on a large numbers of variables, or with categorical variables
containing a large number of distinct values may result in an extremely large output, which
will not be easy to interpret.
So you should consider carefully which variables to select for participation in the analysis.
Next: T-TEST, Previous: MEANS, Up: Statistics [Contents][Index]
NPAR TESTS nonparametric test subcommands . . . [ /STATISTICS={DESCRIPTIVES} ] [ /MISSING={ANALYSIS, LISTWISE} {INCLUDE, EXCLUDE} ] [ /METHOD=EXACT [ TIMER [(n)] ] ]
NPAR TESTS
performs nonparametric tests.
Non parametric tests make very few assumptions about the distribution of the
data.
One or more tests may be specified by using the corresponding subcommand.
If the /STATISTICS
subcommand is also specified, then summary statistics are
produces for each variable that is the subject of any test.
Certain tests may take a long time to execute, if an exact figure is required.
Therefore, by default asymptotic approximations are used unless the
subcommand /METHOD=EXACT
is specified.
Exact tests give more accurate results, but may take an unacceptably long
time to perform. If the TIMER
keyword is used, it sets a maximum time,
after which the test is abandoned, and a warning message printed.
The time, in minutes, should be specified in parentheses after the TIMER
keyword.
If the TIMER
keyword is given without this figure, then a default value of 5 minutes
is used.
Next: Chi-square Test, Up: NPAR TESTS [Contents][Index]
[ /BINOMIAL[(p)]=var_list[(value1[, value2)] ] ]
The /BINOMIAL
subcommand compares the observed distribution of a dichotomous
variable with that of a binomial distribution.
The variable p specifies the test proportion of the binomial
distribution.
The default value of 0.5 is assumed if p is omitted.
If a single value appears after the variable list, then that value is used as the threshold to partition the observed values. Values less than or equal to the threshold value form the first category. Values greater than the threshold form the second category.
If two values appear after the variable list, then they are used as the values which a variable must take to be in the respective category. Cases for which a variable takes a value equal to neither of the specified values, take no part in the test for that variable.
If no values appear, then the variable must assume dichotomous values. If more than two distinct, non-missing values for a variable under test are encountered then an error occurs.
If the test proportion is equal to 0.5, then a two tailed test is reported. For any other test proportion, a one tailed test is reported. For one tailed tests, if the test proportion is less than or equal to the observed proportion, then the significance of observing the observed proportion or more is reported. If the test proportion is more than the observed proportion, then the significance of observing the observed proportion or less is reported. That is to say, the test is always performed in the observed direction.
PSPP uses a very precise approximation to the gamma function to compute the binomial significance. Thus, exact results are reported even for very large sample sizes.
Next: Cochran Q Test, Previous: Binomial test, Up: NPAR TESTS [Contents][Index]
[ /CHISQUARE=var_list[(lo,hi)] [/EXPECTED={EQUAL|f1, f2 … fn}] ]
The /CHISQUARE
subcommand produces a chi-square statistic for the differences
between the expected and observed frequencies of the categories of a variable.
Optionally, a range of values may appear after the variable list.
If a range is given, then non integer values are truncated, and values
outside the specified range are excluded from the analysis.
The /EXPECTED
subcommand specifies the expected values of each
category.
There must be exactly one non-zero expected value, for each observed
category, or the EQUAL
keyword must be specified.
You may use the notation n*f
to specify n
consecutive expected categories all taking a frequency of f.
The frequencies given are proportions, not absolute frequencies. The
sum of the frequencies need not be 1.
If no /EXPECTED
subcommand is given, then equal frequencies
are expected.
A researcher wishes to investigate whether there are an equal number of persons of each sex in a population. The sample chosen for invesigation is that from the physiology.sav dataset. The null hypothesis for the test is that the population comprises an equal number of males and females. The analysis is performed as shown in Example 15.5.
get file='physiology.sav'. npar test /chisquare=sex. |
Example 15.5: Performing a chi-square test to check for equal distribution of sexes
There is only one test variable, viz: sex. The other variables in the dataset are ignored.
![]() |
Screenshot 15.4: Performing a chi-square test using the graphic user interface
In Result 15.4 the summary box shows that in the sample, there are more males than females. However the significance of chi-square result is greater than 0.05 — the most commonly accepted p-value — and therefore there is not enough evidence to reject the null hypothesis and one must conclude that the evidence does not indicate that there is an imbalance of the sexes in the population.
|
Result 15.4: The results of running a chi-square test on sex
Next: Friedman Test, Previous: Chi-square Test, Up: NPAR TESTS [Contents][Index]
[ /COCHRAN = var_list ]
The Cochran Q test is used to test for differences between three or more groups. The data for var_list in all cases must assume exactly two distinct values (other than missing values).
The value of Q is displayed along with its Asymptotic significance based on a chi-square distribution.
Next: Kendall’s W Test, Previous: Cochran Q Test, Up: NPAR TESTS [Contents][Index]
[ /FRIEDMAN = var_list ]
The Friedman test is used to test for differences between repeated measures when there is no indication that the distributions are normally distributed.
A list of variables which contain the measured data must be given. The procedure prints the sum of ranks for each variable, the test statistic and its significance.
Next: Kolmogorov-Smirnov Test, Previous: Friedman Test, Up: NPAR TESTS [Contents][Index]
[ /KENDALL = var_list ]
The Kendall test investigates whether an arbitrary number of related samples come from the same population. It is identical to the Friedman test except that the additional statistic W, Kendall’s Coefficient of Concordance is printed. It has the range [0,1] — a value of zero indicates no agreement between the samples whereas a value of unity indicates complete agreement.
Next: Kruskal-Wallis Test, Previous: Kendall’s W Test, Up: NPAR TESTS [Contents][Index]
[ /KOLMOGOROV-SMIRNOV ({NORMAL [mu, sigma], UNIFORM [min, max], POISSON [lambda], EXPONENTIAL [scale] }) = var_list ]
The one sample Kolmogorov-Smirnov subcommand is used to test whether or not a dataset is drawn from a particular distribution. Four distributions are supported, viz: Normal, Uniform, Poisson and Exponential.
Ideally you should provide the parameters of the distribution against which you wish to test the data. For example, with the normal distribution the mean (mu)and standard deviation (sigma) should be given; with the uniform distribution, the minimum (min)and maximum (max) value should be provided. However, if the parameters are omitted they are imputed from the data. Imputing the parameters reduces the power of the test so should be avoided if possible.
In the following example, two variables score and age are tested to see if they follow a normal distribution with a mean of 3.5 and a standard deviation of 2.0.
NPAR TESTS /KOLMOGOROV-SMIRNOV (normal 3.5 2.0) = score age.
If the variables need to be tested against different distributions, then a separate subcommand must be used. For example the following syntax tests score against a normal distribution with mean of 3.5 and standard deviation of 2.0 whilst age is tested against a normal distribution of mean 40 and standard deviation 1.5.
NPAR TESTS /KOLMOGOROV-SMIRNOV (normal 3.5 2.0) = score /KOLMOGOROV-SMIRNOV (normal 40 1.5) = age.
The abbreviated subcommand K-S
may be used in place of KOLMOGOROV-SMIRNOV
.
Next: Mann-Whitney U Test, Previous: Kolmogorov-Smirnov Test, Up: NPAR TESTS [Contents][Index]
[ /KRUSKAL-WALLIS = var_list BY var (lower, upper) ]
The Kruskal-Wallis test is used to compare data from an arbitrary number of populations. It does not assume normality. The data to be compared are specified by var_list. The categorical variable determining the groups to which the data belongs is given by var. The limits lower and upper specify the valid range of var. If upper is smaller than lower, the PSPP will assume their values to be reversed. Any cases for which var falls outside [lower, upper] are ignored.
The mean rank of each group as well as the chi-squared value and
significance of the test are printed.
The abbreviated subcommand K-W
may be used in place of
KRUSKAL-WALLIS
.
Next: McNemar Test, Previous: Kruskal-Wallis Test, Up: NPAR TESTS [Contents][Index]
[ /MANN-WHITNEY = var_list BY var (group1, group2) ]
The Mann-Whitney subcommand is used to test whether two groups of data come from different populations. The variables to be tested should be specified in var_list and the grouping variable, that determines to which group the test variables belong, in var. Var may be either a string or an alpha variable. Group1 and group2 specify the two values of var which determine the groups of the test data. Cases for which the var value is neither group1 or group2 are ignored.
The value of the Mann-Whitney U statistic, the Wilcoxon W, and the
significance are printed.
You may abbreviated the subcommand MANN-WHITNEY
to
M-W
.
Next: Median Test, Previous: Mann-Whitney U Test, Up: NPAR TESTS [Contents][Index]
[ /MCNEMAR var_list [ WITH var_list [ (PAIRED) ]]]
Use McNemar’s test to analyse the significance of the difference between pairs of correlated proportions.
If the WITH
keyword is omitted, then tests for all
combinations of the listed variables are performed.
If the WITH
keyword is given, and the (PAIRED)
keyword
is also given, then the number of variables preceding WITH
must be the same as the number following it.
In this case, tests for each respective pair of variables are
performed.
If the WITH
keyword is given, but the
(PAIRED)
keyword is omitted, then tests for each combination
of variable preceding WITH
against variable following
WITH
are performed.
The data in each variable must be dichotomous. If there are more than two distinct variables an error will occur and the test will not be run.
Next: Runs Test, Previous: McNemar Test, Up: NPAR TESTS [Contents][Index]
[ /MEDIAN [(value)] = var_list BY variable (value1, value2) ]
The median test is used to test whether independent samples come from
populations with a common median.
The median of the populations against which the samples are to be tested
may be given in parentheses immediately after the
/MEDIAN
subcommand. If it is not given, the median is imputed from the
union of all the samples.
The variables of the samples to be tested should immediately follow the ‘=’ sign. The
keyword BY
must come next, and then the grouping variable. Two values
in parentheses should follow. If the first value is greater than the second,
then a 2 sample test is performed using these two values to determine the groups.
If however, the first variable is less than the second, then a k sample test is
conducted and the group values used are all values encountered which lie in the
range [value1,value2].
Next: Sign Test, Previous: Median Test, Up: NPAR TESTS [Contents][Index]
[ /RUNS ({MEAN, MEDIAN, MODE, value}) = var_list ]
The /RUNS
subcommand tests whether a data sequence is randomly ordered.
It works by examining the number of times a variable’s value crosses a given threshold.
The desired threshold must be specified within parentheses.
It may either be specified as a number or as one of MEAN
, MEDIAN
or MODE
.
Following the threshold specification comes the list of variables whose values are to be
tested.
The subcommand shows the number of runs, the asymptotic significance based on the length of the data.
Next: Wilcoxon Matched Pairs Signed Ranks Test, Previous: Runs Test, Up: NPAR TESTS [Contents][Index]
[ /SIGN var_list [ WITH var_list [ (PAIRED) ]]]
The /SIGN
subcommand tests for differences between medians of the
variables listed.
The test does not make any assumptions about the
distribution of the data.
If the WITH
keyword is omitted, then tests for all
combinations of the listed variables are performed.
If the WITH
keyword is given, and the (PAIRED)
keyword
is also given, then the number of variables preceding WITH
must be the same as the number following it.
In this case, tests for each respective pair of variables are
performed.
If the WITH
keyword is given, but the
(PAIRED)
keyword is omitted, then tests for each combination
of variable preceding WITH
against variable following
WITH
are performed.
Previous: Sign Test, Up: NPAR TESTS [Contents][Index]
[ /WILCOXON var_list [ WITH var_list [ (PAIRED) ]]]
The /WILCOXON
subcommand tests for differences between medians of the
variables listed.
The test does not make any assumptions about the variances of the samples.
It does however assume that the distribution is symmetrical.
If the WITH
keyword is omitted, then tests for all
combinations of the listed variables are performed.
If the WITH
keyword is given, and the (PAIRED)
keyword
is also given, then the number of variables preceding WITH
must be the same as the number following it.
In this case, tests for each respective pair of variables are
performed.
If the WITH
keyword is given, but the
(PAIRED)
keyword is omitted, then tests for each combination
of variable preceding WITH
against variable following
WITH
are performed.
Next: ONEWAY, Previous: NPAR TESTS, Up: Statistics [Contents][Index]
T-TEST /MISSING={ANALYSIS,LISTWISE} {EXCLUDE,INCLUDE} /CRITERIA=CI(confidence) (One Sample mode.) TESTVAL=test_value /VARIABLES=var_list (Independent Samples mode.) GROUPS=var(value1 [, value2]) /VARIABLES=var_list (Paired Samples mode.) PAIRS=var_list [WITH var_list [(PAIRED)] ]
The T-TEST
procedure outputs tables used in testing hypotheses about
means.
It operates in one of three modes:
Each of these modes are described in more detail below. There are two optional subcommands which are common to all modes.
The /CRITERIA
subcommand tells PSPP the confidence interval used
in the tests. The default value is 0.95.
The MISSING
subcommand determines the handling of missing
variables.
If INCLUDE
is set, then user-missing values are included in the
calculations, but system-missing values are not.
If EXCLUDE
is set, which is the default, user-missing
values are excluded as well as system-missing values.
This is the default.
If LISTWISE
is set, then the entire case is excluded from analysis
whenever any variable specified in the /VARIABLES
, /PAIRS
or
/GROUPS
subcommands contains a missing value.
If ANALYSIS
is set, then missing values are excluded only in the analysis for
which they would be needed. This is the default.
Next: Independent Samples Mode, Up: T-TEST [Contents][Index]
The TESTVAL
subcommand invokes the One Sample mode.
This mode is used to test a population mean against a hypothesized
mean.
The value given to the TESTVAL
subcommand is the value against
which you wish to test.
In this mode, you must also use the /VARIABLES
subcommand to
tell PSPP which variables you wish to test.
A researcher wishes to know whether the weight of persons in a population
is different from the national average.
The samples are drawn from the population under investigation and recorded
in the file physiology.sav.
From the Department of Health, she
knows that the national average weight of healthy adults is 76.8kg.
Accordingly the TESTVAL
is set to 76.8.
The null hypothesis therefore is that the mean average weight of the
population from which the sample was drawn is 76.8kg.
As previously noted (see Identifying incorrect data), one
sample in the dataset contains a weight value
which is clearly incorrect. So this is excluded from the analysis
using the SELECT
command.
get file='physiology.sav'. select if (weight > 0). t-test testval = 76.8 /variables = weight. |
Example 15.6: Running a one sample T-Test after excluding all non-positive values
![]() |
Screenshot 15.5: Using the One Sample T-Test dialog box to test weight for a mean of 76.8kg
Results 15.2 shows that the mean of our sample differs from the test value by -1.40kg. However the significance is very high (0.610). So one cannot reject the null hypothesis, and must conclude there is not enough evidence to suggest that the mean weight of the persons in our population is different from 76.8kg.
|
Results 15.2: The results of a one sample T-test of weight using a test value of 76.8kg
Next: Paired Samples Mode, Previous: One Sample Mode, Up: T-TEST [Contents][Index]
The GROUPS
subcommand invokes Independent Samples mode or
‘Groups’ mode.
This mode is used to test whether two groups of values have the
same population mean.
In this mode, you must also use the /VARIABLES
subcommand to
tell PSPP the dependent variables you wish to test.
The variable given in the GROUPS
subcommand is the independent
variable which determines to which group the samples belong.
The values in parentheses are the specific values of the independent
variable for each group.
If the parentheses are omitted and no values are given, the default values
of 1.0 and 2.0 are assumed.
If the independent variable is numeric,
it is acceptable to specify only one value inside the parentheses.
If you do this, cases where the independent variable is
greater than or equal to this value belong to the first group, and cases
less than this value belong to the second group.
When using this form of the GROUPS
subcommand, missing values in
the independent variable are excluded on a listwise basis, regardless
of whether /MISSING=LISTWISE
was specified.
A researcher wishes to know whether within a population, adult males are taller than adult females. The samples are drawn from the population under investigation and recorded in the file physiology.sav.
As previously noted (see Identifying incorrect data), one
sample in the dataset contains a height value
which is clearly incorrect. So this is excluded from the analysis
using the SELECT
command.
get file='physiology.sav'. select if (height >= 200). t-test /variables = height /groups = sex(0,1). |
Example 15.7: Running a independent samples T-Test after excluding all observations less than 200kg
The null hypothesis is that both males and females are on average of equal height.
![]() |
Screenshot 15.6: Using the Independent Sample T-test dialog, to test for differences of height between values of sex
In this case, the grouping variable is sex, so this is entered
as the variable for the GROUP
subcommand. The group values are 0 (male) and
1 (female).
If you are running the proceedure using syntax, then you need to enter the values corresponding to each group within parentheses. If you are using the graphic user interface, then you have to open the “Define Groups” dialog box and enter the values corresponding to each group as shown in Screenshot 15.7. If, as in this case, the dataset has defined value labels for the group variable, then you can enter them by label or by value.
![]() |
Screenshot 15.7: Setting the values of the grouping variable for an Independent Samples T-test
From Result 15.5, one can clearly see that the sample mean height is greater for males than for females. However in order to see if this is a significant result, one must consult the T-Test table.
The T-Test table contains two rows; one for use if the variance of the samples in each group may be safely assumed to be equal, and the second row if the variances in each group may not be safely assumed to be equal.
In this case however, both rows show a 2-tailed significance less than 0.001 and one must therefore reject the null hypothesis and conclude that within the population the mean height of males and of females are unequal.
|
Result 15.5: The results of an independent samples T-test of height by sex
Previous: Independent Samples Mode, Up: T-TEST [Contents][Index]
The PAIRS
subcommand introduces Paired Samples mode.
Use this mode when repeated measures have been taken from the same
samples.
If the WITH
keyword is omitted, then tables for all
combinations of variables given in the PAIRS
subcommand are
generated.
If the WITH
keyword is given, and the (PAIRED)
keyword
is also given, then the number of variables preceding WITH
must be the same as the number following it.
In this case, tables for each respective pair of variables are
generated.
In the event that the WITH
keyword is given, but the
(PAIRED)
keyword is omitted, then tables for each combination
of variable preceding WITH
against variable following
WITH
are generated.
Next: QUICK CLUSTER, Previous: T-TEST, Up: Statistics [Contents][Index]
ONEWAY [/VARIABLES = ] var_list BY var /MISSING={ANALYSIS,LISTWISE} {EXCLUDE,INCLUDE} /CONTRAST= value1 [, value2] ... [,valueN] /STATISTICS={DESCRIPTIVES,HOMOGENEITY} /POSTHOC={BONFERRONI, GH, LSD, SCHEFFE, SIDAK, TUKEY, ALPHA ([value])}
The ONEWAY
procedure performs a one-way analysis of variance of
variables factored by a single independent variable.
It is used to compare the means of a population
divided into more than two groups.
The dependent variables to be analysed should be given in the VARIABLES
subcommand.
The list of variables must be followed by the BY
keyword and
the name of the independent (or factor) variable.
You can use the STATISTICS
subcommand to tell PSPP to display
ancillary information. The options accepted are:
The CONTRAST
subcommand is used when you anticipate certain
differences between the groups.
The subcommand must be followed by a list of numerals which are the
coefficients of the groups to be tested.
The number of coefficients must correspond to the number of distinct
groups (or values of the independent variable).
If the total sum of the coefficients are not zero, then PSPP will
display a warning, but will proceed with the analysis.
The CONTRAST
subcommand may be given up to 10 times in order
to specify different contrast tests.
The MISSING
subcommand defines how missing values are handled.
If LISTWISE
is specified then cases which have missing values for
the independent variable or any dependent variable are ignored.
If ANALYSIS
is specified, then cases are ignored if the independent
variable is missing or if the dependent variable currently being
analysed is missing. The default is ANALYSIS
.
A setting of EXCLUDE
means that variables whose values are
user-missing are to be excluded from the analysis. A setting of
INCLUDE
means they are to be included. The default is EXCLUDE
.
Using the POSTHOC
subcommand you can perform multiple
pairwise comparisons on the data. The following comparison methods
are available:
LSD
Least Significant Difference.
TUKEY
Tukey Honestly Significant Difference.
BONFERRONI
Bonferroni test.
SCHEFFE
Scheffé’s test.
SIDAK
Sidak test.
GH
The Games-Howell test.
Use the optional syntax ALPHA(value)
to indicate that
ONEWAY
should perform the posthoc tests at a confidence level of
value. If ALPHA(value)
is not specified, then the
confidence level used is 0.05.
Next: RANK, Previous: ONEWAY, Up: Statistics [Contents][Index]
QUICK CLUSTER var_list [/CRITERIA=CLUSTERS(k) [MXITER(max_iter)] CONVERGE(epsilon) [NOINITIAL]] [/MISSING={EXCLUDE,INCLUDE} {LISTWISE, PAIRWISE}] [/PRINT={INITIAL} {CLUSTER}] [/SAVE[=[CLUSTER[(membership_var)]] [DISTANCE[(distance_var)]]]
The QUICK CLUSTER
command performs k-means clustering on the
dataset. This is useful when you wish to allocate cases into clusters
of similar values and you already know the number of clusters.
The minimum specification is ‘QUICK CLUSTER’ followed by the names
of the variables which contain the cluster data. Normally you will also
want to specify /CRITERIA=CLUSTERS(k)
where k is the
number of clusters. If this is not specified, then k defaults to 2.
If you use /CRITERIA=NOINITIAL
then a naive algorithm to select
the initial clusters is used. This will provide for faster execution but
less well separated initial clusters and hence possibly an inferior final
result.
QUICK CLUSTER
uses an iterative algorithm to select the clusters centers.
The subcommand /CRITERIA=MXITER(max_iter)
sets the maximum number of iterations.
During classification, PSPP will continue iterating until until max_iter
iterations have been done or the convergence criterion (see below) is fulfilled.
The default value of max_iter is 2.
If however, you specify /CRITERIA=NOUPDATE
then after selecting the initial centers,
no further update to the cluster centers is done. In this case, max_iter, if specified.
is ignored.
The subcommand /CRITERIA=CONVERGE(epsilon)
is used
to set the convergence criterion. The value of convergence criterion is epsilon
times the minimum distance between the initial cluster centers. Iteration stops when
the mean cluster distance between one iteration and the next
is less than the convergence criterion. The default value of epsilon is zero.
The MISSING
subcommand determines the handling of missing variables.
If INCLUDE
is set, then user-missing values are considered at their face
value and not as missing values.
If EXCLUDE
is set, which is the default, user-missing
values are excluded as well as system-missing values.
If LISTWISE
is set, then the entire case is excluded from the analysis
whenever any of the clustering variables contains a missing value.
If PAIRWISE
is set, then a case is considered missing only if all the
clustering variables contain missing values. Otherwise it is clustered
on the basis of the non-missing values.
The default is LISTWISE
.
The PRINT
subcommand requests additional output to be printed.
If INITIAL
is set, then the initial cluster memberships will
be printed.
If CLUSTER
is set, the cluster memberships of the individual
cases are displayed (potentially generating lengthy output).
You can specify the subcommand SAVE
to ask that each case’s cluster membership
and the euclidean distance between the case and its cluster center be saved to
a new variable in the active dataset. To save the cluster membership use the
CLUSTER
keyword and to save the distance use the DISTANCE
keyword.
Each keyword may optionally be followed by a variable name in parentheses to specify
the new variable which is to contain the saved parameter. If no variable name is specified,
then PSPP will create one.
Next: REGRESSION, Previous: QUICK CLUSTER, Up: Statistics [Contents][Index]
RANK [VARIABLES=] var_list [{A,D}] [BY var_list] /TIES={MEAN,LOW,HIGH,CONDENSE} /FRACTION={BLOM,TUKEY,VW,RANKIT} /PRINT[={YES,NO} /MISSING={EXCLUDE,INCLUDE} /RANK [INTO var_list] /NTILES(k) [INTO var_list] /NORMAL [INTO var_list] /PERCENT [INTO var_list] /RFRACTION [INTO var_list] /PROPORTION [INTO var_list] /N [INTO var_list] /SAVAGE [INTO var_list]
The RANK
command ranks variables and stores the results into new
variables.
The VARIABLES
subcommand, which is mandatory, specifies one or
more variables whose values are to be ranked.
After each variable, ‘A’ or ‘D’ may appear, indicating that
the variable is to be ranked in ascending or descending order.
Ascending is the default.
If a BY
keyword appears, it should be followed by a list of variables
which are to serve as group variables.
In this case, the cases are gathered into groups, and ranks calculated
for each group.
The TIES
subcommand specifies how tied values are to be treated. The
default is to take the mean value of all the tied cases.
The FRACTION
subcommand specifies how proportional ranks are to be
calculated. This only has any effect if NORMAL
or PROPORTIONAL
rank
functions are requested.
The PRINT
subcommand may be used to specify that a summary of the rank
variables created should appear in the output.
The function subcommands are RANK
, NTILES
, NORMAL
, PERCENT
, RFRACTION
,
PROPORTION
and SAVAGE
. Any number of function subcommands may appear.
If none are given, then the default is RANK.
The NTILES
subcommand must take an integer specifying the number of
partitions into which values should be ranked.
Each subcommand may be followed by the INTO
keyword and a list of
variables which are the variables to be created and receive the rank
scores. There may be as many variables specified as there are
variables named on the VARIABLES
subcommand. If fewer are specified,
then the variable names are automatically created.
The MISSING
subcommand determines how user missing values are to be
treated. A setting of EXCLUDE
means that variables whose values are
user-missing are to be excluded from the rank scores. A setting of
INCLUDE
means they are to be included. The default is EXCLUDE
.
Next: RELIABILITY, Previous: RANK, Up: Statistics [Contents][Index]
The REGRESSION
procedure fits linear models to data via least-squares
estimation. The procedure is appropriate for data which satisfy those
assumptions typical in linear regression:
The REGRESSION
procedure estimates the coefficients
b_0,…,b_k and produces output relevant to inferences for the
linear model.
Next: Examples, Up: REGRESSION [Contents][Index]
REGRESSION /VARIABLES=var_list /DEPENDENT=var_list /STATISTICS={ALL, DEFAULTS, R, COEFF, ANOVA, BCOV, CI[conf, TOL]} { /ORIGIN | /NOORIGIN } /SAVE={PRED, RESID}
The REGRESSION
procedure reads the active dataset and outputs
statistics relevant to the linear model specified by the user.
The VARIABLES
subcommand, which is required, specifies the list of
variables to be analyzed. Keyword VARIABLES
is required. The
DEPENDENT
subcommand specifies the dependent variable of the linear
model. The DEPENDENT
subcommand is required. All variables listed in
the VARIABLES
subcommand, but not listed in the DEPENDENT
subcommand,
are treated as explanatory variables in the linear model.
All other subcommands are optional:
The STATISTICS
subcommand specifies which statistics are to be displayed.
The following keywords are accepted:
ALL
All of the statistics below.
R
The ratio of the sums of squares due to the model to the total sums of squares for the dependent variable.
COEFF
A table containing the estimated model coefficients and their standard errors.
CI (conf)
This item is only relevant if COEFF has also been selected. It specifies that the confidence interval for the coefficients should be printed. The optional value conf, which must be in parentheses, is the desired confidence level expressed as a percentage.
ANOVA
Analysis of variance table for the model.
BCOV
The covariance matrix for the estimated model coefficients.
TOL
The variance inflation factor and its reciprocal. This has no effect unless COEFF is also given.
DEFAULT
The same as if R, COEFF, and ANOVA had been selected. This is what you get if the /STATISTICS command is not specified, or if it is specified without any parameters.
The ORIGIN
and NOORIGIN
subcommands are mutually
exclusive. ORIGIN
indicates that the regression should be
performed through the origin. You should use this option if, and
only if you have reason to believe that the regression does indeed
pass through the origin — that is to say, the value b_0 above,
is zero. The default is NOORIGIN
.
The SAVE
subcommand causes PSPP to save the residuals or predicted
values from the fitted
model to the active dataset. PSPP will store the residuals in a variable
called ‘RES1’ if no such variable exists, ‘RES2’ if ‘RES1’
already exists,
‘RES3’ if ‘RES1’ and ‘RES2’ already exist, etc. It will
choose the name of
the variable for the predicted values similarly, but with ‘PRED’ as a
prefix.
When SAVE
is used, PSPP ignores TEMPORARY
, treating
temporary transformations as permanent.
Previous: Syntax, Up: REGRESSION [Contents][Index]
The following PSPP syntax will generate the default output and save the predicted values and residuals to the active dataset.
title 'Demonstrate REGRESSION procedure'. data list / v0 1-2 (A) v1 v2 3-22 (10). begin data. b 7.735648 -23.97588 b 6.142625 -19.63854 a 7.651430 -25.26557 c 6.125125 -16.57090 a 8.245789 -25.80001 c 6.031540 -17.56743 a 9.832291 -28.35977 c 5.343832 -16.79548 a 8.838262 -29.25689 b 6.200189 -18.58219 end data. list. regression /variables=v0 v1 v2 /statistics defaults /dependent=v2 /save pred resid /method=enter.
Next: ROC, Previous: REGRESSION, Up: Statistics [Contents][Index]
RELIABILITY /VARIABLES=var_list /SCALE (name) = {var_list, ALL} /MODEL={ALPHA, SPLIT[(n)]} /SUMMARY={TOTAL,ALL} /MISSING={EXCLUDE,INCLUDE}
The RELIABILITY
command performs reliability analysis on the data.
The VARIABLES
subcommand is required. It determines the set of variables
upon which analysis is to be performed.
The SCALE
subcommand determines the variables for which
reliability is to be calculated. If SCALE
is omitted, then analysis for
all variables named in the VARIABLES
subcommand are used.
Optionally, the name parameter may be specified to set a string name
for the scale.
The MODEL
subcommand determines the type of analysis. If ALPHA
is specified,
then Cronbach’s Alpha is calculated for the scale. If the model is SPLIT
,
then the variables are divided into 2 subsets. An optional parameter
n may be given, to specify how many variables to be in the first subset.
If n is omitted, then it defaults to one half of the variables in the
scale, or one half minus one if there are an odd number of variables.
The default model is ALPHA
.
By default, any cases with user missing, or system missing values for
any variables given in the VARIABLES
subcommand are omitted
from the analysis. The MISSING
subcommand determines whether
user missing values are included or excluded in the analysis.
The SUMMARY
subcommand determines the type of summary analysis to be performed.
Currently there is only one type: SUMMARY=TOTAL
, which displays per-item
analysis tested against the totals.
Before analysing the results of a survey – particularly for a multiple choice survey – it is desireable to know whether the respondents have considered their answers or simply provided random answers.
In the following example the survey results from the file hotel.sav are used.
All five survey questions are included in the reliability analysis.
However, before running the analysis, the data must be preprocessed.
An examination of the survey questions reveals that two questions, viz: v3 and v5
are negatively worded, whereas the others are positively worded.
All questions must be based upon the same scale for the analysis to be meaningful.
One could use the RECODE
command (see RECODE), however a simpler way is
to use COMPUTE
(see COMPUTE) and this is what is done in Example 15.8.
get file="hotel.sav". * Recode V3 and V5 inverting the sense of the values. compute v3 = 6 - v3. compute v5 = 6 - v5. reliability /variables= all /model=alpha. |
Example 15.8: Investigating the reliability of survey responses
In this case, all variables in the data set are used. So we can use the special keyword ‘ALL’ (see Backus-Naur Form).
![]() |
Screenshot 15.8: Reliability dialog box with all variables selected
Result 15.6 shows that Cronbach’s Alpha is 0.11 which is a value normally considered too low to indicate consistency within the data. This is possibly due to the small number of survey questions. The survey should be redesigned before serious use of the results are applied.
Scale: ANY
|
Result 15.6: The results of the reliability command on hotel.sav
Previous: RELIABILITY, Up: Statistics [Contents][Index]
ROC var_list BY state_var (state_value) /PLOT = { CURVE [(REFERENCE)], NONE } /PRINT = [ SE ] [ COORDINATES ] /CRITERIA = [ CUTOFF({INCLUDE,EXCLUDE}) ] [ TESTPOS ({LARGE,SMALL}) ] [ CI (confidence) ] [ DISTRIBUTION ({FREE, NEGEXPO }) ] /MISSING={EXCLUDE,INCLUDE}
The ROC
command is used to plot the receiver operating characteristic curve
of a dataset, and to estimate the area under the curve.
This is useful for analysing the efficacy of a variable as a predictor of a state of nature.
The mandatory var_list is the list of predictor variables. The variable state_var is the variable whose values represent the actual states, and state_value is the value of this variable which represents the positive state.
The optional subcommand PLOT
is used to determine if and how the ROC
curve is drawn.
The keyword CURVE
means that the ROC
curve should be drawn, and the optional keyword REFERENCE
,
which should be enclosed in parentheses, says that the diagonal reference line should be drawn.
If the keyword NONE
is given, then no ROC
curve is drawn.
By default, the curve is drawn with no reference line.
The optional subcommand PRINT
determines which additional
tables should be printed. Two additional tables are available. The
SE
keyword says that standard error of the area under the
curve should be printed as well as the area itself. In addition, a
p-value for the null hypothesis that the area under the curve equals
0.5 is printed. The COORDINATES
keyword says that a
table of coordinates of the ROC
curve should be printed.
The CRITERIA
subcommand has four optional parameters:
TESTPOS
parameter may be LARGE
or SMALL
.
LARGE
is the default, and says that larger values in the predictor variables are to be
considered positive. SMALL
indicates that smaller values should be considered positive.
CI
parameter specifies the confidence interval that should be printed.
It has no effect if the SE
keyword in the PRINT
subcommand has not been given.
DISTRIBUTION
parameter determines the method to be used when estimating the area
under the curve.
There are two possibilities, viz: FREE
and NEGEXPO
.
The FREE
method uses a non-parametric estimate, and the NEGEXPO
method a bi-negative
exponential distribution estimate.
The NEGEXPO
method should only be used when the number of positive actual states is
equal to the number of negative actual states.
The default is FREE
.
CUTOFF
parameter is for compatibility and is ignored.
The MISSING
subcommand determines whether user missing values are to
be included or excluded in the analysis. The default behaviour is to
exclude them.
Cases are excluded on a listwise basis; if any of the variables in var_list
or if the variable state_var is missing, then the entire case is
excluded.
Next: Utilities, Previous: Statistics, Up: GNU PSPP [Contents][Index]
Some PSPP procedures work with matrices by producing numeric matrices that report results of data analysis, or by consuming matrices as a basis for further analysis. This chapter documents the format of data files that store these matrices and commands for working with them, as well as PSPP’s general-purpose facility for matrix operations.
Next: MATRIX DATA, Up: Matrices [Contents][Index]
A matrix file is an SPSS system file that conforms to the dictionary and case structure described in this section. Procedures that read matrices from files expect them to be in the matrix file format. Procedures that write matrices also use this format.
Text files that contain matrices can be converted to matrix file format. See MATRIX DATA, for a command to read a text file as a matrix file.
A matrix file’s dictionary must have the following variables in the specified order:
SPLIT FILE
is active. MATRIX DATA
assigns
split variables format F4.0.
ROWTYPE_
, a string variable with width 8. This variable
indicates the kind of matrix or vector that a given case represents.
The supported row types are listed below.
MATRIX DATA
assigns factor variables format F4.0.
VARNAME_
, a string variable. Matrix data includes one row per
continuous variable (see below), naming each continuous variable in
order. This column is blank for vector data. MATRIX DATA
makes
VARNAME_
wide enough for the name of any of the continuous
variables, but at least 8 bytes.
MATRIX DATA
assigns continuous variables format F10.4.
Case weights are ignored in matrix files.
Matrix files support a fixed set of types of matrix and vector data.
The ROWTYPE_
variable in each case of a matrix file indicates
its row type.
The supported matrix row types are listed below. Each type is listed
with the keyword that identifies it in ROWTYPE_
. All supported
types of matrices are square, meaning that each matrix must include
one row per continuous variable, with the VARNAME_
variable
indicating each continuous variable in turn in the same order as the
dictionary.
CORR
Correlation coefficients.
COV
Covariance coefficients.
MAT
General-purpose matrix.
N_MATRIX
Counts.
PROX
Proximities matrix.
The supported vector row types are listed below, along with their
associated keyword. Vector row types only require a single row, whose
VARNAME_
is blank:
COUNT
Unweighted counts.
DFE
Degrees of freedom.
MEAN
Means.
MSE
Mean squared errors.
N
Counts.
STDDEV
Standard deviations.
Only the row types listed above may appear in matrix files. The
MATRIX DATA
command, however, accepts the additional row types
listed below, which it changes into matrix file row types as part of
its conversion process:
N_VECTOR
Synonym for N
.
SD
Synonym for STDDEV
.
N_SCALAR
Accepts a single number from the MATRIX DATA
input and writes
it as an N
row with the number replicated across all the
continuous variables.
Next: MCONVERT, Previous: Matrix Files, Up: Matrices [Contents][Index]
MATRIX DATA VARIABLES=variables [FILE={’file_name’ | INLINE} [/FORMAT=[{LIST | FREE}] [{UPPER | LOWER | FULL}] [{DIAGONAL | NODIAGONAL}]] [/SPLIT=split_vars] [/FACTORS=factor_vars] [/N=n] The following subcommands are only needed when ROWTYPE_ is not specified on the VARIABLES subcommand: [/CONTENTS={CORR,COUNT,COV,DFE,MAT,MEAN,MSE, N_MATRIX,N|N_VECTOR,N_SCALAR,PROX,SD|STDDEV}] [/CELLS=n_cells]
The MATRIX DATA
command convert matrices and vectors from text
format into the matrix file format (See Matrix Files) for use by
procedures that read matrices. It reads a text file or inline data
and outputs to the active file, replacing any data already in the
active dataset. The matrix file may then be used by other commands
directly from the active file, or it may be written to a .sav
file using the SAVE
command.
The text data read by MATRIX DATA
can be delimited by spaces or
commas. A plus or minus sign, except immediately following a ‘d’
or ‘e’, also begins a new value. Optionally, values may be
enclosed in single or double quotes.
MATRIX DATA
can read the types of matrix and vector data
supported in matrix files (see Matrix File Row Types).
The FILE
subcommand specifies the source of the command’s
input. To read input from a text file, specify its name in quotes.
To supply input inline, omit FILE
or specify INLINE
.
Inline data must directly follow MATRIX DATA
, inside BEGIN
DATA
(see BEGIN DATA).
VARIABLES
is the only required subcommand. It names the
variables present in each input record in the order that they appear.
(MATRIX DATA
reorders the variables in the matrix file it
produces, if needed to fit the matrix file format.) The variable list
must include split variables and factor variables, if they are present
in the data, in addition to the continuous variables that form matrix
rows and columns. It may also include a special variable named
ROWTYPE_
.
Matrix data may include split variables or factor variables or both.
List split variables, if any, on the SPLIT
subcommand and
factor variables, if any, on the FACTORS
subcommand. Split
and factor variables must be numeric. Split and factor variables must
also be listed on VARIABLES
, with one exception: if
VARIABLES
does not include ROWTYPE_
, then
SPLIT
may name a single variable that is not in
VARIABLES
(see MATRIX DATA Example 8).
The FORMAT
subcommand accepts settings to describe the format
of the input data:
LIST
(default)FREE
LIST requires each row to begin at the start of a new input line. FREE allows rows to begin in the middle of a line. Either setting allows a single row to continue across multiple input lines.
LOWER
(default)UPPER
FULL
With LOWER, only the lower triangle is read from the input data and the upper triangle is mirrored across the main diagonal. UPPER behaves similarly for the upper triangle. FULL reads the entire matrix.
DIAGONAL
(default)NODIAGONAL
With DIAGONAL, the main diagonal is read from the input data. With NODIAGONAL, which is incompatible with FULL, the main diagonal is not read from the input data but instead set to 1 for correlation matrices and system-missing for others.
The N
subcommand is a way to specify the size of the
population. It is equivalent to specifying an N
vector with
the specified value for each split file.
MATRIX DATA
supports two different ways to indicate the kinds of
matrices and vectors present in the data, depending on whether a
variable with the special name ROWTYPE_
is present in
VARIABLES
. The following subsections explain MATRIX DATA
syntax and behavior in each case.
Next: Without ROWTYPE_
, Up: MATRIX DATA [Contents][Index]
ROWTYPE_
If VARIABLES
includes ROWTYPE_
, each case’s
ROWTYPE_
indicates the type of data contained in the row.
See Matrix File Row Types, for a list of supported row types.
ROWTYPE_
This example shows a simple use of MATRIX DATA
with
ROWTYPE_
plus 8 variables named var01
through
var08
.
Because ROWTYPE_
is the first variable in VARIABLES
,
it appears first on each line. The first three lines in the example
data have ROWTYPE_
values of ‘MEAN’, ‘SD’, and
‘N’. These indicate that these lines contain vectors of means,
standard deviations, and counts, respectively, for var01
through var08
in order.
The remaining 8 lines have a ROWTYPE_ of ‘CORR’ which indicates
that the values are correlation coefficients. Each of the lines
corresponds to a row in the correlation matrix: the first line is for
var01
, the next line for var02
, and so on. The input
only contains values for the lower triangle, including the diagonal,
since FORMAT=LOWER DIAGONAL
is the default.
With ROWTYPE_
, the CONTENTS
subcommand is optional and
the CELLS
subcommand may not be used.
MATRIX DATA VARIABLES=ROWTYPE_ var01 TO var08. BEGIN DATA. MEAN 24.3 5.4 69.7 20.1 13.4 2.7 27.9 3.7 SD 5.7 1.5 23.5 5.8 2.8 4.5 5.4 1.5 N 92 92 92 92 92 92 92 92 CORR 1.00 CORR .18 1.00 CORR -.22 -.17 1.00 CORR .36 .31 -.14 1.00 CORR .27 .16 -.12 .22 1.00 CORR .33 .15 -.17 .24 .21 1.00 CORR .50 .29 -.20 .32 .12 .38 1.00 CORR .17 .29 -.05 .20 .27 .20 .04 1.00 END DATA.
FORMAT=UPPER NODIAGONAL
This syntax produces the same matrix file as example 1, but it uses
FORMAT=UPPER NODIAGONAL
to specify the upper triangle and omit
the diagonal. Because the matrix’s ROWTYPE_
is CORR
,
PSPP automatically fills in the diagonal with 1.
MATRIX DATA VARIABLES=ROWTYPE_ var01 TO var08 /FORMAT=UPPER NODIAGONAL. BEGIN DATA. MEAN 24.3 5.4 69.7 20.1 13.4 2.7 27.9 3.7 SD 5.7 1.5 23.5 5.8 2.8 4.5 5.4 1.5 N 92 92 92 92 92 92 92 92 CORR .17 .50 -.33 .27 .36 -.22 .18 CORR .29 .29 -.20 .32 .12 .38 CORR .05 .20 -.15 .16 .21 CORR .20 .32 -.17 .12 CORR .27 .12 -.24 CORR -.20 -.38 CORR .04 END DATA.
N
subcommandThis syntax uses the N
subcommand in place of an N
vector. It produces the same matrix file as examples 1 and 2.
MATRIX DATA VARIABLES=ROWTYPE_ var01 TO var08 /FORMAT=UPPER NODIAGONAL /N 92. BEGIN DATA. MEAN 24.3 5.4 69.7 20.1 13.4 2.7 27.9 3.7 SD 5.7 1.5 23.5 5.8 2.8 4.5 5.4 1.5 CORR .17 .50 -.33 .27 .36 -.22 .18 CORR .29 .29 -.20 .32 .12 .38 CORR .05 .20 -.15 .16 .21 CORR .20 .32 -.17 .12 CORR .27 .12 -.24 CORR -.20 -.38 CORR .04 END DATA.
This syntax defines two matrices, using the variable ‘s1’ to
distinguish between them. Notice how the order of variables in the
input matches their order on VARIABLES
. This example also
uses FORMAT=FULL
.
MATRIX DATA VARIABLES=s1 ROWTYPE_ var01 TO var04 /SPLIT=s1 /FORMAT=FULL. BEGIN DATA. 0 MEAN 34 35 36 37 0 SD 22 11 55 66 0 N 99 98 99 92 0 CORR 1 .9 .8 .7 0 CORR .9 1 .6 .5 0 CORR .8 .6 1 .4 0 CORR .7 .5 .4 1 1 MEAN 44 45 34 39 1 SD 23 15 51 46 1 N 98 34 87 23 1 CORR 1 .2 .3 .4 1 CORR .2 1 .5 .6 1 CORR .3 .5 1 .7 1 CORR .4 .6 .7 1 END DATA.
This syntax defines a matrix file that includes a factor variable ‘f1’. The data includes mean, standard deviation, and count vectors for two values of the factor variable, plus a correlation matrix for pooled data.
MATRIX DATA VARIABLES=ROWTYPE_ f1 var01 TO var04 /FACTOR=f1. BEGIN DATA. MEAN 0 34 35 36 37 SD 0 22 11 55 66 N 0 99 98 99 92 MEAN 1 44 45 34 39 SD 1 23 15 51 46 N 1 98 34 87 23 CORR . 1 CORR . .9 1 CORR . .8 .6 1 CORR . .7 .5 .4 1 END DATA.
Previous: With ROWTYPE_
, Up: MATRIX DATA [Contents][Index]
ROWTYPE_
If VARIABLES
does not contain ROWTYPE_
, the
CONTENTS
subcommand defines the row types that appear in the
file and their order. If CONTENTS
is omitted,
CONTENTS=CORR
is assumed.
Factor variables without ROWTYPE_
introduce special
requirements, illustrated below in Examples 8 and 9.
ROWTYPE_
This example shows a simple use of MATRIX DATA
with 8 variables
named var01
through var08
, without ROWTYPE_
.
This yields the same matrix file as Example 1 (see MATRIX DATA Example 1).
MATRIX DATA VARIABLES=var01 TO var08 /CONTENTS=MEAN SD N CORR. BEGIN DATA. 24.3 5.4 69.7 20.1 13.4 2.7 27.9 3.7 5.7 1.5 23.5 5.8 2.8 4.5 5.4 1.5 92 92 92 92 92 92 92 92 1.00 .18 1.00 -.22 -.17 1.00 .36 .31 -.14 1.00 .27 .16 -.12 .22 1.00 .33 .15 -.17 .24 .21 1.00 .50 .29 -.20 .32 .12 .38 1.00 .17 .29 -.05 .20 .27 .20 .04 1.00 END DATA.
This syntax defines two matrices, using the variable s1
to
distinguish between them. Each line of data begins with s1
.
This yields the same matrix file as Example 4 (see MATRIX DATA Example 4).
MATRIX DATA VARIABLES=s1 var01 TO var04 /SPLIT=s1 /FORMAT=FULL /CONTENTS=MEAN SD N CORR. BEGIN DATA. 0 34 35 36 37 0 22 11 55 66 0 99 98 99 92 0 1 .9 .8 .7 0 .9 1 .6 .5 0 .8 .6 1 .4 0 .7 .5 .4 1 1 44 45 34 39 1 23 15 51 46 1 98 34 87 23 1 1 .2 .3 .4 1 .2 1 .5 .6 1 .3 .5 1 .7 1 .4 .6 .7 1 END DATA.
Like this previous example, this syntax defines two matrices with
split variable s1
. In this case, though, s1
is not
listed in VARIABLES
, which means that its value does not
appear in the data. Instead, MATRIX DATA
reads matrix data
until the input is exhausted, supplying 1 for the first split, 2 for
the second, and so on.
MATRIX DATA VARIABLES=var01 TO var04 /SPLIT=s1 /FORMAT=FULL /CONTENTS=MEAN SD N CORR. BEGIN DATA. 34 35 36 37 22 11 55 66 99 98 99 92 1 .9 .8 .7 .9 1 .6 .5 .8 .6 1 .4 .7 .5 .4 1 44 45 34 39 23 15 51 46 98 34 87 23 1 .2 .3 .4 .2 1 .5 .6 .3 .5 1 .7 .4 .6 .7 1 END DATA.
ROWTYPE_
Without ROWTYPE_
, factor variables introduce two new wrinkles
to MATRIX DATA
syntax. First, the CELLS
subcommand
must declare the number of combinations of factor variables present in
the data. If there is, for example, one factor variable for which the
data contains three values, one would write CELLS=3
; if there
are two (or more) factor variables for which the data contains five
combinations, one would use CELLS=5
; and so on.
Second, the CONTENTS
subcommand must distinguish within-cell
data from pooled data by enclosing within-cell row types in
parentheses. When different within-cell row types for a single factor
appear in subsequent lines, enclose the row types in a single set of
parentheses; when different factors’ values for a given within-cell
row type appear in subsequent lines, enclose each row type in
individual parentheses.
Without ROWTYPE_
, input lines for pooled data do not include
factor values, not even as missing values, but input lines for
within-cell data do.
The following examples aim to clarify this syntax.
This syntax defines the same matrix file as Example 5 (see MATRIX DATA Example 5), without using ROWTYPE_
. It declares
CELLS=2
because the data contains two values (0 and 1) for
factor variable f1
. Within-cell vector row types MEAN
,
SD
, and N
are in a single set of parentheses on
CONTENTS
because they are grouped together in subsequent
lines for a single factor value. The data lines with the pooled
correlation matrix do not have any factor values.
MATRIX DATA VARIABLES=f1 var01 TO var04 /FACTOR=f1 /CELLS=2 /CONTENTS=(MEAN SD N) CORR. BEGIN DATA. 0 34 35 36 37 0 22 11 55 66 0 99 98 99 92 1 44 45 34 39 1 23 15 51 46 1 98 34 87 23 1 .9 1 .8 .6 1 .7 .5 .4 1 END DATA.
This syntax defines the same matrix file as the previous example. The only difference is that the within-cell vector rows are grouped differently: two rows of means (one for each factor), followed by two rows of standard deviations, followed by two rows of counts.
MATRIX DATA VARIABLES=f1 var01 TO var04 /FACTOR=f1 /CELLS=2 /CONTENTS=(MEAN) (SD) (N) CORR. BEGIN DATA. 0 34 35 36 37 1 44 45 34 39 0 22 11 55 66 1 23 15 51 46 0 99 98 99 92 1 98 34 87 23 1 .9 1 .8 .6 1 .7 .5 .4 1 END DATA.
Next: MATRIX, Previous: MATRIX DATA, Up: Matrices [Contents][Index]
MCONVERT [[MATRIX=] [IN({‘*’|’file’})] [OUT({‘*’|’file’})]] [/{REPLACE,APPEND}].
The MCONVERT
command converts matrix data from a correlation
matrix and a vector of standard deviations into a covariance matrix,
or vice versa.
By default, MCONVERT
both reads and writes the active file. Use
the MATRIX
subcommand to specify other files. To read a matrix
file, specify its name inside parentheses following IN
. To
write a matrix file, specify its name inside parentheses following
OUT
. Use ‘*’ to explicitly specify the active file for
input or output.
When MCONVERT
reads the input, by default it substitutes a
correlation matrix and a vector of standard deviations each time it
encounters a covariance matrix, and vice versa. Specify
/APPEND
to instead have MCONVERT
add the other form of
data without removing the existing data. Use /REPLACE
to
explicitly request removing the existing data.
The MCONVERT
command requires its input to be a matrix file.
Use MATRIX DATA
to convert text input into matrix file format.
See MATRIX DATA, for details.
MATRIX. …matrix commands… END MATRIX.
The following basic matrix commands are supported:
COMPUTE variable[(index[,index])]=expression. CALL procedure(argument, …). PRINT [expression] [/FORMAT=format] [/TITLE=title] [/SPACE={NEWPAGE | n}] [{/RLABELS=string… | /RNAMES=expression}] [{/CLABELS=string… | /CNAMES=expression}].
The following matrix commands offer support for flow control:
DO IF expression. …matrix commands… [ELSE IF expression. …matrix commands…]… [ELSE …matrix commands…] END IF. LOOP [var=first TO last [BY step]] [IF expression]. …matrix commands… END LOOP [IF expression]. BREAK.
The following matrix commands support matrix input and output:
READ variable[(index[,index])] [/FILE=file] /FIELD=first TO last [BY width] [/FORMAT=format] [/SIZE=expression] [/MODE={RECTANGULAR | SYMMETRIC}] [/REREAD]. WRITE expression [/OUTFILE=file] /FIELD=first TO last [BY width] [/MODE={RECTANGULAR | TRIANGULAR}] [/HOLD] [/FORMAT=format]. GET variable[(index[,index])] [/FILE={file | *}] [/VARIABLES=variable…] [/NAMES=expression] [/MISSING={ACCEPT | OMIT | number}] [/SYSMIS={OMIT | number}]. SAVE expression [/OUTFILE={file | *}] [/VARIABLES=variable…] [/NAMES=expression] [/STRINGS=variable…]. MGET [/FILE=file] [/TYPE={COV | CORR | MEAN | STDDEV | N | COUNT}]. MSAVE expression /TYPE={COV | CORR | MEAN | STDDEV | N | COUNT} [/OUTFILE=file] [/VARIABLES=variable…] [/SNAMES=variable…] [/SPLIT=expression] [/FNAMES=variable…] [/FACTOR=expression].
The following matrix commands provide additional support:
DISPLAY [{DICTIONARY | STATUS}]. RELEASE variable….
MATRIX
and END MATRIX
enclose a special PSPP
sub-language, called the matrix language. The matrix language does
not require an active dataset to be defined and only a few of the
matrix language commands work with any datasets that are defined.
Each instance of MATRIX
…END MATRIX
is a separate
program whose state is independent of any instance, so that variables
declared within a matrix program are forgotten at its end.
The matrix language works with matrices, where a matrix is a rectangular array of real numbers. An n×m matrix has n rows and m columns. Some special cases are important: a n×1 matrix is a column vector, a 1×n is a row vector, and a 1×1 matrix is a scalar.
The matrix language also has limited support for matrices that contain
8-byte strings instead of numbers. Strings longer than 8 bytes are
truncated, and shorter strings are padded with spaces. String
matrices are mainly useful for labeling rows and columns when printing
numerical matrices with the MATRIX PRINT
command. Arithmetic
operations on string matrices will not produce useful results. The
user should not mix strings and numbers within a matrix.
The matrix language does not work with cases. A variable in the matrix language represents a single matrix.
The matrix language does not support missing values.
MATRIX
is a procedure, so it cannot be enclosed inside DO
IF
, LOOP
, etc.
Macros may be used within a matrix program, and macros may expand to
include entire matrix programs. The DEFINE
command may not
appear within a matrix program. See DEFINE, for more information
about macros.
The following sections describe the details of the matrix language:
first, the syntax of matrix expressions, then each of the supported
commands. The COMMENT
command (see COMMENT) is also
supported.
COMPUTE
CommandCALL
CommandPRINT
CommandDO IF
CommandLOOP
and BREAK
CommandsREAD
and WRITE
CommandsGET
CommandSAVE
CommandMGET
CommandMSAVE
CommandDISPLAY
CommandRELEASE
CommandNext: Matrix Functions, Up: MATRIX [Contents][Index]
Many matrix commands use expressions. A matrix expression may use the following operators, listed in descending order of operator precedence. Within a single level, operators associate from left to right.
See Matrix Functions, for the available matrix functions. The remaining operators are described in more detail below.
Expressions appear in the matrix language in some contexts where there would be ambiguity whether ‘/’ is an operator or a separator between subcommands. In these contexts, only the operators with higher precedence than ‘/’ are allowed outside parentheses. Later sections call these restricted expressions.
()
**
Next: Integer Sequence Operator ‘:’, Up: Matrix Expressions [Contents][Index]
Use the {} operator to construct matrices. Within the curly braces, commas separate elements within a row and semicolons separate rows. The following examples show a 2×3 matrix, a 1×4 row vector, a 3×1 column vector, and a scalar.
{1, 2, 3; 4, 5, 6} | ⇒ | [1 2 3] [4 5 6] |
{3.14, 6.28, 9.24, 12.57} | ⇒ | [3.14 6.28 9.42 12.57] |
{1.41; 1.73; 2} | ⇒ | [1.41] [1.73] [2.00] |
{5} | ⇒ | 5 |
Curly braces are not limited to holding numeric literals. They can
contain calculations, and they can paste together matrices and vectors
in any way as long as the result is rectangular. For example, if
‘m’ is matrix {1, 2; 3, 4}
, ‘r’ is row vector
{5, 6}
, and ‘c’ is column vector {7, 8}
, then
curly braces can be used as follows:
{m, c; r, 10} | ⇒ | [1 2 7] [3 4 8] [5 6 10] |
{c, 2 * c, T(r)} | ⇒ | [7 14 5] [8 16 6] |
The final example above uses the transposition function T
.
Next: Index Operator ()
, Previous: Matrix Construction Operator {}, Up: Matrix Expressions [Contents][Index]
The syntax first:last:step
yields a row
vector of consecutive integers from first to last counting
by step. The final :step
is optional and
defaults to 1 when omitted.
Each of first, last, and step must be a scalar and should be an integer (any fractional part is discarded). Because ‘:’ has a high precedence, operands other than numeric literals must usually be parenthesized.
When step is positive (or omitted) and end < start, or if step is negative and end > start, then the result is an empty matrix. If step is 0, then PSPP reports an error.
Here are some examples:
1:6 | ⇒ | {1, 2, 3, 4, 5, 6} |
1:6:2 | ⇒ | {1, 3, 5} |
-1:-5:-1 | ⇒ | {-1, -2, -3, -4, -5} |
-1:-5 | ⇒ | {} |
2:1:0 | ⇒ | (error) |
Next: Unary Operators, Previous: Integer Sequence Operator ‘:’, Up: Matrix Expressions [Contents][Index]
()
The result of the submatrix or indexing operator, written
m(rindex, cindex)
, contains the rows of
m whose indexes are given in vector rindex and the columns
whose indexes are given in vector cindex.
In the simplest case, if rindex and cindex are both scalars, the result is also a scalar:
{10, 20; 30, 40}(1, 1) | ⇒ | 10 |
{10, 20; 30, 40}(1, 2) | ⇒ | 20 |
{10, 20; 30, 40}(2, 1) | ⇒ | 30 |
{10, 20; 30, 40}(2, 2) | ⇒ | 40 |
If the index arguments have multiple elements, then the result includes multiple rows or columns:
{10, 20; 30, 40}(1:2, 1) | ⇒ | {10; 30} |
{10, 20; 30, 40}(2, 1:2) | ⇒ | {30, 40} |
{10, 20; 30, 40}(1:2, 1:2) | ⇒ | {10, 20; 30, 40} |
The special argument ‘:’ may stand in for all the rows or columns in the matrix being indexed, like this:
{10, 20; 30, 40}(:, 1) | ⇒ | {10; 30} |
{10, 20; 30, 40}(2, :) | ⇒ | {30, 40} |
{10, 20; 30, 40}(:, :) | ⇒ | {10, 20; 30, 40} |
The index arguments do not have to be in order, and they may contain repeated values, like this:
{10, 20; 30, 40}({2, 1}, 1) | ⇒ | {30; 10} |
{10, 20; 30, 40}(2, {2; 2; 1}) | ⇒ | {40, 40, 30} |
{10, 20; 30, 40}(2:1:-1, :) | ⇒ | {30, 40; 10, 20} |
When the matrix being indexed is a row or column vector, only a single index argument is needed, like this:
{11, 12, 13, 14, 15}(2:4) | ⇒ | {12, 13, 14} |
{11; 12; 13; 14; 15}(2:4) | ⇒ | {12; 13; 14} |
When an index is not an integer, PSPP discards the fractional part. It is an error for an index to be less than 1 or greater than the number of rows or columns:
{11, 12, 13, 14}({2.5, 4.6}) | ⇒ | {12, 14} |
{11; 12; 13; 14}(0) | ⇒ | (error) |
Next: Elementwise Binary Operators, Previous: Index Operator ()
, Up: Matrix Expressions [Contents][Index]
The unary operators take a single operand of any dimensions and operate on each of its elements independently. The unary operators are:
-
Inverts the sign of each element.
+
No change.
NOT
Logical inversion: each positive value becomes 0 and each zero or negative value becomes 1.
Examples:
-{1, -2; 3, -4} | ⇒ | {-1, 2; -3, 4} |
+{1, -2; 3, -4} | ⇒ | {1, -2; 3, -4} |
NOT {1, 0; -1, 1} | ⇒ | {0, 1; 1, 0} |
Next: Matrix Multiplication Operator ‘*’, Previous: Unary Operators, Up: Matrix Expressions [Contents][Index]
The elementwise binary operators require their operands to be matrices with the same dimensions. Alternatively, if one operand is a scalar, then its value is treated as if it were duplicated to the dimensions of the other operand. The result is a matrix of the same size as the operands, in which each element is the result of the applying the operator to the corresponding elements of the operands.
The elementwise binary operators are listed below.
+
Addition.
-
Subtraction.
*
Multiplication, if one operand is a scalar. (Otherwise this is matrix multiplication, described below.)
/
or &/
Division.
&*
Multiplication.
&**
Exponentiation.
<
or LT
Less than.
<=
or LE
Less than or equal.
=
or EQ
Equal.
>
or GT
Greater than.
>=
or GE
Greater than or equal.
<>
or ~=
or NE
Not equal.
AND
True if both operands are true.
OR
True if at least one operand is true.
XOR
True if exactly one operand is true.
Examples:
1 + 2 | ⇒ | 3 |
1 + {3; 4} | ⇒ | {4; 5} |
{66, 77; 88, 99} + 5 | ⇒ | {71, 82; 93, 104} |
{4, 8; 3, 7} + {1, 0; 5, 2} | ⇒ | {5, 8; 8, 9} |
{1, 2; 3, 4} < {4, 3; 2, 1} | ⇒ | {1, 1; 0, 0} |
{1, 3; 2, 4} >= 3 | ⇒ | {0, 1; 0, 1} |
{0, 0; 1, 1} AND {0, 1; 0, 1} | ⇒ | {0, 0; 0, 1} |
Next: Matrix Exponentiation Operator **
, Previous: Elementwise Binary Operators, Up: Matrix Expressions [Contents][Index]
If A
is an m×n matrix and B
is
an n×p matrix, then A*B
is the
m×p matrix multiplication product C
.
PSPP reports an error if the number of columns in A
differs
from the number of rows in B
.
The *
operator performs elementwise multiplication (see above)
if one of its operands is a scalar.
No built-in operator yields the inverse of matrix multiplication.
Instead, multiply by the result of INV
or GINV
.
Some examples:
{1, 2, 3} * {4; 5; 6} | ⇒ | 32 |
{4; 5; 6} * {1, 2, 3} | ⇒ | {4, 8, 12; 5, 10, 15; 6, 12, 18} |
Previous: Matrix Multiplication Operator ‘*’, Up: Matrix Expressions [Contents][Index]
**
The result of A**B
is defined as follows when A
is a
square matrix and B
is an integer scalar:
B > 0
, A**B
is A*…*A
, where there are
B
‘A’s. (PSPP implements this efficiently for large
B
, using exponentiation by squaring.)
B < 0
, A**B
is INV(A**(-B))
.
B = 0
, A**B
is the identity matrix.
PSPP reports an error if A
is not square or B
is not
an integer.
Examples:
{2, 5; 1, 4}**3 | ⇒ | {48, 165; 33, 114} |
{2, 5; 1, 4}**0 | ⇒ | {1, 0; 0, 1} |
10*{4, 7; 2, 6}**-1 | ⇒ | {6, -7; -2, 4} |
Next: The COMPUTE
Command, Previous: Matrix Expressions, Up: MATRIX [Contents][Index]
The matrix language support numerous functions in multiple categories. The following subsections document each of the currently supported functions. The first letter of each parameter’s name indicate the required argument type:
A scalar.
A nonnegative integer scalar. (Non-integers are accepted and silently rounded down to the nearest integer.)
A row or column vector.
A matrix.
Next: Logical Functions, Up: Matrix Functions [Contents][Index]
These functions act on each element of their argument independently, like the elementwise operators (see Elementwise Binary Operators).
Takes the absolute value of each element of M.
ABS({-1, 2; -3, 0}) ⇒ {1, 2; 3, 0}
Computes the inverse sine or tangent, respectively, of each element in M. The results are in radians, between -\pi/2 and +\pi/2, inclusive.
The value of \pi can be computed as 4*ARTAN(1)
.
ARSIN({-1, 0, 1}) ⇒ {-1.57, 0, 1.57} (approximately)
ARTAN({-5, -1, 1, 5}) ⇒ {-1.37, -.79, .79, 1.37} (approximately)
Computes the cosine or sine, respectively, of each element in M, which must be in radians.
COS({0.785, 1.57; 3.14, 1.57 + 3.14}) ⇒ {.71, 0; -1, 0} (approximately)
Computes e^x for each element x in M.
EXP({2, 3; 4, 5}) ⇒ {7.39, 20.09; 54.6, 148.4} (approximately)
Takes the logarithm with base 10 or base e, respectively, of each element in M.
LG10({1, 10, 100, 1000}) ⇒ {0, 1, 2, 3}
LG10(0) ⇒ (error)
LN({EXP(1), 1, 2, 3, 4}) ⇒ {1, 0, .69, 1.1, 1.39} (approximately)
LN(0) ⇒ (error)
Takes each element in M modulo nonzero scalar value s, that is, the remainder of division by s. The sign of the result is the same as the sign of the dividend.
MOD({5, 4, 3, 2, 1, 0}, 3) ⇒ {2, 1, 0, 2, 1, 0}
MOD({5, 4, 3, 2, 1, 0}, -3) ⇒ {2, 1, 0, 2, 1, 0}
MOD({-5, -4, -3, -2, -1, 0}, 3) ⇒ {-2, -1, 0, -2, -1, 0}
MOD({-5, -4, -3, -2, -1, 0}, -3) ⇒ {-2, -1, 0, -2, -1, 0}
MOD({5, 4, 3, 2, 1, 0}, 1.5) ⇒ {.5, 1.0, .0, .5, 1.0, .0}
MOD({5, 4, 3, 2, 1, 0}, 0) ⇒ (error)
Rounds each element of M to an integer. RND
rounds to
the nearest integer, with halves rounded to even integers, and
TRUNC
rounds toward zero.
RND({-1.6, -1.5, -1.4}) ⇒ {-2, -2, -1}
RND({-.6, -.5, -.4}) ⇒ {-1, 0, 0}
RND({.4, .5, .6} ⇒ {0, 0, 1}
RND({1.4, 1.5, 1.6}) ⇒ {1, 2, 2}
TRUNC({-1.6, -1.5, -1.4}) ⇒ {-1, -1, -1}
TRUNC({-.6, -.5, -.4}) ⇒ {0, 0, 0}
TRUNC({.4, .5, .6} ⇒ {0, 0, 0}
TRUNC({1.4, 1.5, 1.6}) ⇒ {1, 1, 1}
Takes the square root of each element of M, which must not be negative.
SQRT({0, 1, 2, 4, 9, 81}) ⇒ {0, 1, 1.41, 2, 3, 9} (approximately)
SQRT(-1) ⇒ (error)
Next: Matrix Construction Functions, Previous: Elementwise Functions, Up: Matrix Functions [Contents][Index]
Returns a scalar with value 1 if all of the elements in M are nonzero, or 0 if at least one element is zero.
ALL({1, 2, 3} < {2, 3, 4}) ⇒ 1
ALL({2, 2, 3} < {2, 3, 4}) ⇒ 0
ALL({2, 3, 3} < {2, 3, 4}) ⇒ 0
ALL({2, 3, 4} < {2, 3, 4}) ⇒ 0
Returns a scalar with value 1 if any of the elements in M is nonzero, or 0 if all of them are zero.
ANY({1, 2, 3} < {2, 3, 4}) ⇒ 1
ANY({2, 2, 3} < {2, 3, 4}) ⇒ 1
ANY({2, 3, 3} < {2, 3, 4}) ⇒ 1
ANY({2, 3, 4} < {2, 3, 4}) ⇒ 0
Next: Minimum, Maximum, and Sum Functions, Previous: Logical Functions, Up: Matrix Functions [Contents][Index]
Returns a block diagonal matrix with as many rows as the sum of its arguments’ row counts and as many columns as the sum of their columns. Each argument matrix is placed along the main diagonal of the result, and all other elements are zero.
BLOCK({1, 2; 3, 4}, 5, {7; 8; 9}, {10, 11}) ⇒ 1 2 0 0 0 0 3 4 0 0 0 0 0 0 5 0 0 0 0 0 0 7 0 0 0 0 0 8 0 0 0 0 0 9 0 0 0 0 0 0 10 11
Returns an identity matrix, whose main diagonal elements are one and whose other elements are zero. The returned matrix has n rows and columns or nr rows and nc columns, respectively.
IDENT(1) ⇒ 1 IDENT(2) ⇒ 1 0 0 1 IDENT(3, 5) ⇒ 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 IDENT(5, 3) ⇒ 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0
Returns an n×n matrix that contains each of the integers 1…n once, in which each column, each row, and each diagonal sums to n(n^2+1)/2. There are many magic squares with given dimensions, but this function always returns the same one for a given value of n.
MAGIC(3) ⇒ {8, 1, 6; 3, 5, 7; 4, 9, 2}
MAGIC(4) ⇒ {1, 5, 12, 16; 15, 11, 6, 2; 14, 8, 9, 3; 4, 10, 7, 13}
Returns an nr×nc matrix whose elements are all s.
MAKE(1, 2, 3) ⇒ {3, 3}
MAKE(2, 1, 4) ⇒ {4; 4}
MAKE(2, 3, 5) ⇒ {5, 5, 5; 5, 5, 5}
Given n-element vector V, returns a n×n matrix whose main diagonal is copied from V. The other elements in the returned vector are zero.
Use CALL SETDIAG
(see CALL SETDIAG) to replace the main
diagonal of a matrix in-place.
MDIAG({1, 2, 3, 4}) ⇒ 1 0 0 0 0 2 0 0 0 0 3 0 0 0 0 4
Returns an nr×nc matrix whose elements come from M, which must have the same number of elements as the new matrix, copying elements from M to the new matrix row by row.
RESHAPE(1:12, 1, 12) ⇒ 1 2 3 4 5 6 7 8 9 10 11 12 RESHAPE(1:12, 2, 6) ⇒ 1 2 3 4 5 6 7 8 9 10 11 12 RESHAPE(1:12, 3, 4) ⇒ 1 2 3 4 5 6 7 8 9 10 11 12 RESHAPE(1:12, 4, 3) ⇒ 1 2 3 4 5 6 7 8 9 10 11 12
Returns M with rows exchanged for columns.
T({1, 2, 3}) ⇒ {1; 2; 3}
T({1; 2; 3}) ⇒ {1, 2, 3}
Returns a nr×nc matrix in which each element is randomly chosen from a uniform distribution of real numbers between 0 and 1. Random number generation honors the current seed setting (see SET SEED).
The following example shows one possible output, but of course every result will be different (given different seeds):
UNIFORM(4, 5)*10 ⇒ 7.71 2.99 .21 4.95 6.34 4.43 7.49 8.32 4.99 5.83 2.25 .25 1.98 7.09 7.61 2.66 1.69 2.64 .88 1.50
Next: Matrix Property Functions, Previous: Matrix Construction Functions, Up: Matrix Functions [Contents][Index]
Returns a row vector with the same number of columns as M, in which each element is the minimum, maximum, sum, or sum of squares, respectively, of the elements in the same column of M.
CMIN({1, 2, 3; 4, 5, 6; 7, 8, 9} ⇒ {1, 2, 3}
CMAX({1, 2, 3; 4, 5, 6; 7, 8, 9} ⇒ {7, 8, 9}
CSUM({1, 2, 3; 4, 5, 6; 7, 8, 9} ⇒ {12, 15, 18}
CSSQ({1, 2, 3; 4, 5, 6; 7, 8, 9} ⇒ {66, 93, 126}
Returns the minimum, maximum, sum, or sum of squares, respectively, of the elements of M.
MMIN({1, 2, 3; 4, 5, 6; 7, 8, 9} ⇒ 1
MMAX({1, 2, 3; 4, 5, 6; 7, 8, 9} ⇒ 9
MSUM({1, 2, 3; 4, 5, 6; 7, 8, 9} ⇒ 45
MSSQ({1, 2, 3; 4, 5, 6; 7, 8, 9} ⇒ 285
Returns a column vector with the same number of rows as M, in which each element is the minimum, maximum, sum, or sum of squares, respectively, of the elements in the same row of M.
RMIN({1, 2, 3; 4, 5, 6; 7, 8, 9} ⇒ {1; 4; 7}
RMAX({1, 2, 3; 4, 5, 6; 7, 8, 9} ⇒ {3; 6; 9}
RSUM({1, 2, 3; 4, 5, 6; 7, 8, 9} ⇒ {6; 15; 24}
RSSQ({1, 2, 3; 4, 5, 6; 7, 8, 9} ⇒ {14; 77; 194}
Returns M^T × M.
SSCP({1, 2, 3; 4, 5, 6}) ⇒ {17, 22, 27; 22, 29, 36; 27, 36, 45}
Returns the sum of the elements along M’s main diagonal,
equivalent to MSUM(DIAG(M))
.
TRACE(MDIAG(1:5)) ⇒ 15
Next: Matrix Rank Ordering Functions, Previous: Minimum, Maximum, and Sum Functions, Up: Matrix Functions [Contents][Index]
Returns the number of row or columns, respectively, in M.
NROW({1, 0; -2, -3; 3, 3}) ⇒ 3 NROW(1:5) ⇒ 1 NCOL({1, 0; -2, -3; 3, 3}) ⇒ 2 NCOL(1:5) ⇒ 5
Returns a column vector containing a copy of M’s main diagonal.
The vector’s length is the lesser of NCOL(M)
and
NROW(M)
.
DIAG({1, 0; -2, -3; 3, 3}) ⇒ {1; -3}
Next: Matrix Algebra Functions, Previous: Matrix Property Functions, Up: Matrix Functions [Contents][Index]
The GRADE
and RANK
functions each take a matrix M
and return a matrix r with the same dimensions. Each element in
r ranges between 1 and the number of elements n in
M, inclusive. When the elements in M all have unique
values, both of these functions yield the same results: the smallest
element in M corresponds to value 1 in r, the next
smallest to 2, and so on, up to the largest to n. When multiple
elements in M have the same value, these functions use different
rules for handling the ties.
Returns a ranking of M, turning duplicate values into sequential ranks. The returned matrix always contains each of the integers 1 through the number of elements in the matrix exactly once.
GRADE({1, 0, 3; 3, 1, 2; 3, 0, 5}) ⇒ {3, 1, 6; 7, 4, 5; 8, 2, 9}
Returns a ranking of M, turning duplicate values into the mean of their sequential ranks.
RNKORDER({1, 0, 3; 3, 1, 2; 3, 0, 5})
⇒ {3.5, 1.5, 7; 7, 3.5, 5; 7, 1.5, 9}
One may use GRADE
to sort a vector:
COMPUTE v(GRADE(v))=v. /* Sort v in ascending order. COMPUTE v(GRADE(-v))=v. /* Sort v in descending order.
Next: Matrix Statistical Distribution Functions, Previous: Matrix Rank Ordering Functions, Up: Matrix Functions [Contents][Index]
Matrix M must be an n×n symmetric positive-definite matrix. Returns an n×n matrix B such that B^T×B=M.
CHOL({4, 12, -16; 12, 37, -43; -16, -43, 98}) ⇒ 2 6 -8 0 1 5 0 0 3
Returns a design matrix for M. The design matrix has the same number of rows as M. Each column c in M, from left to right, yields a group of columns in the output. For each unique value v in c, from top to bottom, add a column to the output in which v becomes 1 and other values become 0.
PSPP issues a warning if a column only contains a single unique value.
DESIGN({1; 2; 3}) ⇒ {1, 0, 0; 0, 1, 0; 0, 0, 1} DESIGN({5; 8; 5}) ⇒ {1, 0; 0, 1; 1, 0} DESIGN({1, 5; 2, 8; 3, 5}) ⇒ {1, 0, 0, 1, 0; 0, 1, 0, 0, 1; 0, 0, 1, 1, 0} DESIGN({5; 5; 5}) ⇒ (warning)
Returns the determinant of square matrix M.
DET({3, 7; 1, -4}) ⇒ -19
Returns a column vector containing the eigenvalues of symmetric matrix M, sorted in ascending order.
Use CALL EIGEN
(see CALL EIGEN) to compute eigenvalues and
eigenvectors of a matrix.
EVAL({2, 0, 0; 0, 3, 4; 0, 4, 9}) ⇒ {11; 2; 1}
Returns the k×n matrix A that is the generalized inverse of n×k matrix M, defined such that M×A×M=M and A×M×A=A.
GINV({1, 2}) ⇒ {.2; .4} (approximately)
{1:9} * GINV(1:9) * {1:9} ⇒ {1:9} (approximately)
M must be a n×m matrix, m ≥ n, with rank n. Returns an n×n orthonormal basis for M, obtained using the Gram-Schmidt process.
GSCH({3, 2; 1, 2}) * SQRT(10) ⇒ {3, -1; 1, 3} (approximately)
Returns the n×n matrix A that is the inverse of n×n matrix M, defined such that M×A = A×M = I, where I is the identity matrix. M must not be singular, that is, \det(M) ≠0.
INV({4, 7; 2, 6}) ⇒ {.6, -.7; -.2, .4} (approximately)
Returns the pm×qn matrix P that is the
Kroneker product of m×n matrix
Ma and p×q matrix Mb. One may
view P as the concatenation of multiple
p×q blocks, each of which is the scalar
product of Mb by a different element of Ma. For example,
when A
is a 2×2 matrix, KRONEKER(A, B)
is
equivalent to {A(1,1)*B, A(1,2)*B; A(2,1)*B, A(2,2)*B}
.
KRONEKER({1, 2; 3, 4}, {0, 5; 6, 7}) ⇒ 0 5 0 10 6 7 12 14 0 15 0 20 18 21 24 28
Returns the rank of matrix M, a integer scalar whose value is the dimension of the vector space spanned by its columns or, equivalently, by its rows.
RANK({1, 0, 1; -2, -3, 1; 3, 3, 0}) ⇒ 2 RANK({1, 1, 0, 2; -1, -1, 0, -2}) ⇒ 1 RANK({1, -1; 1, -1; 0, 0; 2, -2}) ⇒ 1 RANK({1, 2, 1; -2, -3, 1; 3, 5, 0}) ⇒ 2 RANK({1, 0, 2; 2, 1, 0; 3, 2, 1}) ⇒ 3
Ma must be an n×n matrix, with \det(Ma) ≠0, and Mb an n×k matrix. Returns an n×k matrix X such that Ma × X = Mb.
All of the following examples show approximate results:
SOLVE({2, 3; 4, 9}, {6, 2; 15, 5}) ⇒ 1.50 .50 1.00 .33 SOLVE({1, 3, -2; 3, 5, 6; 2, 4, 3}, {5; 7; 8}) ⇒ -15.00 8.00 2.00 SOLVE({2, 1, -1; -3, -1, 2; -2, 1, 2}, {8; -11; -3}) ⇒ 2.00 3.00 -1.00
Given n×k matrix M, returns a \min(n,k)-element column vector containing the singular values of M in descending order.
Use CALL SVD
(see CALL SVD) to compute the full singular
value decomposition of a matrix.
SVAL({1, 1; 0, 0}) ⇒ {1.41; .00} SVAL({1, 0, 1; 0, 1, 1; 0, 0, 0}) ⇒ {1.73; 1.00; .00} SVAL({2, 4; 1, 3; 0, 0; 0, 0}) ⇒ {5.46; .37}
Given r×c matrix M and integer scalar k = nk such that 1 ≤ k ≤ \min(r,c), returns the r×c sweep matrix A.
If M_{kk} ≠0, then:
A_{kk} = 1/M_{kk}, A_{ik} = -M_{ik}/M_{kk} for i ≠k, A_{kj} = M_{kj}/M_{kk} for j ≠k, and A_{ij} = M_{ij} - M_{ik}M_{kj}/M_{kk} for i ≠k and j ≠k.
If M_{kk} = 0, then:
A_{ik} = A_{ki} = 0 and A_{ij} = M_{ij}, for i ≠k and j ≠k.
Given M = {0, 1, 2; 3, 4, 5; 6, 7, 8}, then (approximately):
SWEEP(M, 1) ⇒ .00 .00 .00 .00 4.00 5.00 .00 7.00 8.00 SWEEP(M, 2) ⇒ -.75 -.25 .75 .75 .25 1.25 .75 -1.75 -.75 SWEEP(M, 3) ⇒ -1.50 -.75 -.25 -.75 -.38 -.63 .75 .88 .13
Next: EOF Function, Previous: Matrix Algebra Functions, Up: Matrix Functions [Contents][Index]
The matrix language can calculate several functions of standard statistical distributions using the same syntax and semantics as in PSPP transformation expressions. See Statistical Distribution Functions, for details.
The matrix language extends the PDF, CDF, SIG, IDF, NPDF, and NCDF
functions by allowing the first parameters to each of these functions
to be a vector or matrix with any dimensions. In addition,
CDF.BVNOR
and PDF.BVNOR
allow either or both of their
first two parameters to be vectors or matrices; if both are non-scalar
then they must have the same dimensions. In each case, the result is
a matrix or vector with the same dimensions as the input populated
with elementwise calculations.
Previous: Matrix Statistical Distribution Functions, Up: Matrix Functions [Contents][Index]
This function works with files being used on the READ
statement.
Given a file handle or file name file, returns an integer scalar
1 if the last line in the file has been read or 0 if more lines are
available. Determining this requires attempting to read another line,
which means that REREAD
on the next READ
command
following EOF
on the same file will be ineffective.
The EOF
function gives a matrix program the flexibility to read
a file with text data without knowing the length of the file in
advance. For example, the following program will read all the lines
of data in data.txt, each consisting of three numbers, as rows
in matrix data
:
MATRIX. COMPUTE data={}. LOOP IF NOT EOF('data.txt'). READ row/FILE='data.txt'/FIELD=1 TO 1000/SIZE={1,3}. COMPUTE data={data; row}. END LOOP. PRINT data. END MATRIX.
Next: The CALL
Command, Previous: Matrix Functions, Up: MATRIX [Contents][Index]
COMPUTE
CommandCOMPUTE variable[(index[,index])]=expression.
The COMPUTE
command evaluates an expression and assigns the
result to a variable or a submatrix of a variable. Assigning to a
submatrix uses the same syntax as the index operator (see Index Operator ()
).
Next: The PRINT
Command, Previous: The COMPUTE
Command, Up: MATRIX [Contents][Index]
CALL
CommandA matrix function returns a single result. The CALL
command
implements procedures, which take a similar syntactic form to
functions but yield results by modifying their arguments rather than
returning a value.
Output arguments to a CALL
procedure must be a single variable
name.
The following procedures are implemented via CALL
to allow them
to return multiple results. For these procedures, the output
arguments need not name existing variables; if they do, then their
previous values are replaced:
Computes the eigenvalues and eigenvector of symmetric n×n matrix M. Assigns the eigenvectors of M to the columns of n×n matrix evec and the eigenvalues in descending order to n-element column vector eval.
Use the EVAL
function (see EVAL) to compute just the
eigenvalues of a symmetric matrix.
For example, the following matrix language commands:
CALL EIGEN({1, 0; 0, 1}, evec, eval). PRINT evec. PRINT eval. CALL EIGEN({3, 2, 4; 2, 0, 2; 4, 2, 3}, evec2, eval2). PRINT evec2. PRINT eval2.
yield this output:
evec 1 0 0 1 eval 1 1 evec2 -.6666666667 .0000000000 .7453559925 -.3333333333 -.8944271910 -.2981423970 -.6666666667 .4472135955 -.5962847940 eval2 8.0000000000 -1.0000000000 -1.0000000000
Computes the singular value decomposition of n×k matrix M, assigning S a n×k diagonal matrix and to U and V unitary k×k matrices such that M = U×S×V^T. The main diagonal of Q contains the singular values of M.
Use the SVAL
function (see SVAL) to compute just the
singular values of a matrix.
For example, the following matrix program:
CALL SVD({3, 2, 2; 2, 3, -2}, u, s, v). PRINT (u * s * T(v))/FORMAT F5.1.
yields this output:
(u * s * T(v)) 3.0 2.0 2.0 2.0 3.0 -2.0
The final procedure is implemented via CALL
to allow it to
modify a matrix instead of returning a modified version. For this
procedure, the output argument must name an existing variable.
Replaces the main diagonal of n×p matrix M by the contents of k-element vector V. If k = 1, so that V is a scalar, replaces all of the diagonal elements of M by V. If k < \min(n,p), only the upper k diagonal elements are replaced; if k > \min(n,p), then the extra elements of V are ignored.
Use the MDIAG
function (see MDIAG) to construct a new
matrix with a specified main diagonal.
For example, this matrix program:
COMPUTE x={1, 2, 3; 4, 5, 6; 7, 8, 9}. CALL SETDIAG(x, 10). PRINT x.
outputs the following:
x 10 2 3 4 10 6 7 8 10
Next: The DO IF
Command, Previous: The CALL
Command, Up: MATRIX [Contents][Index]
PRINT
CommandPRINT [expression] [/FORMAT=format] [/TITLE=title] [/SPACE={NEWPAGE | n}] [{/RLABELS=string… | /RNAMES=expression}] [{/CLABELS=string… | /CNAMES=expression}].
The PRINT
command is commonly used to display a matrix. It
evaluates the restricted expression, if present, and outputs it
either as text or a pivot table, depending on the setting of
MDISPLAY
(see SET MDISPLAY).
Use the FORMAT
subcommand to specify a format, such as
F8.2
, for displaying the matrix elements. FORMAT
is
optional for numerical matrices. When it is omitted, PSPP chooses
how to format entries automatically using m, the magnitude of
the largest-magnitude element in the matrix to be displayed:
F
format that fits
m plus a sign. For example, if the matrix is {1:10}, then
m = 10, which fits in 3 columns with room for a sign, the
format is F3.0
.
F13.10
and notes the scale factor in the output.
F13.10
.
The optional TITLE
subcommand specifies a title for the output
text or table, as a quoted string. When it is omitted, the syntax of
the matrix expression is used as the title.
Use the SPACE
subcommand to request extra space above the
matrix output. With a numerical argument, it adds the specified
number of lines of blank space above the matrix. With NEWPAGE
as an argument, it prints the matrix at the top of a new page. The
SPACE
subcommand has no effect when a matrix is output as a
pivot table.
The RLABELS
and RNAMES
subcommands, which are mutually
exclusive, can supply a label to accompany each row in the output.
With RLABELS
, specify the labels as comma-separated strings or
other tokens. With RNAMES
, specify a single expression that
evaluates to a vector of strings. Either way, if there are more
labels than rows, the extra labels are ignored, and if there are more
rows than labels, the extra rows are unlabeled. For output to a pivot
table with RLABELS
, the labels can be any length; otherwise,
the labels are truncated to 8 bytes.
The CLABELS
and CNAMES
subcommands work for labeling
columns as RLABELS
and RNAMES
do for labeling rows.
When the expression is omitted, PRINT
does not output a
matrix. Instead, it outputs only the text specified on TITLE
,
if any, preceded by any space specified on the SPACE
subcommand, if any. Any other subcommands are ignored, and the
command acts as if MDISPLAY
is set to TEXT
regardless of
its actual setting.
The following syntax demonstrates two different ways to label the rows
and columns of a matrix with PRINT
:
MATRIX. COMPUTE m={1, 2, 3; 4, 5, 6; 7, 8, 9}. PRINT m/RLABELS=a, b, c/CLABELS=x, y, z. COMPUTE rlabels={"a", "b", "c"}. COMPUTE clabels={"x", "y", "z"}. PRINT m/RNAMES=rlabels/CNAMES=clabels. END MATRIX.
With MDISPLAY=TEXT
(the default), this program outputs the
following (twice):
m x y z a 1 2 3 b 4 5 6 c 7 8 9
With ‘SET MDISPLAY=TABLES.’ added above ‘MATRIX.’, the output becomes the following (twice):
|
Next: The LOOP
and BREAK
Commands, Previous: The PRINT
Command, Up: MATRIX [Contents][Index]
DO IF
CommandDO IF expression. …matrix commands… [ELSE IF expression. …matrix commands…]… [ELSE …matrix commands…] END IF.
A DO IF
command evaluates its expression argument. If the
DO IF
expression evaluates to true, then PSPP executes the
associated commands. Otherwise, PSPP evaluates the expression on
each ELSE IF
clause (if any) in order, and executes the
commands associated with the first one that yields a true value.
Finally, if the DO IF
and all the ELSE IF
expressions
all evaluate to false, PSPP executes the commands following the
ELSE
clause (if any).
Each expression on DO IF
and ELSE IF
must evaluate to a
scalar. Positive scalars are considered to be true, and scalars that
are zero or negative are considered to be false.
The following matrix language fragment sets ‘b’ to the term following ‘a’ in the Juggler sequence:
DO IF MOD(a, 2) = 0. COMPUTE b = TRUNC(a &** (1/2)). ELSE. COMPUTE b = TRUNC(a &** (3/2)). END IF.
Next: The READ
and WRITE
Commands, Previous: The DO IF
Command, Up: MATRIX [Contents][Index]
LOOP
and BREAK
CommandsLOOP [var=first TO last [BY step]] [IF expression]. …matrix commands… END LOOP [IF expression]. BREAK.
The LOOP
command executes a nested group of matrix commands,
called the loop’s body, repeatedly. It has three optional
clauses that control how many times the loop body executes.
Regardless of these clauses, the global MXLOOPS
setting, which
defaults to 40, also limits the number of iterations of a loop. To
iterate more times, raise the maximum with SET MXLOOPS
outside
of the MATRIX
command (see SET MXLOOPS).
The optional index clause causes var to be assigned successive values on each trip through the loop: first first, then first + step, then first + 2 × step, and so on. The loop ends when var > last, for positive step, or var < last, for negative step. If step is not specified, it defaults to 1. All the index clause expressions must evaluate to scalars, and non-integers are rounded toward zero. If step evaluates as zero (or rounds to zero), then the loop body never executes.
The optional IF
on LOOP
is evaluated before each
iteration through the loop body. If its expression, which must
evaluate to a scalar, is zero or negative, then the loop terminates
without executing the loop body.
The optional IF
on END LOOP
is evaluated after each
iteration through the loop body. If its expression, which must
evaluate to a scalar, is zero or negative, then the loop terminates.
The following computes and prints l(n), whose value is the number of steps in the Juggler sequence for n, for n from 2 to 10 inclusive:
COMPUTE l = {}. LOOP n = 2 TO 10. COMPUTE a = n. LOOP i = 1 TO 100. DO IF MOD(a, 2) = 0. COMPUTE a = TRUNC(a &** (1/2)). ELSE. COMPUTE a = TRUNC(a &** (3/2)). END IF. END LOOP IF a = 1. COMPUTE l = {l; i}. END LOOP. PRINT l.
BREAK
CommandThe BREAK
command may be used inside a loop body, ordinarily
within a DO IF
command. If it is executed, then the loop
terminates immediately, jumping to the command just following
END LOOP
. When multiple LOOP
commands nest,
BREAK
terminates the innermost loop.
The following example is a revision of the one above that shows how
BREAK
could substitute for the index and IF
clauses on
LOOP
and END LOOP
:
COMPUTE l = {}. LOOP n = 2 TO 10. COMPUTE a = n. COMPUTE i = 1. LOOP. DO IF MOD(a, 2) = 0. COMPUTE a = TRUNC(a &** (1/2)). ELSE. COMPUTE a = TRUNC(a &** (3/2)). END IF. DO IF a = 1. BREAK. END IF. COMPUTE i = i + 1. END LOOP. COMPUTE l = {l; i}. END LOOP. PRINT l.
Next: The GET
Command, Previous: The LOOP
and BREAK
Commands, Up: MATRIX [Contents][Index]
READ
and WRITE
CommandsThe READ
and WRITE
commands perform matrix input and
output with text files. They share the following syntax for
specifying how data is divided among input lines:
/FIELD=first TO last [BY width] [/FORMAT=format]
Both commands require the FIELD
subcommand. It specifies the
range of columns, from first to last, inclusive, that the
data occupies on each line of the file. The leftmost column is column
1. The columns must be literal numbers, not expressions. To use
entire lines, even if they might be very long, specify a column range
such as 1 TO 100000
.
The FORMAT
subcommand is optional for numerical matrices. For
string matrix input and output, specify an A
format. In
addition to FORMAT
, the optional BY
specification on
FIELD
determine the meaning of each text line:
BY
nor FORMAT
, the numbers in the text file
are in F
format separated by spaces or commas. For
WRITE
, PSPP uses as many digits of precision as needed to
accurately represent the numbers in the matrix.
BY width
divides the input area into fixed-width fields
with the given width. The input area must be a multiple of
width columns wide. Numbers are read or written as
Fwidth.0
format.
FORMAT="countF"
divides the input area into integer count
equal-width fields per line. The input area must be a multiple of
count columns wide. Another format type may be substituted for
F
.
FORMAT=Fw
[.d
] divides the input area into fixed-width
fields with width w. The input area must be a multiple of w
columns wide. Another format type may be substituted for F
.
The READ
command disregards d.
FORMAT=F
specifies format F
without indicating a field
width. Another format type may be substituted for F
. The
WRITE
command accepts this form, but it has no effect unless
BY
is also used to specify a field width.
If BY
and FORMAT
both specify or imply a field width,
then they must indicate the same field width.
Next: The WRITE
Command, Up: The READ
and WRITE
Commands [Contents][Index]
READ
CommandREAD variable[(index[,index])] [/FILE=file] /FIELD=first TO last [BY width] [/FORMAT=format] [/SIZE=expression] [/MODE={RECTANGULAR | SYMMETRIC}] [/REREAD].
The READ
command reads from a text file into a matrix variable.
Specify the target variable just after the command name, either just a
variable name to create or replace an entire variable, or a variable
name followed by an indexing expression to replace a submatrix of an
existing variable.
The FILE
subcommand is required in the first READ
command that appears within MATRIX
. It specifies the text file
to be read, either as a file name in quotes or a file handle
previously declared on FILE HANDLE
(see FILE HANDLE).
Later READ
commands (in syntax order) use the previous
referenced file if FILE
is omitted.
The FIELD
and FORMAT
subcommands specify how input lines
are interpreted. FIELD
is required, but FORMAT
is
optional. See The READ
and WRITE
Commands, for details.
The SIZE
subcommand is required for reading into an entire
variable. Its restricted expression argument should evaluate to a
2-element vector {n, m}
or
{n; m}
, which indicates a
n×m matrix destination. A scalar n is
also allowed and indicates a n×1 column vector
destination. When the destination is a submatrix, SIZE
is
optional, and if it is present then it must match the size of the
submatrix.
By default, or with MODE=RECTANGULAR
, the command reads an
entry for every row and column. With MODE=SYMMETRIC
, the
command reads only the entries on and below the matrix’s main
diagonal, and copies the entries above the main diagonal from the
corresponding symmetric entries below it. Only square matrices
may use MODE=SYMMETRIC
.
Ordinarily, each READ
command starts from a new line in the
text file. Specify the REREAD
subcommand to instead start from
the last line read by the previous READ
command. This has no
effect for the first READ
command to read from a particular
file. It is also ineffective just after a command that uses the
EOF
matrix function (see EOF Matrix Function) on a
particular file, because EOF
has to try to read the next line
from the file to determine whether the file contains more input.
The following matrix program reads the same matrix {1, 2, 4; 2,
3, 5; 4, 5, 6}
into matrix variables v
, w
, and
x
:
READ v /FILE='input.txt' /FIELD=1 TO 100 /SIZE={3, 3}. READ w /FIELD=1 TO 100 /SIZE={3; 3} /MODE=SYMMETRIC. READ x /FIELD=1 TO 100 BY 1/SIZE={3, 3} /MODE=SYMMETRIC.
given that input.txt contains the following:
1, 2, 4 2, 3, 5 4, 5, 6 1 2 3 4 5 6 1 23 456
The READ
command will read as many lines of input as needed for
a particular row, so it’s also acceptable to break any of the lines
above into multiple lines. For example, the first line 1, 2, 4
could be written with a line break following either or both commas.
The following reads a 5×5 matrix from input2.txt, reversing the order of the rows:
COMPUTE m = MAKE(5, 5, 0). LOOP r = 5 TO 1 BY -1. READ m(r, :) /FILE='input2.txt' /FIELD=1 TO 100. END LOOP.
REREAD
Suppose each of the 5 lines in a file input3.txt starts with an integer count followed by count numbers, e.g.:
1 5 3 1 2 3 5 6 -1 2 5 1 2 8 9 3 1 3 2
Then, the following reads this file into a matrix m
:
COMPUTE m = MAKE(5, 5, 0). LOOP i = 1 TO 5. READ count /FILE='input3.txt' /FIELD=1 TO 1 /SIZE=1. READ m(i, 1:count) /FIELD=3 TO 100 /REREAD. END LOOP.
Previous: The READ
Command, Up: The READ
and WRITE
Commands [Contents][Index]
WRITE
CommandWRITE expression [/OUTFILE=file] /FIELD=first TO last [BY width] [/FORMAT=format] [/MODE={RECTANGULAR | TRIANGULAR}] [/HOLD].
The WRITE
command evaluates expression and writes its value
to a text file in a specified format. Write the expression to
evaluate just after the command name.
The OUTFILE
subcommand is required in the first WRITE
command that appears within MATRIX
. It specifies the text file
to be written, either as a file name in quotes or a file handle
previously declared on FILE HANDLE
(see FILE HANDLE).
Later WRITE
commands (in syntax order) use the previous
referenced file if FILE
is omitted.
The FIELD
and FORMAT
subcommands specify how output
lines are formed. FIELD
is required, but FORMAT
is
optional. See The READ
and WRITE
Commands, for details.
By default, or with MODE=RECTANGULAR
, the command writes an
entry for every row and column. With MODE=TRIANGULAR
, the
command writes only the entries on and below the matrix’s main
diagonal. Entries above the diagonal are not written. Only square
matrices may be written with MODE=TRIANGULAR
.
Ordinarily, each WRITE
command writes complete lines to the
output file. With HOLD
, the final line written by WRITE
will be held back for the next WRITE
command to augment. This
can be useful to write more than one matrix on a single output line.
This matrix program:
WRITE {1, 2; 3, 4} /OUTFILE='matrix.txt' /FIELD=1 TO 80.
writes the following to matrix.txt:
1 2 3 4
This matrix program:
WRITE MAGIC(5) /OUTFILE='matrix.txt' /FIELD=1 TO 80 BY 5 /MODE=TRIANGULAR.
writes the following to matrix.txt:
17 23 5 4 6 13 10 12 19 21 11 18 25 2 9
Next: The SAVE
Command, Previous: The READ
and WRITE
Commands, Up: MATRIX [Contents][Index]
GET
CommandGET variable[(index[,index])] [/FILE={file | *}] [/VARIABLES=variable…] [/NAMES=variable] [/MISSING={ACCEPT | OMIT | number}] [/SYSMIS={OMIT | number}].
The READ
command reads numeric data from an SPSS system file,
SPSS/PC+ system file, or SPSS portable file into a matrix variable or
submatrix:
GET
. The variable need not already exist; if it does, it is
replaced. The variable will have as many columns as there are
variables specified on the VARIABLES
subcommand and as many
rows as there are cases in the input file.
GET
.
The submatrix must have as many columns as variables specified on
VARIABLES
and as many rows as cases in the input file.
Specify the name or handle of the file to be read on FILE
. Use
‘*’, or simply omit the FILE
subcommand, to read from the
active file. Reading from the active file is only permitted if it was
already defined outside MATRIX
.
List the variables to be read as columns in the matrix on the
VARIABLES
subcommand. The list can use TO
for
collections of variables or ALL
for all variables. If
VARIABLES
is omitted, all variables are read. Only numeric
variables may be read.
If a variable is named on NAMES
, then the names of the
variables read as data columns are stored in a string vector within
the given name, replacing any existing matrix variable with that name.
Variable names are truncated to 8 bytes.
The MISSING
and SYSMIS
subcommands control the treatment
of missing values in the input file. By default, any user- or
system-missing data in the variables being read from the input causes
an error that prevents GET
from executing. To accept missing
values, specify one of the following settings on MISSING
:
ACCEPT
Accept user-missing values with no change.
By default, system-missing values still yield an error. Use the
SYSMIS
subcommand to change this treatment:
OMIT
Skip any case that contains a system-missing value.
Recode the system-missing value to number.
OMIT
Skip any case that contains any user- or system-missing value.
Recode all user- and system-missing values to number.
The SYSMIS
subcommand has an effect only with
MISSING=ACCEPT
.
Next: The MGET
Command, Previous: The GET
Command, Up: MATRIX [Contents][Index]
SAVE
CommandSAVE expression [/OUTFILE={file | *}] [/VARIABLES=variable…] [/NAMES=expression] [/STRINGS=variable…].
The SAVE
matrix command evaluates expression and writes the
resulting matrix to an SPSS system file. In the system file, each
matrix row becomes a case and each column becomes a variable.
Specify the name or handle of the SPSS system file on the
OUTFILE
subcommand, or ‘*’ to write the output as the new
active file. The OUTFILE
subcommand is required on the first
SAVE
command, in syntax order, within MATRIX
. For
SAVE
commands after the first, the default output file is the
same as the previous.
When multiple SAVE
commands write to one destination within a
single MATRIX
, the later commands append to the same output
file. All the matrices written to the file must have the same number
of columns. The VARIABLES
, NAMES
, and STRINGS
subcommands are honored only for the first SAVE
command that
writes to a given file.
By default, SAVE
names the variables in the output file
COL1
through COLn
. Use VARIABLES
or
NAMES
to give the variables meaningful names. The
VARIABLES
subcommand accepts a comma-separated list of variable
names. Its alternative, NAMES
, instead accepts an expression
that must evaluate to a row or column string vector of names. The
number of names need not exactly match the number of columns in the
matrix to be written: extra names are ignored; extra columns use
default names.
By default, SAVE
assumes that the matrix to be written is all
numeric. To write string columns, specify a comma-separated list of
the string columns’ variable names on STRINGS
.
Next: The MSAVE
Command, Previous: The SAVE
Command, Up: MATRIX [Contents][Index]
MGET
CommandMGET [/FILE=file] [/TYPE={COV | CORR | MEAN | STDDEV | N | COUNT}].
The MGET
command reads the data from a matrix file
(see Matrix Files) into matrix variables.
All of MGET
’s subcommands are optional. Specify the name or
handle of the matrix file to be read on the FILE
subcommand; if
it is omitted, then the command reads the active file.
By default, MGET
reads all of the data from the matrix file.
Specify a space-delimited list of matrix types on TYPE
to limit
the kinds of data to the one specified:
COV
Covariance matrix.
CORR
Correlation coefficient matrix.
MEAN
Vector of means.
STDDEV
Vector of standard deviations.
N
Vector of case counts.
COUNT
Vector of counts.
MGET
reads the entire matrix file and automatically names,
creates, and populates matrix variables using its contents. It
constructs the name of each variable by concatenating the following:
CV
Covariance matrix.
CR
Correlation coefficient matrix.
MN
Vector of means.
SD
Vector of standard deviations.
NC
Vector of case counts.
CN
Vector of counts.
Fn
, where n is
a number identifying a group of factors: F1
for the first
group, F2
for the second, and so on. This part is omitted for
pooled data (where the factors all have the system-missing value).
Sn
, where
n is a number identifying a split group: S1
for the first
group, S2
for the second, and so on.
If MGET
chooses the name of an existing variable, it issues a
warning and does not change the variable.
Next: The DISPLAY
Command, Previous: The MGET
Command, Up: MATRIX [Contents][Index]
MSAVE
CommandMSAVE expression /TYPE={COV | CORR | MEAN | STDDEV | N | COUNT} [/FACTOR=expression] [/SPLIT=expression] [/OUTFILE=file] [/VARIABLES=variable…] [/SNAMES=variable…] [/FNAMES=variable…].
The MSAVE
command evaluates the expression specified just
after the command name, and writes the resulting matrix to a matrix
file (see Matrix Files).
The TYPE
subcommand is required. It specifies the
ROWTYPE_
to write along with this matrix.
The FACTOR
and SPLIT
subcommands are required on the
first MSAVE
if and only if the matrix file has factor or split
variables, respectively. After that, their values are carried along
from one MSAVE
command to the next in syntax order as defaults.
Each one takes an expression that must evaluate to a vector with the
same number of entries as the matrix has factor or split variables,
respectively. Each MSAVE
only writes data for a single
combination of factor and split variables, so many MSAVE
commands (or one inside a loop) may be needed to write a complete set.
The remaining MSAVE
subcommands define the format of the matrix
file. All of the MSAVE
commands within a given matrix program
write to the same matrix file, so these subcommands are only
meaningful on the first MSAVE
command within a matrix program.
(If they are given again on later MSAVE
commands, then they
must have the same values as on the first.)
The OUTFILE
subcommand specifies the name or handle of the
matrix file to be written. Output must go to an external file, not a
data set or the active file.
The VARIABLES
subcommand specifies a comma-separated list of
the names of the continuous variables to be written to the matrix
file. The TO
keyword can be used to define variables named
with consecutive integer suffixes. These names become column names
and names that appear in VARNAME_
in the matrix file.
ROWTYPE_
and VARNAME_
are not allowed on
VARIABLES
. If VARIABLES
is omitted, then PSPP uses
the names COL1
, COL2
, and so on.
The FNAMES
subcommand may be used to supply a comma-separated
list of factor variable names. The default names are FAC1
,
FAC2
, and so on.
The SNAMES
subcommand can supply a comma-separated list of
split variable names. The default names are SPL1
, SPL2
,
and so on.
Next: The RELEASE
Command, Previous: The MSAVE
Command, Up: MATRIX [Contents][Index]
DISPLAY
CommandDISPLAY [{DICTIONARY | STATUS}].
The DISPLAY
command makes PSPP display a table with the name
and dimensions of each matrix variable. The DICTIONARY
and
STATUS
keywords are accepted but have no effect.
Previous: The DISPLAY
Command, Up: MATRIX [Contents][Index]
RELEASE
CommandRELEASE variable….
The RELEASE
command accepts a comma-separated list of matrix
variable names. It deletes each variable and releases the memory
associated with it.
The END MATRIX
command releases all matrix variables.
Next: Invoking pspp-convert
, Previous: Matrices, Up: GNU PSPP [Contents][Index]
Commands that don’t fit any other category are placed here.
Most of these commands are not affected by commands like IF
and
LOOP
:
they take effect only once, unconditionally, at the time that they are
encountered in the input.
ADD DOCUMENT ’line one’ ’line two’ … ’last line’ .
ADD DOCUMENT
adds one or more lines of descriptive commentary to
the active dataset. Documents added in this way are saved to system files.
They can be viewed using SYSFILE INFO
or DISPLAY
DOCUMENTS
. They can be removed from the active dataset with DROP
DOCUMENTS
.
Each line of documentary text must be enclosed in quotation marks, and may not be more than 80 bytes long. See DOCUMENT.
Next: CD, Previous: ADD DOCUMENT, Up: Utilities [Contents][Index]
CACHE.
This command is accepted, for compatibility, but it has no effect.
CD ’new directory’ .
CD
changes the current directory. The new directory becomes
that specified by the command.
Comment commands: COMMENT comment text … . *comment text … . Comments within a line of syntax: FREQUENCIES /VARIABLES=v0 v1 v2. /* All our categorical variables.
COMMENT
is ignored. It is used to provide information to
the author and other readers of the PSPP syntax file.
COMMENT
can extend over any number of lines. It ends at a dot
at the end of a line or a blank line. The comment may contain any
characters.
PSPP also supports comments within a line of syntax, introduced with ‘/*’. These comments end at the first ‘*/’ or at the end of the line, whichever comes first. A line that contains just this kind of comment is considered blank and ends the current command.
Next: DISPLAY DOCUMENTS, Previous: COMMENT, Up: Utilities [Contents][Index]
DOCUMENT documentary_text.
DOCUMENT
adds one or more lines of descriptive commentary to the
active dataset. Documents added in this way are saved to system files.
They can be viewed using SYSFILE INFO
or DISPLAY
DOCUMENTS
. They can be removed from the active dataset with DROP
DOCUMENTS
.
Specify the documentary text following the DOCUMENT
keyword.
It is interpreted literally—any quotes or other punctuation marks
are included in the file.
You can extend the documentary text over as many lines as necessary,
including blank lines to separate paragraphs.
Lines are truncated at 80 bytes. Don’t forget to terminate
the command with a dot at the end of a line. See ADD DOCUMENT.
Next: DISPLAY FILE LABEL, Previous: DOCUMENT, Up: Utilities [Contents][Index]
DISPLAY DOCUMENTS.
DISPLAY DOCUMENTS
displays the documents in the active dataset. Each
document is preceded by a line giving the time and date that it was
added. See DOCUMENT.
Next: DROP DOCUMENTS, Previous: DISPLAY DOCUMENTS, Up: Utilities [Contents][Index]
DISPLAY FILE LABEL.
DISPLAY FILE LABEL
displays the file label contained in the
active dataset,
if any. See FILE LABEL.
This command is a PSPP extension.
Next: ECHO, Previous: DISPLAY FILE LABEL, Up: Utilities [Contents][Index]
DROP DOCUMENTS.
DROP DOCUMENTS
removes all documents from the active dataset.
New documents can be added with DOCUMENT
(see DOCUMENT).
DROP DOCUMENTS
changes only the active dataset. It does not modify any
system files stored on disk.
Next: ERASE, Previous: DROP DOCUMENTS, Up: Utilities [Contents][Index]
ECHO ’arbitrary text’ .
Use ECHO
to write arbitrary text to the output stream. The text should be enclosed in quotation marks following the normal rules for string tokens (see Tokens).
ERASE FILE file_name.
ERASE FILE
deletes a file from the local file system.
file_name must be quoted.
This command cannot be used if the SAFER (see SET) setting is active.
Next: FILE LABEL, Previous: ERASE, Up: Utilities [Contents][Index]
EXECUTE.
EXECUTE
causes the active dataset to be read and all pending
transformations to be executed.
FILE LABEL file_label.
FILE LABEL
provides a title for the active dataset. This
title is saved into system files and portable files that are
created during this PSPP run.
file_label should not be quoted. If quotes are included, they are literally interpreted and become part of the file label.
Next: HOST, Previous: FILE LABEL, Up: Utilities [Contents][Index]
FINISH.
FINISH
terminates the current PSPP session and returns
control to the operating system.
In the syntax below, the square brackets must be included in the command syntax and do not indicate that that their contents are optional.
HOST COMMAND=[’command’...] TIMELIMIT=secs.
HOST
executes one or more commands, each provided as a string in
the required COMMAND
subcommand, in the shell of the
underlying operating system. PSPP runs each command in a separate
shell process and waits for it to finish before running the next one.
If a command fails (with a nonzero exit status, or because it is
killed by a signal), then PSPP does not run any remaining commands.
PSPP provides /dev/null as the shell’s standard input. If a process needs to read from stdin, redirect from a file or device, or use a pipe.
PSPP displays the shell’s standard output and standard error as PSPP
output. Redirect to a file or /dev/null
or another device if
this is not desired.
The following example runs rsync
to copy a file from a remote
server to the local file data.txt, writing rsync
’s own
output to rsync-log.txt. PSPP displays the command’s error
output, if any. If rsync
needs to prompt the user (e.g. to
obtain a password), the command fails. Only if the rsync
succeeds, PSPP then runs the sha512sum
command.
HOST COMMAND=['rsync remote:data.txt data.txt > rsync-log.txt' 'sha512sum -c data.txt.sha512sum].
By default, PSPP waits as long as necessary for the series of commands
to complete. Use the optional TIMELIMIT
subcommand to limit
the execution time to the specified number of seconds.
PSPP built for mingw does not support all the features of
HOST
.
PSPP rejects this command if the SAFER (see SET) setting is active.
INCLUDE [FILE=]’file_name’ [ENCODING=’encoding’].
INCLUDE
causes the PSPP command processor to read an
additional command file as if it were included bodily in the current
command file.
If errors are encountered in the included file, then command
processing stops and no more commands are processed.
Include files may be nested to any depth, up to the limit of available
memory.
The INSERT
command (see INSERT) is a more flexible
alternative to INCLUDE
. An INCLUDE
command acts the same as
INSERT
with ERROR=STOP CD=NO SYNTAX=BATCH
specified.
The optional ENCODING
subcommand has the same meaning as with INSERT
.
INSERT [FILE=]’file_name’ [CD={NO,YES}] [ERROR={CONTINUE,STOP}] [SYNTAX={BATCH,INTERACTIVE}] [ENCODING={LOCALE, ’charset_name’}].
INSERT
is similar to INCLUDE
(see INCLUDE)
but somewhat more flexible.
It causes the command processor to read a file as if it were embedded in the
current command file.
If CD=YES
is specified, then before including the file, the
current directory becomes the directory of the included
file.
The default setting is ‘CD=NO’.
Note that this directory remains current until it is
changed explicitly (with the CD
command, or a subsequent
INSERT
command with the ‘CD=YES’ option).
It does not revert to its original setting even after the included
file is finished processing.
If ERROR=STOP
is specified, errors encountered in the
inserted file causes processing to immediately cease.
Otherwise processing continues at the next command.
The default setting is ERROR=CONTINUE
.
If SYNTAX=INTERACTIVE
is specified then the syntax contained in
the included file must conform to interactive syntax
conventions. See Syntax Variants.
The default setting is SYNTAX=BATCH
.
ENCODING
optionally specifies the character set used by the included
file. Its argument, which is not case-sensitive, must be in one of
the following forms:
LOCALE
The encoding used by the system locale, or as overridden by the
SET
command (see SET). On GNU/Linux and other Unix-like systems,
environment variables, e.g. LANG
or LC_ALL
, determine the
system locale.
One of the character set names listed by IANA at
http://www.iana.org/assignments/character-sets. Some examples
are ASCII
(United States), ISO-8859-1
(western Europe),
EUC-JP
(Japan), and windows-1252
(Windows). Not all
systems support all character sets.
Auto,encoding
Automatically detects whether a syntax file is encoded in an Unicode
encoding such as UTF-8, UTF-16, or UTF-32. If it is not, then PSPP
generally assumes that the file is encoded in encoding (an IANA
character set name). However, if encoding is UTF-8, and the
syntax file is not valid UTF-8, PSPP instead assumes that the file
is encoded in windows-1252
.
For best results, encoding should be an ASCII-compatible encoding (the most common locale encodings are all ASCII-compatible), because encodings that are not ASCII compatible cannot be automatically distinguished from UTF-8.
Auto
Auto,Locale
Automatic detection, as above, with the default encoding taken from
the system locale or the setting on SET LOCALE
.
When ENCODING is not specified, the default is taken from the
--syntax-encoding command option, if it was specified, and
otherwise it is Auto
.
Next: PERMISSIONS, Previous: INSERT, Up: Utilities [Contents][Index]
OUTPUT MODIFY /SELECT TABLES /TABLECELLS SELECT = [ class... ] FORMAT = fmt_spec.
Please note: In the above synopsis the characters ‘[’ and ‘]’ are literals. They must appear in the syntax to be interpreted.
OUTPUT
changes the appearance of the tables in which results are
printed. In particular, it can be used to set the format and precision
to which results are displayed.
After running this command, the default table appearance parameters will have been modified and each new output table generated uses the new parameters.
Following /TABLECELLS SELECT =
a list of cell classes must
appear, enclosed in square brackets. This list determines the classes
of values should be selected for modification. Each class can be:
Residual values. Default: F40.2.
Correlations. Default: F40.3.
Percentages. Default: PCT40.1.
Significance of tests (p-values). Default: F40.3.
Counts or sums of weights. For a weighted data set, the default is the weight variable’s print format. For an unweighted data set, the default is F40.0.
For most other numeric values that appear in tables, SET FORMAT
may be used to specify the format (see SET FORMAT).
The value of fmt_spec must be a valid output format (see Input and Output Formats). Note that not all possible formats are meaningful for all classes.
Next: PRESERVE and RESTORE, Previous: OUTPUT, Up: Utilities [Contents][Index]
PERMISSIONS FILE=’file_name’ /PERMISSIONS = {READONLY,WRITEABLE}.
PERMISSIONS
changes the permissions of a file.
There is one mandatory subcommand which specifies the permissions to
which the file should be changed.
If you set a file’s permission to READONLY
, then the file
will become unwritable either by you or anyone else on the system.
If you set the permission to WRITEABLE
, then the file becomes
writeable by you; the permissions afforded to others are unchanged.
This command cannot be used if the SAFER
(see SET)
setting is active.
Next: SET, Previous: PERMISSIONS, Up: Utilities [Contents][Index]
PRESERVE. … RESTORE.
PRESERVE
saves all of the settings that SET
(see SET)
can adjust. A later RESTORE
command restores those settings.
PRESERVE
can be nested up to five levels deep.
Next: SHOW, Previous: PRESERVE and RESTORE, Up: Utilities [Contents][Index]
SET (data input) /BLANKS={SYSMIS,’.’,number} /DECIMAL={DOT,COMMA} /FORMAT=fmt_spec /EPOCH={AUTOMATIC,year} /RIB={NATIVE,MSBFIRST,LSBFIRST,VAX} /RRB={NATIVE,ISL,ISB,IDL,IDB,VF,VD,VG,ZS,ZL} (interaction) /MXERRS=max_errs /MXWARNS=max_warnings /WORKSPACE=workspace_size (syntax execution) /LOCALE=’locale’ /MXLOOPS=max_loops /SEED={RANDOM,seed_value} /UNDEFINED={WARN,NOWARN} /FUZZBITS=fuzzbits /SCALEMIN=count (data output) /CC{A,B,C,D,E}={’npre,pre,suf,nsuf’,’npre.pre.suf.nsuf’} /DECIMAL={DOT,COMMA} /FORMAT=fmt_spec /LEADZERO={ON,OFF} /MDISPLAY={TEXT,TABLES} /SMALL=number /WIB={NATIVE,MSBFIRST,LSBFIRST,VAX} /WRB={NATIVE,ISL,ISB,IDL,IDB,VF,VD,VG,ZS,ZL} (output routing) /ERRORS={ON,OFF,TERMINAL,LISTING,BOTH,NONE} /MESSAGES={ON,OFF,TERMINAL,LISTING,BOTH,NONE} /PRINTBACK={ON,OFF,TERMINAL,LISTING,BOTH,NONE} /RESULTS={ON,OFF,TERMINAL,LISTING,BOTH,NONE} (output driver options) /HEADERS={NO,YES,BLANK} /LENGTH={NONE,n_lines} /WIDTH={NARROW,WIDTH,n_characters} /TNUMBERS={VALUES,LABELS,BOTH} /TVARS={NAMES,LABELS,BOTH} /TLOOK={NONE,file} (logging) /JOURNAL={ON,OFF} [’file_name’] (system files) /SCOMPRESSION={ON,OFF} (miscellaneous) /SAFER=ON /LOCALE=’string’ (macros) /MEXPAND={ON,OFF} /MPRINT={ON,OFF} /MITERATE=number /MNEST=number (settings not yet implemented, but accepted and ignored) /BASETEXTDIRECTION={AUTOMATIC,RIGHTTOLEFT,LEFTTORIGHT} /BLOCK=’c’ /BOX={’xxx’,’xxxxxxxxxxx’} /CACHE={ON,OFF} /CELLSBREAK=number /COMPRESSION={ON,OFF} /CMPTRANS={ON,OFF} /HEADER={NO,YES,BLANK}
SET
allows the user to adjust several parameters relating to
PSPP’s execution. Since there are many subcommands to this command, its
subcommands are examined in groups.
For subcommands that take boolean values, ON
and YES
are synonymous,
as are OFF
and NO
, when used as subcommand values.
The data input subcommands affect the way that data is read from data files. The data input subcommands are
This is the value assigned to an item data item that is empty or contains only white space. An argument of SYSMIS or ’.’ causes the system-missing value to be assigned to null items. This is the default. Any real value may be assigned.
This value may be set to DOT
or COMMA
.
Setting it to DOT
causes the decimal point character to be
‘.’ and the grouping character to be ‘,’.
Setting it to COMMA
causes the decimal point character to be ‘,’ and the grouping
character to be ‘.’.
If the setting is COMMA
, then ‘,’ is not treated
as a field separator in the DATA LIST
command (see DATA LIST).
The default value is determined from the system locale.
Allows the default numeric input/output format to be specified. The default is F8.2. See Input and Output Formats.
Specifies the range of years used when a 2-digit year is read from a
data file or used in a date construction expression (see Functions that Produce Dates). If a 4-digit year is specified for the epoch, then
2-digit years are interpreted starting from that year, known as the
epoch. If AUTOMATIC
(the default) is specified, then the epoch begins
69 years before the current date.
PSPP extension to set the byte ordering (endianness) used for reading
data in IB or PIB format (see Binary and Hexadecimal Numeric Formats). In MSBFIRST
ordering, the most-significant byte appears at
the left end of a IB or PIB field. In LSBFIRST
ordering, the
least-significant byte appears at the left end. VAX
ordering is like
MSBFIRST
, except that each pair of bytes is in reverse order. NATIVE
,
the default, is equivalent to MSBFIRST
or LSBFIRST
depending on the
native format of the machine running PSPP.
PSPP extension to set the floating-point format used for reading data in RB format (see Binary and Hexadecimal Numeric Formats). The possibilities are:
The native format of the machine running PSPP. Equivalent to either IDL or IDB.
32-bit IEEE 754 single-precision floating point, in little-endian byte order.
32-bit IEEE 754 single-precision floating point, in big-endian byte order.
64-bit IEEE 754 double-precision floating point, in little-endian byte order.
64-bit IEEE 754 double-precision floating point, in big-endian byte order.
32-bit VAX F format, in VAX-endian byte order.
64-bit VAX D format, in VAX-endian byte order.
64-bit VAX G format, in VAX-endian byte order.
32-bit IBM Z architecture short format hexadecimal floating point, in big-endian byte order.
64-bit IBM Z architecture long format hexadecimal floating point, in big-endian byte order.
Z architecture also supports IEEE 754 floating point. The ZS and ZL formats are only for use with very old input files.
The default is NATIVE.
Interaction subcommands affect the way that PSPP interacts with an online user. The interaction subcommands are
The maximum number of errors before PSPP halts processing of the current command file. The default is 50.
The maximum number of warnings + errors before PSPP halts processing the current command file. The special value of zero means that all warning situations should be ignored. No warnings are issued, except a single initial warning advising you that warnings will not be given. The default value is 100.
Syntax execution subcommands control the way that PSPP commands execute. The syntax execution subcommands are
Overrides the system locale for the purpose of reading and writing
syntax and data files. The argument should be a locale name in the
general form language_country.encoding
, where language
and country are 2-character language and country abbreviations,
respectively, and encoding is an IANA character set name.
Example locales are en_US.UTF-8
(UTF-8 encoded English as
spoken in the United States) and ja_JP.EUC-JP
(EUC-JP encoded
Japanese as spoken in Japan).
The maximum number of iterations for an uncontrolled loop
(see LOOP), and for any loop in the matrix language (see The LOOP
and BREAK
Commands). The default max_loops is 40.
The initial pseudo-random number seed. Set it to a real number or to RANDOM, to obtain an initial seed from the current time of day.
Currently not used.
The maximum number of bits of errors in the least-significant places to accept for rounding up a value that is almost halfway between two possibilities for rounding with the RND operator (see Miscellaneous Mathematical Functions). The default fuzzbits is 6.
The minimum number of distinct valid values for PSPP to assume that a variable has a scale measurement level. See Measurement Level.
The maximum amount of memory (in kilobytes) that PSPP uses to store data being processed. If memory in excess of the workspace size is required, then PSPP starts to use temporary files to store the data. Setting a higher value means that procedures run faster, but may cause other applications to run slower. On platforms without virtual memory management, setting a very large workspace may cause PSPP to abort.
Data output subcommands affect the format of output data. These subcommands are
Set up custom currency formats. See Custom Currency Formats, for details.
The default DOT
setting causes the decimal point character to be
‘.’. A setting of COMMA
causes the decimal point character to be
‘,’.
Allows the default numeric input/output format to be specified. The default is F8.2. See Input and Output Formats.
Controls whether numbers with magnitude less than one are displayed
with a zero before the decimal point. For example, with SET
LEADZERO=OFF
, which is the default, one-half is shown as 0.5, and
with SET LEADZERO=ON
, it is shown as .5. This setting affects
only the F
, COMMA
, and DOT
formats.
Controls how the PRINT
command within
MATRIX
…END MATRIX
outputs matrices. With the
default TEXT
, PRINT
outputs matrices as text. Change
this setting to TABLES
to instead output matrices as pivot
tables. See The PRINT
Command, for more information.
This controls how PSPP formats small numbers in pivot tables, in cases where PSPP does not otherwise have a well-defined format for the numbers. When such a number has a magnitude less than the value set here, PSPP formats the number in scientific notation; otherwise, it formats it in standard notation. The default is 0.0001. Set a value of 0 to disable scientific notation.
PSPP extension to set the byte ordering (endianness) used for writing
data in IB or PIB format (see Binary and Hexadecimal Numeric Formats). In MSBFIRST
ordering, the most-significant byte appears at
the left end of a IB or PIB field. In LSBFIRST
ordering, the
least-significant byte appears at the left end. VAX
ordering is like
MSBFIRST
, except that each pair of bytes is in reverse order. NATIVE
,
the default, is equivalent to MSBFIRST
or LSBFIRST
depending on the
native format of the machine running PSPP.
PSPP extension to set the floating-point format used for writing data in
RB format (see Binary and Hexadecimal Numeric Formats). The choices
are the same as SET RIB
. The default is NATIVE
.
In the PSPP text-based interface, the output routing subcommands affect where output is sent. The following values are allowed for each of these subcommands:
Discard this kind of output.
Write this output to the terminal, but not to listing files and other output devices.
Write this output to listing files and other output devices, but not to the terminal.
Write this type of output to all output devices.
These output routing subcommands are:
Applies to error and warning messages. The default is BOTH
.
Applies to notes. The default is BOTH
.
Determines whether the syntax used for input is printed back as part
of the output. The default is NONE
.
Applies to everything not in one of the above categories, such as the
results of statistical procedures. The default is BOTH
.
These subcommands have no effect on output in the PSPP GUI environment.
Output driver option subcommands affect output drivers’ settings. These subcommands are
The TNUMBERS
option sets the way in which values are displayed in output tables.
The valid settings are VALUES
, LABELS
and BOTH
.
If TNUMBERS
is set to VALUES
, then all values are displayed with their literal value
(which for a numeric value is a number and for a string value an alphanumeric string).
If TNUMBERS
is set to LABELS
, then values are displayed using their assigned labels if any.
(See VALUE LABELS.)
If the value has no label, then the literal value is used for display.
If TNUMBERS
is set to BOTH
, then values are displayed with both their label
(if any) and their literal value in parentheses.
The TVARS
option sets the way in which variables are displayed in output tables.
The valid settings are NAMES
, LABELS
and BOTH
.
If TVARS
is set to NAMES
, then all variables are displayed using their names.
If TVARS
is set to LABELS
, then variables are displayed using their label if one
has been set. If no label has been set, then the name is used.
(See VARIABLE LABELS.)
If TVARS
is set to BOTH
, then variables are displayed with both their label
(if any) and their name in parentheses.
The TLOOK
option sets the style used for subsequent table
output. Specifying NONE
makes PSPP use the default
built-in style. Otherwise, specifying file makes PSPP search
for an .stt or .tlo file in the same way as specifying
--table-look=file the PSPP command line (see Main Options).
Logging subcommands affect logging of commands executed to external files. These subcommands are
These subcommands, which are synonyms, control the journal. The
default is ON
, which causes commands entered interactively to be
written to the journal file. Commands included from syntax files that
are included interactively and error messages printed by PSPP are also
written to the journal file, prefixed by ‘>’. OFF
disables use
of the journal.
The journal is named pspp.jnl by default. A different name may be specified.
System file subcommands affect the default format of system files produced by PSPP. These subcommands are
Whether system files created by SAVE
or XSAVE
are
compressed by default. The default is ON
.
Security subcommands affect the operations that commands are allowed to perform. The security subcommands are
Setting this option disables the following operations:
ERASE
command.
HOST
command.
PERMISSIONS
command.
Be aware that this setting does not guarantee safety (commands can still overwrite files, for instance) but it is an improvement. When set, this setting cannot be reset during the same session, for obvious security reasons.
This item is used to set the default character encoding. The encoding may be specified either as an encoding name or alias (see http://www.iana.org/assignments/character-sets), or as a locale name. If given as a locale name, only the character encoding of the locale is relevant.
System files written by PSPP use this encoding. System files read by PSPP, for which the encoding is unknown, are interpreted using this encoding.
The full list of valid encodings and locale names/alias are operating system dependent. The following are all examples of acceptable syntax on common GNU/Linux systems.
SET LOCALE='iso-8859-1'. SET LOCALE='ru_RU.cp1251'. SET LOCALE='japanese'.
Contrary to intuition, this command does not affect any aspect of the system’s locale.
The following subcommands affect the interpretation of macros.
Controls whether macros are expanded. The default is ON.
Controls whether the expansion of macros is included in output. This is separate from whether command syntax in general is included in output. The default is OFF.
Limits the number of iterations executed in !DO
loops within
macros. This does not affect other language constructs such as
LOOP
. This must be set to a positive integer. The default is
1000.
Limits the number of levels of nested macro expansions. This must be set to a positive integer. The default is 50.
The following subcommands are not yet implemented, but PSPP accepts them and ignores the settings.
SHOW [ALL] [BLANKS] [CC] [CCA] [CCB] [CCC] [CCD] [CCE] [COPYING] [DECIMAL] [DIRECTORY] [ENVIRONMENT] [FORMAT] [FUZZBITS] [LENGTH] [MEXPAND] [MPRINT] [MITERATE] [MNEST] [MXERRS] [MXLOOPS] [MXWARNS] [N] [SCOMPRESSION] [SYSTEM] [TEMPDIR] [UNDEFINED] [VERSION] [WARRANTY] [WEIGHT] [WIDTH]
SHOW
can be used to display the current state of PSPP’s execution
parameters. Parameters that can be changed using SET
(see SET), can be examined using SHOW
using the subcommand
with the same name. SHOW
supports the following additional
subcommands:
ALL
Show all settings.
CC
Show all custom currency settings (CCA
through CCE
).
DIRECTORY
Shows the current working directory.
ENVIRONMENT
Shows the operating system details.
N
Reports the number of cases in the active dataset. The reported number is not weighted. If no dataset is defined, then ‘Unknown’ is reported.
SYSTEM
Shows information about how PSPP was built. This information is useful in bug reports. See Bugs, for details.
TEMPDIR
Shows the path of the directory where temporary files are stored.
VERSION
Shows the version of this installation of PSPP.
WARRANTY
Show details of the lack of warranty for PSPP.
COPYING
/ LICENSE
Display the terms of PSPP’s copyright licence (see Your rights and obligations).
Specifying SHOW
without any subcommands is equivalent to SHOW ALL
.
SUBTITLE ’subtitle_string’. or SUBTITLE subtitle_string.
SUBTITLE
provides a subtitle to a particular PSPP
run. This subtitle appears at the top of each output page below the
title, if headers are enabled on the output device.
Specify a subtitle as a string in quotes. The alternate syntax that did not require quotes is now obsolete. If it is used then the subtitle is converted to all uppercase.
TITLE ’title_string’. or TITLE title_string.
TITLE
provides a title to a particular PSPP run.
This title appears at the top of each output page, if headers are enabled
on the output device.
Specify a title as a string in quotes. The alternate syntax that did not require quotes is now obsolete. If it is used then the title is converted to all uppercase.
Next: Invoking pspp-output
, Previous: Utilities, Up: GNU PSPP [Contents][Index]
pspp-convert
pspp-convert
is a command-line utility accompanying
PSPP. It reads an SPSS or SPSS/PC+ system file or SPSS portable
file or encrypted SPSS syntax file input and
writes a copy of it to another output in a different format.
Synopsis:
pspp-convert [options] input output pspp-convert --help pspp-convert --version
The format of input is automatically detected, when possible.
The character encoding of old SPSS system files cannot always be
guessed correctly, and SPSS/PC+ system files do not include any
indication of their encoding. Use -e encoding
to specify
the encoding in this case.
By default, the intended format for output is inferred based on its extension:
csv
txt
Comma-separated value. Each value is formatted according to its variable’s print format. The first line in the file contains variable names.
sav
sys
SPSS system file.
por
SPSS portable file.
sps
SPSS syntax file. (Only encrypted syntax files may be converted to this format.)
pspp-convert
can convert most input formats to most output
formats. Encrypted SPSS file formats are exceptions: if the input
file is in an encrypted format, then the output file will be the same
format (decrypted). To decrypt such a file, specify the encrypted
file as input. The output will be the equivalent plaintext
file. Options for the output format are ignored in this case.
The password for encrypted files can be specified a few different
ways. If the password is known, use the -p option
(documented below) or allow pspp-convert
to prompt for it.
If the password is unknown, use the -a and -l
options to specify how to search for it, or --password-list
to specify a file of passwords to try.
Use -O format
to override the inferred format or to
specify the format for unrecognized extensions.
pspp-convert
accepts the following general options:
Sets the output format, where format is one of the extensions listed above, e.g.: -O csv. Use --help to list the supported output formats.
By default, all cases are copied from input to output. Specifying this option to limit the number of cases written to output to maxcases.
Overrides the encoding in which character strings in input are interpreted. This option is necessary because old SPSS system files, and SPSS/PC+ system files, do not self-identify their encoding.
By default, pspp-convert
includes all the variables from the
input file. Use this option to list specific variables to include;
any variables not listed will be dropped. The variables in the output
file will also be reordered into the given order. The variable list
may use TO
in the same way as in PSPP syntax, e.g. if the
dictionary contains consecutive variables a
, b
,
c
, and d
, then --keep='a to d' will include all
of them (and no others).
Drops the specified variables from the output.
When --keep and --drop are used together, --keep is processed first.
Prints a usage message on stdout and exits.
Prints version information on stdout and exits.
The following options affect CSV output:
By default, pspp-convert
writes user-missing values to CSV
output files as their regular values. With this option,
pspp-convert
recodes them to system-missing values (which
are written as a single space).
By default, pspp-convert
writes the variable names as the
first line of output. With this option, pspp-convert
omits
this line.
By default, pspp-convert
writes variables’ values to CSV
output files. With this option, pspp-convert
writes value
labels.
By default, pspp-convert
writes numeric variables as plain
numbers. This option makes pspp-convert
honor variables’
print formats.
This option sets the character used as a decimal point in output. The default is ‘.’.
This option sets the character used to separate fields in output. The default is ‘,’, unless the decimal point is ‘,’, in which case ‘;’ is used.
The option sets the character used to quote fields that contain the delimiter. The default is ‘"’.
The following options specify how to obtain the password for encrypted files:
Specifies the password to use to decrypt an encrypted SPSS system file
or syntax file. If this option is not specified,
pspp-convert
will prompt interactively for the password as
necessary.
Be aware that command-line options, including passwords, may be visible to other users on multiuser systems.
When used with -a (or --password-alphabet) and -l (or --password-length), this option specifies the starting point for the search. This can be used to restart a search that was interrupted.
Specifies the alphabet of symbols over which to search for an encrypted file’s password. alphabet may include individual characters and ranges delimited by ‘-’. For example, -a a-z searches lowercase letters, -a A-Z0-9 searches uppercase letters and digits, and -a ' -~' searches all printable ASCII characters.
Specifies the maximum length of the passwords to try.
Specifies a file to read containing a list of passwords to try, one per line. If file is -, reads from stdin.
Next: Invoking pspp-dump-sav
, Previous: Invoking pspp-convert
, Up: GNU PSPP [Contents][Index]
pspp-output
pspp-output
is a command-line utility accompanying PSPP.
It supports multiple operations on SPSS viewer or .spv files,
here called SPV files. SPSS 16 and later writes SPV files to
represent the contents of its output editor.
SPSS 15 and earlier versions instead use .spo files.
pspp-output
does not support this format.
pspp-options
may be invoked in the following ways:
pspp-output detect file pspp-output [options] dir file pspp-output [options] convert source destination pspp-output [options] get-table-look source destination pspp-output [options] convert-table-look source destination pspp-output --help pspp-output --version
Each of these forms is documented separately below.
pspp-output
also has several undocumented command forms that
developers may find useful for debugging.
detect
Commanddir
Commandconvert
Commandget-table-look
Commandconvert-table-look
Command
Next: The dir
Command, Up: Invoking pspp-output
[Contents][Index]
detect
Commandpspp-output detect file
When file is an SPV file, pspp-output
exits
successfully without outputting anything. When file is not an
SPV file or some other error occurs, pspp-output
prints an
error message and exits with a failure indication.
Next: The convert
Command, Previous: The detect
Command, Up: Invoking pspp-output
[Contents][Index]
dir
Commandpspp-output [options] dir file
Prints on stdout a table of contents for SPV file file. By default, this table lists every object in the file, except for hidden objects. See Input Selection Options, for information on the options available to select a subset of objects.
The following additional option for dir
is intended mainly
for use by PSPP developers:
Also show the names of the Zip members associated with each object.
Next: The get-table-look
Command, Previous: The dir
Command, Up: Invoking pspp-output
[Contents][Index]
convert
Commandpspp-output [options] convert source destination
Reads SPV file source and converts it to another format, writing the output to destination.
By default, the intended format for destination is inferred
based on its extension, in the same way that the pspp
program does for its output files. See Invoking pspp
, for details.
See Input Selection Options, for information on the options available to select a subset of objects to include in the output. The following additional options are accepted:
Overrides the format inferred from the output file’s extension. Use
--help to list the available formats. See Invoking pspp
,
for details of the available output formats.
Sets an option for the output file format. See Invoking pspp
, for
details of the available output options.
By default, if the source is corrupt or otherwise cannot be processed, the destination is not written. With -F or --force, the destination is written as best it can, even with errors.
Reads a table style from file and applies it to all of the output tables. The file should be a TableLook .stt or .tlo file.
Next: The convert-table-look
Command, Previous: The convert
Command, Up: Invoking pspp-output
[Contents][Index]
get-table-look
Commandpspp-output [options] get-table-look source destination
Reads SPV file source, applies any selection options (see Input Selection Options), picks the first table from the selected object, extracts the TableLook from that table, and writes it to destination (typically with an .stt extension) in the TableLook XML format.
Use ‘-’ for source to instead write the default look to destination.
The user may use the TableLook file to change the style of tables in
other files, by passing it to the --table-look option on the
convert
command.
Next: Input Selection Options, Previous: The get-table-look
Command, Up: Invoking pspp-output
[Contents][Index]
convert-table-look
Commandpspp-output [options] convert-table-look source destination
Reads .stt or .tlo file source, and writes it back to destination (typically with an .stt extension) in the TableLook XML format. This is useful for converting a TableLook .tlo file from SPSS 15 or earlier into the newer .stt format.
Previous: The convert-table-look
Command, Up: Invoking pspp-output
[Contents][Index]
The dir
and convert
commands, by default, operate
on all of the objects in the source SPV file, except for objects that
are not visible in the output viewer window. The user may specify
these options to select a subset of the input objects. When multiple
options are used, only objects that satisfy all of them are selected:
Include only objects of the given class; with leading ‘^’, include only objects not in the class. Use commas to separate multiple classes. The supported classes are:
charts headings logs models tables texts trees warnings outlineheaders pagetitle notes unknown other
Use --select=help to print this list of classes.
Include only objects with the specified command, subtype, or label. With a leading ‘^’, include only the objects that do not match. Multiple values may be specified separated by commas. An asterisk at the end of a value acts as a wildcard.
The --command option matches command identifiers, case
insensitively. All of the objects produced by a single command use
the same, unique command identifier. Command identifiers are always
in English regardless of the language used for output. They often
differ from the command name in PSPP syntax. Use the
pspp-output
program’s dir
command to print command
identifiers in particular output.
The --subtypes option matches particular tables within a
command, case insensitively. Subtypes are not necessarily unique: two
commands that produce similar output tables may use the same subtype.
Subtypes are always in English and dir
will print them.
The --labels option matches the labels in table output (that is, the table titles). Labels are affected by the output language, variable names and labels, split file settings, and other factors.
Include only objects from the nth command that matches --command (or the nth command overall if --command is not specified), where n is 1 for the first command, 2 for the second, and so on.
Include the specified instance of an object that matches the
other criteria within a single command. The instance may be a
number (1 for the first instance, 2 for the second, and so on) or
last
for the last instance.
Include hidden output objects in the output. By default, they are excluded.
Separates two sets of selection options. Objects selected by either set of options are included in the output.
The following additional input selection options are intended mainly for use by PSPP developers:
Include only objects that cause an error when read. With the
convert
command, this is most useful in conjunction with the
--force option.
Include only the objects that include a listed Zip file member.
More than one name may be included, comma-separated. The members in
an SPV file may be listed with the dir
command by adding the
--show-members option or with the zipinfo
program
included with many operating systems. Error messages that
pspp-output
prints when it reads SPV files also often
include member names.
Displays the name of the Zip member or members associated with each object just above the object itself.
Next: Not Implemented, Previous: Invoking pspp-output
, Up: GNU PSPP [Contents][Index]
pspp-dump-sav
pspp-dump-sav
is a command-line utility accompanying
PSPP. It is not installed by default, so it may be missing
from your PSPP installation.
It reads one or more SPSS system files and prints their
contents. The output format is useful for debugging system file
readers and writers and for discovering how to interpret unknown or
poorly understood records. End users may find the output useful for
providing the PSPP developers information about system files that PSPP
does not accurately read.
Synopsis:
pspp-dump-sav [-d[maxcases] | --data[=maxcases]] file… pspp-dump-sav --help | -h pspp-dump-sav --version | -v
The following options are accepted:
By default, pspp-dump-sav
does not print any of the data in a
system file, only the file headers. Specify this option to print the
data as well. If maxcases is specified, then it limits the
number of cases printed.
Prints a usage message on stdout and exits.
Prints version information on stdout and exits.
Some errors that prevent files from being interpreted successfully
cause pspp-dump-sav
to exit without reading any additional
files given on the command line.
Next: Bugs, Previous: Invoking pspp-dump-sav
, Up: GNU PSPP [Contents][Index]
This chapter lists parts of the PSPP language that are not yet implemented.
2SLS
Two stage least squares regression
ACF
Autocorrelation function
ALSCAL
Multidimensional scaling
ANACOR
Correspondence analysis
ANOVA
Factorial analysis of variance
CASEPLOT
Plot time series
CASESTOVARS
Restructure complex data
CATPCA
Categorical principle components analysis
CATREG
Categorical regression
CCF
Time series cross correlation
CLEAR TRANSFORMATIONS
Clears transformations from active dataset
CLUSTER
Hierarchical clustering
CONJOINT
Analyse full concept data
CORRESPONDENCE
Show correspondence
COXREG
Cox proportional hazards regression
CREATE
Create time series data
CSDESCRIPTIVES
Complex samples descriptives
CSGLM
Complex samples GLM
CSLOGISTIC
Complex samples logistic regression
CSPLAN
Complex samples design
CSSELECT
Select complex samples
CSTABULATE
Tabulate complex samples
CURVEFIT
Fit curve to line plot
DATE
Create time series data
DETECTANOMALY
Find unusual cases
DISCRIMINANT
Linear discriminant analysis
EDIT
obsolete
END FILE TYPE
Ends complex data input
FILE TYPE
Complex data input
FIT
Goodness of Fit
GENLOG
Categorical model fitting
GET TRANSLATE
Read other file formats
GGRAPH
Custom defined graphs
HILOGLINEAR
Hierarchical loglinear models
HOMALS
Homogeneity analysis
IGRAPH
Interactive graphs
INFO
Local Documentation
KEYED DATA LIST
Read nonsequential data
KM
Kaplan-Meier
LOGLINEAR
General model fitting
MANOVA
Multivariate analysis of variance
MAPS
Geographical display
MIXED
Mixed linear models
MODEL CLOSE
Close server connection
MODEL HANDLE
Define server connection
MODEL LIST
Show existing models
MODEL NAME
Specify model label
MULTIPLE CORRESPONDENCE
Multiple correspondence analysis
MULT RESPONSE
Multiple response analysis
MVA
Missing value analysis
NAIVEBAYES
Small sample bayesian prediction
NLR
Non Linear Regression
NOMREG
Multinomial logistic regression
NONPAR CORR
Nonparametric correlation
NUMBERED
OLAP CUBES
On-line analytical processing
OMS
Output management
ORTHOPLAN
Orthogonal effects design
OVERALS
Nonlinear canonical correlation
PACF
Partial autocorrelation
PARTIAL CORR
Partial correlation
PLANCARDS
Conjoint analysis planning
PLUM
Estimate ordinal regression models
POINT
Marker in keyed file
PPLOT
Plot time series variables
PREDICT
Specify forecast period
PREFSCAL
Multidimensional unfolding
PRINCALS
PCA by alternating least squares
PROBIT
Probit analysis
PROCEDURE OUTPUT
Specify output file
PROXIMITIES
Pairwise similarity
PROXSCAL
Multidimensional scaling of proximity data
RATIO STATISTICS
Descriptives of ratios
READ MODEL
Read new model
RECORD TYPE
Defines a type of record within FILE TYPE
REFORMAT
Read obsolete files
REPEATING DATA
Specify multiple cases per input record
REPORT
Pretty print working file
RMV
Replace missing values
SCRIPT
Run script file
SEASON
Estimate seasonal factors
SELECTPRED
Select predictor variables
SPCHART
Plot control charts
SPECTRA
Plot spectral density
STEMLEAF
Plot stem-and-leaf display
SUMMARIZE
Univariate statistics
SURVIVAL
Survival analysis
TDISPLAY
Display active models
TREE
Create classification tree
TSAPPLY
Apply time series model
TSET
Set time sequence variables
TSHOW
Show time sequence variables
TSMODEL
Estimate time series model
TSPLOT
Plot time sequence variables
TWOSTEP CLUSTER
Cluster observations
UNIANOVA
Univariate analysis
UNNUMBERED
obsolete
VALIDATEDATA
Identify suspicious cases
VARCOMP
Estimate variance
VARSTOCASES
Restructure complex data
VERIFY
Report time series
WLS
Weighted least squares regression
XGRAPH
High resolution charts
Next: Function Index, Previous: Not Implemented, Up: GNU PSPP [Contents][Index]
Occasionally you may encounter a bug in PSPP.
If you believe you have found a bug, please make sure that it really is a bug. Sometimes, what may appear to be a bug, turns out to be a misunderstanding of how to use the program. If you are unsure, ask for advice on the pspp-users mailing list. Information about the mailing list is at http://lists.gnu.org/mailman/listinfo/pspp-users.
It is also valuable to try the newest version of PSPP, since the problem may have already been fixed. You can always find the newest version of PSPP by visiting the PSPP website. You might have obtained PSPP from a downstream packager, such as a GNU/Linux distribution; if your downstream package is not up-to-date, please ask the distributor to update to the newest version.
If the problem persists in the up-to-date version, check to see if it has already been reported. Reported issues are listed at http://savannah.gnu.org/bugs/?group=pspp. If it has already been reported, you might consider adding a comment with additional information or even just to mention that you are also experiencing the problem, since the PSPP developers are often inclined to work on issues that are important to a large number of users.
For known issues in individual language features, see the relevant section in see The PSPP language.
If the problem exists in a recent version and it has not already been reported, then please report it.
The best way to send a bug report is using the web page at http://savannah.gnu.org/bugs/?group=pspp. Alternatively, bug reports may be sent by email to bug-gnu-pspp@gnu.org.
A high-quality bug report allows the developers to understand, reproduce, and ultimately fix the problem. We recommend including the following:
With PSPP command syntax, SHOW SYSTEM.
will output
everything we ordinarily need. In the PSPPIRE GUI,
Help → System Info produces the same output.
Here is one example of a bug report that includes all of the elements above:
I'm running PSPP on a system where
The bug I'm seeing is that executing the following syntax: DATA LIST FREE /x *. BEGIN DATA. 1 2 3 END DATA. LIST. results in: 4 5 6 but I think the output should be: 1 2 3 |
The following bug report, on the other hand, does not provide enough information for PSPP developers to understand the problem. This means that the developers cannot identify or fix the problem without additional rounds of questions, which is more work for both the reporter and the developer:
I downloaded the latest version of PSPP and entered a sequence of numbers, but when I analyse them it gives the wrong output. |
PSPP developers value all users’ feedback, but cannot promise an immediate response. The bug reporting is not a consultancy or support service, although you can make private arrangements for such services. Since PSPP is free software, consultants have access to the information they need to provide such support.
For general enquiries or help, please use the pspp-users mailing list instead of the bug mailing list or bug tracker.
The PSPP bug tracker and bug reporting mailing list are public. To privately report a security vulnerability in GNU PSPP, please send your report to the closed mailing list pspp-security@gnu.org. The PSPP developers will help you assess your report and fix problems prior to public disclosure.
Next: Command Index, Previous: Bugs, Up: GNU PSPP [Contents][Index]
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Copyright © 2000, 2001, 2002, 2007, 2008 Free Software Foundation, Inc. http://fsf.org/ Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed.
The purpose of this License is to make a manual, textbook, or other functional and useful document free in the sense of freedom: to assure everyone the effective freedom to copy and redistribute it, with or without modifying it, either commercially or noncommercially. Secondarily, this License preserves for the author and publisher a way to get credit for their work, while not being considered responsible for modifications made by others.
This License is a kind of “copyleft”, which means that derivative works of the document must themselves be free in the same sense. It complements the GNU General Public License, which is a copyleft license designed for free software.
We have designed this License in order to use it for manuals for free software, because free software needs free documentation: a free program should come with manuals providing the same freedoms that the software does. But this License is not limited to software manuals; it can be used for any textual work, regardless of subject matter or whether it is published as a printed book. We recommend this License principally for works whose purpose is instruction or reference.
This License applies to any manual or other work, in any medium, that contains a notice placed by the copyright holder saying it can be distributed under the terms of this License. Such a notice grants a world-wide, royalty-free license, unlimited in duration, to use that work under the conditions stated herein. The “Document”, below, refers to any such manual or work. Any member of the public is a licensee, and is addressed as “you”. You accept the license if you copy, modify or distribute the work in a way requiring permission under copyright law.
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The “Invariant Sections” are certain Secondary Sections whose titles are designated, as being those of Invariant Sections, in the notice that says that the Document is released under this License. If a section does not fit the above definition of Secondary then it is not allowed to be designated as Invariant. The Document may contain zero Invariant Sections. If the Document does not identify any Invariant Sections then there are none.
The “Cover Texts” are certain short passages of text that are listed, as Front-Cover Texts or Back-Cover Texts, in the notice that says that the Document is released under this License. A Front-Cover Text may be at most 5 words, and a Back-Cover Text may be at most 25 words.
A “Transparent” copy of the Document means a machine-readable copy, represented in a format whose specification is available to the general public, that is suitable for revising the document straightforwardly with generic text editors or (for images composed of pixels) generic paint programs or (for drawings) some widely available drawing editor, and that is suitable for input to text formatters or for automatic translation to a variety of formats suitable for input to text formatters. A copy made in an otherwise Transparent file format whose markup, or absence of markup, has been arranged to thwart or discourage subsequent modification by readers is not Transparent. An image format is not Transparent if used for any substantial amount of text. A copy that is not “Transparent” is called “Opaque”.
Examples of suitable formats for Transparent copies include plain ASCII without markup, Texinfo input format, LaTeX input format, SGML or XML using a publicly available DTD, and standard-conforming simple HTML, PostScript or PDF designed for human modification. Examples of transparent image formats include PNG, XCF and JPG. Opaque formats include proprietary formats that can be read and edited only by proprietary word processors, SGML or XML for which the DTD and/or processing tools are not generally available, and the machine-generated HTML, PostScript or PDF produced by some word processors for output purposes only.
The “Title Page” means, for a printed book, the title page itself, plus such following pages as are needed to hold, legibly, the material this License requires to appear in the title page. For works in formats which do not have any title page as such, “Title Page” means the text near the most prominent appearance of the work’s title, preceding the beginning of the body of the text.
The “publisher” means any person or entity that distributes copies of the Document to the public.
A section “Entitled XYZ” means a named subunit of the Document whose title either is precisely XYZ or contains XYZ in parentheses following text that translates XYZ in another language. (Here XYZ stands for a specific section name mentioned below, such as “Acknowledgements”, “Dedications”, “Endorsements”, or “History”.) To “Preserve the Title” of such a section when you modify the Document means that it remains a section “Entitled XYZ” according to this definition.
The Document may include Warranty Disclaimers next to the notice which states that this License applies to the Document. These Warranty Disclaimers are considered to be included by reference in this License, but only as regards disclaiming warranties: any other implication that these Warranty Disclaimers may have is void and has no effect on the meaning of this License.
You may copy and distribute the Document in any medium, either commercially or noncommercially, provided that this License, the copyright notices, and the license notice saying this License applies to the Document are reproduced in all copies, and that you add no other conditions whatsoever to those of this License. You may not use technical measures to obstruct or control the reading or further copying of the copies you make or distribute. However, you may accept compensation in exchange for copies. If you distribute a large enough number of copies you must also follow the conditions in section 3.
You may also lend copies, under the same conditions stated above, and you may publicly display copies.
If you publish printed copies (or copies in media that commonly have printed covers) of the Document, numbering more than 100, and the Document’s license notice requires Cover Texts, you must enclose the copies in covers that carry, clearly and legibly, all these Cover Texts: Front-Cover Texts on the front cover, and Back-Cover Texts on the back cover. Both covers must also clearly and legibly identify you as the publisher of these copies. The front cover must present the full title with all words of the title equally prominent and visible. You may add other material on the covers in addition. Copying with changes limited to the covers, as long as they preserve the title of the Document and satisfy these conditions, can be treated as verbatim copying in other respects.
If the required texts for either cover are too voluminous to fit legibly, you should put the first ones listed (as many as fit reasonably) on the actual cover, and continue the rest onto adjacent pages.
If you publish or distribute Opaque copies of the Document numbering more than 100, you must either include a machine-readable Transparent copy along with each Opaque copy, or state in or with each Opaque copy a computer-network location from which the general network-using public has access to download using public-standard network protocols a complete Transparent copy of the Document, free of added material. If you use the latter option, you must take reasonably prudent steps, when you begin distribution of Opaque copies in quantity, to ensure that this Transparent copy will remain thus accessible at the stated location until at least one year after the last time you distribute an Opaque copy (directly or through your agents or retailers) of that edition to the public.
It is requested, but not required, that you contact the authors of the Document well before redistributing any large number of copies, to give them a chance to provide you with an updated version of the Document.
You may copy and distribute a Modified Version of the Document under the conditions of sections 2 and 3 above, provided that you release the Modified Version under precisely this License, with the Modified Version filling the role of the Document, thus licensing distribution and modification of the Modified Version to whoever possesses a copy of it. In addition, you must do these things in the Modified Version:
If the Modified Version includes new front-matter sections or appendices that qualify as Secondary Sections and contain no material copied from the Document, you may at your option designate some or all of these sections as invariant. To do this, add their titles to the list of Invariant Sections in the Modified Version’s license notice. These titles must be distinct from any other section titles.
You may add a section Entitled “Endorsements”, provided it contains nothing but endorsements of your Modified Version by various parties—for example, statements of peer review or that the text has been approved by an organization as the authoritative definition of a standard.
You may add a passage of up to five words as a Front-Cover Text, and a passage of up to 25 words as a Back-Cover Text, to the end of the list of Cover Texts in the Modified Version. Only one passage of Front-Cover Text and one of Back-Cover Text may be added by (or through arrangements made by) any one entity. If the Document already includes a cover text for the same cover, previously added by you or by arrangement made by the same entity you are acting on behalf of, you may not add another; but you may replace the old one, on explicit permission from the previous publisher that added the old one.
The author(s) and publisher(s) of the Document do not by this License give permission to use their names for publicity for or to assert or imply endorsement of any Modified Version.
You may combine the Document with other documents released under this License, under the terms defined in section 4 above for modified versions, provided that you include in the combination all of the Invariant Sections of all of the original documents, unmodified, and list them all as Invariant Sections of your combined work in its license notice, and that you preserve all their Warranty Disclaimers.
The combined work need only contain one copy of this License, and multiple identical Invariant Sections may be replaced with a single copy. If there are multiple Invariant Sections with the same name but different contents, make the title of each such section unique by adding at the end of it, in parentheses, the name of the original author or publisher of that section if known, or else a unique number. Make the same adjustment to the section titles in the list of Invariant Sections in the license notice of the combined work.
In the combination, you must combine any sections Entitled “History” in the various original documents, forming one section Entitled “History”; likewise combine any sections Entitled “Acknowledgements”, and any sections Entitled “Dedications”. You must delete all sections Entitled “Endorsements.”
You may make a collection consisting of the Document and other documents released under this License, and replace the individual copies of this License in the various documents with a single copy that is included in the collection, provided that you follow the rules of this License for verbatim copying of each of the documents in all other respects.
You may extract a single document from such a collection, and distribute it individually under this License, provided you insert a copy of this License into the extracted document, and follow this License in all other respects regarding verbatim copying of that document.
A compilation of the Document or its derivatives with other separate and independent documents or works, in or on a volume of a storage or distribution medium, is called an “aggregate” if the copyright resulting from the compilation is not used to limit the legal rights of the compilation’s users beyond what the individual works permit. When the Document is included in an aggregate, this License does not apply to the other works in the aggregate which are not themselves derivative works of the Document.
If the Cover Text requirement of section 3 is applicable to these copies of the Document, then if the Document is less than one half of the entire aggregate, the Document’s Cover Texts may be placed on covers that bracket the Document within the aggregate, or the electronic equivalent of covers if the Document is in electronic form. Otherwise they must appear on printed covers that bracket the whole aggregate.
Translation is considered a kind of modification, so you may distribute translations of the Document under the terms of section 4. Replacing Invariant Sections with translations requires special permission from their copyright holders, but you may include translations of some or all Invariant Sections in addition to the original versions of these Invariant Sections. You may include a translation of this License, and all the license notices in the Document, and any Warranty Disclaimers, provided that you also include the original English version of this License and the original versions of those notices and disclaimers. In case of a disagreement between the translation and the original version of this License or a notice or disclaimer, the original version will prevail.
If a section in the Document is Entitled “Acknowledgements”, “Dedications”, or “History”, the requirement (section 4) to Preserve its Title (section 1) will typically require changing the actual title.
You may not copy, modify, sublicense, or distribute the Document except as expressly provided under this License. Any attempt otherwise to copy, modify, sublicense, or distribute it is void, and will automatically terminate your rights under this License.
However, if you cease all violation of this License, then your license from a particular copyright holder is reinstated (a) provisionally, unless and until the copyright holder explicitly and finally terminates your license, and (b) permanently, if the copyright holder fails to notify you of the violation by some reasonable means prior to 60 days after the cessation.
Moreover, your license from a particular copyright holder is reinstated permanently if the copyright holder notifies you of the violation by some reasonable means, this is the first time you have received notice of violation of this License (for any work) from that copyright holder, and you cure the violation prior to 30 days after your receipt of the notice.
Termination of your rights under this section does not terminate the licenses of parties who have received copies or rights from you under this License. If your rights have been terminated and not permanently reinstated, receipt of a copy of some or all of the same material does not give you any rights to use it.
The Free Software Foundation may publish new, revised versions of the GNU Free Documentation License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns. See http://www.gnu.org/copyleft/.
Each version of the License is given a distinguishing version number. If the Document specifies that a particular numbered version of this License “or any later version” applies to it, you have the option of following the terms and conditions either of that specified version or of any later version that has been published (not as a draft) by the Free Software Foundation. If the Document does not specify a version number of this License, you may choose any version ever published (not as a draft) by the Free Software Foundation. If the Document specifies that a proxy can decide which future versions of this License can be used, that proxy’s public statement of acceptance of a version permanently authorizes you to choose that version for the Document.
“Massive Multiauthor Collaboration Site” (or “MMC Site”) means any World Wide Web server that publishes copyrightable works and also provides prominent facilities for anybody to edit those works. A public wiki that anybody can edit is an example of such a server. A “Massive Multiauthor Collaboration” (or “MMC”) contained in the site means any set of copyrightable works thus published on the MMC site.
“CC-BY-SA” means the Creative Commons Attribution-Share Alike 3.0 license published by Creative Commons Corporation, a not-for-profit corporation with a principal place of business in San Francisco, California, as well as future copyleft versions of that license published by that same organization.
“Incorporate” means to publish or republish a Document, in whole or in part, as part of another Document.
An MMC is “eligible for relicensing” if it is licensed under this License, and if all works that were first published under this License somewhere other than this MMC, and subsequently incorporated in whole or in part into the MMC, (1) had no cover texts or invariant sections, and (2) were thus incorporated prior to November 1, 2008.
The operator of an MMC Site may republish an MMC contained in the site under CC-BY-SA on the same site at any time before August 1, 2009, provided the MMC is eligible for relicensing.
To use this License in a document you have written, include a copy of the License in the document and put the following copyright and license notices just after the title page:
Copyright (C) year your name. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license is included in the section entitled ``GNU Free Documentation License''.
If you have Invariant Sections, Front-Cover Texts and Back-Cover Texts, replace the “with…Texts.” line with this:
with the Invariant Sections being list their titles, with the Front-Cover Texts being list, and with the Back-Cover Texts being list.
If you have Invariant Sections without Cover Texts, or some other combination of the three, merge those two alternatives to suit the situation.
If your document contains nontrivial examples of program code, we recommend releasing these examples in parallel under your choice of free software license, such as the GNU General Public License, to permit their use in free software.
These files contain purely fictitious data. They should not be used for research purposes.
This example assumes that it is already proven that B is not greater than A.
One must use care when correcting such data input errors rather than msimply marking them as missing. For example, if an occupation has been entered “Barister”, did the person mean “Barrister” or did she mean “Barista”?
Since BMI is a quantity with a ratio scale and has units, the term “index” is a misnomer, but that is what it is called.
There are other, possibly better, ways to achieve a similar result
using the MEANS
or EXAMINE
commands.
The number of
bins is chosen according to the Freedman-Diaconis rule:
2 \times IQR(x)n^{-1/3}, where IQR(x) is the interquartile range of x
and n is the number of samples. Note that
EXAMINE
uses a different algorithm to determine bin sizes.
HISTOGRAM
uses Sturges’ rule to determine the number of
bins, as approximately 1 + \log2(n), where n is the number of samples.
Note that FREQUENCIES
uses a different algorithm to find the bin size.
This is not necessarily a meaningful table. To make it easier to read, short variable labels are used.