This manual is for GNU PSPP version 2.0.1-gff8d3d, software for statistical analysis.
Copyright © 1997, 1998, 2004, 2005, 2009, 2012, 2013, 2014, 2016, 2019, 2020, 2023 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
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, 2.0.1-gff8d3d, 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.
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).
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.
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.
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
.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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).
Use the FINISH
command to exit PSPP:
PSPP> finish.
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.
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.
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.
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.
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.
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”.
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.
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.
This chapter discusses elements common to many PSPP commands. Later chapters describe individual commands in detail.
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.
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.
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.
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.
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
.
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.
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.
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.
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.
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.
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.
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.
-$9.99
.
+Infinity
or -Infinity
for infinities, from
NaN
for “not a number,” or from Unknown
for other values
(if any are supported by the system). In fields under 3 columns wide,
special values are output as asterisks.
The custom currency formats are closely related to the basic numeric formats, but they allow users to customize the output format. The SET command configures custom currency formats, using the syntax
SET CCx="
string"
.
where x is A, B, C, D, or E, and string is no more than 16 characters long.
string must contain exactly three commas or exactly three periods (but not both), except that a single quote character may be used to “escape” a following comma, period, or single quote. If three commas are used, commas are used for grouping in output, and a period is used as the decimal point. Uses of periods reverses these roles.
The commas or periods divide string into four fields, called the negative prefix, prefix, suffix, and negative suffix, respectively. The prefix and suffix are added to output whenever space is available. The negative prefix and negative suffix are always added to a negative number when the output includes a nonzero digit.
The following syntax shows how custom currency formats could be used to reproduce basic numeric formats:
SET CCA="-,,,". /* Same as COMMA. SET CCB="-...". /* Same as DOT. SET CCC="-,$,,". /* 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Advanced mathematical functions take numeric arguments and produce numeric results.
(exponent)
¶Returns e (approximately 2.71828) raised to power exponent.
(number)
¶Takes the base-10 logarithm of number. If number is not positive, the result is system-missing.
(number)
¶Takes the base-e logarithm of number. If number is not positive, the result is system-missing.
(number)
¶Yields the base-e logarithm of the complete gamma of number. If number is a negative integer, the result is system-missing.
(number)
¶Takes the square root of number. If number is negative, the result is system-missing.
Miscellaneous mathematical functions take numeric arguments and produce numeric results.
(number)
¶Results in the absolute value of number.
(numerator, denominator)
¶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.
(number)
¶Returns the remainder when number is divided by 10. If number is negative, MOD10(number) is negative or zero.
(number [, mult[, fuzzbits]])
¶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.
(number [, mult[, fuzzbits]])
¶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.
Trigonometric functions take numeric arguments and produce numeric results.
(number)
¶(number)
¶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.
(number)
¶(number)
¶Takes the arcsine, in radians, of number. Results in system-missing if number is not between -1 and 1 inclusive.
(angle)
¶Takes the cosine of angle which should be in radians.
(angle)
¶Takes the sine of angle which should be in radians.
(angle)
¶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.
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.
(expr)
¶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.
(expr [, expr]…)
¶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.
(expr [, expr]…)
¶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.
(expr)
¶Returns 1 if expr has the system-missing value, 0 otherwise.
(variable)
¶(vector(index))
¶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.
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.
(value, set [, set]…)
¶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
(value, low, high [, low, high]…)
¶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.
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.
(number, number[, …])
¶Results in the coefficient of variation of the values of number. (The coefficient of variation is the standard deviation divided by the mean.)
(value, value[, …])
¶Results in the value of the greatest value. The values may be numeric or string.
(number, number[, …])
¶Results in the mean of the values of number.
(number, 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.
(number, number[, …])
¶Results in the value of the least value. The values may be numeric or string.
(number, number[, …])
¶Results in the standard deviation of the values of number.
(number, number[, …])
¶Results in the sum of the values of number.
(number, number[, …])
¶Results in the variance of the values of number.
String functions take various arguments and return various results.
(string, string[, …])
¶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.
(haystack, needle)
¶(haystack, needle)
¶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.
(haystack, needles, needle_len)
¶(haystack, needle, needle_len)
¶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.
(string)
¶Returns the number of bytes in string.
(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.
(string, length[, padding])
¶(string, length[, padding])
¶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.
(string[, padding])
¶(string[, padding])
¶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.
(string, format)
¶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.
(haystack, needle, replacement[, n])
¶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.
(number, format)
¶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"
.
(string, n)
¶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.
(string, start)
¶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.
(string, start, count)
¶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"
.
(string)
¶Returns string, changing lowercase letters to uppercase letters.
For compatibility, PSPP considers dates before 15 Oct 1582 invalid. Most time and date functions will not accept earlier dates.
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
These functions take numeric arguments and return numeric values that represent times.
(ndays)
¶Returns a time corresponding to ndays days.
(nhours, nmins, nsecs)
¶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.
These functions take numeric arguments in PSPP time format and give numeric results.
(time)
¶Results in the number of days and fractional days in time.
(time)
¶Results in the number of hours and fractional hours in time.
(time)
¶Results in the number of minutes and fractional minutes in time.
(time)
¶Results in the number of seconds and fractional seconds in time.
(CTIME.SECONDS
does nothing; CTIME.SECONDS(x)
is
equivalent to x
.)
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.
(day, month, year)
¶(month, day, year)
¶Results in a date value corresponding to the midnight before day day of month month of year year.
(month, year)
¶Results in a date value corresponding to the midnight before the first day of month month of year year.
(quarter, year)
¶Results in a date value corresponding to the midnight before the first day of quarter quarter of year year.
(week, year)
¶Results in a date value corresponding to the midnight before the first day of week week of year year.
(year, yday)
¶Results in a date value corresponding to the day yday of year year.
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.
(time-or-date)
¶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.
(time-or-date)
¶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.
(date)
¶Results in the day of the year (as an integer between 1 and 366) corresponding to date.
(date)
¶Results in the day of the month (as an integer between 1 and 31) corresponding to date.
(time-or-date)
¶Results in the number of minutes (as an integer between 0 and 59) after the last hour in time-or-date.
(date)
¶Results in the month of the year (as an integer between 1 and 12) corresponding to date.
(date)
¶Results in the quarter of the year (as an integer between 1 and 4) corresponding to date.
(time-or-date)
¶Results in the number of whole seconds after the last whole minute (as an integer between 0 and 59) in time-or-date.
(date)
¶Results in the number of whole days from 14 Oct 1582 to date.
(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.
(date)
¶Results in the week of the year (as an integer between 1 and 53) corresponding to date.
(date)
¶Results in the day of week (as an integer between 1 and 7) corresponding to date, where 1 represents Sunday.
(date)
¶Returns the year (as an integer 1582 or greater) corresponding to date.
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:
(date2, date1, unit)
¶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.
(date, quantity, unit[, method])
¶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.
(variable[, n])
¶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, month, day)
¶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.
VALUELABEL
(variable) ¶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.
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:
(x)
¶(x, a, b)
¶(p, a, b)
¶(a, b)
¶(x, a, b, lambda)
¶(x, a, b, lambda)
¶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.
(x0, x1, rho)
¶(x0, x1, rho)
¶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.
(x, a, b)
¶(x, a, b)
¶(p, a, b)
¶(a, b)
¶Cauchy distribution with location parameter a and scale parameter b. Constraints: b > 0, 0 < p < 1.
(x, df)
¶(x, df)
¶(p, df)
¶(df)
¶(x, df, lambda)
¶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.
(x, a)
¶(x, a)
¶(p, a)
¶(a)
¶Exponential distribution with scale parameter a. The inverse of a represents the rate of decay. Constraints: a > 0, x >= 0, 0 <= p < 1.
(x, a, b)
¶(a, b)
¶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.
(x, df1, df2)
¶(x, df1, df2)
¶(x, df1, df2)
¶(p, df1, df2)
¶(df1, df2)
¶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.
(x, a, b)
¶(x, a, b)
¶(p, a, b)
¶(a, b)
¶Gamma distribution with shape parameter a and scale parameter b. Constraints: a > 0, b > 0, x >= 0, 0 <= p < 1.
(x, a, b)
¶(x, a, b)
¶(p, a, b)
¶(a, b)
¶Laplace distribution with location parameter a and scale parameter b. Constraints: b > 0, 0 < p < 1.
(c, alpha)
¶Levy symmetric alpha-stable distribution with scale c and exponent alpha. Constraints: 0 < alpha <= 2.
(c, alpha, beta)
¶Levy skew alpha-stable distribution with scale c, exponent alpha, and skewness parameter beta. Constraints: 0 < alpha <= 2, -1 <= beta <= 1.
(x, a, b)
¶(x, a, b)
¶(p, a, b)
¶(a, b)
¶Logistic distribution with location parameter a and scale parameter b. Constraints: b > 0, 0 < p < 1.
(x, a, b)
¶(x, a, b)
¶(p, a, b)
¶(a, b)
¶Lognormal distribution with parameters a and b. Constraints: a > 0, b > 0, x >= 0, 0 <= p < 1.
(x, mu, sigma)
¶(x, mu, sigma)
¶(p, mu, sigma)
¶(mu, sigma)
¶Normal distribution with mean mu and standard deviation sigma. Constraints: b > 0, 0 < p < 1. Three additional functions are available as shorthand:
(x)
¶Equivalent to CDF.NORMAL(x, 0, 1).
(p)
¶Equivalent to IDF.NORMAL(p, 0, 1).
(sigma)
¶Equivalent to RV.NORMAL(0, sigma).
(x, a, sigma)
¶(a, 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.
(x, a, b)
¶(x, a, b)
¶(p, a, b)
¶(a, b)
¶Pareto distribution with threshold parameter a and shape parameter b. Constraints: a > 0, b > 0, x >= a, 0 <= p < 1.
(x, sigma)
¶(x, sigma)
¶(p, sigma)
¶(sigma)
¶Rayleigh distribution with scale parameter sigma. This distribution is a PSPP extension. Constraints: sigma > 0, x > 0.
(x, a, sigma)
¶(a, sigma)
¶Rayleigh tail distribution with lower limit a and scale parameter sigma. This distribution is a PSPP extension. Constraints: a > 0, sigma > 0, x > a.
(x, df)
¶(x, df)
¶(p, df)
¶(df)
¶T-distribution with df degrees of freedom. The noncentral distribution takes an additional parameter lambda. Constraints: df > 0, 0 < p < 1.
(x, a, b)
¶(x, a, b)
¶(p, a, b)
¶Type-1 Gumbel distribution with parameters a and b. This distribution is a PSPP extension. Constraints: 0 < p < 1.
(x, a, b)
¶(x, a, b)
¶(p, a, b)
¶Type-2 Gumbel distribution with parameters a and b. This distribution is a PSPP extension. Constraints: x > 0, 0 < p < 1.
(x, a, b)
¶(x, a, b)
¶(p, a, b)
¶(a, b)
¶Uniform distribution with parameters a and b. Constraints: a <= x <= b, 0 <= p <= 1. An additional function is available as shorthand:
(b)
¶Equivalent to RV.UNIFORM(0, b).
(x, a, b)
¶(x, a, b)
¶(p, a, b)
¶(a, b)
¶Weibull distribution with parameters a and b. Constraints: a > 0, b > 0, x >= 0, 0 <= p < 1.
The following discrete distributions are available:
(x)
¶(x, p)
¶(p)
¶Bernoulli distribution with probability of success p. Constraints: x = 0 or 1, 0 <= p <= 1.
(x, n, p)
¶(x, n, p)
¶(n, p)
¶Binomial distribution with n trials and probability of success p. Constraints: integer n > 0, 0 <= p <= 1, integer x <= n.
(x, n, p)
¶(x, n, p)
¶(n, p)
¶Geometric distribution with probability of success p. Constraints: 0 <= p <= 1, integer x > 0.
(x, a, b, c)
¶(x, a, b, c)
¶(a, b, c)
¶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.
(x, p)
¶(p)
¶Logarithmic distribution with probability parameter p. Constraints: 0 <= p < 1, x >= 1.
(x, n, p)
¶(x, n, p)
¶(n, p)
¶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.
(x, mu)
¶(x, mu)
¶(mu)
¶Poisson distribution with mean mu. Constraints: mu > 0, integer x >= 0.
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
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.
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.
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.
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
.
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.
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.
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.
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.
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.
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.
END CASE.
END CASE
is used only within INPUT PROGRAM
to output the
current case. See INPUT PROGRAM, for details.
END FILE.
END FILE
is used only within INPUT PROGRAM
to terminate
the current input program. See INPUT PROGRAM.
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.
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.
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.
NEW FILE.
NEW FILE
command clears the dictionary and data from the current
active dataset.
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.
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.
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.
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.
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.
The commands in this chapter read, write, and examine system files and portable files.
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.
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.
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.
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.
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.
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'.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Only variables in the output file can conflict, so DROP
or
KEEP
, as described below, can also resolve a conflict.
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.
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.
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.
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.
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.
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.
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.
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).
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).
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.
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).
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.
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.
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.
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
.
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.
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’]…
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
.
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
.
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 ) ]
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.
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.
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 a 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.
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.
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.
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.
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.
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. |
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.
|
Note that some values for the standard deviation are blank. This is because there is only one case with the respective occupation.
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. It
ignores TEMPORARY
(see TEMPORARY), so that “temporary”
transformations become permanent.
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. |
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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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 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.
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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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. |
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.
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”.
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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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. |
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:
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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).
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.
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. |
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.
|
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.
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.
This chapter documents PSPP commands that temporarily or permanently select data records from the active dataset for analysis.
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.
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.
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.
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. |
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
|
Note that the personnel.sav file from which the data were read is unaffected. The transformation affects only the active file.
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. |
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.
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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.
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. |
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.
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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. |
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
|
This chapter documents PSPP commands used for conditional execution, looping, and flow of control.
BREAK.
BREAK
terminates execution of the innermost currently executing
LOOP
construct.
BREAK
is allowed only inside LOOP
…END LOOP
.
See LOOP, for more details.
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
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.
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.
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.
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.
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
isa 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
isa '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
isa '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
isa '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
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.
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.
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.
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.
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.
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.
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).
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.
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).
This chapter documents the statistical procedures that PSPP supports so far.
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. |
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.
|
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.
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 a general idea of the way in which these nominal variables are distributed.
get file='personnel.sav'. frequencies /variables = sex occupation /statistics = none. |
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.
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.
|
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.
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.
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.
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
.
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. |
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.
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.
|
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=
variablesDISPLAY
={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.
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.
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.
|
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
.
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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