PSPP

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GNU PSPP

This manual is for GNU PSPP version 1.6.2, software for statistical analysis.

Copyright © 1997, 1998, 2004, 2005, 2009, 2012, 2013, 2014, 2016, 2019, 2020 Free Software Foundation, Inc.

Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license is included in the section entitled "GNU Free Documentation License".

Table of Contents


1 Introduction

PSPP is a tool for statistical analysis of sampled data. It reads the data, analyzes the data according to commands provided, and writes the results to a listing file, to the standard output or to a window of the graphical display.

The language accepted by PSPP is similar to those accepted by SPSS statistical products. The details of PSPP’s language are given later in this manual.

PSPP produces tables and charts as output, which it can produce in several formats; currently, ASCII, PostScript, PDF, HTML, DocBook and TeX are supported.

The current version of PSPP, 1.6.2, is incomplete in terms of its statistical procedure support. PSPP is a work in progress. The authors hope to fully support all features in the products that PSPP replaces, eventually. The authors welcome questions, comments, donations, and code submissions. See Submitting Bug Reports, for instructions on contacting the authors.


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2 Your rights and obligations

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).


3 Invoking 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.


3.1 Main Options

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.

Non-option arguments
syntax-file
Output options
-o, --output=output-file
-O option=value
-O format=format
-O device={terminal|listing}
--no-output
--table-look=file
-e, --error-file=error-file
Language options
-I, --include=dir
-I-, --no-include
-b, --batch
-i, --interactive
-r, --no-statrc
-a, --algorithm={compatible|enhanced}
-x, --syntax={compatible|enhanced}
--syntax-encoding=encoding
Informational options
-h, --help
-V, --version
Other options
-s, --safer
--testing-mode
syntax-file

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.

-o output-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.

-O option=value

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.

-O format=format

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.

-O device={terminal|listing}

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.

--no-output

Disables output entirely, if neither -o nor -O is also used. If one of those options is used, --no-output has no effect.

--table-look=file

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).

-e error-file
--error-file=error-file

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.

-I dir
--include=dir

Appends dir to the set of directories searched by the INCLUDE (see INCLUDE) and INSERT (see INSERT) commands.

-I-
--no-include

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).

-b
--batch
-i
--interactive

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.

-r
--no-statrc

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.

-a {enhanced|compatible}
--algorithm={enhanced|compatible}

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.

-x {enhanced|compatible}
--syntax={enhanced|compatible}

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.

--syntax-encoding=encoding

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.

--help

Prints a message describing PSPP command-line syntax and the available device formats, then exits.

-V
--version

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.

-s
--safer

Disables certain unsafe operations. This includes the ERASE and HOST commands, as well as use of pipes as input and output files.

--testing-mode

Invoke heuristics to assist with testing PSPP. For use by make check and similar scripts.


3.2 PDF, PostScript, SVG, and PNG Output Options

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 about 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.

-O format={pdf|ps|svg|png}

Specify the output format. This is only necessary if the file name given on -o does not end in .pdf, .ps, .svg, or .png.

-O paper-size=paper-size

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.

-O foreground-color=color

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.

-O orientation=orientation

Either portrait or landscape. Default: portrait.

-O left-margin=dimension
-O right-margin=dimension
-O top-margin=dimension
-O bottom-margin=dimension

Sets the margins around the page. See below for the allowed forms of dimension Default: 0.5in.

-O prop-font=font-name

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.

-O font-size=font-size

Sets the size of the default fonts, in thousandths of a point. Default: 10000 (10 point).

-O trim=true

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.

-O outline=boolean

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.)

-O font-resolution=dpi

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.


3.3 Plain Text Output Options

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.

-O format=txt

Specify the output format. This is only necessary if the file name given on -o does not end in .txt or .list.

-O charts={template.png|none}

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.

-O foreground-color=color
-O background-color=color

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.

-O width=columns

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.

-O box={ascii|unicode}

Sets the characters used for lines in tables. If set to ascii the characters ‘-’, ‘|’, and ‘+’ for single-width lines and ‘=’ and ‘#’ for double-width lines are used. If set to unicode then Unicode box drawing characters will be used. The default is unicode if the locale’s character encoding is "UTF-8" or ascii otherwise.

-O emphasis={none|bold|underline}

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.


3.4 TeX Output Options

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.


3.5 HTML Output Options

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.

-O format=html

Specify the output format. This is only necessary if the file name given on -o does not end in .html.

-O charts={template.png|none}

Sets the name used for chart files. See Plain Text Output Options, for details.

-O borders=boolean

Decorate the tables with borders. If set to false, the tables produced will have no borders. The default value is true.

-O bare=boolean

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.

-O css=boolean

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.


3.6 OpenDocument Output Options

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.


3.7 Comma-Separated Value Output 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.

-O format=csv

Specify the output format. This is only necessary if the file name given on -o does not end in .csv.

-O separator=field-separator

Sets the character used to separate fields. Default: a comma (‘,’).

-O quote=qualifier

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.

-O titles=boolean

Whether table titles (brief descriptions) should be printed. Default: on.

-O captions=boolean

Whether table captions (more extensive descriptions) should be printed. Default: on.

The CSV format used is an extension to that specified in RFC 4180:

Tables

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.

Titles

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:’.

Captions

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:’.

Footnotes

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

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.

Messages

Errors, warnings, and notes are printed the same way as text.

Charts

Charts are not included in CSV output.

Successive output items are separated by a blank line.


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4 Invoking psppire

4.1 The graphic user interface

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.


5 Using PSPP

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 //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.


5.1 Preparation of Data Files

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:

  • The variables — these are the parameters of a quantity which has been measured or estimated in some way. For example height, weight and geographic location are all variables.
  • The observations (also called ‘cases’) of the variables — each observation represents an instance when the variables were measured or observed.

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.


5.1.1 Defining Variables

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.

  • The words ‘data list list’ are an example of the 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.
  • The ‘/’ character is important. It marks the start of the list of variables which you wish to define.
  • The text ‘forename’ is the name of the first variable, and ‘(A12)’ says that the variable forename is a string variable and that its maximum length is 12 bytes. The second variable’s name is specified by the text ‘height’. Since no format is given, this variable has the default format. Normally the default format expects numeric data, which should be entered in the locale of the operating system. Thus, the example is correct for English locales and other locales which use a period (‘.’) as the decimal separator. However if you are using a system with a locale which uses the comma (‘,’) as the decimal separator, then you should in the subsequent lines substitute ‘.’ with ‘,’. Alternatively, you could explicitly tell PSPP that the height variable is to be read using a period as its decimal separator by appending the text ‘DOT8.3’ after the word ‘height’. For more information on data formats, see Input and Output Formats.
  • Normally, PSPP displays the prompt PSPP> whenever it’s expecting a command. However, when it’s expecting data, the prompt changes to data> so that you know to enter data and not a command.
  • At the end of every command there is a terminating ‘.’ which tells PSPP that the end of a command has been encountered. You should not enter ‘.’ when data is expected (ie. when the data> prompt is current) since it is appropriate only for terminating commands.

5.1.2 Listing the data

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:

Data List
Case Number forename height
1 Ahmed 188.00
2 Bertram 167.00
3 Catherine 134.23
4 David 109.10

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.


5.1.3 Reading data from a text file

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.


5.1.4 Reading data from a pre-prepared PSPP file

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.


5.1.5 Saving data to a PSPP file.

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.


5.1.6 Reading data from other sources

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).


5.1.7 Exiting PSPP

Use the FINISH command to exit PSPP:

PSPP> finish.

5.2 Data Screening and Transformation

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.


5.2.1 Identifying incorrect data

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='//share/pspp/examples/physiology.sav'.
PSPP> descriptives sex, weight, height.

For this example, PSPP produces the following output:

Descriptive Statistics
N Mean Std Dev Minimum Maximum
Sex of subject 40 .45 .50 Male Female
Weight in kilograms 40 72.12 26.70 -55.6 92.1
Height in millimeters 40 1677.12 262.87 179 1903
Valid N (listwise) 40
Missing N (listwise) 0

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):

Extreme Values
Case Number Value
Height in millimeters Highest 1 14 1903
2 15 1884
3 12 1802
Lowest 1 30 179
2 31 1598
3 28 1601
Weight in kilograms Highest 1 13 92.1
2 5 92.1
3 17 91.7
Lowest 1 38 -55.6
2 39 54.5
3 33 55.4

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.


5.2.2 Dealing with suspicious data

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.


5.2.3 Inverting negatively coded variables

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='//share/pspp/examples/hotel.sav'.
PSPP> display dictionary.

It yields the following output:

Variables
Name Position Label Measurement Level Role Width Alignment Print Format Write Format
v1 1 I am satisfied with the level of service Ordinal Input 8 Right F8.0 F8.0
v2 2 The value for money was good Ordinal Input 8 Right F8.0 F8.0
v3 3 The staff were slow in responding Ordinal Input 8 Right F8.0 F8.0
v4 4 My concerns were dealt with in an efficient manner Ordinal Input 8 Right F8.0 F8.0
v5 5 There was too much noise in the rooms Ordinal Input 8 Right F8.0 F8.0
Value Labels
Variable Value Label
I am satisfied with the level of service 1 Strongly Disagree
2 Disagree
3 No Opinion
4 Agree
5 Strongly Agree
The value for money was good 1 Strongly Disagree
2 Disagree
3 No Opinion
4 Agree
5 Strongly Agree
The staff were slow in responding 1 Strongly Disagree
2 Disagree
3 No Opinion
4 Agree
5 Strongly Agree
My concerns were dealt with in an efficient manner 1 Strongly Disagree
2 Disagree
3 No Opinion
4 Agree
5 Strongly Agree
There was too much noise in the rooms 1 Strongly Disagree
2 Disagree
3 No Opinion
4 Agree
5 Strongly Agree

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.


5.2.4 Testing data consistency

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='//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

Case Processing Summary
Cases N Percent
Valid 17 100.0%
Excluded 0 .0%
Total 17 100.0%
Reliability Statistics
Cronbach's Alpha N of Items
.81 3

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.


5.2.5 Testing for normality

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='//share/pspp/examples/repairs.sav'.
PSPP> examine mtbf
                /statistics=descriptives.

This produces the following output:

Descriptives
Statistic Std. Error
Mean time between failures (months) Mean 8.78 1.10
95% Confidence Interval for Mean Lower Bound 6.53
Upper Bound 11.04
5% Trimmed Mean 8.20
Median 8.29
Variance 36.34
Std. Deviation 6.03
Minimum 1.63
Maximum 26.47
Range 24.84
Interquartile Range 6.03
Skewness 1.65 .43
Kurtosis 3.41 .83

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:

Descriptives
Statistic Std. Error
mtbf_ln Mean 1.95 .13
95% Confidence Interval for Mean Lower Bound 1.69
Upper Bound 2.22
5% Trimmed Mean 1.96
Median 2.11
Variance .49
Std. Deviation .70
Minimum .49
Maximum 3.28
Range 2.79
Interquartile Range .88
Skewness -.37 .43
Kurtosis .01 .83

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.


5.3 Hypothesis Testing

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”.


5.3.1 Testing for differences of means

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:

  • The mean heights of males and females in the population are equal.
  • The mean body temperature of males and females in the population are equal.

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='//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:

Group Statistics
Group N Mean Std. Deviation S.E. Mean
Height in millimeters Male 22 1796.49 49.71 10.60
Female 17 1610.77 25.43 6.17
Internal body temperature in degrees Celcius Male 22 36.68 1.95 .42
Female 18 37.43 1.61 .38
Independent Samples Test
Levene's Test for Equality of Variances T-Test for Equality of Means
F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference 95% Confidence Interval of the Difference
Lower Upper
Height in millimeters Equal variances assumed .97 .331 14.02 37.00 .000 185.72 13.24 158.88 212.55
Equal variances not assumed 15.15 32.71 .000 185.72 12.26 160.76 210.67
Internal body temperature in degrees Celcius Equal variances assumed .31 .581 -1.31 38.00 .198 -.75 .57 -1.91 .41
Equal variances not assumed -1.33 37.99 .190 -.75 .56 -1.89 .39

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.


5.3.2 Linear Regression

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='//share/pspp/examples/repairs.sav'.
PSPP> regression /variables = mtbf duty_cycle /dependent = mttr.

This attempt yields the following output (in part):

Coefficients (Mean time to repair (hours) )
Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
(Constant) 10.59 3.11 .00 3.40 .002
Mean time between failures (months) 3.02 .20 .95 14.88 .000
Ratio of working to non-working time -1.12 3.69 -.02 -.30 .763

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):

Coefficients (Mean time to repair (hours) )
Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
(Constant) 9.90 2.10 .00 4.71 .000
Mean time between failures (months) 3.01 .20 .94 15.21 .000

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.


6 The PSPP language

This chapter discusses elements common to many PSPP commands. Later chapters describe individual commands in detail.


6.1 Tokens

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

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:

ALL AND BY EQ GE GT LE LT NE NOT OR TO WITH
Keywords

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

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

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.

Punctuators and Operators

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.


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6.2 Forming commands of tokens

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.


6.3 Syntax Variants

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.


6.4 Types of Commands

Commands in PSPP are divided roughly into six categories:

Utility commands

Set or display various global options that affect PSPP operations. May appear anywhere in a syntax file. See Utility commands.

File definition 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.

Input program commands

Though rarely used, these provide tools for reading data files in arbitrary textual or binary formats. See INPUT PROGRAM.

Transformations

Perform operations on data and write data to output files. Transformations are not carried out until a procedure is executed.

Restricted transformations

Transformations that cannot appear in certain contexts. See Order of Commands, for details.

Procedures

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.


6.5 Order of Commands

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:

Utility commands
  • Valid in any state.
  • Do not cause state transitions. Exception: when N OF CASES is executed in the procedure state, it causes a transition to the transformation state.
DATA LIST
  • Valid in any state.
  • When executed in the initial or procedure state, causes a transition to the transformation state.
  • Clears the active dataset if executed in the procedure or transformation state.
INPUT PROGRAM
  • Invalid in input-program and file-type states.
  • Causes a transition to the intput-program state.
  • Clears the active dataset.
FILE TYPE
  • Invalid in intput-program and file-type states.
  • Causes a transition to the file-type state.
  • Clears the active dataset.
Other file definition commands
  • Invalid in input-program and file-type states.
  • Cause a transition to the transformation state.
  • Clear the active dataset, except for ADD FILES, MATCH FILES, and UPDATE.
Transformations
  • Invalid in initial and file-type states.
  • Cause a transition to the transformation state.
Restricted transformations
  • Invalid in initial, input-program, and file-type states.
  • Cause a transition to the transformation state.
Procedures
  • Invalid in initial, input-program, and file-type states.
  • Cause a transition to the procedure state.

6.6 Handling missing observations

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.


6.7 Datasets

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.


6.7.1 Attributes of Variables

Each variable has a number of attributes, including:

Name

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.

Type

Numeric or string.

Width

(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.

Position

Variables in the dictionary are arranged in a specific order. DISPLAY can be used to show this order: see DISPLAY.

Initialization

Either reinitialized to 0 or spaces for each case, or left at its existing value. See LEAVE.

Missing values

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.

Variable label

A string that describes the variable. See VARIABLE LABELS.

Value label

Optionally, these associate each possible value of the variable with a string. See VALUE LABELS.

Print format

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.

Write format

Similar to print format, but used by the WRITE command (see WRITE).

Custom attributes

User-defined associations between names and values. See VARIABLE ATTRIBUTE.

Role

The intended role of a variable for use in dialog boxes in graphical user interfaces. See VARIABLE ROLE.


6.7.2 Variables Automatically Defined by PSPP

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.


6.7.3 Lists of variable names

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.


6.7.4 Input and Output Formats

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.


6.7.4.1 Basic Numeric Formats

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:

  • Numbers in COMMA format are additionally grouped every three digits by inserting a grouping character. The grouping character is ordinarily a comma, but it can be changed to a period (see SET DECIMAL).
  • DOT format is like COMMA format, but it interchanges the role of the decimal point and grouping characters. That is, the current grouping character is used as a decimal point and vice versa.
  • DOLLAR format is like COMMA format, but it prefixes the number with ‘$’.
  • PCT format is like F format, but adds ‘%’ after the number.
  • The E format always produces output in scientific notation.

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:

  • COMMA, DOLLAR, and DOT formats ignore grouping characters within the integer part of the input field. The identity of the grouping character depends on the format.
  • DOLLAR format allows a dollar sign to precede the number. In a negative number, the dollar sign may precede or follow the minus sign.
  • PCT format allows a percent sign to follow the number.

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:

  • As input formats, the basic numeric formats allow no more decimal places than the field width. As output formats, the field width must be greater than the number of decimal places; that is, large enough to allow for a decimal point and the number of requested decimal places. DOLLAR and PCT formats must allow an additional column for ‘$’ or ‘%’.
  • The default output format for a given input format increases the field width enough to make room for optional input characters. If an input format calls for decimal places, the width is increased by 1 to make room for an implied decimal point. COMMA, DOT, and DOLLAR formats also increase the output width to make room for grouping characters. DOLLAR and PCT further increase the output field width by 1 to make room for ‘$’ or ‘%’. The increased output width is capped at 40, the maximum field width.
  • The E format is exceptional. For output, E format has a minimum width of 7 plus the number of decimal places. The default output format for an E input format is an E format with at least 3 decimal places and thus a minimum width of 10.

More details of basic numeric output formatting are given below:

  • Output rounds to nearest, with ties rounded away from zero. Thus, 2.5 is output as 3 in F1.0 format, and -1.125 as -1.13 in F5.1 format.
  • The system-missing value is output as a period in a field of spaces, placed in the decimal point’s position, or in the rightmost column if no decimal places are requested. A period is used even if the decimal point character is a comma.
  • A number that does not fill its field is right-justified within the field.
  • A number is too large for its field causes decimal places to be dropped to make room. If dropping decimals does not make enough room, scientific notation is used if the field is wide enough. If a number does not fit in the field, even in scientific notation, the overflow is indicated by filling the field with asterisks (‘*’).
  • COMMA, DOT, and DOLLAR formats insert grouping characters only if space is available for all of them. Grouping characters are never inserted when all decimal places must be dropped. Thus, 1234.56 in COMMA5.2 format is output as ‘ 1235’ without a comma, even though there is room for one, because all decimal places were dropped.
  • DOLLAR or PCT format drop the ‘$’ or ‘%’ only if the number would not fit at all without it. Scientific notation with ‘$’ or ‘%’ is preferred to ordinary decimal notation without it.
  • Except in scientific notation, a decimal point is included only when it is followed by a digit. If the integer part of the number being output is 0, and a decimal point is included, then PSPP ordinarily drops the zero before the decimal point. However, in 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.

  • A negative number includes a minus sign only in the presence of a nonzero digit: -0.01 is output as ‘-.01’ in F4.2 format but as ‘  .0’ in F4.1 format. Thus, a “negative zero” never includes a minus sign.
  • In negative numbers output in DOLLAR format, the dollar sign follows the negative sign. Thus, -9.99 in DOLLAR6.2 format is output as -$9.99.
  • In scientific notation, the exponent is output as ‘E’ followed by ‘+’ or ‘-’ and exactly three digits. Numbers with magnitude less than 10**-999 or larger than 10**999 are not supported by most computers, but if they are supported then their output is considered to overflow the field and they are output as asterisks.
  • On most computers, no more than 15 decimal digits are significant in output, even if more are printed. In any case, output precision cannot be any higher than input precision; few data sets are accurate to 15 digits of precision. Unavoidable loss of precision in intermediate calculations may also reduce precision of output.
  • Special values such as infinities and “not a number” values are usually converted to the system-missing value before printing. In a few circumstances, these values are output directly. In fields of width 3 or greater, special values are output as however many characters fit from +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.

6.7.4.2 Custom Currency Formats

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 EUR3,145.59EUR3,145.59-
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.


6.7.4.3 Legacy Numeric Formats

The N and Z numeric formats provide compatibility with legacy file formats. They have much in common:

  • Output is rounded to the nearest representable value, with ties rounded away from zero.
  • Numbers too large to display are output as a field filled with asterisks (‘*’).
  • The decimal point is always implicitly the specified number of digits from the right edge of the field, except that Z format input allows an explicit decimal point.
  • Scientific notation may not be used.
  • The system-missing value is output as a period in a field of spaces. The period is placed just to the right of the implied decimal point in Z format, or at the right end in N format or in Z format if no decimal places are requested. A period is used even if the decimal point character is a comma.
  • Field width may range from 1 to 40. Decimal places may range from 0 up to the field width, to a maximum of 16.
  • When a legacy numeric format used for input is converted to an output format, it is changed into the equivalent F format. The field width is increased by 1 if any decimal places are specified, to make room for a decimal point. For Z format, the field width is increased by 1 more column, to make room for a negative sign. The output field width is capped at 40 columns.

N Format

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.

Z Format

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.


6.7.4.4 Binary and Hexadecimal Numeric Formats

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.

P and PK Formats

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).

IB and PIB Formats

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.

RB Format

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.

PIBHEX and RBHEX Formats

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.


6.7.4.5 Time and Date Formats

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 FormatTemplateExample
MTIMEMM:SS.ss91:17.01
TIMEhh:MM:SS.ss01:31:17.01
DTIMEDD HH:MM:SS.ss00 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 FormatTemplateExample
DATEdd-mmm-yyyy01-OCT-1978
ADATEmm/dd/yyyy10/01/1978
EDATEdd.mm.yyyy01.10.1978
JDATEyyyyjjj1978274
SDATEyyyy/mm/dd1978/10/01
QYRq Q yyyy3 Q 1978
MOYRmmm yyyyOCT 1978
WKYRww WK yyyy40 WK 1978
DATETIMEdd-mmm-yyyy HH:MM:SS.ss01-OCT-1978 04:31:17.01
YMDHMSyyyy-mm-dd HH:MM:SS.ss1978-01-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:

FormatMin. Input WidthMin. Output WidthOption
DATE894-digit year
ADATE884-digit year
EDATE884-digit year
JDATE554-digit year
SDATE884-digit year
QYR464-digit year
MOYR664-digit year
WKYR684-digit year
DATETIME1717seconds
YMDHMS1216seconds
MTIME45
TIME55seconds
DTIME88seconds

In the table, “Option” describes what increased output width enables:

4-digit year

A field 2 columns wider than the minimum includes a 4-digit year. (DATETIME and YMDHMS formats always include a 4-digit year.)

seconds

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.


6.7.4.6 Date Component Formats

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.


6.7.4.7 String Formats

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.


6.7.5 Scratch Variables

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.


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6.8 Files Used by PSPP

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:

command file
syntax file

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 file

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.

listing file

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 file

System files are binary files that store a dictionary and a set of cases. GET and SAVE read and write system files.

portable file

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.


6.9 File Handles

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.


6.10 Backus-Naur Form

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:

  • Words in all-uppercase are PSPP keyword tokens. In BNF, these are often called terminals. There are some special terminals, which are written in lowercase for clarity:
    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).

  • Other words in all lowercase refer to BNF definitions, called productions. These productions are also known as nonterminals. Some nonterminals are very common, so they are defined here in English for clarity:
    var-list

    A list of one or more variable names or the keyword ALL.

    expression

    An expression. See Mathematical Expressions, for details.

  • ::=’ means “is defined as”. The left side of ‘::=’ gives the name of the nonterminal being defined. The right side of ‘::=’ gives the definition of that nonterminal. If the right side is empty, then one possible expansion of that nonterminal is nothing. A BNF definition is called a production.
  • So, the key difference between a terminal and a nonterminal is that a terminal cannot be broken into smaller parts—in fact, every terminal is a single token (see Tokens). On the other hand, nonterminals are composed of a (possibly empty) sequence of terminals and nonterminals. Thus, terminals indicate the deepest level of syntax description. (In parsing theory, terminals are the leaves of the parse tree; nonterminals form the branches.)
  • The first nonterminal defined in a set of productions is called the start symbol. The start symbol defines the entire syntax for that command.

7 Mathematical Expressions

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.


7.1 Boolean Values

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.


7.2 Missing Values in Expressions

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.


7.3 Grouping Operators

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.


7.4 Arithmetic 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.


7.5 Logical Operators

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.


7.6 Relational Operators

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.


7.7 Functions

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.


7.7.1 Mathematical Functions

Advanced mathematical functions take numeric arguments and produce numeric results.

Function: EXP (exponent)

Returns e (approximately 2.71828) raised to power exponent.

Function: LG10 (number)

Takes the base-10 logarithm of number. If number is not positive, the result is system-missing.

Function: LN (number)

Takes the base-e logarithm of number. If number is not positive, the result is system-missing.

Function: LNGAMMA (number)

Yields the base-e logarithm of the complete gamma of number. If number is a negative integer, the result is system-missing.

Function: SQRT (number)

Takes the square root of number. If number is negative, the result is system-missing.


7.7.2 Miscellaneous Mathematical Functions

Miscellaneous mathematical functions take numeric arguments and produce numeric results.

Function: ABS (number)

Results in the absolute value of number.

Function: MOD (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.

Function: MOD10 (number)

Returns the remainder when number is divided by 10. If number is negative, MOD10(number) is negative or zero.

Function: RND (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.

Function: TRUNC (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.


7.7.3 Trigonometric Functions

Trigonometric functions take numeric arguments and produce numeric results.

Function: ARCOS (number)
Function: ACOS (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.

Function: ARSIN (number)
Function: ASIN (number)

Takes the arcsine, in radians, of number. Results in system-missing if number is not between -1 and 1 inclusive.

Function: ARTAN (number)
Function: ATAN (number)

Takes the arctangent, in radians, of number.

Function: COS (angle)

Takes the cosine of angle which should be in radians.

Function: SIN (angle)

Takes the sine of angle which should be in radians.

Function: TAN (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.


7.7.4 Missing-Value Functions

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.

Function: MISSING (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.

Function: NMISS (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.

Function: NVALID (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.

Function: SYSMIS (expr)

Returns 1 if expr has the system-missing value, 0 otherwise.

Function: VALUE (variable)
Function: VALUE (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.


7.7.5 Set-Membership Functions

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.

Function: ANY (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

Function: RANGE (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.


7.7.6 Statistical Functions

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.

Function: CFVAR (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.)

Function: MAX (value, value[, …])

Results in the value of the greatest value. The values may be numeric or string.

Function: MEAN (number, number[, …])

Results in the mean of the values of number.

Function: MEDIAN (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.

Function: MIN (number, number[, …])

Results in the value of the least value. The values may be numeric or string.

Function: SD (number, number[, …])

Results in the standard deviation of the values of number.

Function: SUM (number, number[, …])

Results in the sum of the values of number.

Function: VARIANCE (number, number[, …])

Results in the variance of the values of number.


7.7.7 String Functions

String functions take various arguments and return various results.

Function: CONCAT (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.

Function: INDEX (haystack, needle)
Function: RINDEX (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.

Function: INDEX (haystack, needles, needle_len)
Function: RINDEX (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.

Function: LENGTH (string)

Returns the number of bytes in string.

Function: LOWER (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.

Function: LPAD (string, length[, padding])
Function: RPAD (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.

Function: LTRIM (string[, padding])
Function: RTRIM (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.

Function: NUMBER (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.

Function: REPLACE (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.

Function: STRING (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".

Function: STRUNC (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.

Function: SUBSTR (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.

Function: SUBSTR (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".

Function: UPCASE (string)

Returns string, changing lowercase letters to uppercase letters.


7.7.8 Time & Date Functions

For compatibility, PSPP considers dates before 15 Oct 1582 invalid. Most time and date functions will not accept earlier dates.


7.7.8.1 How times & dates are defined and represented

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

7.7.8.2 Functions that Produce Times

These functions take numeric arguments and return numeric values that represent times.

Function: TIME.DAYS (ndays)

Returns a time corresponding to ndays days.

Function: TIME.HMS (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.


7.7.8.3 Functions that Examine Times

These functions take numeric arguments in PSPP time format and give numeric results.

Function: CTIME.DAYS (time)

Results in the number of days and fractional days in time.

Function: CTIME.HOURS (time)

Results in the number of hours and fractional hours in time.

Function: CTIME.MINUTES (time)

Results in the number of minutes and fractional minutes in time.

Function: CTIME.SECONDS (time)

Results in the number of seconds and fractional seconds in time. (CTIME.SECONDS does nothing; CTIME.SECONDS(x) is equivalent to x.)


7.7.8.4 Functions that Produce Dates

These functions take numeric arguments and give numeric results that represent dates. Arguments taken by these functions are:

day

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.

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.

quarter

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.

week

Refers to a week of the year between 1 and 53.

yday

Refers to a day of the year between 1 and 366.

year

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.

Function: DATE.DMY (day, month, year)
Function: DATE.MDY (month, day, year)

Results in a date value corresponding to the midnight before day day of month month of year year.

Function: DATE.MOYR (month, year)

Results in a date value corresponding to the midnight before the first day of month month of year year.

Function: DATE.QYR (quarter, year)

Results in a date value corresponding to the midnight before the first day of quarter quarter of year year.

Function: DATE.WKYR (week, year)

Results in a date value corresponding to the midnight before the first day of week week of year year.

Function: DATE.YRDAY (year, yday)

Results in a date value corresponding to the day yday of year year.


7.7.8.5 Functions that Examine Dates

These functions take numeric arguments in PSPP date or time format and give numeric results. These names are used for arguments:

date

A numeric value in PSPP date format.

time

A numeric value in PSPP time format.

time-or-date

A numeric value in PSPP time or date format.

Function: XDATE.DATE (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.

Function: XDATE.HOUR (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.

Function: XDATE.JDAY (date)

Results in the day of the year (as an integer between 1 and 366) corresponding to date.

Function: XDATE.MDAY (date)

Results in the day of the month (as an integer between 1 and 31) corresponding to date.

Function: XDATE.MINUTE (time-or-date)

Results in the number of minutes (as an integer between 0 and 59) after the last hour in time-or-date.

Function: XDATE.MONTH (date)

Results in the month of the year (as an integer between 1 and 12) corresponding to date.

Function: XDATE.QUARTER (date)

Results in the quarter of the year (as an integer between 1 and 4) corresponding to date.

Function: XDATE.SECOND (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.

Function: XDATE.TDAY (date)

Results in the number of whole days from 14 Oct 1582 to date.

Function: XDATE.TIME (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.

Function: XDATE.WEEK (date)

Results in the week of the year (as an integer between 1 and 53) corresponding to date.

Function: XDATE.WKDAY (date)

Results in the day of week (as an integer between 1 and 7) corresponding to date, where 1 represents Sunday.

Function: XDATE.YEAR (date)

Returns the year (as an integer 1582 or greater) corresponding to date.


7.7.8.6 Time and Date Arithmetic

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:

Function: DATEDIFF (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.

Function: DATESUM (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.


7.7.9 Miscellaneous Functions

Function: LAG (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.

Function: YRMODA (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.

Function: 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.


7.7.10 Statistical Distribution Functions

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:

PDF.dist (x[, param…])

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.

CDF.dist (x[, param…])

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.

SIG.dist (x[, param…)

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.

IDF.dist (p[, param…])

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.

RV.dist ([param…])

Random variate function for dist. The range depends on the distribution.

NPDF.dist (x[, param…])

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.

NCDF.dist (x[, param…])

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.


7.7.10.1 Continuous Distributions

The following continuous distributions are available:

Function: PDF.BETA (x)
Function: CDF.BETA (x, a, b)
Function: IDF.BETA (p, a, b)
Function: RV.BETA (a, b)
Function: NPDF.BETA (x, a, b, lambda)
Function: NCDF.BETA (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.

Function: PDF.BVNOR (x0, x1, rho)
Function: CDF.BVNOR (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.

Function: PDF.CAUCHY (x, a, b)
Function: CDF.CAUCHY (x, a, b)
Function: IDF.CAUCHY (p, a, b)
Function: RV.CAUCHY (a, b)

Cauchy distribution with location parameter a and scale parameter b. Constraints: b > 0, 0 < p < 1.

Function: CDF.CHISQ (x, df)
Function: SIG.CHISQ (x, df)
Function: IDF.CHISQ (p, df)
Function: RV.CHISQ (df)
Function: NCDF.CHISQ (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.

Function: PDF.EXP (x, a)
Function: CDF.EXP (x, a)
Function: IDF.EXP (p, a)
Function: RV.EXP (a)

Exponential distribution with scale parameter a. The inverse of a represents the rate of decay. Constraints: a > 0, x >= 0, 0 <= p < 1.

Function: PDF.XPOWER (x, a, b)
Function: RV.XPOWER (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.

Function: PDF.F (x, df1, df2)
Function: CDF.F (x, df1, df2)
Function: SIG.F (x, df1, df2)
Function: IDF.F (p, df1, df2)
Function: RV.F (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.

Function: PDF.GAMMA (x, a, b)
Function: CDF.GAMMA (x, a, b)
Function: IDF.GAMMA (p, a, b)
Function: RV.GAMMA (a, b)

Gamma distribution with shape parameter a and scale parameter b. Constraints: a > 0, b > 0, x >= 0, 0 <= p < 1.

Function: PDF.LANDAU (x)
Function: RV.LANDAU ()

Landau distribution.

Function: PDF.LAPLACE (x, a, b)
Function: CDF.LAPLACE (x, a, b)
Function: IDF.LAPLACE (p, a, b)
Function: RV.LAPLACE (a, b)

Laplace distribution with location parameter a and scale parameter b. Constraints: b > 0, 0 < p < 1.

Function: RV.LEVY (c, alpha)

Levy symmetric alpha-stable distribution with scale c and exponent alpha. Constraints: 0 < alpha <= 2.

Function: RV.LVSKEW (c, alpha, beta)

Levy skew alpha-stable distribution with scale c, exponent alpha, and skewness parameter beta. Constraints: 0 < alpha <= 2, -1 <= beta <= 1.

Function: PDF.LOGISTIC (x, a, b)
Function: CDF.LOGISTIC (x, a, b)
Function: IDF.LOGISTIC (p, a, b)
Function: RV.LOGISTIC (a, b)

Logistic distribution with location parameter a and scale parameter b. Constraints: b > 0, 0 < p < 1.

Function: PDF.LNORMAL (x, a, b)
Function: CDF.LNORMAL (x, a, b)
Function: IDF.LNORMAL (p, a, b)
Function: RV.LNORMAL (a, b)

Lognormal distribution with parameters a and b. Constraints: a > 0, b > 0, x >= 0, 0 <= p < 1.

Function: PDF.NORMAL (x, mu, sigma)
Function: CDF.NORMAL (x, mu, sigma)
Function: IDF.NORMAL (p, mu, sigma)
Function: RV.NORMAL (mu, sigma)

Normal distribution with mean mu and standard deviation sigma. Constraints: b > 0, 0 < p < 1. Three additional functions are available as shorthand:

Function: CDFNORM (x)

Equivalent to CDF.NORMAL(x, 0, 1).

Function: PROBIT (p)

Equivalent to IDF.NORMAL(p, 0, 1).

Function: NORMAL (sigma)

Equivalent to RV.NORMAL(0, sigma).

Function: PDF.NTAIL (x, a, sigma)
Function: RV.NTAIL (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.

Function: PDF.PARETO (x, a, b)
Function: CDF.PARETO (x, a, b)
Function: IDF.PARETO (p, a, b)
Function: RV.PARETO (a, b)

Pareto distribution with threshold parameter a and shape parameter b. Constraints: a > 0, b > 0, x >= a, 0 <= p < 1.

Function: PDF.RAYLEIGH (x, sigma)
Function: CDF.RAYLEIGH (x, sigma)
Function: IDF.RAYLEIGH (p, sigma)
Function: RV.RAYLEIGH (sigma)

Rayleigh distribution with scale parameter sigma. This distribution is a PSPP extension. Constraints: sigma > 0, x > 0.

Function: PDF.RTAIL (x, a, sigma)
Function: RV.RTAIL (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.

Function: PDF.T (x, df)
Function: CDF.T (x, df)
Function: IDF.T (p, df)
Function: RV.T (df)

T-distribution with df degrees of freedom. The noncentral distribution takes an additional parameter lambda. Constraints: df > 0, 0 < p < 1.

Function: PDF.T1G (x, a, b)
Function: CDF.T1G (x, a, b)
Function: IDF.T1G (p, a, b)

Type-1 Gumbel distribution with parameters a and b. This distribution is a PSPP extension. Constraints: 0 < p < 1.

Function: PDF.T2G (x, a, b)
Function: CDF.T2G (x, a, b)
Function: IDF.T2G (p, a, b)

Type-2 Gumbel distribution with parameters a and b. This distribution is a PSPP extension. Constraints: x > 0, 0 < p < 1.

Function: PDF.UNIFORM (x, a, b)
Function: CDF.UNIFORM (x, a, b)
Function: IDF.UNIFORM (p, a, b)
Function: RV.UNIFORM (a, b)

Uniform distribution with parameters a and b. Constraints: a <= x <= b, 0 <= p <= 1. An additional function is available as shorthand:

Function: UNIFORM (b)

Equivalent to RV.UNIFORM(0, b).

Function: PDF.WEIBULL (x, a, b)
Function: CDF.WEIBULL (x, a, b)
Function: IDF.WEIBULL (p, a, b)
Function: RV.WEIBULL (a, b)

Weibull distribution with parameters a and b. Constraints: a > 0, b > 0, x >= 0, 0 <= p < 1.


7.7.10.2 Discrete Distributions

The following discrete distributions are available:

Function: PDF.BERNOULLI (x)
Function: CDF.BERNOULLI (x, p)
Function: RV.BERNOULLI (p)

Bernoulli distribution with probability of success p. Constraints: x = 0 or 1, 0 <= p <= 1.

Function: PDF.BINOM (x, n, p)
Function: CDF.BINOM (x, n, p)
Function: RV.BINOM (n, p)

Binomial distribution with n trials and probability of success p. Constraints: integer n > 0, 0 <= p <= 1, integer x <= n.

Function: PDF.GEOM (x, n, p)
Function: CDF.GEOM (x, n, p)
Function: RV.GEOM (n, p)

Geometric distribution with probability of success p. Constraints: 0 <= p <= 1, integer x > 0.

Function: PDF.HYPER (x, a, b, c)
Function: CDF.HYPER (x, a, b, c)
Function: RV.HYPER (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.

Function: PDF.LOG (x, p)
Function: RV.LOG (p)

Logarithmic distribution with probability parameter p. Constraints: 0 <= p < 1, x >= 1.

Function: PDF.NEGBIN (x, n, p)
Function: CDF.NEGBIN (x, n, p)
Function: RV.NEGBIN (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.

Function: PDF.POISSON (x, mu)
Function: CDF.POISSON (x, mu)
Function: RV.POISSON (mu)

Poisson distribution with mean mu. Constraints: mu > 0, integer x >= 0.


7.8 Operator Precedence

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.

  1. ()
  2. **
  3. -
  4. * /
  5. + -
  6. = >= > <= < <>
  7. NOT
  8. AND
  9. OR

8 Data Input and Output

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.


8.1 BEGIN DATA

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.

8.2 CLOSE FILE HANDLE

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.


8.3 DATAFILE ATTRIBUTE

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.


8.4 DATASET commands

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:

ASIS

Do not change how the window is displayed. This is the default for DATASET NAME and DATASET ACTIVATE.

FRONT

Raise the dataset’s window to the top. Make it the default dataset for running syntax.

MINIMIZED

Display the window “minimized” to an icon. Prefer other datasets for running syntax. This is the default for DATASET COPY and DATASET DECLARE.

HIDDEN

Hide the dataset’s window. Prefer other datasets for running syntax.


8.5 DATA LIST

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.


8.5.1 DATA LIST FIXED

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 DATAEND 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.

Tx

Set the current column on this line to column x, with column numbers considered to begin with 1 at the left margin.

NEWRECx

Skip forward x lines in the current record, resetting the active column to the left margin.

Repeat count

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.

(spec1, …, specN)

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.


Examples

  1. 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.
    
    Defines the following variables:
    • 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.

  2. DAT LIS FIL="survey.dat"
            /ID 1-5 NAME 7-36 (A) SURNAME 38-67 (A) MINITIAL 69 (A)
            /Q01 TO Q50 7-56
            /.
    
    Defines the following variables:
    • 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.
    • Fifty variables 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.

    This example shows keywords abbreviated to their first 3 letters.


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8.5.2 DATA LIST FREE

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.


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8.5.3 DATA LIST LIST

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.


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8.6 END CASE

END CASE.

END CASE is used only within INPUT PROGRAM to output the current case. See INPUT PROGRAM, for details.


8.7 END FILE

END FILE.

END FILE is used only within INPUT PROGRAM to terminate the current input program. See INPUT PROGRAM.


8.8 FILE HANDLE

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, the default, the data file is read as a text file. Each text line is read as one record.

    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.

  • In IMAGE mode, the data file is treated as a series of fixed-length binary records. LRECL should be used to specify the record length in bytes, with a default of 1024. On input, it is an error if an IMAGE file’s length is not a integer multiple of the record length. On output, each record is padded with spaces or truncated, if necessary, to make it exactly the correct length.
  • In BINARY mode, the data file is treated as a series of variable-length binary records. LRECL may be specified, but its value is ignored. The data for each record is both preceded and followed by a 32-bit signed integer in little-endian byte order that specifies the length of the record. (This redundancy permits records in these files to be efficiently read in reverse order, although PSPP always reads them in forward order.) The length does not include either integer.
  • Mode 360 reads and writes files in formats first used for tapes in the 1960s on IBM mainframe operating systems and still supported today by the modern successors of those operating systems. For more information, see OS/400 Tape and Diskette Device Programming, available on IBM’s website.

    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:

    F
    FIXED

    This record format is equivalent to IMAGE mode, except for EBCDIC translation.

    IBM documentation calls this *F (fixed-length, deblocked) format.

    V
    VARIABLE

    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.

    VS
    SPANNED

    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.


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8.9 INPUT PROGRAM

INPUT PROGRAM.
… input commands …
END INPUT PROGRAM.

INPUT PROGRAMEND 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.

Example 1: Read two files in parallel to the end of the shorter

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.

Example 2: Read two files in parallel, supplementing the shorter

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.

Example 3: Concatenate two files (version 1)

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.

Example 4: Concatenate two files (version 2)

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.

Example 5: Generate random variates

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.

8.10 LIST

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.


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8.11 NEW FILE

NEW FILE.

NEW FILE command clears the dictionary and data from the current active dataset.


8.12 PRINT

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.


8.15 REREAD

REREAD [FILE=handle] [COLUMN=column] [ENCODING=’encoding’].

The REREAD transformation allows the previous input line in a data file already processed by DATA LIST or another input command to be re-read for further processing.

The FILE subcommand, which is optional, is used to specify the file to have its line re-read. The file must be specified as the name of a file handle (see File Handles). If FILE is not specified then the last file specified on DATA LIST is assumed (last file specified lexically, not in terms of flow-of-control).

By default, the line re-read is re-read in its entirety. With the COLUMN subcommand, a prefix of the line can be exempted from re-reading. Specify an expression (see Mathematical Expressions) evaluating to the first column that should be included in the re-read line. Columns are numbered from 1 at the left margin.

The ENCODING subcommand may only be used if the FILE subcommand is also used. It specifies the character encoding of the file. See INSERT, for information on supported encodings.

Issuing REREAD multiple times will not back up in the data file. Instead, it will re-read the same line multiple times.


Next: , Previous: , Up: Data Input and Output   [Contents][Index]

8.16 REPEATING DATA

REPEATING DATA
        /STARTS=start-end
        /OCCURS=n_occurs
        /FILE=’file_name’
        /LENGTH=length
        /CONTINUED[=cont_start-cont_end]
        /ID=id_start-id_end=id_var
        /{TABLE,NOTABLE}
        /DATA=var_spec…

where each var_spec takes one of the forms
        var_list start-end [type_spec]
        var_list (fortran_spec)

REPEATING DATA parses groups of data repeating in a uniform format, possibly with several groups on a single line. Each group of data corresponds with one case. REPEATING DATA may only be used within an INPUT PROGRAM structure (see INPUT PROGRAM). When used with DATA LIST, it can be used to parse groups of cases that share a subset of variables but differ in their other data.

The STARTS subcommand is required. Specify a range of columns, using literal numbers or numeric variable names. This range specifies the columns on the first line that are used to contain groups of data. The ending column is optional. If it is not specified, then the record width of the input file is used. For the inline file (see BEGIN DATA) this is 80 columns; for a file with fixed record widths it is the record width; for other files it is 1024 characters by default.

The OCCURS subcommand is required. It must be a number or the name of a numeric variable. Its value is the number of groups present in the current record.

The DATA subcommand is required. It must be the last subcommand specified. It is used to specify the data present within each repeating group. Column numbers are specified relative to the beginning of a group at column 1. Data is specified in the same way as with DATA LIST FIXED (see DATA LIST FIXED).

All other subcommands are optional.

FILE specifies the file to read, either a file name as a string or a file handle (see File Handles). If FILE is not present then the default is the last file handle used on DATA LIST (lexically, not in terms of flow of control).

By default REPEATING DATA will output a table describing how it will parse the input data. Specifying NOTABLE will disable this behavior; specifying TABLE will explicitly enable it.

The LENGTH subcommand specifies the length in characters of each group. If it is not present then length is inferred from the DATA subcommand. LENGTH can be a number or a variable name.

Normally all the data groups are expected to be present on a single line. Use the CONTINUED command to indicate that data can be continued onto additional lines. If data on continuation lines starts at the left margin and continues through the entire field width, no column specifications are necessary on CONTINUED. Otherwise, specify the possible range of columns in the same way as on STARTS.

When data groups are continued from line to line, it is easy for cases to get out of sync through careless hand editing. The ID subcommand allows a case identifier to be present on each line of repeating data groups. REPEATING DATA will check for the same identifier on each line and report mismatches. Specify the range of columns that the identifier will occupy, followed by an equals sign (‘=’) and the identifier variable name. The variable must already have been declared with NUMERIC or another command.

REPEATING DATA should be the last command given within an INPUT PROGRAM. It should not be enclosed within a LOOP structure (see LOOP). Use DATA LIST before, not after, REPEATING DATA.


8.17 WRITE

WRITE
        OUTFILE=’file_name’
        RECORDS=n_lines
        {NOTABLE,TABLE}
        /[line_no] argarg 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.

9 System and Portable File I/O

The commands in this chapter read, write, and examine system files and portable files.


9.1 APPLY DICTIONARY

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:

  • If a system file variable has a variable label, then it replaces the variable label of the active dataset variable. If the system file variable does not have a variable label, then the active dataset variable’s variable label, if any, is retained.
  • If the system file variable has custom attributes (see VARIABLE ATTRIBUTE), then those attributes replace the active dataset variable’s custom attributes. If the system file variable does not have custom attributes, then the active dataset variable’s custom attributes, if any, is retained.
  • If the active dataset variable is numeric or short string, then value labels and missing values, if any, are copied to the active dataset variable. If the system file variable does not have value labels or missing values, then those in the active dataset variable, if any, are not disturbed.

In addition to properties of variables, some properties of the active file dictionary as a whole are updated:

  • If the system file has custom attributes (see DATAFILE ATTRIBUTE), then those attributes replace the active dataset variable’s custom attributes.
  • If the active dataset has a weighting variable (see WEIGHT), and the system file does not, or if the weighting variable in the system file does not exist in the active dataset, then the active dataset weighting variable, if any, is retained. Otherwise, the weighting variable in the system file becomes the active dataset weighting variable.

APPLY DICTIONARY takes effect immediately. It does not read the active dataset. The system file is not modified.


9.2 EXPORT

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.


9.3 GET

GET
        /FILE={’file_name’,file_handle}
        /DROP=var_list
        /KEEP=var_list
        /RENAME=(src_names=target_names)…
        /ENCODING=’encoding

GET clears the current dictionary and active dataset and replaces them with the dictionary and data from a specified file.

The FILE subcommand is the only required subcommand. Specify the SPSS system file, SPSS/PC+ system file, or SPSS portable file to be read as a string file name or a file handle (see File Handles).

By default, all the variables in a file are read. The DROP subcommand can be used to specify a list of variables that are not to be read. By contrast, the KEEP subcommand can be used to specify variable that are to be read, with all other variables not read.

Normally variables in a file retain the names that they were saved under. Use the RENAME subcommand to change these names. Specify, within parentheses, a list of variable names followed by an equals sign (‘=’) and the names that they should be renamed to. Multiple parenthesized groups of variable names can be included on a single RENAME subcommand. Variables’ names may be swapped using a RENAME subcommand of the form /RENAME=(A B=B A).

Alternate syntax for the RENAME subcommand allows the parentheses to be eliminated. When this is done, only a single variable may be renamed at once. For instance, /RENAME=A=B. This alternate syntax is deprecated.

DROP, KEEP, and RENAME are executed in left-to-right order. Each may be present any number of times. GET never modifies a file on disk. Only the active dataset read from the file is affected by these subcommands.

PSPP automatically detects the encoding of string data in the file, when possible. The character encoding of old SPSS system files cannot always be guessed correctly, and SPSS/PC+ system files do not include any indication of their encoding. Specify the ENCODING subcommand with an IANA character set name as its string argument to override the default. Use SYSFILE INFO to analyze the encodings that might be valid for a system file. The ENCODING subcommand is a PSPP extension.

GET does not cause the data to be read, only the dictionary. The data is read later, when a procedure is executed.

Use of GET to read a portable file is a PSPP extension.


Next: , Previous: , Up: System and Portable File I/O   [Contents][Index]

9.4 GET DATA

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:

GNM

Spreadsheet files created by Gnumeric (http://gnumeric.org).

ODS

Spreadsheet files in OpenDocument format (http://opendocumentformat.org).

PSQL

Relations from PostgreSQL databases (http://postgresql.org).

TXT

Textual data files in columnar and delimited formats.

Each supported file type has additional subcommands, explained in separate sections below.


9.4.1 Spreadsheet Files

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.


9.4.2 Postgres Database Queries

GET DATA /TYPE=PSQL
         /CONNECT={connection info}
         /SQL={query}
         [/ASSUMEDSTRWIDTH=w]
         [/UNENCRYPTED]
         [/BSIZE=n].

The PSQL type is used to import data from a postgres database server. The server may be located locally or remotely. Variables are automatically created based on the table column names or the names specified in the SQL query. Postgres data types of high precision, loose precision when imported into PSPP. Not all the postgres data types are able to be represented in PSPP. If a datum cannot be represented then GET DATA issues a warning and that datum is set to SYSMIS.

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'.

9.4.3 Textual Data Files

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.


9.4.3.1 Reading Delimited Data

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.

Examples

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.

9.4.3.2 Reading Fixed Columnar Data

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.

Examples

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.

9.5 IMPORT

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.


9.6 SAVE

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.


9.7 SAVE DATA COLLECTION

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.


9.8 SAVE TRANSLATE

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:

CSV

Comma-separated value format,

TAB

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.


9.8.1 Writing Comma- and Tab-Separated Data Files

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:

CELLS=VALUES FORMAT=PLAIN (the default settings)

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.

CELLS=VALUES FORMAT=VARIABLE

Writes variables using their print formats. Leading and trailing spaces are removed from numeric values, and trailing spaces are removed from string values.

CELLS=LABEL FORMAT=PLAIN
CELLS=LABEL FORMAT=VARIABLE

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.


9.9 SYSFILE INFO

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.


9.10 XEXPORT

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.


9.11 XSAVE

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.


10 Combining Data Files

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:

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.


10.1 Common Syntax

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:

  • The variables in the new active dataset are the union of all the input files’ variables, matched based on their name. When a single input file contains a variable with a given name, the output file will contain exactly that variable. When more than one input file contains a variable with a given name, those variables must all have the same type (numeric or string) and, for string variables, the same width. Variables are matched after renaming with the RENAME subcommand. Thus, RENAME can be used to resolve conflicts.
  • The variable label for each output variable is taken from the first specified input file that has a variable label for that variable, and similarly for value labels and missing values.
  • The file label of the new active dataset (see FILE LABEL) is that of the first specified FILE that has a file label.
  • The documents in the new active dataset (see DOCUMENT) are the concatenation of all the input files’ documents, in the order in which the FILE subcommands are specified.
  • If all of the input files are weighted on the same variable, then the new active dataset is weighted on that variable. Otherwise, the new active dataset is not weighted.

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.


10.2 ADD FILES

ADD FILES

Per input file:
        /FILE={*,’file_name’}
        [/RENAME=(src_names=target_names)…]
        [/IN=var_name]
        [/SORT]

Once per command:
        [/BY var_list[({D|A})] [var_list[({D|A})]…]]
        [/DROP=var_list]
        [/KEEP=var_list]
        [/FIRST=var_name]
        [/LAST=var_name]
        [/MAP]

ADD FILES adds cases from multiple input files. The output, which replaces the active dataset, consists all of the cases in all of the input files.

ADD FILES shares the bulk of its syntax with other PSPP commands for combining multiple data files. See Common Syntax, above, for an explanation of this common syntax.

When BY is not used, the output of ADD FILES consists of all the cases from the first input file specified, followed by all the cases from the second file specified, and so on. When BY is used, the output is additionally sorted on the BY variables.

When ADD FILES creates an output case, variables that are not part of the input file from which the case was drawn are set to the system-missing value for numeric variables or spaces for string variables.


Next: , Previous: , Up: Combining Data Files   [Contents][Index]

10.3 MATCH FILES

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:

  • If 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.
  • If 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 TABLEs, 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.


10.4 UPDATE

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:

  • When a match is found, then the values of the variables present in the transaction file replace those variables’ values in the new active file. If there are matching cases in more than more transaction file, PSPP applies the replacements from the first transaction file, then from the second transaction file, and so on. Similarly, if a single transaction file has cases with duplicate 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.

  • If a case in the master file has no matching case in any transaction file, then it is copied unchanged to the output.
  • If a case in a transaction file has no matching case in the master file, then it causes a new case to be added to the output, initialized from the values in the transaction file.

11 Manipulating Variables

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.


11.1 DISPLAY

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.

NAMES

The variables’ names are displayed.

INDEX

The variables’ names are displayed along with a value describing their position within the active dataset dictionary.

LABELS

Variable names, positions, and variable labels are displayed.

VARIABLES

Variable names, positions, print and write formats, and missing values are displayed.

DICTIONARY

Variable names, positions, print and write formats, missing values, variable labels, and value labels are displayed.

SCRATCH

Variable names are displayed, for scratch variables only (see Scratch Variables).

ATTRIBUTES
@ATTRIBUTES

Datafile and variable attributes are displayed. The first form of the command omits those attributes whose names begin with @ or $@. In the second for, all datafile and variable attributes are displayed.

With the VECTOR keyword, DISPLAY lists all the currently declared vectors. If the SORTED keyword is given, the vectors are listed in alphabetical order; otherwise, they are listed in textual order of definition within the PSPP syntax file.

For related commands, see DISPLAY DOCUMENTS and DISPLAY FILE LABEL.


Next: , Previous: , Up: Manipulating Variables   [Contents][Index]

11.2 NUMERIC

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.


11.3 STRING

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).

11.4 MODIFY VARS

You can use MODIFY VARS to reorder, rename, or delete variables.

MODIFY VARS
        /REORDER={FORWARD,BACKWARD} {POSITIONAL,ALPHA} (var_list)…
        /RENAME=(old_names=new_names)…
        /{DROP,KEEP}=var_list
        /MAP

At least one subcommand must be specified, and no subcommand may be specified more than once. DROP and KEEP may not both be specified.

The REORDER subcommand changes the order of variables in the active dataset. Specify one or more lists of variable names in parentheses. By default, each list of variables is rearranged into the specified order. To put the variables into the reverse of the specified order, put keyword BACKWARD before the parentheses. To put them into alphabetical order in the dictionary, specify keyword ALPHA before the parentheses. BACKWARD and ALPHA may also be combined.

To rename variables in the active dataset, specify RENAME, an equals sign (‘=’), and lists of the old variable names and new variable names separated by another 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.

The DROP subcommand deletes a specified list of variables from the active dataset. MODIFY VARS may not be used to delete all variables from the dictionary; use NEW FILE to do that (see NEW FILE).

The KEEP subcommand keeps the specified list of variables in the active dataset. Any unlisted variables are deleted from the active dataset.

MAP is currently ignored.

If either DROP or KEEP is specified, the data is read; otherwise it is not.

MODIFY VARS may not be specified following TEMPORARY (see TEMPORARY).


11.5 RENAME VARIABLES

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).


11.6 SORT VARIABLES

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:

NAME

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’.

TYPE

Sorts numeric variables before string variables, and shorter string variables before longer ones.

FORMAT

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.

LABEL

Sorts variables without a variable label before those with one. See VARIABLE LABELS.

VALUES

Sorts variables without value labels before those with some. See VALUE LABELS.

MISSING

Sorts variables without missing values before those with some. See MISSING VALUES.

MEASURE

Sorts nominal variables first, followed by ordinal variables, followed by scale variables. See VARIABLE LEVEL.

ROLE

Groups variables according to their role. See VARIABLE ROLE.

COLUMNS

Sorts variables in ascending display width. See VARIABLE WIDTH.

ALIGNMENT

Sorts variables according to their alignment, first left-aligned, then right-aligned, then centered. See VARIABLE ALIGNMENT.

ATTRIBUTE name

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.


11.7 DELETE VARIABLES

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).


11.8 VARIABLE LABELS

In addition to a variable’s name, each variable can have a label. Whereas the name is limited to certain constraints (see Attributes of Variables) a variable’s label has no such constraints. Typically, the names are concise, easy to type mnemonics for the variable and the labels are longer, more verbose descriptions.

VARIABLE LABELS
        var_listvar_label’
        [ /var_listvar_label’]
        .
        .
        .
        [ /var_listvar_label’]

VARIABLE LABELS associates explanatory names with variables. This name, called a variable label, is displayed by statistical procedures.

To assign a variable label to a group of variables, specify a list of variable names and the variable label as a string. To assign different labels to different variables in the same command, precede the subsequent variable list with a slash (‘/’).


11.10 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.


11.11 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.


11.12 VALUE LABELS

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 valuelabel’ [valuelabel’]…

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.


11.13 ADD VALUE LABELS

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 valuelabel’ [valuelabel’]…

11.14 MISSING VALUES

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 or LOWEST may take the place of num1;
HI or HIGHEST 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.


11.15 VARIABLE ATTRIBUTE

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.


11.16 VARIABLE ALIGNMENT

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 ) ]

11.17 VARIABLE WIDTH

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.


11.18 VARIABLE LEVEL

VARIABLE LEVEL
        var_list ( SCALE | NOMINAL | ORDINAL )
        [ /var_list ( SCALE | NOMINAL | ORDINAL ) ]
        .
        .
        .
        [ /var_list ( SCALE | NOMINAL | ORDINAL ) ]

VARIABLE LEVEL sets the measurement level of variables. Currently, this has no effect except for certain third party software.


11.19 VARIABLE ROLE

VARIABLE ROLE
        /role var_list
        [/role var_list]…

VARIABLE ROLE sets the intended role of a variable for use in dialog boxes in graphical user interfaces. Each role specifies one of the following roles for the variables that follow it:

INPUT

An input variable, such as an independent variable.

TARGET

An output variable, such as an dependent variable.

BOTH

A variable used for input and output.

NONE

No role assigned. (This is a variable’s default role.)

PARTITION

Used to break the data into groups for testing.

SPLIT

No meaning except for certain third party software. (This role’s meaning is unrelated to SPLIT FILE.)

The PSPPIRE GUI does not yet use variable roles as intended.


11.20 VECTOR

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.


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11.21 MRSETS

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:

  • A multiple dichotomy set is analogous to a survey question with a set of checkboxes. Each variable in the set is treated in a Boolean fashion: one value (the "counted value") means that the box was checked, and any other value means that it was not.
  • A multiple category set represents a survey question where the respondent is instructed to list up to n choices. Each variable represents one of the responses.
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:

  • NAME specifies the name used in syntax for the new multiple dichotomy set. The name must begin with ‘$’; it must otherwise follow the rules for identifiers (see Tokens).
  • 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.


11.22 LEAVE

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.


12 Data transformations

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.


12.1 AGGREGATE

AGGREGATE
        OUTFILE={*,’file_name’,file_handle} [MODE={REPLACE, ADDVARIABLES}]
        /PRESORTED
        /DOCUMENT
        /MISSING=COLUMNWISE
        /BREAK=var_list
        /dest_var[’label’]…=agr_func(src_vars, args…)…

AGGREGATE summarizes groups of cases into single cases. Cases are divided into groups that have the same values for one or more variables called break variables. Several functions are available for summarizing case contents.

The OUTFILE subcommand is required and must appear first. Specify a system file or portable file by file name or file handle (see File Handles), or a dataset by its name (see Datasets). The aggregated cases are written to this file. If ‘*’ is specified, then the aggregated cases replace the active dataset’s data. Use of OUTFILE to write a portable file is a PSPP extension.

If OUTFILE=* is given, then the subcommand MODE may also be specified. The mode subcommand has two possible values: ADDVARIABLES or REPLACE. In REPLACE mode, the entire active dataset is replaced by a new dataset which contains just the break variables and the destination varibles. In this mode, the new file contains as many cases as there are unique combinations of the break variables. In ADDVARIABLES mode, the destination variables are appended to the existing active dataset. Cases which have identical combinations of values in their break variables, receive identical values for the destination variables. The number of cases in the active dataset remains unchanged. Note that if ADDVARIABLES is specified, then the data must be sorted on the break variables.

By default, the active dataset is sorted based on the break variables before aggregation takes place. If the active dataset is already sorted or otherwise grouped in terms of the break variables, specify PRESORTED to save time. PRESORTED is assumed if MODE=ADDVARIABLES is used.

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, only a single case (for SD and SD., two cases) need be non-missing in each group for the aggregate variable to be non-missing. Specifying /MISSING=COLUMNWISE inverts this behavior, so that the aggregate variable becomes missing if any aggregated value is missing.

If PRESORTED, DOCUMENT, or MISSING are specified, they must appear between OUTFILE and BREAK.

At least one break variable must be specified on BREAK, a required subcommand. The values of these variables are used to divide the active dataset into groups to be summarized. In addition, at least one dest_var must be specified.

One or more sets of aggregation variables must be specified. 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. Some aggregation functions expect additional arguments following the source variable names.

Aggregation variables typically are created 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 MEAN, MEDIAN, SD, and SUM aggregation functions may only be applied to numeric variables. All the rest may be applied to numeric and string variables.

The available aggregation functions are as follows:

FGT(var_name, value)

Fraction of values greater than the specified constant. The default format is F5.3.

FIN(var_name, low, high)

Fraction of values within the specified inclusive range of constants. The default format is F5.3.

FLT(var_name, value)

Fraction of values less than the specified constant. The default format is F5.3.

FIRST(var_name)

First non-missing value 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 case with particular values for the break variables before sorting is also the first case in that break group after sorting.

FOUT(var_name, low, high)

Fraction of values strictly outside the specified range of constants. The default format is F5.3.

LAST(var_name)

Last non-missing value 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.

MAX(var_name)

Maximum value. The aggregation variable receives the complete dictionary information from the source variable.

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.

MIN(var_name)

Minimum value. The aggregation variable receives the complete dictionary information from the source variable.

N(var_name)

Number of non-missing values. The default format is F7.0 if weighting is not enabled, F8.2 if it is (see WEIGHT).

N

Number 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).

NMISS(var_name)

Number of missing values. The default format is F7.0 if weighting is not enabled, F8.2 if it is (see WEIGHT).

NU(var_name)

Number of non-missing values. Each case is considered to have a weight of 1, regardless of the current weighting variable (see WEIGHT). The default format is F7.0.

NU

Number of cases aggregated to form this group. Each case is considered to have a weight of 1, regardless of the current weighting variable. The default format is F7.0.

NUMISS(var_name)

Number of missing values. Each case is considered to have a weight of 1, regardless of the current weighting variable. The default format is F7.0.

PGT(var_name, value)

Percentage between 0 and 100 of values greater than the specified constant. The default format is F5.1.

PIN(var_name, low, high)

Percentage of values within the specified inclusive range of constants. The default format is F5.1.

PLT(var_name, value)

Percentage of values less than the specified constant. The default format is F5.1.

POUT(var_name, low, high)

Percentage of values strictly outside the specified range of constants. The default format is F5.1.

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.

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).

12.1.1 Aggregate Example

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_salaray = mean (salary)
        /occ_median_salary = median (salary)
        /occ_std_dev_salary = sd (salary).

list.

Example 12.1: Calculating aggregated statistics from the personnel.sav file.

Since we chose the ‘MODE = REPLACE’ option, in Results 12.1 cases for the individual persons are no longer present. They have each been replaced by a single case per aggregated value.

Data List
occupation occ_mean_salaray occ_median_salary occ_std_dev_salary
Artist 37836.18 34712.50 7631.48
Baker 45075.20 45075.20 4411.21
Barrister 39504.00 39504.00 .
Carpenter 39349.11 36190.04 7453.40
Cleaner 41142.50 39647.49 14378.98
Cook 40357.79 43194.00 11064.51
Manager 46452.14 45657.56 6901.69
Mathematician 34531.06 34763.06 5267.68
Painter 45063.55 45063.55 15159.67
Payload Specialist 34355.72 34355.72 .
Plumber 40413.91 40410.00 4726.05
Scientist 36687.07 36803.83 10873.54
Scrientist 42530.65 42530.65 .
Tailor 34586.79 34586.79 3728.98

Results 12.1: Aggregated mean, median and standard deviation per occupation.

Note that some values for the standard deviation are blank. This is because there is only one case with the respective occupation.


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12.2 AUTORECODE

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.

12.2.1 Autorecode Example

In the file personnel.sav, the variable occupation is a string variable. Except for data of a purely commentary nature, string variables are generally a bad idea. One reason is that data entry errors are easily overlooked. This has happened in personnel.sav; one entry which should read “Scientist” has been mistyped as “Scrientist”. In Example 12.2 first, this error is corrected by the DO IF clause, 3 then we use AUTORECODE to create a new numeric variable which takes recoded values of occupation. Finally, we remove the old variable and rename the new variable to the name of the old variable.

get file='personnel.sav'.

* Correct a typing error in the original file.
do if occupation = "Scrientist".
 compute occupation = "Scientist".
end if.

autorecode
	variables = occupation into occ
	/blank = missing.

* Delete the old variable.
delete variables occupation.

* Rename the new variable to the old variable's name.
rename variables (occ = occupation).

* Inspect the new variable.
display dictionary /variables=occupation.

Example 12.2: Changing a string variable to a numeric variable using AUTORECODE after correcting a data entry error

screenshots/autorecode-ad

Screenshot 12.1: Autorecode dialog box set to recode occupation to occ

Notice in Result 12.1, how the new variable has been automatically allocated value labels which correspond to the strings of the old variable. This means that in future analyses the descriptive strings are reported instead of the numeric values.

Variables
Name Position Measurement Level Role Width Alignment Print Format Write Format
occupation 6 Scale Input 8 Right F2.0 F2.0
Value Labels
Variable Value Label
occupation 1 Artist
2 Baker
3 Barrister
4 Carpenter
5 Cleaner
6 Cook
7 Manager
8 Mathematician
9 Painter
10 Payload Specialist
11 Plumber
12 Scientist
13 Tailor

Result 12.1: The properties of the occupation variable following AUTORECODE


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12.3 COMPUTE

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).

12.3.1 Compute Examples

The dataset physiology.sav contains the height and weight of persons. For some purposes, neither height nor weight alone is of interest. Epidemiologists are often more interested in the body mass index which can sometimes be used as a predictor for clinical conditions. The body mass index is defined as the weight of the person in kilograms divided by the square of the person’s height in metres. 4

get file='physiology.sav'.

* height is in mm so we must divide by 1000 to get metres.
compute bmi = weight / (height/1000)**2.
variable label bmi "Body Mass Index".

descriptives /weight height bmi.

Example 12.3: Computing the body mass index from weight and height

Example 12.3 shows how you can use COMPUTE to generate a new variable called bmi and have every case’s value calculated from the existing values of weight and height. It also shows how you can add a label to this new variable (see VARIABLE LABELS), so that a more descriptive label appears in subsequent analyses, and this can be seen in the ouput from the DESCRIPTIVES command in Results 12.2.

screenshots/compute-ad

Screenshot 12.2: Using the dialog box to generate a new variable and compute its values

The expression which follows the ‘=’ sign can be as complicated as necessary. See Mathematical Expressions for a precise description of the language accepted. Normally it is easiest to enter the code directly, however there is a dialog box available if desired. This is illustrated in Screenshot 12.2. One advantage is that it offers a list of mathematical functions which can be selected and pasted into the expression.

Descriptive Statistics
N Mean Std Dev Minimum Maximum
Weight in kilograms 40 72.12 26.70 -55.6 92.1
Height in millimeters 40 1677.12 262.87 179 1903
Body Mass Index 40 67.46 274.08 -21.62 1756.82
Valid N (listwise) 40
Missing N (listwise) 0

Results 12.2: An analysis which includes bmi in its results


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12.4 COUNT

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 words LO  or LOWEST
      and num2 is a numeric expression  or HI or HIGHEST.

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:

  • - The number of occurrences of 1 between A and B is counted.
  • - A is assigned this value.
  • - The number of occurrences of 1 between B and the new value of A is counted.
  • - B is assigned this value.

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.

12.4.1 Count Examples

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 v1v2 which are 2 or lower.

get file="hotel.sav".

count low_counts = v1 v2 v3 (low thru 2).

list /variables v1 v2 v3 low_counts.

Example 12.4: Counting low values to responses v1, v2 and v3

In Example 12.4 the COUNT transformation creates a new variable, low_counts and its values are shown using the LIST command.

If using the graphic user interface, a two step process must be used to set up the COUNT transformation. The first dialog box (Screenshot 12.3) provides for the variables to be chosen. Then, one must click on the button marked “Define Values...” to reveal the dialog box for selecting the values to count.

screenshots/count-ad

Screenshot 12.3: The variables v1, v2 and v3 selected, ready to define values to count

In this dialog box, you must select the values you wish to count — in this case all values up to and including 2 — as shown in Screenshot 12.4 and click “Add”. As many ranges or may be added as you desire. When all desired ranges have been added click “Continue”.

screenshots/count-define-ad

Screenshot 12.4: Count “Define Values” dialog with ‘lowest thru 2’ selected

In Result 12.2 we can see the values of low_counts after the COUNT transformation has completed. The first value is 1, because there is only one variable amoung v1, v2 and 3 which has a value of 2 or less. The second value is 2, because both v1 and v2 are 2 or less.

Data List
v1 v2 v3 low_counts
4 2 3 1.00
1 1 4 2.00
4 2 2 2.00
3 1 3 1.00
5 3 1 1.00
2 2 5 2.00
3 2 4 1.00
1 4 5 1.00
3 2 3 1.00
2 5 4 1.00
4 2 2 2.00
2 1 4 2.00
1 2 5 2.00
2 3 3 1.00
4 1 1 2.00
1 1 5 2.00
1 5 5 1.00

Result 12.2: The values of v1, v2, v3 and low_counts after the COUNT transformation has run


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12.5 FLIP

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.

12.5.1 Flip Examples

In Example 12.5, data has been entered using DATA LIST (see DATA LIST) such that the first variable in the dataset is a string variable containing a description of the other data for the case. Clearly this is not a convenient arrangement for performing statistical analyses, so it would have been better to think a little more carefully about how the data should have been arranged. However often the data is provided by some third party source, and you have no control over the form. Fortunately, we can use FLIP to exchange the variables and cases in the active dataset.

data list notable list /heading (a16) v1 v2 v3 v4 v5 v6
begin data.
date-of-birth 1970 1989 2001 1966 1976 1982
sex 1 0 0 1 0 1
score 10 10 9 3 8 9
end data.

echo 'Before FLIP:'.
display variables.
list.

flip /variables = all /newnames = heading.

echo 'After FLIP:'.
display variables.
list.

Example 12.5: Using FLIP to exchange variables and cases in a dataset

As you can see in Results 12.3 before the FLIP command has run there are seven variables (six containing data and one for the heading) and three cases. Afterwards there are four variables (one per case, plus the CASE_LBL variable) and six cases. You can delete the CASE_LBL variable (see DELETE VARIABLES) if you don’t need it.

Before FLIP:

Variables
Name Position Print Format Write Format
heading 1 A16 A16
v1 2 F8.2 F8.2
v2 3 F8.2 F8.2
v3 4 F8.2 F8.2
v4 5 F8.2 F8.2
v5 6 F8.2 F8.2
v6 7 F8.2 F8.2
Data List
heading v1 v2 v3 v4 v5 v6
date-of-birth 1970.00 1989.00 2001.00 1966.00 1976.00 1982.00
sex 1.00 .00 .00 1.00 .00 1.00
score 10.00 10.00 9.00 3.00 8.00 9.00

After FLIP:

Variables
Name Position Print Format Write Format
CASE_LBL 1 A8 A8
date_of_birth 2 F8.2 F8.2
sex 3 F8.2 F8.2
score 4 F8.2 F8.2
Data List
CASE_LBL date_of_birth sex score
v1 1970.00 1.00 10.00
v2 1989.00 .00 10.00
v3 2001.00 .00 9.00
v4 1966.00 1.00 3.00
v5 1976.00 .00 8.00
v6 1982.00 1.00 9.00

Results 12.3: The results of using FLIP to exchange variables and cases in a dataset


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12.6 IF

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).


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12.7 RECODE

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:

number

If the source variables are numeric then src_value may be a literal number.

string

If the source variables are string variables then src_value may be a literal string (like all strings, enclosed in single or double quotes).

num1 THRU num2

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.

MISSING

The literal keyword ‘MISSING’ matches both system missing and user missing values. It is valid for both numeric and string variables.

SYSMIS

The literal keyword ‘SYSMIS’ matches system missing values. It is valid for both numeric variables only.

ELSE

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:

number

A literal numeric value to which the source values should be changed. This implies the destination variable must be numeric.

string

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.

SYSMIS

The keyword ‘SYSMIS’ changes the value to the system missing value. This implies the destination variable must be numeric.

COPY

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.


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12.8 SORT CASES

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.

12.8.1 Sorting Example

In Example 12.6 the data from the file physiology.sav is sorted by two variables, viz sex in descending order and temperature in ascending order.

get file='physiology.sav'.
sort cases by sex (D) temperature(A).
list.

Example 12.6: Sorting cases by two variables.

In Results 12.4 you can see that all the cases with a sex of ‘1’ (female) appear before those with a sex of ‘0’ (male). This is because they have been sorted in descending order. Within each sex, the data is sorted on the temperature variable, this time in ascending order.

Data List
sex height weight temperature
1 1606 56.1 34.56
1 179 56.3 35.15
1 1609 55.4 35.46
1 1606 56.0 36.06
1 1607 56.3 36.26
1 1604 56.0 36.57
1 1604 56.6 36.81
1 1606 56.3 36.88
1 1604 57.8 37.32
1 1598 55.6 37.37
1 1607 55.9 37.84
1 1605 54.5 37.86
1 1603 56.1 38.80
1 1604 58.1 38.85
1 1605 57.7 38.98
1 1709 55.6 39.45
1 1604 -55.6 39.72
1 1601 55.9 39.90
0 1799 90.3 32.59
0 1799 89.0 33.61
0 1799 90.6 34.04
0 1801 90.5 34.42
0 1802 87.7 35.03
0 1793 90.1 35.11
0 1801 92.1 35.98
0 1800 89.5 36.10
0 1645 92.1 36.68
0 1698 90.2 36.94
0 1800 89.6 37.02
0 1800 88.9 37.03
0 1801 88.9 37.12
0 1799 90.4 37.33
0 1903 91.5 37.52
0 1799 90.9 37.53
0 1800 91.0 37.60
0 1799 90.4 37.68
0 1801 91.7 38.98
0 1801 90.9 39.03
0 1799 89.3 39.77
0 1884 88.6 39.97

Results 12.4: The physiology.sav file after sorting.

Note that SORT CASES, like all other transformations, affects only the active file. It does not have any effect upon the physiology.sav file itself. For that, you would have to rewrite the file using the SAVE command (see SAVE).

When using the graphic user interface, it is often simpler to perform a sort directly from the data view. To do this, switch to the data view. Select the column corresponding to the variable by which you want to sort and click button 1 and then click button 3. A popup menu will appear like that shown in Screenshot 12.5. Select either “Sort Ascending” or “Sort Descending” from this menu.

screenshots/sort-simple-ad

Screenshot 12.5: Sorting the data on a single variable height

However, sometimes you will want to sort on two or more variables, and that is not possible using this method. In this case, you must either use some code or the “Sort Cases” dialog from the Data menu. Screenshot 12.6 shows the dialog box set up to perform a sort on both sex and height. Note that the order in which you enter the variables is important. In this case, the data will be first sorted on sex, and then all cases for which sex is the same will then be sorted by height.

screenshots/sort-ad

Screenshot 12.6: Sorting the data on two variables sex and height


13 Selecting data for analysis

This chapter documents PSPP commands that temporarily or permanently select data records from the active dataset for analysis.


13.1 FILTER

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.


13.2 N OF CASES

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.


13.3 SAMPLE

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:

  1. If N is equal to the number of currently-selected cases in the active dataset, exactly m cases are selected.
  2. If N is greater than the number of currently-selected cases in the active dataset, an equivalent proportion of cases are selected.
  3. If N is less than the number of currently-selected cases in the active, exactly m cases are selected from the first N cases in the active dataset.

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.


13.4 SELECT IF

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).

13.4.1 Example Select-If

A shop steward is interested in the salaries of younger personnel in a firm. The file personnel.sav provides the salaries of all the workers and their dates of birth. The syntax in Example 13.1 shows how SELECT IF can be used to limit analysis only to those persons born after December 31, 1999.

get file = 'personnel.sav'.

echo 'Salaries of all personnel'.
descriptives salary.

echo 'Salaries of personnel born after December 31 1999'.
select if dob > date.dmy (31,12,1999).
descriptives salary.

Example 13.1: Using SELECT IF to select persons born on or after a certain date.

From Result 13.1 one can see that there are 56 persons listed in the dataset, and 17 of them were born after December 31, 1999.

Salaries of all personnel

Descriptive Statistics
N Mean Std Dev Minimum Maximum
Annual salary before tax 56 40028.97 8721.17 $23,451 $57,044
Valid N (listwise) 56
Missing N (listwise) 0

Salaries of personnel born after December 31 1999

Descriptive Statistics
N Mean Std Dev Minimum Maximum
Annual salary before tax 17 31828.59 4454.80 $23,451 $39,504
Valid N (listwise) 17
Missing N (listwise) 0

Result 13.1: Salary descriptives before and after the SELECT IF transformation.

Note that the personnel.sav file from which the data were read is unaffected. The transformation affects only the active file.


13.5 SPLIT 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. If provided, either keyword are ignored.

Groups are formed only by adjacent cases. To create a split using a variable where like values are not adjacent in the working file, you should 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).

13.5.1 Example Split

The file horticulture.sav contains data describing the yield of a number of horticultural specimens which have been subjected to various treatments. If we wanted to investigate linear statistics of the yeild, one way to do this is using the DESCRIPTIVES (see DESCRIPTIVES). However, it is reasonable to expect the mean to be different depending on the treatment. So we might want to perform three separate procedures — one for each treatment. 5 Example 13.2 shows how this can be done automatically using the SPLIT FILE command.

get file='horticulture.sav'.

* Ensure cases are sorted before splitting.
sort cases by treatment.

split file by treatment.

* Run descriptives on the yield variable
descriptives /variable = yield.

Example 13.2: Running DESCRIPTIVES on each value of treatment

In Example 13.3 you can see that the table of descriptive statistics appears 3 times — once for each value of treatment. In this example ‘N’, the number of observations are identical in all splits. This is because that experiment was deliberately designed that way. However in general one can expect a different ‘N’ for each split.

Split Values
Variable Value
treatment control
Descriptive Statistics
N Mean Std Dev Minimum Maximum
yield 30 51.23 8.28 37.86 68.59
Valid N (listwise) 30
Missing N (listwise) 0
Split Values
Variable Value
treatment conventional
Descriptive Statistics
N Mean Std Dev Minimum Maximum
yield 30 53.57 8.92 36.30 70.66
Valid N (listwise) 30
Missing N (listwise) 0
Split Values
Variable Value
treatment traditional
Descriptive Statistics
N Mean Std Dev Minimum Maximum
yield 30 56.87 8.88 39.08 75.93
Valid N (listwise) 30
Missing N (listwise) 0

Example 13.3: The results of running DESCRIPTIVES with an active split

Unless TEMPORARY was used, after a split has been defined for a dataset it remains active until explicitly disabled. In the graphical user interface, the active split variable (if any) is displayed in the status bar (see Screenshot 13.1. If a dataset is saved to a system file (see SAVE) whilst a split is active, the split stastus is stored in the file and will be automatically loaded when that file is loaded.

screenshots/split-status-bar-ad

Screenshot 13.1: The status bar indicating that the data set is split using the treatment variable


13.6 TEMPORARY

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.

13.6.1 Example Temporary

In Example 13.4 there are two COMPUTE transformation. One of them immediatly follows a TEMPORARY command, and therefore has effect only for the next procedure, which in this case is the first DESCRIPTIVES command.

data list notable /x 1-2.
begin data.
 2
 4
10
15
20
24
end data.

compute x=x/2.

temporary.
compute x=x+3.

descriptives x.
descriptives x.

Example 13.4: Running a COMPUTE transformation after TEMPORARY

The data read by the first DESCRIPTIVES procedure are 4, 5, 8, 10.5, 13, 15. The data read by the second DESCRIPTIVES procedure are 1, 2, 5, 7.5, 10, 12. This is because the second COMPUTE transformation has no effect on the second DESCRIPTIVES procedure. You can check these figures in Result 13.2.

Descriptive Statistics
N Mean Std Dev Minimum Maximum
x 6 9.25 4.38 4 15
Valid N (listwise) 6
Missing N (listwise) 0
Descriptive Statistics
N Mean Std Dev Minimum Maximum
x 6 6.25 4.38 1 12
Valid N (listwise) 6
Missing N (listwise) 0

Result 13.2: The results of running two consecutive DESCRIPTIVES commands after a temporary transformation


13.7 WEIGHT

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.

13.7.1 Example Weights

One could define a dataset containing an inventory of stock items. It would be reasonable to use a string variable for a description of the item, and a numeric variable for the number in stock, like in Example 13.5.

data list notable list /item (a16) quantity (f8.0).
begin   data
nuts    345
screws  10034
washers 32012
bolts   876
end data.

echo 'Unweighted frequency table'.
frequencies /variables = item /format=dfreq.

weight by quantity.

echo 'Weighted frequency table'.
frequencies /variables = item /format=dfreq.

Example 13.5: Setting the weight on the variable quantity

One analysis which most surely would be of interest is the relative amounts or each item in stock. However without setting a weight variable, FREQUENCIES (see FREQUENCIES) does not tell us what we want to know, since there is only one case for each stock item. Example 13.6 shows the difference between the weighted and unweighted frequency tables.

Unweighted frequency table

item
Frequency Percent Valid Percent Cumulative Percent
Valid bolts 1 25.0% 25.0% 25.0%
nuts 1 25.0% 25.0% 50.0%
screws 1 25.0% 25.0% 75.0%
washers 1 25.0% 25.0% 100.0%
Total 4 100.0%

Weighted frequency table

item
Frequency Percent Valid Percent Cumulative Percent
Valid washers 32012 74.0% 74.0% 74.0%
screws 10034 23.2% 23.2% 97.2%
bolts 876 2.0% 2.0% 99.2%
nuts 345 .8% .8% 100.0%
Total 43267 100.0%

Example 13.6: Weighted and unweighted frequency tables of items


14 Conditional and Looping Constructs

This chapter documents PSPP commands used for conditional execution, looping, and flow of control.


14.1 BREAK

BREAK.

BREAK terminates execution of the innermost currently executing LOOP construct.

BREAK is allowed only inside LOOPEND LOOP. See LOOP, for more details.


14.2 DEFINE


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14.2.1 Overview

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

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14.2.2 Introduction

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:

  • Macro arguments that are substituted into the body whenever they are named. The values of a macro’s arguments are specified each time it is called. See Macro Arguments.
  • Macro functions, expanded when the macro is called. See Macro Functions.
  • !IF constructs, for conditional expansion. See Macro Conditional Expansion.
  • Two forms of !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.


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14.2.3 Macro Bodies

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 DATAEND 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.

DEFINE !vars() v1 v2 v3 !ENDDEFINE.
!commands

* We can redefine the variables 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.


14.2.4 Macro Arguments

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:

  • A positional argument has a required value that follows the macro’s name. Use the !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.
    
  • A keyword argument has a name. In the macro call, its value is specified with the syntax 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.


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14.2.5 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.


14.2.6 Macro Functions

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 callexpansion.

Macro Function: !BLANKS (count)

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))          → '_____'
Macro Function: !CONCAT (arg…)

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
Macro Function: !EVAL (arg)

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'
Macro Function: !HEAD (arg)
Macro Function: !TAIL (arg)

!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
Macro Function: !INDEX (haystack, needle)

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
Macro Function: !LENGTH (arg)

Expands to a number token representing the number of characters in arg.

!LENGTH(123)                 → 3
!LENGTH(123.00)              → 6
!LENGTH( 123 )               → 3
!LENGTH("123")               → 5
!LENGTH(xyzzy)               → 5
!LENGTH("xyzzy")             → 7
!LENGTH("xy""zzy")           → 9
!LENGTH(!UNQUOTE("xyzzy"))   → 5
!LENGTH(!UNQUOTE("xy""zzy")) → 6
!LENGTH(!1)                  → 5 if !1 is a b c
!LENGTH(!1)                  → 0 if !1 is empty
!LENGTH(!NULL)               → 0
Macro Function: !NULL

Expands to an empty character sequence.

!NULL                        → empty
!QUOTE(!NULL)                → ''
Macro Function: !QUOTE (arg)
Macro Function: !UNQUOTE (arg)

The !QUOTE function expands to its argument surrounded by apostrophes, doubling any apostrophes inside the argument to make sure that it is valid PSPP syntax for a string. If the argument was already a quoted string, !QUOTE expands to it unchanged.

Given a quoted string argument, the !UNQUOTED function expands to the string’s contents, with the quotes removed and any doubled quote marks reduced to singletons. If the argument was not a quoted string, !UNQUOTE expands to the argument unchanged.

!QUOTE(123.0)                → '123.0'
!QUOTE( 123 )                → '123'
!QUOTE('a b c')              → 'a b c'
!QUOTE("a b c")              → "a b c"
!QUOTE(!1)                   → 'a ''b'' c' if !1 is a 'b' c

!UNQUOTE(123.0)              → 123.0
!UNQUOTE( 123 )              → 123
!UNQUOTE('a b c')            → a b c
!UNQUOTE("a b c")            → a b c
!UNQUOTE(!1)                 → a 'b' c if !1 is a 'b' c

!QUOTE(!UNQUOTE(123.0))      → '123.0'
!QUOTE(!UNQUOTE( 123 ))      → '123'
!QUOTE(!UNQUOTE('a b c'))    → 'a b c'
!QUOTE(!UNQUOTE("a b c"))    → 'a b c'
!QUOTE(!UNQUOTE(!1))         → 'a ''b'' c' if !1 is a 'b' c
Macro Function: !SUBSTR (arg, start[, count])

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
Macro Function: !UPCASE (arg)

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

14.2.7 Macro Expressions

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.


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14.2.8 Macro Conditional Expansion

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.


14.2.9 Macro Loops

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.

Loops Over Ranges

!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.

Loops Over Tokens

!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.


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14.2.10 Macro Variable Assignment

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.


14.2.11 Macro Settings

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.

PRESERVE...RESTORE

SET MEXPAND, etc. doesn’t work inside macro bodies.


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14.2.12 Additional Notes

14.2.12.1 Calling Macros from Macros

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 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.

14.2.12.2 Command Terminators

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 ‘.’.

14.2.12.3 Expansion Contexts

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.

14.2.12.4 PRESERVE and RESTORE

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.


14.3 DO IF

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).


14.4 DO REPEAT

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 DATAEND 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.


14.5 LOOP

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).


15 Statistics

This chapter documents the statistical procedures that PSPP supports so far.


15.1 DESCRIPTIVES

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.

15.1.1 Descriptives Example

The physiology.sav file contains various physiological data for a sample of persons. Running the DESCRIPTIVES command on the variables height and temperature with the default options allows one to see simple linear statistics for these two variables. In Example 15.1, these variables are specfied on the VARIABLES subcommand and the SAVE option has been used, to request that Z scores be calculated.

After the command has completed, this example runs DESCRIPTIVES again, this time on the zheight and ztemperature variables, which are the two normalized (Z-score) variables generated by the first DESCRIPTIVES command.

get file='physiology.sav'.

descriptives
        /variables = height temperature
        /save.

descriptives
        /variables = zheight ztemperature.

Example 15.1: Running two DESCRIPTIVES commands, one with the SAVE subcommand

screenshots/descriptives-ad

Screenshot 15.1: The Descriptives dialog box with two variables and Z-Scores option selected

In Result 15.1, we can see that there are 40 valid data for each of the variables and no missing values. The mean average of the height and temperature is 16677.12 and 37.02 respectively. The descriptive statistics for temperature seem reasonable. However there is a very high standard deviation for height and a suspiciously low minimum. This is due to a data entry error in the data (see Identifying incorrect data).

In the second Descriptive Statistics command, one can see that the mean and standard deviation of both Z score variables is 0 and 1 respectively. All Z score statistics should have these properties since they are normalized versions of the original scores.

Mapping of Variables to Z-scores
Source Target
Height in millimeters Zheight
Internal body temperature in degrees Celcius Ztemperature
Descriptive Statistics
N Mean Std Dev Minimum Maximum
Height in millimeters 40 1677.12 262.87 179 1903
Internal body temperature in degrees Celcius 40 37.02 1.82 32.59 39.97
Valid N (listwise) 40
Missing N (listwise) 0
Descriptive Statistics
N Mean Std Dev Minimum Maximum
Z-score of Height in millimeters 40 1.93E-015 1.00 -5.70 .86
Z-score of Internal body temperature in degrees Celcius 40 1.37E-015 1.00 -2.44 1.62
Valid N (listwise) 40
Missing N (listwise) 0

Result 15.1: Descriptives statistics including two normalized variables (Z-scores)


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15.2 FREQUENCIES

FREQUENCIES
        /VARIABLES=var_list
        /FORMAT={TABLE,NOTABLE,LIMIT(limit)}
                {AVALUE,DVALUE,AFREQ,DFREQ}
        /MISSING={EXCLUDE,INCLUDE}
        /STATISTICS={DEFAULT,MEAN,SEMEAN,MEDIAN,MODE,STDDEV,VARIANCE,
                     KURTOSIS,SKEWNESS,RANGE,MINIMUM,MAXIMUM,SUM,
                     SESKEWNESS,SEKURTOSIS,ALL,NONE}
        /NTILES=ntiles
        /PERCENTILES=percent…
        /HISTOGRAM=[MINIMUM(x_min)] [MAXIMUM(x_max)]
                   [{FREQ[(y_max)],PERCENT[(y_max)]}] [{NONORMAL,NORMAL}]
        /PIECHART=[MINIMUM(x_min)] [MAXIMUM(x_max)]
                  [{FREQ,PERCENT}] [{NOMISSING,MISSING}]
        /BARCHART=[MINIMUM(x_min)] [MAXIMUM(x_max)]
                  [{FREQ,PERCENT}]
        /ORDER={ANALYSIS,VARIABLE}


(These options are not currently implemented.)
        /HBAR=…
        /GROUPED=…

The FREQUENCIES procedure outputs frequency tables for specified variables. FREQUENCIES can also calculate and display descriptive statistics (including median and mode) and percentiles, and various graphical representations of the frequency distribution.

The VARIABLES subcommand is the only required subcommand. Specify the variables to be analyzed.

The FORMAT subcommand controls the output format. It has several possible settings:

  • TABLE, the default, causes a frequency table to be output for every variable specified. NOTABLE prevents them from being output. LIMIT with a numeric argument causes them to be output except when there are more than the specified number of values in the table.
  • Normally frequency tables are sorted in ascending order by value. This is AVALUE. DVALUE tables are sorted in descending order by value. AFREQ and DFREQ tables are sorted in ascending and descending order, respectively, by frequency count.

The MISSING subcommand controls the handling of user-missing values. When EXCLUDE, the default, is set, user-missing values are not included in frequency tables or statistics. When INCLUDE is set, user-missing are included. System-missing values are never included in statistics, but are listed in frequency tables.

The available STATISTICS are the same as available in DESCRIPTIVES (see DESCRIPTIVES), with the addition of MEDIAN, the data’s median value, and MODE, the mode. (If there are multiple modes, the smallest value is reported.) By default, the mean, standard deviation of the mean, minimum, and maximum are reported for each variable.

PERCENTILES causes the specified percentiles to be reported. The percentiles should be presented at a list of numbers between 0 and 100 inclusive. The NTILES subcommand causes the percentiles to be reported at the boundaries of the data set divided into the specified number of ranges. For instance, /NTILES=4 would cause quartiles to be reported.

The HISTOGRAM subcommand causes the output to include a histogram for each specified numeric variable. The X axis by default ranges from the minimum to the maximum value observed in the data, but the MINIMUM and MAXIMUM keywords can set an explicit range. 6 Histograms are not created for string variables.

Specify NORMAL to superimpose a normal curve on the histogram.

The PIECHART subcommand adds a pie chart for each variable to the data. Each slice represents one value, with the size of the slice proportional to the value’s frequency. By default, all non-missing values are given slices. The MINIMUM and MAXIMUM keywords can be used to limit the displayed slices to a given range of values. The keyword NOMISSING causes missing values to be omitted from the piechart. This is the default. If instead, MISSING is specified, then the pie chart includes a single slice representing all system missing and user-missing cases.

The BARCHART subcommand produces a bar chart for each variable. The MINIMUM and MAXIMUM keywords can be used to omit categories whose counts which lie outside the specified limits. The FREQ option (default) causes the ordinate to display the frequency of each category, whereas the PERCENT option displays relative percentages.

The FREQ and PERCENT options on HISTOGRAM and PIECHART are accepted but not currently honoured.

The ORDER subcommand is accepted but ignored.

15.2.1 Frequencies Example

Example 15.2 runs a frequency analysis on the sex and occupation variables from the personnel.sav file. This is useful to get an general idea of the way in which these nominal variables are distributed.

get file='personnel.sav'.

frequencies /variables = sex occupation
            /statistics = none.

Example 15.2: Running frequencies on the sex and occupation variables

If you are using the graphic user interface, the dialog box is set up such that by default, several statistics are calculated. Some are not particularly useful for categorical variables, so you may want to disable those.

screenshots/frequencies-ad

Screenshot 15.2: The frequencies dialog box with the sex and occupation variables selected

From Result 15.2 it is evident that there are 33 males, 21 females and 2 persons for whom their sex has not been entered.

One can also see how many of each occupation there are in the data. When dealing with string variables used as nominal values, running a frequency analysis is useful to detect data input entries. Notice that one occupation value has been mistyped as “Scrientist”. This entry should be corrected, or marked as missing before using the data.

sex
Frequency Percent Valid Percent Cumulative Percent
Valid Male 33 58.9% 61.1% 61.1%
Female 21 37.5% 38.9% 100.0%
Missing . 2 3.6%
Total 56 100.0%
occupation
Frequency Percent Valid Percent Cumulative Percent
Valid Artist 8 14.3% 14.3% 14.3%
Baker 2 3.6% 3.6% 17.9%
Barrister 1 1.8% 1.8% 19.6%
Carpenter 4 7.1% 7.1% 26.8%
Cleaner 4 7.1% 7.1% 33.9%
Cook 7 12.5% 12.5% 46.4%
Manager 8 14.3% 14.3% 60.7%
Mathematician 4 7.1% 7.1% 67.9%
Painter 2 3.6% 3.6% 71.4%
Payload Specialist 1 1.8% 1.8% 73.2%
Plumber 5 8.9% 8.9% 82.1%
Scientist 7 12.5% 12.5% 94.6%
Scrientist 1 1.8% 1.8% 96.4%
Tailor 2 3.6% 3.6% 100.0%
Total 56 100.0%

Result 15.2: The relative frequencies of sex and occupation


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15.3 EXAMINE

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:

  • All weight values must be integer.
  • The cumulative weight value must be in the range [3, 5000]

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.


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15.4 GRAPH

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.


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15.4.1 Scatterplot

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.


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15.4.2 Histogram

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.

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15.4.3 Bar Chart

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.


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15.5 CORRELATIONS

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.


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15.6 CROSSTABS

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:

COUNT

Frequency count.

ROW

Row percent.

COLUMN

Column percent.

TOTAL

Table percent.

EXPECTED

Expected value.

RESIDUAL

Residual.

SRESIDUAL

Standardized residual.

ASRESIDUAL

Adjusted standardized residual.

ALL

All of the above.

NONE

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:

CHISQ

Pearson chi-square, likelihood ratio, Fisher’s exact test, continuity correction, linear-by-linear association.

PHI

Phi.

CC

Contingency coefficient.

LAMBDA

Lambda.

UC

Uncertainty coefficient.

BTAU

Tau-b.

CTAU

Tau-c.

RISK

Risk estimate.

GAMMA

Gamma.

D

Somers’ D.

KAPPA

Cohen’s Kappa.

ETA

Eta.

CORR

Spearman correlation, Pearson’s r.

ALL

All of the above.

NONE

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:

  • Significance of some symmetric and directional measures is not calculated.
  • Asymptotic standard error is not calculated for Goodman and Kruskal’s tau or symmetric Somers’ d.
  • Approximate T is not calculated for symmetric uncertainty coefficient.

Fixes for any of these deficiencies would be welcomed.

15.6.1 Crosstabs Example

A researcher wishes to know if, in an industry, a person’s sex is related to the person’s occupation. To investigate this, she has determined that the personnel.sav is a representative, randomly selected sample of persons. The researcher’s null hypothesis is that a person’s sex has no relation to a person’s occupation. She uses a chi-squared test of independence to investigate the hypothesis.

get file="personnel.sav".

crosstabs
	/tables= occupation by sex
	/cells = count expected
	/statistics=chisq.


Example 15.3: Running crosstabs on the sex and occupation variables

The syntax in Example 15.3 conducts a chi-squared test of independence. The line /tables = occupation by sex indicates that occupation and sex are the variables to be tabulated. To do this using the graphic user interface you must place these variable names respectively in the ‘Row’ and ‘Column’ fields as shown in Screenshot 15.3.

screenshots/crosstabs-ad

Screenshot 15.3: The Crosstabs dialog box with the sex and occupation variables selected

Similarly, the ‘Cells’ button shows a dialog box to select the count and expected options. All other cell options can be deselected for this test.

You would use the ‘Format’ and ‘Statistics’ buttons to select options for the FORMAT and STATISTICS subcommands. In this example, the ‘Statistics’ requires only the ‘Chisq’ option to be checked. All other options should be unchecked. No special settings are required from the ‘Format’ dialog.

As shown in Results 15.1 CROSSTABS generates a contingency table containing the observed count and the expected count of each sex and each occupation. The expected count is the count which would be observed if the null hypothesis were true.

The significance of the Pearson Chi-Square value is very much larger than the normally accepted value of 0.05 and so one cannot reject the null hypothesis. Thus the researcher must conclude that a person’s sex has no relation to the person’s occupation.

Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
occupation × sex 54 96.4% 2 3.6% 56 100.0%
occupation × sex
sex Total
Male Female
occupation Artist Count 2 6 8
Expected 4.89 3.11 .15
Baker Count 1 1 2
Expected 1.22 .78 .04
Barrister Count 0 1 1
Expected .61 .39 .02
Carpenter Count 3 1 4
Expected 2.44 1.56 .07
Cleaner Count 4 0 4
Expected 2.44 1.56 .07
Cook Count 3 2 5
Expected 3.06 1.94 .09
Manager Count 4 4 8
Expected 4.89 3.11 .15
Mathematician Count 3 1 4
Expected 2.44 1.56 .07
Painter Count 1 1 2
Expected 1.22 .78 .04
Payload Specialist Count 1 0 1
Expected .61 .39 .02
Plumber Count 5 0 5
Expected 3.06 1.94 .09
Scientist Count 5 2 7
Expected 4.28 2.72 .13
Scrientist Count 0 1 1
Expected .61 .39 .02
Tailor Count 1 1 2
Expected 1.22 .78 .04
Total Count 33 21 54
Expected .61 .39 1.00
Chi-Square Tests
Value df Asymptotic Sig. (2-tailed)
Pearson Chi-Square 15.59 13 .272
Likelihood Ratio 19.66 13 .104
N of Valid Cases 54

Results 15.1: The results of a test of independence between sex and occupation


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15.7 FACTOR

FACTOR  {
         VARIABLES=var_list,
         MATRIX IN ({CORR,COV}={*,file_spec})
        }

        [ /METHOD = {CORRELATION, COVARIANCE} ]

        [ /ANALYSIS=var_list ]

        [ /EXTRACTION={PC, PAF}]

        [ /ROTATION={VARIMAX, EQUAMAX, QUARTIMAX, PROMAX[(k)], NOROTATE}]

        [ /PRINT=[INITIAL] [EXTRACTION] [ROTATION] [UNIVARIATE] [CORRELATION] [COVARIANCE] [DET] [KMO] [AIC] [SIG] [ALL] [DEFAULT] ]

        [ /PLOT=[EIGEN] ]

        [ /FORMAT=[SORT] [BLANK(n)] [DEFAULT] ]

        [ /CRITERIA=[FACTORS(n)] [MINEIGEN(l)] [ITERATE(m)] [ECONVERGE (delta)] [DEFAULT] ]

        [ /MISSING=[{LISTWISE, PAIRWISE}] [{INCLUDE, EXCLUDE}] ]

The FACTOR command performs Factor Analysis or Principal Axis Factoring on a dataset. It may be used to find common factors in the data or for data reduction purposes.

The VARIABLES subcommand is required (unless the MATRIX IN subcommand is used). It lists the variables which are to partake in the analysis. (The ANALYSIS subcommand may optionally further limit the variables that participate; it is useful primarily in conjunction with MATRIX IN.)

If MATRIX IN instead of VARIABLES is specified, then the analysis is performed on a pre-prepared correlation or covariance matrix file instead of on individual data cases. Typically the matrix file will have been generated by MATRIX DATA (see MATRIX DATA) or provided by a third party. If specified, MATRIX IN must be followed by ‘COV’ or ‘CORR’, then by ‘=’ and file_spec all in parentheses. file_spec may either be an asterisk, which indicates the currently loaded dataset, or it may be a file name to be loaded. See MATRIX DATA, for the expected format of the file.

The /EXTRACTION subcommand is used to specify the way in which factors (components) are extracted from the data. If PC is specified, then Principal Components Analysis is used. If PAF is specified, then Principal Axis Factoring is used. By default Principal Components Analysis is used.

The /ROTATION subcommand is used to specify the method by which the extracted solution is rotated. Three orthogonal rotation methods are available: VARIMAX (which is the default), EQUAMAX, and QUARTIMAX. There is one oblique rotation method, viz: PROMAX. Optionally you may enter the power of the promax rotation k, which must be enclosed in parentheses. The default value of k is 5. If you don’t want any rotation to be performed, the word NOROTATE prevents the command from performing any rotation on the data.

The /METHOD subcommand should be used to determine whether the covariance matrix or the correlation matrix of the data is to be analysed. By default, the correlation matrix is analysed.

The /PRINT subcommand may be used to select which features of the analysis are reported:

  • UNIVARIATE A table of mean values, standard deviations and total weights are printed.
  • INITIAL Initial communalities and eigenvalues are printed.
  • EXTRACTION Extracted communalities and eigenvalues are printed.
  • ROTATION Rotated communalities and eigenvalues are printed.
  • CORRELATION The correlation matrix is printed.
  • COVARIANCE The covariance matrix is printed.
  • DET The determinant of the correlation or covariance matrix is printed.
  • AIC The anti-image covariance and anti-image correlation matrices are printed.
  • KMO The Kaiser-Meyer-Olkin measure of sampling adequacy and the Bartlett test of sphericity is printed.
  • SIG The significance of the elements of correlation matrix is printed.
  • ALL All of the above are printed.
  • DEFAULT Identical to INITIAL and EXTRACTION.

If /PLOT=EIGEN is given, then a “Scree” plot of the eigenvalues is printed. This can be useful for visualizing the factors and deciding which factors (components) should be retained.

The /FORMAT subcommand determined how data are to be displayed in loading matrices. If SORT is specified, then the variables are sorted in descending order of significance. If BLANK(n) is specified, then coefficients whose absolute value is less than n are not printed. If the keyword DEFAULT is specified, or if no /FORMAT subcommand is specified, then no sorting is performed, and all coefficients are printed.

You can use the /CRITERIA subcommand to specify how the number of extracted factors (components) are chosen. If FACTORS(n) is specified, where n is an integer, then n factors are extracted. Otherwise, the MINEIGEN setting is used. MINEIGEN(l) requests that all factors whose eigenvalues are greater than or equal to l are extracted. The default value of l is 1. The ECONVERGE setting has effect only when using iterative algorithms for factor extraction (such as Principal Axis Factoring). ECONVERGE(delta) specifies that iteration should cease when the maximum absolute value of the communality estimate between one iteration and the previous is less than delta. The default value of delta is 0.001.

The ITERATE(m) may appear any number of times and is used for two different purposes. It is used to set the maximum number of iterations (m) for convergence and also to set the maximum number of iterations for rotation. Whether it affects convergence or rotation depends upon which subcommand follows the ITERATE subcommand. If EXTRACTION follows, it affects convergence. If ROTATION follows, it affects rotation. If neither ROTATION nor EXTRACTION follow a ITERATE subcommand, then the entire subcommand is ignored. The default value of m is 25.

The MISSING subcommand determines the handling of missing variables. If INCLUDE is set, then user-missing values are included in the calculations, but system-missing values are not. If EXCLUDE is set, which is the default, user-missing values are excluded as well as system-missing values. This is the default. If LISTWISE is set, then the entire case is excluded from analysis whenever any variable specified in the VARIABLES subcommand contains a missing value.

If PAIRWISE is set, then a case is considered missing only if either of the values for the particular coefficient are missing. The default is LISTWISE.


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15.8 GLM

GLM dependent_vars BY fixed_factors
     [/METHOD = SSTYPE(type)]
     [/DESIGN = interaction_0 [interaction_1 [... interaction_n]]]
     [/INTERCEPT = {INCLUDE|EXCLUDE}]
     [/MISSING = {INCLUDE|EXCLUDE}]

The GLM procedure can be used for fixed effects factorial Anova.

The dependent_vars are the variables to be analysed. You may analyse several variables in the same command in which case they should all appear before the BY keyword.

The fixed_factors list must be one or more categorical variables. Normally it does not make sense to enter a scalar variable in the fixed_factors and doing so may cause PSPP to do a lot of unnecessary processing.

The METHOD subcommand is used to change the method for producing the sums of squares. Available values of type are 1, 2 and 3. The default is type 3.

You may specify a custom design using the DESIGN subcommand. The design comprises a list of interactions where each interaction is a list of variables separated by a ‘*’. For example the command

GLM subject BY sex age_group race
    /DESIGN = age_group sex group age_group*sex age_group*race

specifies the model subject = age_group + sex + race + age_group*sex + age_group*race. If no DESIGN subcommand is specified, then the default is all possible combinations of the fixed factors. That is to say

GLM subject BY sex age_group race

implies the model subject = age_group + sex + race + age_group*sex + age_group*race + sex*race + age_group*sex*race.

The MISSING subcommand determines the handling of missing variables. If INCLUDE is set then, for the purposes of GLM analysis, only system-missing values are considered to be missing; user-missing values are not regarded as missing. If EXCLUDE is set, which is the default, then user-missing values are considered to be missing as well as system-missing values. A case for which any dependent variable or any factor variable has a missing value is excluded from the analysis.


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15.9 LOGISTIC REGRESSION

LOGISTIC REGRESSION [VARIABLES =] dependent_var WITH predictors

     [/CATEGORICAL = categorical_predictors]

     [{/NOCONST | /ORIGIN | /NOORIGIN }]

     [/PRINT = [SUMMARY] [DEFAULT] [CI(confidence)] [ALL]]

     [/CRITERIA = [BCON(min_delta)] [ITERATE(max_interations)]
                  [LCON(min_likelihood_delta)] [EPS(min_epsilon)]
                  [CUT(cut_point)]]

     [/MISSING = {INCLUDE|EXCLUDE}]

Bivariate Logistic Regression is used when you want to explain a dichotomous dependent variable in terms of one or more predictor variables.

The minimum command is

LOGISTIC REGRESSION y WITH x1 x2xn.

Here, y is the dependent variable, which must be dichotomous and x1xn are the predictor variables whose coefficients the procedure estimates.

By default, a constant term is included in the model. Hence, the full model is {\bf y} = b_0 + b_1 {\bf x_1} + b_2 {\bf x_2} + \dots + b_n {\bf x_n}

Predictor variables which are categorical in nature should be listed on the /CATEGORICAL subcommand. Simple variables as well as interactions between variables may be listed here.

If you want a model without the constant term b_0, use the keyword /ORIGIN. /NOCONST is a synonym for /ORIGIN.

An iterative Newton-Raphson procedure is used to fit the model. The /CRITERIA subcommand is used to specify the stopping criteria of the procedure, and other parameters. The value of cut_point is used in the classification table. It is the threshold above which predicted values are considered to be 1. Values of cut_point must lie in the range [0,1]. During iterations, if any one of the stopping criteria are satisfied, the procedure is considered complete. The stopping criteria are:

  • The number of iterations exceeds max_iterations. The default value of max_iterations is 20.
  • The change in the all coefficient estimates are less than min_delta. The default value of min_delta is 0.001.
  • The magnitude of change in the likelihood estimate is less than min_likelihood_delta. The default value of min_delta is zero. This means that this criterion is disabled.
  • The differential of the estimated probability for all cases is less than min_epsilon. In other words, the probabilities are close to zero or one. The default value of min_epsilon is 0.00000001.

The PRINT subcommand controls the display of optional statistics. Currently there is one such option, CI, which indicates that the confidence interval of the odds ratio should be displayed as well as its value. CI should be followed by an integer in parentheses, to indicate the confidence level of the desired confidence interval.

The MISSING subcommand determines the handling of missing variables. If INCLUDE is set, then user-missing values are included in the calculations, but system-missing values are not. If EXCLUDE is set, which is the default, user-missing values are excluded as well as system-missing values. This is the default.


15.10 MEANS

MEANS [TABLES =]
      {var_list}
        [ BY {var_list} [BY {var_list} [BY {var_list} … ]]]

      [ /{var_list}
         [ BY {var_list} [BY {var_list} [BY {var_list} … ]]] ]

      [/CELLS = [MEAN] [COUNT] [STDDEV] [SEMEAN] [SUM] [MIN] [MAX] [RANGE]
        [VARIANCE] [KURT] [SEKURT]
        [SKEW] [SESKEW] [FIRST] [LAST]
        [HARMONIC] [GEOMETRIC]
        [DEFAULT]
        [ALL]
        [NONE] ]

      [/MISSING = [INCLUDE] [DEPENDENT]]

You can use the MEANS command to calculate the arithmetic mean and similar statistics, either for the dataset as a whole or for categories of data.

The simplest form of the command is

MEANS v.

which calculates the mean, count and standard deviation for v. If you specify a grouping variable, for example

MEANS v BY g.

then the means, counts and standard deviations for v after having been grouped by g are calculated. Instead of the mean, count and standard deviation, you could specify the statistics in which you are interested:

MEANS x y BY g
      /CELLS = HARMONIC SUM MIN.

This example calculates the harmonic mean, the sum and the minimum values of x and y grouped by g.

The CELLS subcommand specifies which statistics to calculate. The available statistics are:

  • MEAN The arithmetic mean.
  • COUNT The count of the values.
  • STDDEV The standard deviation.
  • SEMEAN The standard error of the mean.
  • SUM The sum of the values.
  • MIN The minimum value.
  • MAX The maximum value.
  • RANGE The difference between the maximum and minimum values.
  • VARIANCE The variance.
  • FIRST The first value in the category.
  • LAST The last value in the category.
  • SKEW The skewness.
  • SESKEW The standard error of the skewness.
  • KURT The kurtosis
  • SEKURT The standard error of the kurtosis.
  • HARMONIC The harmonic mean.
  • GEOMETRIC The geometric mean.

In addition, three special keywords are recognized:

  • DEFAULT This is the same as MEAN COUNT STDDEV.
  • ALL All of the above statistics are calculated.
  • NONE No statistics are calculated (only a summary is shown).

More than one table can be specified in a single command. Each table is separated by a ‘/’. For example

MEANS TABLES =
      c d e BY x
      /a b BY x y
      /f BY y BY z.

has three tables (the ‘TABLE =’ is optional). The first table has three dependent variables c, d and e and a single categorical variable x. The second table has two dependent variables a and b, and two categorical variables x and y. The third table has a single dependent variables f and a categorical variable formed by the combination of y and z.

By default values are omitted from the analysis only if missing values (either system missing or user missing) for any of the variables directly involved in their calculation are encountered. This behaviour can be modified with the /MISSING subcommand. Three options are possible: TABLE, INCLUDE and DEPENDENT.

/MISSING = INCLUDE says that user missing values, either in the dependent variables or in the categorical variables should be taken at their face value, and not excluded.

/MISSING = DEPENDENT says that user missing values, in the dependent variables should be taken at their face value, however cases which have user missing values for the categorical variables should be omitted from the calculation.

15.10.1 Example Means

The dataset in repairs.sav contains the mean time between failures (mtbf) for a sample of artifacts produced by different factories and trialed under different operating conditions. Since there are four combinations of categorical variables, by simply looking at the list of data, it would be hard to how the scores vary for each category. Example 15.4 shows one way of tabulating the mtbf in a way which is easier to understand.

get file='repairs.sav'.

means tables = mtbf
      by factory by environment.

Example 15.4: Running MEANS on the mtbf score with categories factory and environment

The results are shown in Result 15.3. The figures shown indicate the mean, standard deviation and number of samples in each category. These figures however do not indicate whether the results are statistically significant. For that, you would need to use the procedures ONEWAY, GLM or T-TEST depending on the hypothesis being tested.

Case Processing Summary
Cases
Included Excluded Total
N Percent N Percent N Percent
mtbf * factory * environment 30 100.0% 0 .0% 30 100.0%
Report
Manufacturing facility Operating Environment Mean N Std. Deviation
0 Temperate 7.26 9 2.57
Tropical 7.47 7 2.68
Total 7.35 16 2.53
1 Temperate 13.38 6 7.77
Tropical 8.20 8 8.39
Total 10.42 14 8.26
Total Temperate 9.71 15 5.91
Tropical 7.86 15 6.20
Total 8.78 30 6.03

Result 15.3: The mtbf categorised by factory and environment

Note that there is no limit to the number of variables for which you can calculate statistics, nor to the number of categorical variables per layer, nor the number of layers. However, running MEANS on a large numbers of variables, or with categorical variables containing a large number of distinct values may result in an extremely large output, which will not be easy to interpret. So you should consider carefully which variables to select for participation in the analysis.


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15.11 NPAR TESTS

NPAR TESTS

     nonparametric test subcommands
     .
     .
     .

     [ /STATISTICS={DESCRIPTIVES} ]

     [ /MISSING={ANALYSIS, LISTWISE} {INCLUDE, EXCLUDE} ]

     [ /METHOD=EXACT [ TIMER [(n)] ] ]

NPAR TESTS performs nonparametric tests. Non parametric tests make very few assumptions about the distribution of the data. One or more tests may be specified by using the corresponding subcommand. If the /STATISTICS subcommand is also specified, then summary statistics are produces for each variable that is the subject of any test.

Certain tests may take a long time to execute, if an exact figure is required. Therefore, by default asymptotic approximations are used unless the subcommand /METHOD=EXACT is specified. Exact tests give more accurate results, but may take an unacceptably long time to perform. If the TIMER keyword is used, it sets a maximum time, after which the test is abandoned, and a warning message printed. The time, in minutes, should be specified in parentheses after the TIMER keyword. If the TIMER keyword is given without this figure, then a default value of 5 minutes is used.


15.11.1 Binomial test

     [ /BINOMIAL[(p)]=var_list[(value1[, value2)] ] ]

The /BINOMIAL subcommand compares the observed distribution of a dichotomous variable with that of a binomial distribution. The variable p specifies the test proportion of the binomial distribution. The default value of 0.5 is assumed if p is omitted.

If a single value appears after the variable list, then that value is used as the threshold to partition the observed values. Values less than or equal to the threshold value form the first category. Values greater than the threshold form the second category.

If two values appear after the variable list, then they are used as the values which a variable must take to be in the respective category. Cases for which a variable takes a value equal to neither of the specified values, take no part in the test for that variable.

If no values appear, then the variable must assume dichotomous values. If more than two distinct, non-missing values for a variable under test are encountered then an error occurs.

If the test proportion is equal to 0.5, then a two tailed test is reported. For any other test proportion, a one tailed test is reported. For one tailed tests, if the test proportion is less than or equal to the observed proportion, then the significance of observing the observed proportion or more is reported. If the test proportion is more than the observed proportion, then the significance of observing the observed proportion or less is reported. That is to say, the test is always performed in the observed direction.

PSPP uses a very precise approximation to the gamma function to compute the binomial significance. Thus, exact results are reported even for very large sample sizes.


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15.11.2 Chi-square Test

     [ /CHISQUARE=var_list[(lo,hi)] [/EXPECTED={EQUAL|f1, f2fn}] ]

The /CHISQUARE subcommand produces a chi-square statistic for the differences between the expected and observed frequencies of the categories of a variable. Optionally, a range of values may appear after the variable list. If a range is given, then non integer values are truncated, and values outside the specified range are excluded from the analysis.

The /EXPECTED subcommand specifies the expected values of each category. There must be exactly one non-zero expected value, for each observed category, or the EQUAL keyword must be specified. You may use the notation n*f to specify n consecutive expected categories all taking a frequency of f. The frequencies given are proportions, not absolute frequencies. The sum of the frequencies need not be 1. If no /EXPECTED subcommand is given, then equal frequencies are expected.

15.11.2.1 Chi-square Example

A researcher wishes to investigate whether there are an equal number of persons of each sex in a population. The sample chosen for invesigation is that from the physiology.sav dataset. The null hypothesis for the test is that the population comprises an equal number of males and females. The analysis is performed as shown in Example 15.5.

get file='physiology.sav'.

npar test
     /chisquare=sex.

Example 15.5: Performing a chi-square test to check for equal distribution of sexes

There is only one test variable, viz: sex. The other variables in the dataset are ignored.

screenshots/chisquare-ad

Screenshot 15.4: Performing a chi-square test using the graphic user interface

In Result 15.4 the summary box shows that in the sample, there are more males than females. However the significance of chi-square result is greater than 0.05 — the most commonly accepted p-value — and therefore there is not enough evidence to reject the null hypothesis and one must conclude that the evidence does not indicate that there is an imbalance of the sexes in the population.

Sex of subject
Value Observed N Expected N Residual
Male 22 20.00 2.00
Female 18 20.00 -2.00
Total 40
Test Statistics
Chi-square df Asymp. Sig.
Sex of subject .40 1 .527

Result 15.4: The results of running a chi-square test on sex


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15.11.3 Cochran Q Test

     [ /COCHRAN = var_list ]

The Cochran Q test is used to test for differences between three or more groups. The data for var_list in all cases must assume exactly two distinct values (other than missing values).

The value of Q is displayed along with its Asymptotic significance based on a chi-square distribution.


15.11.4 Friedman Test

     [ /FRIEDMAN = var_list ]

The Friedman test is used to test for differences between repeated measures when there is no indication that the distributions are normally distributed.

A list of variables which contain the measured data must be given. The procedure prints the sum of ranks for each variable, the test statistic and its significance.


15.11.5 Kendall’s W Test

     [ /KENDALL = var_list ]

The Kendall test investigates whether an arbitrary number of related samples come from the same population. It is identical to the Friedman test except that the additional statistic W, Kendall’s Coefficient of Concordance is printed. It has the range [0,1] — a value of zero indicates no agreement between the samples whereas a value of unity indicates complete agreement.


15.11.6 Kolmogorov-Smirnov Test

     [ /KOLMOGOROV-SMIRNOV ({NORMAL [mu, sigma], UNIFORM [min, max], POISSON [lambda], EXPONENTIAL [scale] }) = var_list ]

The one sample Kolmogorov-Smirnov subcommand is used to test whether or not a dataset is drawn from a particular distribution. Four distributions are supported, viz: Normal, Uniform, Poisson and Exponential.

Ideally you should provide the parameters of the distribution against which you wish to test the data. For example, with the normal distribution the mean (mu)and standard deviation (sigma) should be given; with the uniform distribution, the minimum (min)and maximum (max) value should be provided. However, if the parameters are omitted they are imputed from the data. Imputing the parameters reduces the power of the test so should be avoided if possible.

In the following example, two variables score and age are tested to see if they follow a normal distribution with a mean of 3.5 and a standard deviation of 2.0.

  NPAR TESTS
        /KOLMOGOROV-SMIRNOV (normal 3.5 2.0) = score age.

If the variables need to be tested against different distributions, then a separate subcommand must be used. For example the following syntax tests score against a normal distribution with mean of 3.5 and standard deviation of 2.0 whilst age is tested against a normal distribution of mean 40 and standard deviation 1.5.

  NPAR TESTS
        /KOLMOGOROV-SMIRNOV (normal 3.5 2.0) = score
        /KOLMOGOROV-SMIRNOV (normal 40 1.5) =  age.

The abbreviated subcommand K-S may be used in place of KOLMOGOROV-SMIRNOV.


15.11.7 Kruskal-Wallis Test

     [ /KRUSKAL-WALLIS = var_list BY var (lower, upper) ]

The Kruskal-Wallis test is used to compare data from an arbitrary number of populations. It does not assume normality. The data to be compared are specified by var_list. The categorical variable determining the groups to which the data belongs is given by var. The limits lower and upper specify the valid range of var. If upper is smaller than lower, the PSPP will assume their values to be reversed. Any cases for which var falls outside [lower, upper] are ignored.

The mean rank of each group as well as the chi-squared value and significance of the test are printed. The abbreviated subcommand K-W may be used in place of KRUSKAL-WALLIS.


15.11.8 Mann-Whitney U Test

     [ /MANN-WHITNEY = var_list BY var (group1, group2) ]

The Mann-Whitney subcommand is used to test whether two groups of data come from different populations. The variables to be tested should be specified in var_list and the grouping variable, that determines to which group the test variables belong, in var. Var may be either a string or an alpha variable. Group1 and group2 specify the two values of var which determine the groups of the test data. Cases for which the var value is neither group1 or group2 are ignored.

The value of the Mann-Whitney U statistic, the Wilcoxon W, and the significance are printed. You may abbreviated the subcommand MANN-WHITNEY to M-W.


15.11.9 McNemar Test

     [ /MCNEMAR var_list [ WITH var_list [ (PAIRED) ]]]

Use McNemar’s test to analyse the significance of the difference between pairs of correlated proportions.

If the WITH keyword is omitted, then tests for all combinations of the listed variables are performed. If the WITH keyword is given, and the (PAIRED) keyword is also given, then the number of variables preceding WITH must be the same as the number following it. In this case, tests for each respective pair of variables are performed. If the WITH keyword is given, but the (PAIRED) keyword is omitted, then tests for each combination of variable preceding WITH against variable following WITH are performed.

The data in each variable must be dichotomous. If there are more than two distinct variables an error will occur and the test will not be run.


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15.11.10 Median Test

     [ /MEDIAN [(value)] = var_list BY variable (value1, value2) ]

The median test is used to test whether independent samples come from populations with a common median. The median of the populations against which the samples are to be tested may be given in parentheses immediately after the /MEDIAN subcommand. If it is not given, the median is imputed from the union of all the samples.

The variables of the samples to be tested should immediately follow the ‘=’ sign. The keyword BY must come next, and then the grouping variable. Two values in parentheses should follow. If the first value is greater than the second, then a 2 sample test is performed using these two values to determine the groups. If however, the first variable is less than the second, then a k sample test is conducted and the group values used are all values encountered which lie in the range [value1,value2].


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15.11.11 Runs Test

     [ /RUNS ({MEAN, MEDIAN, MODE, value})  = var_list ]

The /RUNS subcommand tests whether a data sequence is randomly ordered.

It works by examining the number of times a variable’s value crosses a given threshold. The desired threshold must be specified within parentheses. It may either be specified as a number or as one of MEAN, MEDIAN or MODE. Following the threshold specification comes the list of variables whose values are to be tested.

The subcommand shows the number of runs, the asymptotic significance based on the length of the data.


15.11.12 Sign Test

     [ /SIGN var_list [ WITH var_list [ (PAIRED) ]]]

The /SIGN subcommand tests for differences between medians of the variables listed. The test does not make any assumptions about the distribution of the data.

If the WITH keyword is omitted, then tests for all combinations of the listed variables are performed. If the WITH keyword is given, and the (PAIRED) keyword is also given, then the number of variables preceding WITH must be the same as the number following it. In this case, tests for each respective pair of variables are performed. If the WITH keyword is given, but the (PAIRED) keyword is omitted, then tests for each combination of variable preceding WITH against variable following WITH are performed.


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15.11.13 Wilcoxon Matched Pairs Signed Ranks Test

     [ /WILCOXON var_list [ WITH var_list [ (PAIRED) ]]]

The /WILCOXON subcommand tests for differences between medians of the variables listed. The test does not make any assumptions about the variances of the samples. It does however assume that the distribution is symmetrical.

If the WITH keyword is omitted, then tests for all combinations of the listed variables are performed. If the WITH keyword is given, and the (PAIRED) keyword is also given, then the number of variables preceding WITH must be the same as the number following it. In this case, tests for each respective pair of variables are performed. If the WITH keyword is given, but the (PAIRED) keyword is omitted, then tests for each combination of variable preceding WITH against variable following WITH are performed.


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15.12 T-TEST

T-TEST
        /MISSING={ANALYSIS,LISTWISE} {EXCLUDE,INCLUDE}
        /CRITERIA=CI(confidence)


(One Sample mode.)
        TESTVAL=test_value
        /VARIABLES=var_list


(Independent Samples mode.)
        GROUPS=var(value1 [, value2])
        /VARIABLES=var_list


(Paired Samples mode.)
        PAIRS=var_list [WITH var_list [(PAIRED)] ]

The T-TEST procedure outputs tables used in testing hypotheses about means. It operates in one of three modes:

  • One Sample mode.
  • Independent Groups mode.
  • Paired mode.

Each of these modes are described in more detail below. There are two optional subcommands which are common to all modes.

The /CRITERIA subcommand tells PSPP the confidence interval used in the tests. The default value is 0.95.

The MISSING subcommand determines the handling of missing variables. If INCLUDE is set, then user-missing values are included in the calculations, but system-missing values are not. If EXCLUDE is set, which is the default, user-missing values are excluded as well as system-missing values. This is the default.

If LISTWISE is set, then the entire case is excluded from analysis whenever any variable specified in the /VARIABLES, /PAIRS or /GROUPS subcommands contains a missing value. If ANALYSIS is set, then missing values are excluded only in the analysis for which they would be needed. This is the default.


15.12.1 One Sample Mode

The TESTVAL subcommand invokes the One Sample mode. This mode is used to test a population mean against a hypothesized mean. The value given to the TESTVAL subcommand is the value against which you wish to test. In this mode, you must also use the /VARIABLES subcommand to tell PSPP which variables you wish to test.

15.12.1.1 Example - One Sample T-test

A researcher wishes to know whether the weight of persons in a population is different from the national average. The samples are drawn from the population under investigation and recorded in the file physiology.sav. From the Department of Health, she knows that the national average weight of healthy adults is 76.8kg. Accordingly the TESTVAL is set to 76.8. The null hypothesis therefore is that the mean average weight of the population from which the sample was drawn is 76.8kg.

As previously noted (see Identifying incorrect data), one sample in the dataset contains a weight value which is clearly incorrect. So this is excluded from the analysis using the SELECT command.

get file='physiology.sav'.

select if (weight > 0).

t-test testval = 76.8
	/variables = weight.

Example 15.6: Running a one sample T-Test after excluding all non-positive values

screenshots/one-sample-t-ad

Screenshot 15.5: Using the One Sample T-Test dialog box to test weight for a mean of 76.8kg

Results 15.2 shows that the mean of our sample differs from the test value by -1.40kg. However the significance is very high (0.610). So one cannot reject the null hypothesis, and must conclude there is not enough evidence to suggest that the mean weight of the persons in our population is different from 76.8kg.

One-Sample Statistics
N Mean Std. Deviation S.E. Mean
Weight in kilograms 39 75.40 17.08 2.73
One-Sample Test
Test Value = 76.8
t df Sig. (2-tailed) Mean Difference 95% Confidence Interval of the Difference
Lower Upper
Weight in kilograms -.51 38 .610 -1.40 -6.94 4.13

Results 15.2: The results of a one sample T-test of weight using a test value of 76.8kg


15.12.2 Independent Samples Mode

The GROUPS subcommand invokes Independent Samples mode or ‘Groups’ mode. This mode is used to test whether two groups of values have the same population mean. In this mode, you must also use the /VARIABLES subcommand to tell PSPP the dependent variables you wish to test.

The variable given in the GROUPS subcommand is the independent variable which determines to which group the samples belong. The values in parentheses are the specific values of the independent variable for each group. If the parentheses are omitted and no values are given, the default values of 1.0 and 2.0 are assumed.

If the independent variable is numeric, it is acceptable to specify only one value inside the parentheses. If you do this, cases where the independent variable is greater than or equal to this value belong to the first group, and cases less than this value belong to the second group. When using this form of the GROUPS subcommand, missing values in the independent variable are excluded on a listwise basis, regardless of whether /MISSING=LISTWISE was specified.

15.12.2.1 Example - Independent Samples T-test

A researcher wishes to know whether within a population, adult males are taller than adult females. The samples are drawn from the population under investigation and recorded in the file physiology.sav.

As previously noted (see Identifying incorrect data), one sample in the dataset contains a height value which is clearly incorrect. So this is excluded from the analysis using the SELECT command.

get file='physiology.sav'.

select if (height >= 200).

t-test /variables = height
       /groups = sex(0,1).

Example 15.7: Running a independent samples T-Test after excluding all observations less than 200kg

The null hypothesis is that both males and females are on average of equal height.

screenshots/independent-samples-t-ad

Screenshot 15.6: Using the Independent Sample T-test dialog, to test for differences of height between values of sex

In this case, the grouping variable is sex, so this is entered as the variable for the GROUP subcommand. The group values are 0 (male) and 1 (female).

If you are running the proceedure using syntax, then you need to enter the values corresponding to each group within parentheses. If you are using the graphic user interface, then you have to open the “Define Groups” dialog box and enter the values corresponding to each group as shown in Screenshot 15.7. If, as in this case, the dataset has defined value labels for the group variable, then you can enter them by label or by value.

screenshots/define-groups-t-ad

Screenshot 15.7: Setting the values of the grouping variable for an Independent Samples T-test

From Result 15.5, one can clearly see that the sample mean height is greater for males than for females. However in order to see if this is a significant result, one must consult the T-Test table.

The T-Test table contains two rows; one for use if the variance of the samples in each group may be safely assumed to be equal, and the second row if the variances in each group may not be safely assumed to be equal.

In this case however, both rows show a 2-tailed significance less than 0.001 and one must therefore reject the null hypothesis and conclude that within the population the mean height of males and of females are unequal.

Group Statistics
Group N Mean Std. Deviation S.E. Mean
Height in millimeters Male 22 1796.49 49.71 10.60
Female 17 1610.77 25.43 6.17
Independent Samples Test
Levene's Test for Equality of Variances T-Test for Equality of Means
F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference 95% Confidence Interval of the Difference
Lower Upper
Height in millimeters Equal variances assumed .97 .331 14.02 37.00 .000 185.72 13.24 158.88 212.55
Equal variances not assumed 15.15 32.71 .000 185.72 12.26 160.76 210.67

Result 15.5: The results of an independent samples T-test of height by sex


15.12.3 Paired Samples Mode

The PAIRS subcommand introduces Paired Samples mode. Use this mode when repeated measures have been taken from the same samples. If the WITH keyword is omitted, then tables for all combinations of variables given in the PAIRS subcommand are generated. If the WITH keyword is given, and the (PAIRED) keyword is also given, then the number of variables preceding WITH must be the same as the number following it. In this case, tables for each respective pair of variables are generated. In the event that the WITH keyword is given, but the (PAIRED) keyword is omitted, then tables for each combination of variable preceding WITH against variable following WITH are generated.


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15.13 ONEWAY

ONEWAY
        [/VARIABLES = ] var_list BY var
        /MISSING={ANALYSIS,LISTWISE} {EXCLUDE,INCLUDE}
        /CONTRAST= value1 [, value2] ... [,valueN]
        /STATISTICS={DESCRIPTIVES,HOMOGENEITY}
        /POSTHOC={BONFERRONI, GH, LSD, SCHEFFE, SIDAK, TUKEY, ALPHA ([value])}

The ONEWAY procedure performs a one-way analysis of variance of variables factored by a single independent variable. It is used to compare the means of a population divided into more than two groups.

The dependent variables to be analysed should be given in the VARIABLES subcommand. The list of variables must be followed by the BY keyword and the name of the independent (or factor) variable.

You can use the STATISTICS subcommand to tell PSPP to display ancillary information. The options accepted are:

  • DESCRIPTIVES Displays descriptive statistics about the groups factored by the independent variable.
  • HOMOGENEITY Displays the Levene test of Homogeneity of Variance for the variables and their groups.

The CONTRAST subcommand is used when you anticipate certain differences between the groups. The subcommand must be followed by a list of numerals which are the coefficients of the groups to be tested. The number of coefficients must correspond to the number of distinct groups (or values of the independent variable). If the total sum of the coefficients are not zero, then PSPP will display a warning, but will proceed with the analysis. The CONTRAST subcommand may be given up to 10 times in order to specify different contrast tests. The MISSING subcommand defines how missing values are handled. If LISTWISE is specified then cases which have missing values for the independent variable or any dependent variable are ignored. If ANALYSIS is specified, then cases are ignored if the independent variable is missing or if the dependent variable currently being analysed is missing. The default is ANALYSIS. A setting of EXCLUDE means that variables whose values are user-missing are to be excluded from the analysis. A setting of INCLUDE means they are to be included. The default is EXCLUDE.

Using the POSTHOC subcommand you can perform multiple pairwise comparisons on the data. The following comparison methods are available:

  • LSD Least Significant Difference.
  • TUKEY Tukey Honestly Significant Difference.
  • BONFERRONI Bonferroni test.
  • SCHEFFE Scheffé’s test.
  • SIDAK Sidak test.
  • GH The Games-Howell test.

Use the optional syntax ALPHA(value) to indicate that ONEWAY should perform the posthoc tests at a confidence level of value. If ALPHA(value) is not specified, then the confidence level used is 0.05.


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15.14 QUICK CLUSTER

QUICK CLUSTER var_list
      [/CRITERIA=CLUSTERS(k) [MXITER(max_iter)] CONVERGE(epsilon) [NOINITIAL]]
      [/MISSING={EXCLUDE,INCLUDE} {LISTWISE, PAIRWISE}]
      [/PRINT={INITIAL} {CLUSTER}]
      [/SAVE[=[CLUSTER[(membership_var)]] [DISTANCE[(distance_var)]]]

The QUICK CLUSTER command performs k-means clustering on the dataset. This is useful when you wish to allocate cases into clusters of similar values and you already know the number of clusters.

The minimum specification is ‘QUICK CLUSTER’ followed by the names of the variables which contain the cluster data. Normally you will also want to specify /CRITERIA=CLUSTERS(k) where k is the number of clusters. If this is not specified, then k defaults to 2.

If you use /CRITERIA=NOINITIAL then a naive algorithm to select the initial clusters is used. This will provide for faster execution but less well separated initial clusters and hence possibly an inferior final result.

QUICK CLUSTER uses an iterative algorithm to select the clusters centers. The subcommand /CRITERIA=MXITER(max_iter) sets the maximum number of iterations. During classification, PSPP will continue iterating until until max_iter iterations have been done or the convergence criterion (see below) is fulfilled. The default value of max_iter is 2.

If however, you specify /CRITERIA=NOUPDATE then after selecting the initial centers, no further update to the cluster centers is done. In this case, max_iter, if specified. is ignored.

The subcommand /CRITERIA=CONVERGE(epsilon) is used to set the convergence criterion. The value of convergence criterion is epsilon times the minimum distance between the initial cluster centers. Iteration stops when the mean cluster distance between one iteration and the next is less than the convergence criterion. The default value of epsilon is zero.

The MISSING subcommand determines the handling of missing variables. If INCLUDE is set, then user-missing values are considered at their face value and not as missing values. If EXCLUDE is set, which is the default, user-missing values are excluded as well as system-missing values.

If LISTWISE is set, then the entire case is excluded from the analysis whenever any of the clustering variables contains a missing value. If PAIRWISE is set, then a case is considered missing only if all the clustering variables contain missing values. Otherwise it is clustered on the basis of the non-missing values. The default is LISTWISE.

The PRINT subcommand requests additional output to be printed. If INITIAL is set, then the initial cluster memberships will be printed. If CLUSTER is set, the cluster memberships of the individual cases are displayed (potentially generating lengthy output).

You can specify the subcommand SAVE to ask that each case’s cluster membership and the euclidean distance between the case and its cluster center be saved to a new variable in the active dataset. To save the cluster membership use the CLUSTER keyword and to save the distance use the DISTANCE keyword. Each keyword may optionally be followed by a variable name in parentheses to specify the new variable which is to contain the saved parameter. If no variable name is specified, then PSPP will create one.


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15.15 RANK

RANK
        [VARIABLES=] var_list [{A,D}] [BY var_list]
        /TIES={MEAN,LOW,HIGH,CONDENSE}
        /FRACTION={BLOM,TUKEY,VW,RANKIT}
        /PRINT[={YES,NO}
        /MISSING={EXCLUDE,INCLUDE}

        /RANK [INTO var_list]
        /NTILES(k) [INTO var_list]
        /NORMAL [INTO var_list]
        /PERCENT [INTO var_list]
        /RFRACTION [INTO var_list]
        /PROPORTION [INTO var_list]
        /N [INTO var_list]
        /SAVAGE [INTO var_list]

The RANK command ranks variables and stores the results into new variables.

The VARIABLES subcommand, which is mandatory, specifies one or more variables whose values are to be ranked. After each variable, ‘A’ or ‘D’ may appear, indicating that the variable is to be ranked in ascending or descending order. Ascending is the default. If a BY keyword appears, it should be followed by a list of variables which are to serve as group variables. In this case, the cases are gathered into groups, and ranks calculated for each group.

The TIES subcommand specifies how tied values are to be treated. The default is to take the mean value of all the tied cases.

The FRACTION subcommand specifies how proportional ranks are to be calculated. This only has any effect if NORMAL or PROPORTIONAL rank functions are requested.

The PRINT subcommand may be used to specify that a summary of the rank variables created should appear in the output.

The function subcommands are RANK, NTILES, NORMAL, PERCENT, RFRACTION, PROPORTION and SAVAGE. Any number of function subcommands may appear. If none are given, then the default is RANK. The NTILES subcommand must take an integer specifying the number of partitions into which values should be ranked. Each subcommand may be followed by the INTO keyword and a list of variables which are the variables to be created and receive the rank scores. There may be as many variables specified as there are variables named on the VARIABLES subcommand. If fewer are specified, then the variable names are automatically created.

The MISSING subcommand determines how user missing values are to be treated. A setting of EXCLUDE means that variables whose values are user-missing are to be excluded from the rank scores. A setting of INCLUDE means they are to be included. The default is EXCLUDE.


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15.16 REGRESSION

The REGRESSION procedure fits linear models to data via least-squares estimation. The procedure is appropriate for data which satisfy those assumptions typical in linear regression:

  • The data set contains n observations of a dependent variable, say Y_1,…,Y_n, and n observations of one or more explanatory variables. Let X_{11}, X_{12}, …, X_{1n} denote the n observations of the first explanatory variable; X_{21},…,X_{2n} denote the n observations of the second explanatory variable; X_{k1},…,X_{kn} denote the n observations of the kth explanatory variable.
  • The dependent variable Y has the following relationship to the explanatory variables: Y_i = b_0 + b_1 X_{1i} + ... + b_k X_{ki} + Z_i where b_0, b_1, …, b_k are unknown coefficients, and Z_1,…,Z_n are independent, normally distributed noise terms with mean zero and common variance. The noise, or error terms are unobserved. This relationship is called the linear model.

The REGRESSION procedure estimates the coefficients b_0,…,b_k and produces output relevant to inferences for the linear model.


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15.16.1 Syntax

REGRESSION
        /VARIABLES=var_list
        /DEPENDENT=var_list
        /STATISTICS={ALL, DEFAULTS, R, COEFF, ANOVA, BCOV, CI[conf, TOL]}
        { /ORIGIN | /NOORIGIN }
        /SAVE={PRED, RESID}

The REGRESSION procedure reads the active dataset and outputs statistics relevant to the linear model specified by the user.

The VARIABLES subcommand, which is required, specifies the list of variables to be analyzed. Keyword VARIABLES is required. The DEPENDENT subcommand specifies the dependent variable of the linear model. The DEPENDENT subcommand is required. All variables listed in the VARIABLES subcommand, but not listed in the DEPENDENT subcommand, are treated as explanatory variables in the linear model.

All other subcommands are optional:

The STATISTICS subcommand specifies which statistics are to be displayed. The following keywords are accepted:

ALL

All of the statistics below.

R

The ratio of the sums of squares due to the model to the total sums of squares for the dependent variable.

COEFF

A table containing the estimated model coefficients and their standard errors.

CI (conf)

This item is only relevant if COEFF has also been selected. It specifies that the confidence interval for the coefficients should be printed. The optional value conf, which must be in parentheses, is the desired confidence level expressed as a percentage.

ANOVA

Analysis of variance table for the model.

BCOV

The covariance matrix for the estimated model coefficients.

TOL

The variance inflation factor and its reciprocal. This has no effect unless COEFF is also given.

DEFAULT

The same as if R, COEFF, and ANOVA had been selected. This is what you get if the /STATISTICS command is not specified, or if it is specified without any parameters.

The ORIGIN and NOORIGIN subcommands are mutually exclusive. ORIGIN indicates that the regression should be performed through the origin. You should use this option if, and only if you have reason to believe that the regression does indeed pass through the origin — that is to say, the value b_0 above, is zero. The default is NOORIGIN.

The SAVE subcommand causes PSPP to save the residuals or predicted values from the fitted model to the active dataset. PSPP will store the residuals in a variable called ‘RES1’ if no such variable exists, ‘RES2’ if ‘RES1’ already exists, ‘RES3’ if ‘RES1’ and ‘RES2’ already exist, etc. It will choose the name of the variable for the predicted values similarly, but with ‘PRED’ as a prefix. When SAVE is used, PSPP ignores TEMPORARY, treating temporary transformations as permanent.


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15.16.2 Examples

The following PSPP syntax will generate the default output and save the predicted values and residuals to the active dataset.

title 'Demonstrate REGRESSION procedure'.
data list / v0 1-2 (A) v1 v2 3-22 (10).
begin data.
b  7.735648 -23.97588
b  6.142625 -19.63854
a  7.651430 -25.26557
c  6.125125 -16.57090
a  8.245789 -25.80001
c  6.031540 -17.56743
a  9.832291 -28.35977
c  5.343832 -16.79548
a  8.838262 -29.25689
b  6.200189 -18.58219
end data.
list.
regression /variables=v0 v1 v2 /statistics defaults /dependent=v2
           /save pred resid /method=enter.

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15.17 RELIABILITY

RELIABILITY
        /VARIABLES=var_list
        /SCALE (name) = {var_list, ALL}
        /MODEL={ALPHA, SPLIT[(n)]}
        /SUMMARY={TOTAL,ALL}
        /MISSING={EXCLUDE,INCLUDE}

The RELIABILITY command performs reliability analysis on the data.

The VARIABLES subcommand is required. It determines the set of variables upon which analysis is to be performed.

The SCALE subcommand determines the variables for which reliability is to be calculated. If SCALE is omitted, then analysis for all variables named in the VARIABLES subcommand are used. Optionally, the name parameter may be specified to set a string name for the scale.

The MODEL subcommand determines the type of analysis. If ALPHA is specified, then Cronbach’s Alpha is calculated for the scale. If the model is SPLIT, then the variables are divided into 2 subsets. An optional parameter n may be given, to specify how many variables to be in the first subset. If n is omitted, then it defaults to one half of the variables in the scale, or one half minus one if there are an odd number of variables. The default model is ALPHA.

By default, any cases with user missing, or system missing values for any variables given in the VARIABLES subcommand are omitted from the analysis. The MISSING subcommand determines whether user missing values are included or excluded in the analysis.

The SUMMARY subcommand determines the type of summary analysis to be performed. Currently there is only one type: SUMMARY=TOTAL, which displays per-item analysis tested against the totals.

15.17.1 Example - Reliability

Before analysing the results of a survey – particularly for a multiple choice survey – it is desireable to know whether the respondents have considered their answers or simply provided random answers.

In the following example the survey results from the file hotel.sav are used. All five survey questions are included in the reliability analysis. However, before running the analysis, the data must be preprocessed. An examination of the survey questions reveals that two questions, viz: v3 and v5 are negatively worded, whereas the others are positively worded. All questions must be based upon the same scale for the analysis to be meaningful. One could use the RECODE command (see RECODE), however a simpler way is to use COMPUTE (see COMPUTE) and this is what is done in Example 15.8.

get file="hotel.sav".

* Recode V3 and V5 inverting the sense of the values.
compute v3 = 6 - v3.
compute v5 = 6 - v5.

reliability
	/variables= all
	/model=alpha.

Example 15.8: Investigating the reliability of survey responses

In this case, all variables in the data set are used. So we can use the special keyword ‘ALL’ (see Backus-Naur Form).

screenshots/reliability-ad

Screenshot 15.8: Reliability dialog box with all variables selected

Result 15.6 shows that Cronbach’s Alpha is 0.11 which is a value normally considered too low to indicate consistency within the data. This is possibly due to the small number of survey questions. The survey should be redesigned before serious use of the results are applied.

Scale: ANY

Case Processing Summary
Cases N Percent
Valid 17 100.0%
Excluded 0 .0%
Total 17 100.0%
Reliability Statistics
Cronbach's Alpha N of Items
.11 5

Result 15.6: The results of the reliability command on hotel.sav


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15.18 ROC

ROC     var_list BY state_var (state_value)
        /PLOT = { CURVE [(REFERENCE)], NONE }
        /PRINT = [ SE ] [ COORDINATES ]
        /CRITERIA = [ CUTOFF({INCLUDE,EXCLUDE}) ]
          [ TESTPOS ({LARGE,SMALL}) ]
          [ CI (confidence) ]
          [ DISTRIBUTION ({FREE, NEGEXPO }) ]
        /MISSING={EXCLUDE,INCLUDE}

The ROC command is used to plot the receiver operating characteristic curve of a dataset, and to estimate the area under the curve. This is useful for analysing the efficacy of a variable as a predictor of a state of nature.

The mandatory var_list is the list of predictor variables. The variable state_var is the variable whose values represent the actual states, and state_value is the value of this variable which represents the positive state.

The optional subcommand PLOT is used to determine if and how the ROC curve is drawn. The keyword CURVE means that the ROC curve should be drawn, and the optional keyword REFERENCE, which should be enclosed in parentheses, says that the diagonal reference line should be drawn. If the keyword NONE is given, then no ROC curve is drawn. By default, the curve is drawn with no reference line.

The optional subcommand PRINT determines which additional tables should be printed. Two additional tables are available. The SE keyword says that standard error of the area under the curve should be printed as well as the area itself. In addition, a p-value for the null hypothesis that the area under the curve equals 0.5 is printed. The COORDINATES keyword says that a table of coordinates of the ROC curve should be printed.

The CRITERIA subcommand has four optional parameters:

  • The TESTPOS parameter may be LARGE or SMALL. LARGE is the default, and says that larger values in the predictor variables are to be considered positive. SMALL indicates that smaller values should be considered positive.
  • The CI parameter specifies the confidence interval that should be printed. It has no effect if the SE keyword in the PRINT subcommand has not been given.
  • The DISTRIBUTION parameter determines the method to be used when estimating the area under the curve. There are two possibilities, viz: FREE and NEGEXPO. The FREE method uses a non-parametric estimate, and the NEGEXPO method a bi-negative exponential distribution estimate. The NEGEXPO method should only be used when the number of positive actual states is equal to the number of negative actual states. The default is FREE.
  • The CUTOFF parameter is for compatibility and is ignored.

The MISSING subcommand determines whether user missing values are to be included or excluded in the analysis. The default behaviour is to exclude them. Cases are excluded on a listwise basis; if any of the variables in var_list or if the variable state_var is missing, then the entire case is excluded.


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16 Matrices

Some PSPP procedures work with matrices by producing numeric matrices that report results of data analysis, or by consuming matrices as a basis for further analysis. This chapter documents the format of data files that store these matrices and commands for working with them, as well as PSPP’s general-purpose facility for matrix operations.


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16.1 Matrix Files

A matrix file is an SPSS system file that conforms to the dictionary and case structure described in this section. Procedures that read matrices from files expect them to be in the matrix file format. Procedures that write matrices also use this format.

Text files that contain matrices can be converted to matrix file format. See MATRIX DATA, for a command to read a text file as a matrix file.

A matrix file’s dictionary must have the following variables in the specified order:

  1. Zero or more numeric split variables. These are included by procedures when SPLIT FILE is active. MATRIX DATA assigns split variables format F4.0.
  2. ROWTYPE_, a string variable with width 8. This variable indicates the kind of matrix or vector that a given case represents. The supported row types are listed below.
  3. Zero or more numeric factor variables. These are included by procedures that divide data into cells. For within-cell data, factor variables are filled with non-missing values; for pooled data, they are missing. MATRIX DATA assigns factor variables format F4.0.
  4. VARNAME_, a string variable. Matrix data includes one row per continuous variable (see below), naming each continuous variable in order. This column is blank for vector data. MATRIX DATA makes VARNAME_ wide enough for the name of any of the continuous variables, but at least 8 bytes.
  5. One or more numeric continuous variables. These are the variables whose data was analyzed to produce the matrices. MATRIX DATA assigns continuous variables format F10.4.

Case weights are ignored in matrix files.

Row Types

Matrix files support a fixed set of types of matrix and vector data. The ROWTYPE_ variable in each case of a matrix file indicates its row type.

The supported matrix row types are listed below. Each type is listed with the keyword that identifies it in ROWTYPE_. All supported types of matrices are square, meaning that each matrix must include one row per continuous variable, with the VARNAME_ variable indicating each continuous variable in turn in the same order as the dictionary.

CORR

Correlation coefficients.

COV

Covariance coefficients.

MAT

General-purpose matrix.

N_MATRIX

Counts.

PROX

Proximities matrix.

The supported vector row types are listed below, along with their associated keyword. Vector row types only require a single row, whose VARNAME_ is blank:

COUNT

Unweighted counts.

DFE

Degrees of freedom.

MEAN

Means.

MSE

Mean squared errors.

N

Counts.

STDDEV

Standard deviations.

Only the row types listed above may appear in matrix files. The MATRIX DATA command, however, accepts the additional row types listed below, which it changes into matrix file row types as part of its conversion process:

N_VECTOR

Synonym for N.

SD

Synonym for STDDEV.

N_SCALAR

Accepts a single number from the MATRIX DATA input and writes it as an N row with the number replicated across all the continuous variables.


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16.2 MATRIX DATA

MATRIX DATA
        VARIABLES=variables
        [FILE={’file_name’ | INLINE}
        [/FORMAT=[{LIST | FREE}]
                 [{UPPER | LOWER | FULL}]
                 [{DIAGONAL | NODIAGONAL}]]
        [/SPLIT=split_vars]
        [/FACTORS=factor_vars]
        [/N=n]

The following subcommands are only needed when ROWTYPE_ is not
specified on the VARIABLES subcommand:
        [/CONTENTS={CORR,COUNT,COV,DFE,MAT,MEAN,MSE,
                    N_MATRIX,N|N_VECTOR,N_SCALAR,PROX,SD|STDDEV}]
        [/CELLS=n_cells]

The MATRIX DATA command convert matrices and vectors from text format into the matrix file format (See Matrix Files) for use by procedures that read matrices. It reads a text file or inline data and outputs to the active file, replacing any data already in the active dataset. The matrix file may then be used by other commands directly from the active file, or it may be written to a .sav file using the SAVE command.

The text data read by MATRIX DATA can be delimited by spaces or commas. A plus or minus sign, except immediately following a ‘d’ or ‘e’, also begins a new value. Optionally, values may be enclosed in single or double quotes.

MATRIX DATA can read the types of matrix and vector data supported in matrix files (see Matrix File Row Types).

The FILE subcommand specifies the source of the command’s input. To read input from a text file, specify its name in quotes. To supply input inline, omit FILE or specify INLINE. Inline data must directly follow MATRIX DATA, inside BEGIN DATA (see BEGIN DATA).

VARIABLES is the only required subcommand. It names the variables present in each input record in the order that they appear. (MATRIX DATA reorders the variables in the matrix file it produces, if needed to fit the matrix file format.) The variable list must include split variables and factor variables, if they are present in the data, in addition to the continuous variables that form matrix rows and columns. It may also include a special variable named ROWTYPE_.

Matrix data may include split variables or factor variables or both. List split variables, if any, on the SPLIT subcommand and factor variables, if any, on the FACTORS subcommand. Split and factor variables must be numeric. Split and factor variables must also be listed on VARIABLES, with one exception: if VARIABLES does not include ROWTYPE_, then SPLIT may name a single variable that is not in VARIABLES (see MATRIX DATA Example 8).

The FORMAT subcommand accepts settings to describe the format of the input data:

LIST (default)
FREE

LIST requires each row to begin at the start of a new input line. FREE allows rows to begin in the middle of a line. Either setting allows a single row to continue across multiple input lines.

LOWER (default)
UPPER
FULL

With LOWER, only the lower triangle is read from the input data and the upper triangle is mirrored across the main diagonal. UPPER behaves similarly for the upper triangle. FULL reads the entire matrix.

DIAGONAL (default)
NODIAGONAL

With DIAGONAL, the main diagonal is read from the input data. With NODIAGONAL, which is incompatible with FULL, the main diagonal is not read from the input data but instead set to 1 for correlation matrices and system-missing for others.

The N subcommand is a way to specify the size of the population. It is equivalent to specifying an N vector with the specified value for each split file.

MATRIX DATA supports two different ways to indicate the kinds of matrices and vectors present in the data, depending on whether a variable with the special name ROWTYPE_ is present in VARIABLES. The following subsections explain MATRIX DATA syntax and behavior in each case.


16.2.1 With ROWTYPE_

If VARIABLES includes ROWTYPE_, each case’s ROWTYPE_ indicates the type of data contained in the row. See Matrix File Row Types, for a list of supported row types.

Example 1: Defaults with ROWTYPE_

This example shows a simple use of MATRIX DATA with ROWTYPE_ plus 8 variables named var01 through var08.

Because ROWTYPE_ is the first variable in VARIABLES, it appears first on each line. The first three lines in the example data have ROWTYPE_ values of ‘MEAN’, ‘SD’, and ‘N’. These indicate that these lines contain vectors of means, standard deviations, and counts, respectively, for var01 through var08 in order.

The remaining 8 lines have a ROWTYPE_ of ‘CORR’ which indicates that the values are correlation coefficients. Each of the lines corresponds to a row in the correlation matrix: the first line is for var01, the next line for var02, and so on. The input only contains values for the lower triangle, including the diagonal, since FORMAT=LOWER DIAGONAL is the default.

With ROWTYPE_, the CONTENTS subcommand is optional and the CELLS subcommand may not be used.

MATRIX DATA
    VARIABLES=ROWTYPE_ var01 TO var08.
BEGIN DATA.
MEAN  24.3   5.4  69.7  20.1  13.4   2.7  27.9   3.7
SD     5.7   1.5  23.5   5.8   2.8   4.5   5.4   1.5
N       92    92    92    92    92    92    92    92
CORR  1.00
CORR   .18  1.00
CORR  -.22  -.17  1.00
CORR   .36   .31  -.14  1.00
CORR   .27   .16  -.12   .22  1.00
CORR   .33   .15  -.17   .24   .21  1.00
CORR   .50   .29  -.20   .32   .12   .38  1.00
CORR   .17   .29  -.05   .20   .27   .20   .04  1.00
END DATA.

Example 2: FORMAT=UPPER NODIAGONAL

This syntax produces the same matrix file as example 1, but it uses FORMAT=UPPER NODIAGONAL to specify the upper triangle and omit the diagonal. Because the matrix’s ROWTYPE_ is CORR, PSPP automatically fills in the diagonal with 1.

MATRIX DATA
    VARIABLES=ROWTYPE_ var01 TO var08
    /FORMAT=UPPER NODIAGONAL.
BEGIN DATA.
MEAN  24.3   5.4  69.7  20.1  13.4   2.7  27.9   3.7
SD     5.7   1.5  23.5   5.8   2.8   4.5   5.4   1.5
N       92    92    92    92    92    92    92    92
CORR         .17   .50  -.33   .27   .36  -.22   .18
CORR               .29   .29  -.20   .32   .12   .38
CORR                     .05   .20  -.15   .16   .21
CORR                           .20   .32  -.17   .12
CORR                                 .27   .12  -.24
CORR                                      -.20  -.38
CORR                                             .04
END DATA.

Example 3: N subcommand

This syntax uses the N subcommand in place of an N vector. It produces the same matrix file as examples 1 and 2.

MATRIX DATA
    VARIABLES=ROWTYPE_ var01 TO var08
    /FORMAT=UPPER NODIAGONAL
    /N 92.
BEGIN DATA.
MEAN  24.3   5.4  69.7  20.1  13.4   2.7  27.9   3.7
SD     5.7   1.5  23.5   5.8   2.8   4.5   5.4   1.5
CORR         .17   .50  -.33   .27   .36  -.22   .18
CORR               .29   .29  -.20   .32   .12   .38
CORR                     .05   .20  -.15   .16   .21
CORR                           .20   .32  -.17   .12
CORR                                 .27   .12  -.24
CORR                                      -.20  -.38
CORR                                             .04
END DATA.

Example 4: Split variables

This syntax defines two matrices, using the variable ‘s1’ to distinguish between them. Notice how the order of variables in the input matches their order on VARIABLES. This example also uses FORMAT=FULL.

MATRIX DATA
    VARIABLES=s1 ROWTYPE_  var01 TO var04
    /SPLIT=s1
    /FORMAT=FULL.
BEGIN DATA.
0 MEAN 34 35 36 37
0 SD   22 11 55 66
0 N    99 98 99 92
0 CORR  1 .9 .8 .7
0 CORR .9  1 .6 .5
0 CORR .8 .6  1 .4
0 CORR .7 .5 .4  1
1 MEAN 44 45 34 39
1 SD   23 15 51 46
1 N    98 34 87 23
1 CORR  1 .2 .3 .4
1 CORR .2  1 .5 .6
1 CORR .3 .5  1 .7
1 CORR .4 .6 .7  1
END DATA.

Example 5: Factor variables

This syntax defines a matrix file that includes a factor variable ‘f1’. The data includes mean, standard deviation, and count vectors for two values of the factor variable, plus a correlation matrix for pooled data.

MATRIX DATA
    VARIABLES=ROWTYPE_ f1 var01 TO var04
    /FACTOR=f1.
BEGIN DATA.
MEAN 0 34 35 36 37
SD   0 22 11 55 66
N    0 99 98 99 92
MEAN 1 44 45 34 39
SD   1 23 15 51 46
N    1 98 34 87 23
CORR .  1
CORR . .9  1
CORR . .8 .6  1
CORR . .7 .5 .4  1
END DATA.

Previous: , Up: MATRIX DATA   [Contents][Index]

16.2.2 Without ROWTYPE_

If VARIABLES does not contain ROWTYPE_, the CONTENTS subcommand defines the row types that appear in the file and their order. If CONTENTS is omitted, CONTENTS=CORR is assumed.

Factor variables without ROWTYPE_ introduce special requirements, illustrated below in Examples 8 and 9.

Example 6: Defaults without ROWTYPE_

This example shows a simple use of MATRIX DATA with 8 variables named var01 through var08, without ROWTYPE_. This yields the same matrix file as Example 1 (see MATRIX DATA Example 1).

MATRIX DATA
    VARIABLES=var01 TO var08
   /CONTENTS=MEAN SD N CORR.
BEGIN DATA.
24.3   5.4  69.7  20.1  13.4   2.7  27.9   3.7
 5.7   1.5  23.5   5.8   2.8   4.5   5.4   1.5
  92    92    92    92    92    92    92    92
1.00
 .18  1.00
-.22  -.17  1.00
 .36   .31  -.14  1.00
 .27   .16  -.12   .22  1.00
 .33   .15  -.17   .24   .21  1.00
 .50   .29  -.20   .32   .12   .38  1.00
 .17   .29  -.05   .20   .27   .20   .04  1.00
END DATA.

Example 7: Split variables with explicit values

This syntax defines two matrices, using the variable s1 to distinguish between them. Each line of data begins with s1. This yields the same matrix file as Example 4 (see MATRIX DATA Example 4).

MATRIX DATA
    VARIABLES=s1 var01 TO var04
    /SPLIT=s1
    /FORMAT=FULL
    /CONTENTS=MEAN SD N CORR.
BEGIN DATA.
0 34 35 36 37
0 22 11 55 66
0 99 98 99 92
0  1 .9 .8 .7
0 .9  1 .6 .5
0 .8 .6  1 .4
0 .7 .5 .4  1
1 44 45 34 39
1 23 15 51 46
1 98 34 87 23
1  1 .2 .3 .4
1 .2  1 .5 .6
1 .3 .5  1 .7
1 .4 .6 .7  1
END DATA.

Example 8: Split variable with sequential values

Like this previous example, this syntax defines two matrices with split variable s1. In this case, though, s1 is not listed in VARIABLES, which means that its value does not appear in the data. Instead, MATRIX DATA reads matrix data until the input is exhausted, supplying 1 for the first split, 2 for the second, and so on.

MATRIX DATA
    VARIABLES=var01 TO var04
    /SPLIT=s1
    /FORMAT=FULL
    /CONTENTS=MEAN SD N CORR.
BEGIN DATA.
34 35 36 37
22 11 55 66
99 98 99 92
 1 .9 .8 .7
.9  1 .6 .5
.8 .6  1 .4
.7 .5 .4  1
44 45 34 39
23 15 51 46
98 34 87 23
 1 .2 .3 .4
.2  1 .5 .6
.3 .5  1 .7
.4 .6 .7  1
END DATA.

16.2.2.1 Factor variables without ROWTYPE_

Without ROWTYPE_, factor variables introduce two new wrinkles to MATRIX DATA syntax. First, the CELLS subcommand must declare the number of combinations of factor variables present in the data. If there is, for example, one factor variable for which the data contains three values, one would write CELLS=3; if there are two (or more) factor variables for which the data contains five combinations, one would use CELLS=5; and so on.

Second, the CONTENTS subcommand must distinguish within-cell data from pooled data by enclosing within-cell row types in parentheses. When different within-cell row types for a single factor appear in subsequent lines, enclose the row types in a single set of parentheses; when different factors’ values for a given within-cell row type appear in subsequent lines, enclose each row type in individual parentheses.

Without ROWTYPE_, input lines for pooled data do not include factor values, not even as missing values, but input lines for within-cell data do.

The following examples aim to clarify this syntax.

Example 9: Factor variables, grouping within-cell records by factor

This syntax defines the same matrix file as Example 5 (see MATRIX DATA Example 5), without using ROWTYPE_. It declares CELLS=2 because the data contains two values (0 and 1) for factor variable f1. Within-cell vector row types MEAN, SD, and N are in a single set of parentheses on CONTENTS because they are grouped together in subsequent lines for a single factor value. The data lines with the pooled correlation matrix do not have any factor values.

MATRIX DATA
    VARIABLES=f1 var01 TO var04
    /FACTOR=f1
    /CELLS=2
    /CONTENTS=(MEAN SD N) CORR.
BEGIN DATA.
0 34 35 36 37
0 22 11 55 66
0 99 98 99 92
1 44 45 34 39
1 23 15 51 46
1 98 34 87 23
   1
  .9  1
  .8 .6  1
  .7 .5 .4  1
END DATA.

Example 10: Factor variables, grouping within-cell records by row type

This syntax defines the same matrix file as the previous example. The only difference is that the within-cell vector rows are grouped differently: two rows of means (one for each factor), followed by two rows of standard deviations, followed by two rows of counts.

MATRIX DATA
    VARIABLES=f1 var01 TO var04
    /FACTOR=f1
    /CELLS=2
    /CONTENTS=(MEAN) (SD) (N) CORR.
BEGIN DATA.
0 34 35 36 37
1 44 45 34 39
0 22 11 55 66
1 23 15 51 46
0 99 98 99 92
1 98 34 87 23
   1
  .9  1
  .8 .6  1
  .7 .5 .4  1
END DATA.

Next: , Previous: , Up: Matrices   [Contents][Index]

16.3 MCONVERT

MCONVERT
    [[MATRIX=]
     [IN({‘*’|’file’})]
     [OUT({‘*’|’file’})]]
    [/{REPLACE,APPEND}].

The MCONVERT command converts matrix data from a correlation matrix and a vector of standard deviations into a covariance matrix, or vice versa.

By default, MCONVERT both reads and writes the active file. Use the MATRIX subcommand to specify other files. To read a matrix file, specify its name inside parentheses following IN. To write a matrix file, specify its name inside parentheses following OUT. Use ‘*’ to explicitly specify the active file for input or output.

When MCONVERT reads the input, by default it substitutes a correlation matrix and a vector of standard deviations each time it encounters a covariance matrix, and vice versa. Specify /APPEND to instead have MCONVERT add the other form of data without removing the existing data. Use /REPLACE to explicitly request removing the existing data.

The MCONVERT command requires its input to be a matrix file. Use MATRIX DATA to convert text input into matrix file format. See MATRIX DATA, for details.


Previous: , Up: Matrices   [Contents][Index]

16.4 MATRIX

MATRIX.matrix commandsEND MATRIX.

The following basic matrix commands are supported:

COMPUTE variable[(index[,index])]=expression.
CALL procedure(argument, …).
PRINT [expression]
      [/FORMAT=format]
      [/TITLE=title]
      [/SPACE={NEWPAGE | n}]
      [{/RLABELS=string| /RNAMES=expression}]
      [{/CLABELS=string| /CNAMES=expression}].

The following matrix commands offer support for flow control:

DO IF expression.matrix commands…
[ELSE IF expression.matrix commands…]…
[ELSEmatrix commands…]
END IF.

LOOP [var=first TO last [BY step]] [IF expression].matrix commandsEND LOOP [IF expression].

BREAK.

The following matrix commands support matrix input and output:

READ variable[(index[,index])]
     [/FILE=file]
     /FIELD=first TO last [BY width]
     [/FORMAT=format]
     [/SIZE=expression]
     [/MODE={RECTANGULAR | SYMMETRIC}]
     [/REREAD].
WRITE expression
      [/OUTFILE=file]
      /FIELD=first TO last [BY width]
      [/MODE={RECTANGULAR | TRIANGULAR}]
      [/HOLD]
      [/FORMAT=format].
GET variable[(index[,index])]
    [/FILE={file | *}]
    [/VARIABLES=variable…]
    [/NAMES=expression]
    [/MISSING={ACCEPT | OMIT | number}]
    [/SYSMIS={OMIT | number}].
SAVE expression
     [/OUTFILE={file | *}]
     [/VARIABLES=variable…]
     [/NAMES=expression]
     [/STRINGS=variable…].
MGET [/FILE=file]
     [/TYPE={COV | CORR | MEAN | STDDEV | N | COUNT}].
MSAVE expression
      /TYPE={COV | CORR | MEAN | STDDEV | N | COUNT}
      [/OUTFILE=file]
      [/VARIABLES=variable…]
      [/SNAMES=variable…]
      [/SPLIT=expression]
      [/FNAMES=variable…]
      [/FACTOR=expression].

The following matrix commands provide additional support:

DISPLAY [{DICTIONARY | STATUS}].
RELEASE variable.

MATRIX and END MATRIX enclose a special PSPP sub-language, called the matrix language. The matrix language does not require an active dataset to be defined and only a few of the matrix language commands work with any datasets that are defined. Each instance of MATRIXEND MATRIX is a separate program whose state is independent of any instance, so that variables declared within a matrix program are forgotten at its end.

The matrix language works with matrices, where a matrix is a rectangular array of real numbers. An n×m matrix has n rows and m columns. Some special cases are important: a n×1 matrix is a column vector, a n is a row vector, and a 1×1 matrix is a scalar.

The matrix language also has limited support for matrices that contain 8-byte strings instead of numbers. Strings longer than 8 bytes are truncated, and shorter strings are padded with spaces. String matrices are mainly useful for labeling rows and columns when printing numerical matrices with the MATRIX PRINT command. Arithmetic operations on string matrices will not produce useful results. The user should not mix strings and numbers within a matrix.

The matrix language does not work with cases. A variable in the matrix language represents a single matrix.

The matrix language does not support missing values.

MATRIX is a procedure, so it cannot be enclosed inside DO IF, LOOP, etc.

Macros may be used within a matrix program, and macros may expand to include entire matrix programs. The DEFINE command may not appear within a matrix program. See DEFINE, for more information about macros.

The following sections describe the details of the matrix language: first, the syntax of matrix expressions, then each of the supported commands. The COMMENT command (see COMMENT) is also supported.


16.4.1 Matrix Expressions

Many matrix commands use expressions. A matrix expression may use the following operators, listed in descending order of operator precedence. Within a single level, operators associate from left to right.

  • Function call () and matrix construction {}
  • Indexing ()
  • Unary + and -
  • Integer sequence :
  • Exponentiation ** and &**
  • Multiplication * and &*, and division / and &/
  • Addition + and subtraction -
  • Relational < <= = >= > <>
  • Logical NOT
  • Logical AND
  • Logical OR and XOR

See Matrix Functions, for the available matrix functions. The remaining operators are described in more detail below.

Expressions appear in the matrix language in some contexts where there would be ambiguity whether ‘/’ is an operator or a separator between subcommands. In these contexts, only the operators with higher precedence than ‘/’ are allowed outside parentheses. Later sections call these restricted expressions.


16.4.1.1 Matrix Construction Operator {}

Use the {} operator to construct matrices. Within the curly braces, commas separate elements within a row and semicolons separate rows. The following examples show a 2×3 matrix, a 1×4 row vector, a 3×1 column vector, and a scalar.

{1, 2, 3; 4, 5, 6}[1 2 3]
[4 5 6]
 
{3.14, 6.28, 9.24, 12.57}[3.14 6.28 9.42 12.57]  
{1.41; 1.73; 2}[1.41]
[1.73]
[2.00]
 
{5}5

Curly braces are not limited to holding numeric literals. They can contain calculations, and they can paste together matrices and vectors in any way as long as the result is rectangular. For example, if ‘m’ is matrix {1, 2; 3, 4}, ‘r’ is row vector {5, 6}, and ‘c’ is column vector {7, 8}, then curly braces can be used as follows:

{m, c; r, 10}[1 2 7]
[3 4 8]
[5 6 10]
 
{c, 2 * c, T(r)}[7 14 5]
[8 16 6]

The final example above uses the transposition function T.


16.4.1.2 Integer Sequence Operator ‘:

The syntax first:last:step yields a row vector of consecutive integers from first to last counting by step. The final :step is optional and defaults to 1 when omitted.

Each of first, last, and step must be a scalar and should be an integer (any fractional part is discarded). Because ‘:’ has a high precedence, operands other than numeric literals must usually be parenthesized.

When step is positive (or omitted) and end < start, or if step is negative and end > start, then the result is an empty matrix. If step is 0, then PSPP reports an error.

Here are some examples:

1:6{1, 2, 3, 4, 5, 6}
1:6:2{1, 3, 5}
-1:-5:-1{-1, -2, -3, -4, -5}
-1:-5{}
2:1:0(error)

16.4.1.3 Index Operator ()

The result of the submatrix or indexing operator, written m(rindex, cindex), contains the rows of m whose indexes are given in vector rindex and the columns whose indexes are given in vector cindex.

In the simplest case, if rindex and cindex are both scalars, the result is also a scalar:

{10, 20; 30, 40}(1, 1)10
{10, 20; 30, 40}(1, 2)20
{10, 20; 30, 40}(2, 1)30
{10, 20; 30, 40}(2, 2)40

If the index arguments have multiple elements, then the result includes multiple rows or columns:

{10, 20; 30, 40}(1:2, 1){10; 30}
{10, 20; 30, 40}(2, 1:2){30, 40}
{10, 20; 30, 40}(1:2, 1:2){10, 20; 30, 40}

The special argument ‘:’ may stand in for all the rows or columns in the matrix being indexed, like this:

{10, 20; 30, 40}(:, 1){10; 30}
{10, 20; 30, 40}(2, :){30, 40}
{10, 20; 30, 40}(:, :){10, 20; 30, 40}

The index arguments do not have to be in order, and they may contain repeated values, like this:

{10, 20; 30, 40}({2, 1}, 1){30; 10}
{10, 20; 30, 40}(2, {2; 2; 1}){40, 40, 30}
{10, 20; 30, 40}(2:1:-1, :){30, 40; 10, 20}

When the matrix being indexed is a row or column vector, only a single index argument is needed, like this:

{11, 12, 13, 14, 15}(2:4){12, 13, 14}
{11; 12; 13; 14; 15}(2:4){12; 13; 14}

When an index is not an integer, PSPP discards the fractional part. It is an error for an index to be less than 1 or greater than the number of rows or columns:

{11, 12, 13, 14}({2.5, 4.6}){12, 14}
{11; 12; 13; 14}(0)(error)

16.4.1.4 Unary Operators

The unary operators take a single operand of any dimensions and operate on each of its elements independently. The unary operators are:

-

Inverts the sign of each element.

+

No change.

NOT

Logical inversion: each positive value becomes 0 and each zero or negative value becomes 1.

Examples:

-{1, -2; 3, -4}{-1, 2; -3, 4}
+{1, -2; 3, -4}{1, -2; 3, -4}
NOT {1, 0; -1, 1}{0, 1; 1, 0}

16.4.1.5 Elementwise Binary Operators

The elementwise binary operators require their operands to be matrices with the same dimensions. Alternatively, if one operand is a scalar, then its value is treated as if it were duplicated to the dimensions of the other operand. The result is a matrix of the same size as the operands, in which each element is the result of the applying the operator to the corresponding elements of the operands.

The elementwise binary operators are listed below.

  • The arithmetic operators, for familiar arithmetic operations:
    +

    Addition.

    -

    Subtraction.

    *

    Multiplication, if one operand is a scalar. (Otherwise this is matrix multiplication, described below.)

    / or &/

    Division.

    &*

    Multiplication.

    &**

    Exponentiation.

  • The relational operators, whose results are 1 when a comparison is true and 0 when it is false:
    < or LT

    Less than.

    <= or LE

    Less than or equal.

    = or EQ

    Equal.

    > or GT

    Greater than.

    >= or GE

    Greater than or equal.

    <> or ~= or NE

    Not equal.

  • The logical operators, which treat positive operands as true and nonpositive operands as false. They yield 0 for false and 1 for true:
    AND

    True if both operands are true.

    OR

    True if at least one operand is true.

    XOR

    True if exactly one operand is true.

Examples:

1 + 23
1 + {3; 4}{4; 5}
{66, 77; 88, 99} + 5{71, 82; 93, 104}
{4, 8; 3, 7} + {1, 0; 5, 2}{5, 8; 8, 9}
{1, 2; 3, 4} < {4, 3; 2, 1}{1, 1; 0, 0}
{1, 3; 2, 4} >= 3{0, 1; 0, 1}
{0, 0; 1, 1} AND {0, 1; 0, 1}{0, 0; 0, 1}

16.4.1.6 Matrix Multiplication Operator ‘*

If A is an m×n matrix and B is an n×p matrix, then A*B is the m×p matrix multiplication product C. PSPP reports an error if the number of columns in A differs from the number of rows in B.

The * operator performs elementwise multiplication (see above) if one of its operands is a scalar.

No built-in operator yields the inverse of matrix multiplication. Instead, multiply by the result of INV or GINV.

Some examples:

{1, 2, 3} * {4; 5; 6}32
{4; 5; 6} * {1, 2, 3}{4,  8, 12;
 5, 10, 15;