extension commands from ibm spss

Extension Commands from IBM SPSS

Rich Cohen ([email protected]), Information Developer, IBM

14 August 2012

Extension commands are IBM® SPSS® Statistics commands that are implemented in the Python ®, R or Java programming languages. They allow users who are proficient in Python, R or Java to share external functions with users of standard SPSS Statistics command syntax. Once an extension command is installed, you run the command in the same manner as any built-in command such as DESCRIPTIVES.

Extension commands require the SPSS Statistics Integration Plug-in(s) for the language(s) in which the command is implemented—Python, R or Java. For version 18 on Windows, the IBM® SPSS® Statistics - Integration Plug-in for Python is installed with IBM® SPSS® Statistics Essentials for Python and the IBM® SPSS® Statistics - Integration Plug-in for R is installed with IBM® SPSS® Statistics - Essentials for R. For version 19 and higher and for all platforms, the Integration Plug-in for Python is installed with Essentials for Python and the Integration Plug-in for R is installed with Essentials for R. � Essentials for R is available from the SPSS community at

http://www.ibm.com/developerworks/spssdevcentral.� For versions prior to 21, Essentials for Python is available from the SPSS community. For

version 21 and higher, Essentials for Python is provided with your SPSS Statistics product, either as a downloadable file or on your product DVD/CD.

The IBM® SPSS® Statistics - Integration Plug-in for Java is installed with SPSS Statistics and SPSS Statistics Server and requires no separate installation or configuration. It is available for version 21 and higher.

This article provides a list of extension commands authored by IBM SPSS that are available for download from the SPSS community. Most of the extension commands listed here have an accompanying dialog box (specified by an .spd file) that generates command syntax for the extension command in the same manner as built-in dialogs. Once installed, the dialog box is accessible from the SPSS Statistics menus. See Installing Extension Commands for installation information.

A number of the commands listed here are installed with Essentials for Python or Essentials for R. In some cases a newer version of one of these commands will be available from the SPSS community.

Complete syntax help for each of the extension commands listed here is available, once the command is installed, by executing the command and including the /HELP subcommand—for example: FUZZY /HELP.

Note: Extension commands authored by other users may also be available from the SPSS community.

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FUZZY

FUZZY matches cases in one dataset by utilizing random draws from a second, typically much larger dataset, based on a specified set of key variables. The command supports fuzzy matching for numeric key variables.

Requirements: Version 16.0.1 or higher and the corresponding Integration Plug-in for Python.

Note: For users with SPSS Statistics version 19 or higher, the FUZZY command is installed as part of Essentials for Python.

GATHERMD

GATHERMD builds a dataset containing the variable names, labels, and optionally selected custom variable attributes from one or more SPSS Statistics, SAS, or Stata data files.

Requirements: Version 16.0.1 or higher and the corresponding Integration Plug-in for Python

Note: For users with SPSS Statistics version 19 or higher, the GATHERMD command is installed as part of Essentials for Python.

PLS

PLS estimates partial least squares (PLS, also known as “projection to latent structure”) regression models. PLS is a predictive technique that is an alternative to ordinary least squares (OLS) regression, canonical correlation, or structural equation modeling, and it is particularly useful when predictor variables are highly correlated or when the number of predictors exceeds the number of cases.

Requirements: Version 17.0.2 or higher, the corresponding Integration Plug-in for Python, and the NumPy and SciPy Python packages.

PROPOR

PROPOR calculates confidence intervals for proportions and differences in proportions.

Requirements: Version 16 or higher and the corresponding Integration Plug-in for Python.

SCRIPTEX

SCRIPTEX runs a Python script, optionally passing it a set of parameters. Python scripts make use of the interface exposed by the Python SpssClient module. They operate on output objects—for example, allowing you to customize pivot tables.


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SETSMACRO

SETSMACRO defines a macro consisting of the variable names in one or more variable sets associated with the active dataset. At run-time, instances of the macro will be replaced by the list of variable names. Variable sets are defined from the Utilities > Define Variable Sets menu item in SPSS Statistics.

Requirements: Version 16 or higher and the corresponding Integration Plug-in for Python

SPSSINC ANON

SPSSINC ANON provides a procedure for anonymizing variable values and names. It is useful, for example, when data need to be obscured for privacy or other reasons.

Requirements: Version 17.0.1 or higher and the corresponding Integration Plug-in for Python

SPSSINC APRIORI

SPSSINC APRIORI discovers association rules in a dataset, and returns those rules with the highest information content. Association rules associate a particular conclusion (the purchase of a particular product) with a set of conditions (the purchase of several other products).

Requirements: Version 17 or higher, the corresponding Integration Plug-in for Python and Integration Plug-in for R, and the R arules package.

SPSSINC BREUSCH PAGAN

SPSSINC BREUSCH PAGAN estimates a linear model and performs the Breusch-Pagan heteroscedasticity test.

Requirements: Version 17 or higher, the corresponding Integration Plug-in for Python and Integration Plug-in for R, and the R car package.

Note: For users with SPSS Statistics version 18 or higher, the SPSSINC BREUSCH PAGAN command and the R car package are installed as part of Essentials for R. The version of SPSSINC BREUSCH PAGAN installed with Essentials for R does not require the Integration Plug-in for Python.

SPSSINC CENSOR TABLES

SPSSINC CENSOR TABLES censors specified cells in a pivot table based on the values of statistics in related cells. Censored cells are replaced with a specified string, which defaults to a blank string.


Note: SPSSINC CENSOR TABLES is packaged together with the SPSSINC MERGE TABLES extension command.

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SPSSINC COMPARE DATASETS

SPSSINC COMPARE DATASETS compares two open datasets. You can specify whether the comparison includes the case data, the variable properties or both.

Requirements: Version 17 or higher and the corresponding Integration Plug-in for Python

Note: For Version 16 users, the COMPDS extension command provides similar functionality to SPSSINC COMPARE DATASETS but without an associated dialog.

Note: For users with SPSS Statistics version 19 or higher, the SPSSINC COMPARE DATASETS command is installed as part of Essentials for Python.

SPSSINC CREATE DUMMIES

SPSSINC CREATE DUMMIES creates a set of dummy variables representing the distinct values of a specified variable. It is useful, for example, in converting a categorical variable into a set of variables appropriate for use in the Regression procedure.


Note: For users with SPSS Statistics version 19 or higher, the SPSSINC CREATE DUMMIES command is installed as part of Essentials for Python.

SPSSINC GETURI DATA

SPSSINC GETURI DATA opens data files stored on the Internet. You can open files in SPSS, Excel, SAS, and Stata formats.


SPSSINC HETCOR

SPSSINC HETCOR calculates correlations between nominal, ordinal, and scale variables, accounting for the measurement levels of the variables. Specifically, it calculates Pearson correlations between scale variables, polyserial correlations between scale and categorical variables (nominal or ordinal), and polychoric correlations between categorical variables.

Requirements: Version 17 or higher, the corresponding Integration Plug-in for Python and Integration Plug-in for R, and the R polycor package.

Note: For users with SPSS Statistics version 18 or higher, the SPSSINC HETCOR command and the R polycor package are installed as part of Essentials for R.

SPSSINC MERGE TABLES

SPSSINC MERGE TABLES merges the contents of one pivot table in the Viewer into another.


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Note: SPSSINC MERGE TABLES is packaged together with the SPSSINC CENSOR TABLES extension command.

For users with SPSS Statistics version 19 or higher, the SPSSINC MERGE TABLES command is installed as part of Essentials for Python.

SPSSINC MFP GLM

SPSSINC MFP GLM provides a procedure for generalized linear models with multiple fractional polynomial regressors.

Requirements: Version 17 or higher, the corresponding Integration Plug-in for Python and Integration Plug-in for R, and the R mfp package.

SPSSINC MODIFY OUTPUT

SPSSINC MODIFY OUTPUT allows you to modify the text of specified items in the outline pane of the Viewer as well as the titles (such as pivot table titles) or text (such as for title items) of the associated items. Replacement text can contain html or rtf formatting notation. Optionally, you can hide specified items and you can insert page breaks before specified title items.


Note: For users with SPSS Statistics version 19 or higher, the SPSSINC MODIFY OUTPUT command is installed as part of Essentials for Python.

SPSSINC MODIFY TABLES

SPSSINC MODIFY TABLES allows you to modify the appearance of data cells and row and column labels. You can modify text style, text color or background color. You can also set column widths or the width of row labels and you can hide specified rows or columns.

Note: For improved formatting of correlation tables produced by the CORRELATIONS and NONPAR CORR commands, consider using the Format Correlations dialog in conjunction with SPSSINC MODIFY TABLES. It is available from the Utilities for SPSS Statistics on the SPSS community.


Note: For users with SPSS Statistics version 19 or higher, the SPSSINC MODIFY TABLES command is installed as part of Essentials for Python.

SPSSINC PROCESS FILES

SPSSINC PROCESS FILES iterates through a collection of data files and applies the same set of syntax commands to each file. In particular, SPSSINC PROCESS FILES can be used with the output from SPSSINC SPLIT DATASET to apply a block of transformation and/or procedure syntax to a set of SAV files representing the split groups from an initial dataset.


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Note: SPSSINC PROCESS FILES is packaged together with the SPSSINC SPLIT DATASET extension command.

SPSSINC PROGRAM

SPSSINC PROGRAM allows you to run Python programs using traditional command syntax without having to convert the program into an extension command. The command includes the ability to pass parameters to the Python program.


SPSSINC QQPLOT2

SPSSINC QQPLOT2 creates Q-Q plots for two variables or two groups of cases for one variable. It is useful when comparing their empirical distributions. In contrast, Q-Q plots created with the built-in PPLOT command compare a variable with a theoretical distribution.

Requirements: Version 17 or higher and the corresponding Integration Plug-in for Python and Integration Plug-in for R.

SPSSINC QUANTREG

SPSSINC QUANTREG estimates one or more conditional quantiles (0 <= q <= 1) for a linear model. In contrast, ordinary regression estimates the conditional mean.

Requirements: Version 17 or higher, the corresponding Integration Plug-in for Python and Integration Plug-in for R, and the R quantreg package.

Note: For users with SPSS Statistics version 18 or higher, the SPSSINC QUANTREG command and the R quantreg package are installed as part of Essentials for R. The version of SPSSINC QUANTREG installed with Essentials for R does not require the Integration Plug-in for Python.

SPSSINC RAKE

SPSSINC RAKE calculates case weights to match control totals for categories of one to five variables. The technique is commonly used in survey analysis where the sample may cover segments of the target population in proportions that differ from the proportions of those segments in the population. If the dataset is already weighted, the new weight variable incorporates the existing weights.

Requirements: Version 17 or higher with the Advanced Statistics option, and the Integration Plug-in for Python.

SPSSINC RANFOR

SPSSINC RANFOR generates forests of classification or regression trees using Breiman’s random forest algorithm. Predictions can be obtained with the SPSSINC RANPRED extension command. Note: “Random Forest” is a trademark of Breiman and Cutler.

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Requirements: Version 17 or higher, the corresponding Integration Plug-in for Python and Integration Plug-in for R, and the R randomForest package.

SPSSINC RANPRED

SPSSINC RANPRED calculates predictions from classification or regression trees generated from the SPSSINC RANFOR extension command.

Requirements: Version 17 or higher, the corresponding Integration Plug-in for Python and Integration Plug-in for R, and the R randomForest package.

Note: SPSSINC RANPRED is packaged together with the SPSSINC RANFOR extension command.

SPSSINC RASCH

SPSSINC RASCH estimates the parameters of the Rasch model for item response data.

Requirements: Version 17 or higher, the corresponding Integration Plug-in for Python and Integration Plug-in for R, and the R ltm package.

Note: For users with SPSS Statistics version 18 or higher, the SPSSINC RASCH command and the R ltm package are installed as part of Essentials for R. The version of SPSSINC RASCH installed with Essentials for R does not require the Integration Plug-in for Python.

SPSSINC RECODEEX

SPSSINC RECODEEX extends the capabilities of the built-in RECODE command. Specifically, it allows the use of date and time literals for values, and it can automatically generate value labels and variable labels for output variables. There is no dialog box accompanying this command.


SPSSINC ROBUST REGR

SPSSINC ROBUST REGR estimates a linear regression model, robustly, using an M estimator.

Requirements: Version 17 or higher, the corresponding Integration Plug-in for Python and Integration Plug-in for R, and the R MASS package.

Note: For users with SPSS Statistics version 18 or higher, the SPSSINC ROBUST REGR command is installed as part of Essentials for R. The version of SPSSINC ROBUST REGR installed with Essentials for R does not require the Integration Plug-in for Python.

SPSSINC SPLIT DATASET

SPSSINC SPLIT DATASET creates a set of SAV files by splitting the active dataset according to the values of a splitting variable, leaving the active file unchanged. Output from SPSSINC SPLIT DATASET can be used with the SPSSINC PROCESS FILES extension command to apply a block of transformation and/or procedure syntax to each split group.

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SPSSINC TOBIT REGR

SPSSINC TOBIT REGR estimates a regression model where the dependent variable has a fixed lower bound, upper bound, or both. It can be thought of as a combination of a probit model with a linear regression model.

Requirements: Version 17 or higher, the corresponding Integration Plug-in for Python and Integration Plug-in for R, and the R AER package.

Note: For users with SPSS Statistics version 18 or higher, the SPSSINC TOBIT REGR command and the R AER package are installed as part of Essentials for R. The version of SPSSINC TOBIT REGR installed with Essentials for R does not require the Integration Plug-in for Python.

SPSSINC TRANS

SPSSINC TRANS applies a Python function to the cases in the active dataset and saves the results to one or more new or existing variables. This allows you to apply Python functions to your case data, much like you do with built-in functions such as those available with the COMPUTE command.


Note: For users with SPSS Statistics version 19 or higher, the SPSSINC TRANS command is installed as part of Essentials for Python.

SPSSINC TRANSLATE OUTPUT

SPSSINC TRANSLATE OUTPUT translates specified contents of the Viewer using a set of user-defined translation files. This allows you to translate output into languages other than those supported by SPSS Statistics. The mechanism supports translation of outline entries, tables, titles and headings. It does not support translation of charts, trees or model viewer items. A detailed description of this translation mechanism is included in the Zip file containing this extension command.


SPSSINC TURF

SPSSINC TURF performs a TURF analysis (Total Unduplicated Reach and Frequency), which finds groups of response variables that have the highest coverage in a sample. In contrast to simple frequencies, it accounts for the overlap in responses to different items.

Requirements: Version 17 or higher and the corresponding Integration Plug-in for Python. It can also be used with version 16 with two minor changes, described in the ReadMe file included with the extension command.

Note: For users with SPSS Statistics version 19 or higher, the SPSSINC TURF command is installed as part of Essentials for Python.

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STATS DISTFIT

STATS DISTFIT fits selected probability distributions to one or more variables. The output includes the estimated parameters and a goodness-of-fit test. Optionally, Q-Q plots are produced..

Requirements: Version 17 or higher, the corresponding Integration Plug-in for R, and the R MASS package. The extension command will work with version 17, but the dialog box requires at least version 18.

STATS FIND FILE

STATS FIND FILE creates a file handle pointing to the location where a file is found following a specified search strategy. The strategy consists of a list of locations to look in. This can be a list of folders and/or a list of environment variables whose values are lists of folders. By using this command, you can create jobs that do not have to know exactly where their input data and syntax files reside.


STATS GRM

STATS GRM fits the Graded Response model for ordinal polytomous data via an IRT approach.

Requirements: Version 17 or higher, the corresponding Integration Plug-in for R, and the R ltm package.

STATS HECKMAN REGR

STATS HECKMAN REGR performs Heckman censored regression, which is a generalization of Tobit regression. Censoring is modeled using probit analysis, and the observed outcomes are modeled with regression. It can also perform switching regression where two different regression models apply to subgroups determined by a probit analysis.

Requirements: Version 19 or higher, the corresponding Integration Plug-in for R, and the R sampleSelection package.

STATS IRM

STATS IRM fits three-parameter item response models using the tpm function from the R ltm package. It is assumed that the values of the item variables is 0,1. By default, the procedure produces estimates of the model coefficients, and you can request optional output such as the item fit statistics, plots of the factor scores, and item characteristic curves, and save person-fit statistics to a new dataset.

Requirements: Version 17 or higher, the corresponding Integration Plug-in for R, and the R ltm package.

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STATS LATENT CLASS

STATS LATENT CLASS estimates latent class and latent class regression modules using the R poLCA package.

Requirements: Version 18 or higher, the corresponding Integration Plug-in for R and the R poLCA package.

Note: For users with SPSS Statistics version 21 or higher, the STATS LATENT CLASS command and the R poLCA package are installed as part of Essentials for R.

STATS REGRESS PLOT

STATS REGRESS PLOT produces a set of small plots of one y variable against a set of x variables. Scatters are used for scale variables, and bar, line, or boxplots for categorical ones. Options for scatters include coloring, sizing, shaping, and labeling points by other variables, various fit lines, and control over the size.

Requirements: Version 18.0.3 or higher, and the corresponding Integration Plug-in for Python.

Note: For users with SPSS Statistics version 21 or higher, the STATS REGRESS PLOT command is installed as part of Essentials for Python.

STATS RELIMP

STATS RELIMP calculates importance measures for regression independent variables using the R relaimpo package.

Requirements: Version 18 or higher, the corresponding Integration Plug-in for R and the R relaimpo package.

Note: For users with SPSS Statistics version 21 or higher, the STATS RELIMP command and the R relaimpo package are installed as part of Essentials for R.

STATS SUBGROUP PLOTS

STATS SUBGROUP PLOTS produces a set of small plots for each subgroup in a partition of the dataset. Each plot shows the distribution in the subgroup overlaid on the distribution in the entire sample. The type of plot depends on the variable’s measurement level.

Requirements: Version 18.0.3 or higher, and the corresponding Integration Plug-in for Python.

Note: For users with SPSS Statistics version 21 or higher, the STATS SUBGROUP PLOTS command is installed as part of Essentials for Python.

STATS TABLE CALC

STATS TABLE CALC performs computations based on the cells of a pivot table and replaces existing cell values or inserts new columns or rows into the pivot table.

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Requirements: Version 18 or higher, and the corresponding Integration Plug-in for Python. Insertions require SPSS Statistics version 21 or higher.

TEXT

TEXT creates text comments in the Viewer. Unlike the COMMENT and ECHO commands, it produces a separate text block in the Viewer with its own outline entry.


Installing Extension Commands

The steps to install an extension command depend on how the command is packaged. If the downloaded file for the extension command is an extension bundle (.spe) file, or a Zip file that contains an extension bundle, then install the command from Utilities>Extension Bundles>Install Extension Bundle (requires IBM® SPSS® Statistics 18 or higher) by selecting the extension bundle in the Open an Extension Bundle dialog.

If the command is packaged in a Zip file that does not contain an extension bundle (or you do not have SPSS Statistics 18 or higher), then do the following:

E For Windows or UNIX (including Linux), unzip the contents into the extensions directory under the SPSS Statistics installation directory. For Mac, the installation directory refers to the Contents directory in the SPSS Statistics application bundle. For version 18 on Mac, however, unzip the contents to /Library/Application Support/SPSSInc/PASWStatistics/18/extensions. And for version 19 and higher on Mac, unzip the contents to /Library/Application Support/IBM/SPSS/Statistics/<version>/extensions, where <version> is the two digit SPSS Statistics version—for example, 21. � For Windows and UNIX, for release 21 and higher, if you do not have write permissions to

the SPSS Statistics installation directory then you can unzip the contents to the following general user-writable locations: Windows 7 and Windows Vista. Unzip the contents to C:\Users\<user>\AppData\Local\IBM\SPSS\Statistics\<version>\extensions where <user> is the user name and <version> is the two digit SPSS Statistics version—for example, 21. Note that you may need to create the directories in the specified path. Windows XP. Unzip the contents to C:\Documents and Settings\<user>\Local Settings\Application Data\IBM\SPSS\Statistics\<version>\extensions where <user> is the user name and <version> is the two digit SPSS Statistics version—for example, 21. Note that you may need to create the directories in the specified path. UNIX (including Linux). Unzip the contents to ~/.IBM/SPSS/Statistics/<version>/extensions where <version> is the two digit SPSS Statistics version—for example, 21. Note that you may need to create the directories in the specified path.

� For Windows, UNIX and Mac, and for release 18 and higher, if you do not have write permissions to the SPSS Statistics installation directory or would like to store the XML file and the implementation code elsewhere, then you can unzip the contents to an alternate location that you specify with the SPSS_EXTENSIONS_PATH environment variable. For

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multiple locations, separate each with a semicolon on Windows and a colon on UNIX and Mac when specifying the environment variable. When present, the paths specified in SPSS_EXTENSIONS_PATH take precedence over the SPSS Statistics installation directory. The extensions subdirectory of the installation directory is always searched after any locations specified in the environment variable, followed by the application data directories described above.

E If the Zip file includes a custom dialog package (.spd) file, then install the custom dialog from Utilities>Custom Dialogs>Install Custom Dialog. When presented with the Open dialog, navigate to the location where you unzipped the extension command package and select the custom dialog package (.spd) file. For more information, see Core System>Creating and Managing Custom Dialogs>Managing Custom Dialogs, in the SPSS Statistics Help system. See the ReadMe file included with the Zip file for the menu location of the custom dialog.

Most of the extension commands that require the IBM® SPSS® Statistics - Integration Plug-in for R also require specific R packages. These are freely available from http://www.r-project.org/.

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