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Extension Bundles from IBM SPSS

Contents

Extension bundles from IBM SPSS . . . 1 Index . . . . . . . . . . . . . . . 23 Listing of extension bundles . . . . . . . . . 2 Trademarks . . . . . . . . . . . . . . 22

iii

iv Extension Bundles from IBM SPSS

Extension bundles from IBM SPSS

Rich Cohen ([email protected]), Advisory Software Engineer IBM Corp. 3 March 2015

This article provides a list of extension bundles that are authored by IBM SPSS and that are available for download from the SPSS community (http://www.ibm.com/developerworks/spssdevcentral). Extension bundles package custom components, such as custom dialogs and extension commands, so that they can be easily installed. Most of the extension bundles listed here contain an extension command and an accompanying dialog box that generates command syntax for the extension command in the same manner as built-in dialogs. After an extension bundle is installed, the dialog box is accessible from the IBM® SPSS® Statistics menus, and the extension command can be run in the same manner as any built-in command such as DESCRIPTIVES.

The extension bundles listed in this article require the IBM SPSS Statistics - Integration Plug-in for Python or the IBM SPSS Statistics - Integration Plug-in for R. Some extension bundles require both Plug-ins. For information on installing Integration Plug-ins, see the topic "How to Get Integration Plug-ins" in the IBM SPSS Statistics help system. The topic is located under Help>Core System>Frequently Asked Questions.

A number of the extension bundles listed here are installed with IBM SPSS Statistics - Essentials for Python or IBM SPSS Statistics - Essentials for R and are noted as such in the description for the extension bundle. Note that a newer version of one of these extension bundles might be available from the SPSS community.

To install an extension bundle, you must have IBM SPSS Statistics 18 or higher. v For users with IBM SPSS Statistics 18 - 21, you first download the extension bundle from the SPSS

community. You then install the extension bundle by choosing Utilities>Extension Bundles>Install Extension Bundle from the SPSS Statistics menus and pointing to the location where you saved the extension bundle.

v For users with IBM SPSS Statistics 22 or higher, you can install extension bundles from the Download Extension Bundles dialog, accessible from Utilities>Extension Bundles>Download and Install Extension Bundles within SPSS Statistics. You can also download the extension bundle from the SPSS community and install the extension bundle by choosing Utilities>Extension Bundles>Install Local Extension Bundle from the SPSS Statistics menus.

Note:

v The extension bundle installer attempts to download and install any R packages that are required by the extension bundle and not found on your computer. For users of Windows Vista or later, you might be required to run SPSS Statistics as administrator to successfully install required R packages. This action is accomplished by right-clicking the icon for SPSS Statistics and selecting Run as administrator.

v If the extension bundle requires specific R packages and you do not have internet access, then you must obtain the necessary packages from someone who does. Packages can be downloaded from and then installed from within R. For details, see the R Installation and Administration guide, distributed with R.

v The Download Extension Bundles dialog (requires version 22 or higher) also displays any updates that are available for the extension bundles that are already installed.

v If you are installing extension bundles on SPSS Statistics Server, you can use a script to install multiple extension bundles at once. For information, see Core System > Utilities > Extension bundles > Installing local extension bundles > Batch installation of extension bundles in the Help system.

© Copyright IBM Corporation 1989, 2015 1

http://www.ibm.com/developerworks/spssdevcentral

Help for each of the dialogs, included in the extension bundles listed here, is available by clicking Help on the associated dialog box. The help is not, however, integrated with the SPSS Statistics Help system.

Complete syntax help for each of the extension commands included in the extension bundles listed here, is available by running the command and including the /HELP subcommand. For example:

STATS TABLE CALC /HELP.

For version 23 or higher, the syntax help for a command is available by positioning the cursor help for

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

generalopen

generalopen provides a convenience dialog box that installs on the File menu and provides a unified way of opening a data, syntax, or output file without visiting submenus. Data files can be sav or zsav. A password can be supplied.

Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in 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 18 or higher, the corresponding IBM SPSS Statistics - Integration Plug-in for Python, and the NumPy and SciPy Python packages. Instructions for obtaining NumPy and SciPy and special configuration instructions for users of IBM SPSS Statistics version 22 or higher are provided in the document enabling_pls.pdf, located in the PLS directory under the location where the PLS extension command is installed (see the output from the SHOW EXTPATHS command for a listing of possible locations).

Note: For users with IBM SPSS Statistics version 21 or higher, the PLS extension bundle is installed as part of IBM SPSS Statistics - Essentials for Python.

PROPOR

PROPOR calculates confidence intervals for proportions and differences in proportions.


PSM

PSM does propensity score matching for cases and controls.


Note: For users with IBM SPSS Statistics version 22 or higher, the PSM extension bundle is installed as part of IBM SPSS Statistics - Essentials 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.


Extension bundles from IBM SPSS 3

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 IBM SPSS Statistics.

Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for Python

simulateActiveDataset

simulateActiveDataset generates a new dataset based on the active dataset. Distributions are fit to the selected variables, including cross-variable correlation and contingency tables. Values are then drawn randomly from these distributions.

Requirements: Version 22 or higher.

Note: For users with IBM SPSS Statistics version 23 or higher, the simulateActiveDataset extension bundle is installed as part of IBM SPSS Statistics - Essentials 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.


Note: For users with IBM SPSS Statistics version 23 or higher, the SPSSINC ANON extension bundle is installed as part of IBM SPSS Statistics - Essentials 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 18 or higher, the corresponding IBM SPSS Statistics - 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 18 or higher, the corresponding IBM SPSS Statistics - Integration Plug-in for R, and the R car package.

Note: The SPSSINC BREUSCH PAGAN extension bundle and the R car package are installed as part of IBM SPSS Statistics - Essentials for R.

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.

Extension Bundles from IBM SPSS 4


Note: For users with IBM SPSS Statistics version 22 or higher, the SPSSINC CENSOR TABLES extension bundle is installed as part of IBM SPSS Statistics - Essentials for Python.

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 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for Python

Note: For users with IBM SPSS Statistics versions 19, 20 or 21, the SPSSINC COMPARE DATASETS extension bundle is installed as part of IBM SPSS Statistics - Essentials for Python. For users with IBM SPSS Statistics version 21 or higher, consider using the built-in COMPARE DATASETS command.

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. It can also create two-way and three-way dummies.


Note: For users with IBM SPSS Statistics version 19 or higher, the SPSSINC CREATE DUMMIES extension bundle is installed as part of IBM SPSS Statistics - 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.


Note: For users with IBM SPSS Statistics version 23 or higher, the SPSSINC GETURI DATA extension bundle is installed as part of IBM SPSS Statistics - Essentials for Python.

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 18 or higher, the corresponding IBM SPSS Statistics - Integration Plug-in for Python and IBM SPSS Statistics - Integration Plug-in for R, and the R polycor package.

Note: The SPSSINC HETCOR extension bundle and the R polycor package are installed as part of IBM SPSS Statistics - Essentials for R.


SPSSINC MERGE TABLES

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


Note: For users with IBM SPSS Statistics version 19 or higher, the SPSSINC MERGE TABLES extension bundle is installed as part of IBM SPSS Statistics - Essentials for Python. See also the STATS TABLE CALC extension bundle.

SPSSINC MFP GLM

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

Requirements: Version 18 or higher, the corresponding IBM SPSS Statistics - Integration Plug-in for Python and IBM SPSS Statistics - 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 IBM SPSS Statistics versions 19, 20, or 21, the SPSSINC MODIFY OUTPUT extension bundle is installed as part of IBM SPSS Statistics - Essentials for Python. For users with IBM SPSS Statistics version 22 or higher, consider using the built-in OUTPUT MODIFY command.

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 (provided in the FormatCorrelations extension bundle) in conjunction with SPSSINC MODIFY TABLES.


Note: For users with IBM SPSS Statistics versions 19, 20, or 21, the SPSSINC MODIFY TABLES extension bundle is installed as part of IBM SPSS Statistics - Essentials for Python. For users with IBM SPSS Statistics version 22 or higher, consider using the built-in OUTPUT MODIFY command.

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. File handles and macro definitions are provided for the file being processed.


Note: For users with IBM SPSS Statistics version 22 or higher, the SSPSSINC PROCESS FILES extension bundle is installed as part of IBM SPSS Statistics - Essentials for Python.

SPSSINC PROCESS FILES SEARCH

SPSSINC PROCESS FILES SEARCH provides a way to search through the cases in a collection of similarly structured data files and display information from those matching the search criteria. You specify the files to search, the case selection criterion, and the variables to display for the selected cases. This extension requires the SPSSINC PROCESS FILES extension.


Note: For users with IBM SPSS Statistics version 22 or higher, the SSPSSINC PROCESS FILES SEARCH extension bundle is installed as part of IBM SPSS Statistics - Essentials for Python.

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.


Note: For users with IBM SPSS Statistics version 23 or higher, the SPSSINC PROGRAM extension bundle is installed as part of IBM SPSS Statistics - Essentials for Python.

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 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for Python and IBM SPSS Statistics - 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 18 or higher, the corresponding IBM SPSS Statistics - Integration Plug-in for R, and the R quantreg package.

Note: The SPSSINC QUANTREG extension bundle and the R quantreg package are installed as part of IBM SPSS Statistics - Essentials for R.


SPSSINC RAKE

SPSSINC RAKE calculates case weights to match control totals for categories of one to ten 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 18 or higher with the Advanced Statistics option, and the IBM SPSS Statistics Integration Plug-in for Python.

Note: For users with IBM SPSS Statistics version 22 or higher, the SPSSINC RAKE extension bundle is installed as part of IBM SPSS Statistics - Essentials 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.

Requirements: Version 18 or higher, the corresponding IBM SPSS Statistics - 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 18 or higher, the corresponding IBM SPSS Statistics - Integration Plug-in for R, and the R randomForest package.

SPSSINC RASCH

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

Requirements: Version 18 or higher, the corresponding IBM SPSS Statistics - Integration Plug-in for R,and the R ltm package.

Note: The SPSSINC RASCH extension bundle and the R ltm package are installed as part of IBM SPSSStatistics - Essentials for R. See also the STATS EXRASCH extension bundle.

.

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 18 or higher, the corresponding IBM SPSS Statistics - Integration Plug-in for R, and the R MASS package.

Note: The SPSSINC ROBUST REGR extension bundle is installed as part of IBM SPSS Statistics - Essentials for R. The R MASS package is included with the R distribution.

SPSSINC SELECT VARIABLES

SPSSINC SELECT VARIABLES provides a way to select variables based on a combination of criteria. The criteria can include names, patterns in the names, variable type, measurement level, role, and custom attributes. Using this method, jobs can be generalized so that they do not require hard-wired variable names.


Note: For users with IBM SPSS Statistics version 23 or higher, the SPSSINC SELECT VARIABLES extension bundle is installed as part of IBM SPSS Statistics - Essentials 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.


Note: For users with IBM SPSS Statistics version 22 or higher, the SPSSINC SPLIT DATASET extension bundle is installed as part of IBM SPSS Statistics - Essentials for Python.

SPSSINC SUMMARY TTEST

SPSSINC SUMMARY TTEST calculates a t test from just the summary information of the two samples: means, standard deviations, and case counts.


Note: For users with IBM SPSS Statistics version 23 or higher, the SPSSINC SUMMARY TTEST extension bundle is installed as part of IBM SPSS Statistics - Essentials for Python.

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 18 or higher, the corresponding IBM SPSS Statistics - Integration Plug-in for R, and the R AER package.

Note: The SPSSINC TOBIT REGR extension bundle and the R AER package are installed as part of IBM SPSS Statistics - Essentials for R.


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 IBM SPSS Statistics version 19 or higher, the SPSSINC TRANS extension bundle is installed as part of IBM SPSS Statistics - 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 IBM 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 provided in the document translator.pdf, located in the SPSSINC_TRANSLATE_OUTPUT directory under the location where the SPSSINC TRANSLATE OUTPUT extension command is installed (see the output from the SHOW EXTPATHS command for a listing of possible locations).


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.


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

STATS ADJUST WIDTHS

STATS ADJUST WIDTHS synchronizes the widths of selected string variables across a set of files or datasets. This is mainly useful when combining files with the MATCH or ADD commands.


Note: For users with IBM SPSS Statistics version 23 or higher, the STATS ADJUST WIDTHS extension bundle is installed as part of IBM SPSS Statistics - Essentials for Python.

STATS BAGPLOT

STATS BAGPLOT creates bagplots. A bagplot is a two-dimensional generalization of a boxplot. With more than two variables, a matrix of bagplots is created.

Requirements: Version 18 or higher, the corresponding IBM SPSS Statistics - Integration Plug-in for R, and the R aplpack package.

10 Extension Bundles from IBM SPSS

Note: For users with IBM SPSS Statistics version 23 or higher, the STATS BAGPLOT extension bundle and the R aplpack package are installed as part of IBM SPSS Statistics - Essentials for R.

STATS BENCHMRK

STATS BENCHMRK runs one or two syntax files repeatedly and records various statistics on time, memory, and i/o resource usage by the computer processes associated with Statistics. It requires the Python Extensions for Windows, available from Sourceforge.


STATS CANCORR

STATS CANCORR computes canonical correlations for two sets of variables. It can create scores, and it can produce a syntax file that can be used to score other datasets with the same variable names.


Note: For users with IBM SPSS Statistics version 23 or higher, the STATS CANCORR extension bundle is installed as part of IBM SPSS Statistics - Essentials for Python.

STATS CARTPROD

STATS CARTPROD takes two sets of variables either from the active dataset or the active dataset plus one other dataset and creates a new data file containing the Cartesian product of the variables.


Note: For users with IBM SPSS Statistics version 23 or higher, the STATS CARTPROD extension bundle is installed as part of IBM SPSS Statistics - Essentials for Python.

STATS CATEGORY ORDER

STATS CATEGORY ORDER takes a list of ordinary variables or multiple dichotomy sets. For ordinary variables, it creates a macro listing the variables in sorted order of the count. This macro can be used in the CATEGORIES subcommand of CTABLES to control the order in the table. Unlike CTABLES, it can move special values out of the sort order. For multiple dichotomy sets, it creates a new set with the categories similarly reordered. See also the STATS MCSET CONVERT extension bundle.


Note: For users with IBM SPSS Statistics version 23 or higher, the STATS CATEGORY ORDER extension bundle is installed as part of IBM SPSS Statistics - Essentials for Python.

STATS CLUS CIL

STATS CLUS CIL computes and optionally plots silhouette measures useful in assessing the quality of a cluster analysis. It provides several different dissimilarity measures and plots that work with the output from a cluster analysis procedure.



Note: For users with IBM SPSS Statistics version 23 or higher, the STATS CLUS CIL extension bundle is installed as part of IBM SPSS Statistics - Essentials for Python.

STATS COXREGR

STATS COXREGR procedure calculates Cox regession. It has some features not available in the Statistics COXREG procedure. It supports left and/or right censoring via a counting process model. Time dependent variables are supported via either a formula or multiple observations per subject.

Requirements: Version 18 or higher, the corresponding IBM SPSS Statistics - Integration Plug-in for R, and the R survival package.

Note: For users with IBM SPSS Statistics version 23 or higher, the STATS COXREGR extension bundle and the R survival package are installed as part of IBM SPSS Statistics - Essentials for R.

STATS DATA DATE

STATS DATA DATE defines the date structure of the dataset based on the first value of an SPSS Statistics date variable.


Note: For users with IBM SPSS Statistics version 23 or higher, the STATS DATA DATE extension bundle is installed as part of IBM SPSS Statistics - Essentials for Python.

STATS DATASET

STATS DATASET combines the functions of DATASET NAME, DATASET ACTIVATE, DATASET CLOSE, and DATASET DISPLAY into a single command. It also allow you to close all the datasets except for a specified list without having to list every dataset individually.


Note: For users with IBM SPSS Statistics version 23 or higher, the STATS DATASET extension bundle is installed as part of IBM SPSS Statistics - Essentials for Python.

Note: For users with IBM SPSS Statistics version 23 or higher, the STATS DATASET extension bundle is installed as part of IBM SPSS Statistics - Essentials for Python.

STATS DBPRED

STATS DBPRED uses the results of the companion procedure, STATS DBSCAN, to predict cluster values for new cases.

Requirements: Version 18 or higher, the corresponding IBM SPSS Statistics - Integration Plug-in for R, and the R fpc package.

STATS DBSCAN

STATS DBSCAN calculates density-based clusters allowing for noise. The companion procedure, STATS DBPRED, can be used to predict values for new cases.

Requirements: Version 18 or higher, the corresponding IBM SPSS Statistics - Integration Plug-in for R, and the R fpc package.


STATS DISAGG

STATS DISAGG disaggregates a time series into a series at a higher frequency. It creates a new dataset containing a time series at a higher frequency than the original. It can use higher frequency indicator variables with which the low frequency is correlated to distribute the values or distribute less accurately without such indicators. Several methods are supported.

Requirements: Version 18 or higher, the corresponding IBM SPSS Statistics - Integration Plug-in for R, and the R tempdisagg package.

Note: For users with IBM SPSS Statistics version 23 or higher, the STATS DISAGG extension bundle and the R tempdisagg package are installed as part of IBM SPSS Statistics - Essentials for R.

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 18 or higher, the corresponding IBM SPSS Statistics - Integration Plug-in for R, and the R MASS package, which is included with the R distribution.

STATS EQNSYSTEM

STATS EQNSYSTEM estimates a system of linear equations by 3SLS, 2SLS, SUR, or OLS. The dialog box handles only two equations, but an unlimited number can be used in syntax.

Requirements: Version 18 or higher, the corresponding IBM SPSS Statistics - Integration Plug-in for R, and the following R packages: systemfit, Matrix, car, lmtest, zoo.

Note: For users with IBM SPSS Statistics version 23 or higher, the STATS EQNSYSTEM extension bundle and the required R packages are installed as part of IBM SPSS Statistics - Essentials for R.

STATS EXRASCH

STATS EXRASCH calculates standard Rasch models and five extensions: RM: Binary Rasch, 0/1 item values; LLTM: Linear Logistic Test, 0/1 item values; RSM: Polytomous Rating Scale, more than two values; LRSM: Linear Rating Scale, more than two values; PCM: Polytomous Partial Credit, more than two values; LPCM: Polytomous Linear Partial Credit, more than two values.

Requirements: Version 18 or higher, the corresponding IBM SPSS Statistics - Integration Plug-in for R, and the R eRm package.

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 FIRTHLOG

STATS FIRTHLOG calculates the Firth logistic regression model, which can address the separation issues that can arise in standard logistic regression.


Requirements: Version 18 or higher, the corresponding IBM SPSS Statistics - Integration Plug-in for R, and the R logistf package.

Note: For users with IBM SPSS Statistics version 23 or higher, the STATS FIRTHLOG extension bundle and the R logistf package are installed as part of IBM SPSS Statistics - Essentials for R.

STATS FLEISS KAPPA

STATS FLEISS KAPPA computes Fleiss Multi-Rater Kappa Statistics. It provides an overall estimate of kappa, along with asymptotic standard error, Z statistic, significance or p value under the null hypothesis of chance agreement and confidence interval for kappa.


STATS GARCH

STATS GARCH calculates a variety of univariate GARCH models. The models can be used for forecasting.

Requirements: Version 18 or higher, the corresponding IBM SPSS Statistics - Integration Plug-in for R, and the R rugarch package.

Note: For users with IBM SPSS Statistics version 23 or higher, the STATS GARCH extension bundle and the R rugarch package are installed as part of IBM SPSS Statistics - Essentials for R.

STATS GBM

STATS GBM produces a set (ensemble) of individually weak regression models that in the aggregate can predict a dependent variable more accurately than standard models. It covers distributions from gaussian to Bernoulli; ten distributions in total. It is based on the R gbm package which follows the Friedman gradient boosting method. This package performs the estimation. The accompanying package, STATS GBMPRED, is used to calculate predictions based on the results of this procedure.

Requirements: Version 18 or higher, the corresponding IBM SPSS Statistics - Integration Plug-in for R, and the R gbm package.

STATS GBMPRED

STATS GBMPRED is a companion to the STATS GBM package, which creates generalized boosted regression models. The package reads the results of STATS GBM and creates a new dataset of predictions for new data. It is based on the R gbm package, which is also used by STATS GBM.

Requirements: Version 18 or higher, the corresponding IBM SPSS Statistics - Integration Plug-in for R, and the R gbm package.

STATS GET R

STATS GET R can display information about an R workspace file or dataset and can convert a data frame to a Statistics dataset. It can also display a list of all datasets associated with the installed R packages.

Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for R.

Note: For users with IBM SPSS Statistics version 23 or higher, the STATS GET R extension bundle is installed as part of IBM SPSS Statistics - Essentials for R.


STATS GET TRIPLES

STATS GET TRIPLES reads a file in Triple-S format. Triple-S is an open standard for data files, primarily surveys. This command reads a fixed-format or csv file conforming to this standard. It can also generate and save a syntax file for reading the data.


Note: For users with IBM SPSS Statistics version 23 or higher, the STATS GET TRIPLES extension bundle is installed as part of IBM SPSS Statistics - Essentials for Python.

STATS GRM

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

Requirements: Version 18 or higher, the corresponding IBM SPSS Statistics - 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 IBM SPSS Statistics - Integration Plug-in for R, and the R sampleSelection package.

Note: For users with IBM SPSS Statistics version 22 or higher, the STATS HECKMAN REGR extension bundle and the R sampleSelection package are installed as part of IBM SPSS Statistics - Essentials for R.

STATS IF

STATS IF conditionally executes blocks of code. It accepts a list of logical conditions and executes the associated block of code for the first condition that is true. Conditions are written as Python expressions in which the values are from previously executed Python code or Python apis.


Note: For users with IBM SPSS Statistics version 23 or higher, the STATS IF extension bundle is installed as part of IBM SPSS Statistics - Essentials for Python.

STATS INEQUALITY

STATS INEQUALITY computes up to seven inequality measures, including the Gini coefficient, for a variable. Confidence intervals are available for Gini coefficients. Split files are supported, making group comparisons easy.

Requirements: Version 18 or higher, the corresponding IBM SPSS Statistics - Integration Plug-in for R, and the following R packages: ineq, DescTools.

Note: For users with IBM SPSS Statistics version 23 or higher, the STATS INEQUALITY extension bundle and the required R packages are installed as part of IBM SPSS Statistics - Essentials for R.


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 are 0 or 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 18 or higher, the corresponding IBM SPSS Statistics - Integration Plug-in for R, and the R ltm package.

STATS KERNEL DENSITY

STATS KERNEL DENSITY calculates kernel-smoothed densities. It can save the densities as datasets and plot them. The calculations are similar to the smoothing available via GPL.


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 IBM SPSS Statistics - Integration Plug-in for R and the R poLCA package.

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

STATS LOESS

STATS LOESS fits a loess curve to a model with one to four independent variables and creates a new dataset. The dataset contains the predicted values for the estimation data or new data as well as the prediction input data.


STATS LP

STATS LP solves linear and integer programming problems and mixed linear and integer problems. Bounds can be imposed on the objective function variables. It can even be used to solve systems of linear equations by using all equality constraints.

Requirements: Version 19 or higher, the corresponding IBM SPSS Statistics - Integration Plug-in for Python and IBM SPSS Statistics - Integration Plug-in for R, and the R lpSolveAPI package.

STATS MCSET CONVERT

STATS MCSET CONVERT converts a multiple category set into a multiple dichotomy set representing the same information. This can be useful in conjunction with the STATS CATEGORY ORDER extension command or in order to tabulate individual values or to create dummy variables for use in procedures such as regression.



Note: For users with IBM SPSS Statistics version 23 or higher, the STATS MCSET CONVERT extension bundle is installed as part of IBM SPSS Statistics - Essentials for Python.

STATS OPEN PROJECT

STATS OPEN PROJECT opens a project. A project consists of Statistics syntax, files of type syntax and output, and other projects. Any syntax is run, and listed files are opened. A project can be set to open automatically when Statistics starts.


Note: For users with IBM SPSS Statistics version 23 or higher, the STATS OPEN PROJECT extension bundle is installed as part of IBM SPSS Statistics - Essentials for Python.

STATS OPTBINEX

STATS OPTBINEX constructs optimal bins for categorical or continuous target. It uses the TREE CHAID algorithm on a categorical or continuous target to group categories or ranges of predictor values that have statistically indistinguishable effects on the target. It can carry out the transformations and save that syntax for future use. It also provides value labels for the new variables. Predictor variables are analyzed one at a time. The results are similar to those from OPTIMAL BINNING for continuous variables and ADP, but has the advantage of exposing the transformations and providing labels. The command requires the Decision Trees option, because it uses the TREE procedure.


STATS OPTIMAL DESIGNMC

STATS OPTIMAL DESIGNMC generates an optimal experimental design dataset using Monte Carlo methods and the Federov algorithm.

Requirements: Version 18 or higher, the corresponding IBM SPSS Statistics - Integration Plug-in for R, and the R AlgDesign package.

STATS OUTPUT ATTRS

STATS OUTPUT ATTRS sets various attributes that apply to printing and exporting of the Viewer contents. These include header and footer text, page margins and orientation, starting page number, and spacing between output objects. All of these attributes can be set interactively using the Page Attributes and Page Setup dialogs on the Viewer File menu. This dialog can generate syntax to allow these attributes to be set in batch or production jobs.


Note: For users with IBM SPSS Statistics version 22 or higher, the STATS OUTPUT ATTRS extension bundle is installed as part of IBM SPSS Statistics - Essentials for Python. Also, for IBM SPSS Statistics version 22 or higher, some of the attributes that are set with STATS OUTPUT ATTRS can be set from the Viewer tab on the Options dialog (Edit>Options).


STATS PADJUST

STATS PADJUST calculates adjusted p values that account for multiple testing. It includes false discovery rate and family-wise error rate adjustments. The input is a dataset of p values or a list of values given in the command.


Note: For users with IBM SPSS Statistics version 23 or higher, the STATS PADJUST extension bundle is installed as part of IBM SPSS Statistics - Essentials for R.

STATS PMML DISPLAY

STATS PMML DISPLAY reads one or more PMML files and displays summary information about the models. The output varies according to the type of model, and some PMML files might not be able to be processed with this procedure. The procedure may be useful when a PMML model is being used, for example, for scoring but the original model estimation information is not available.


STATS PROPOR REGR

STATS PROPOR REGR estimates a linear model with a link function for equations where the dependent variable is a proportion with a beta distribution. Mean and precision parameters of the distribution are estimated. Residuals and fitted values can be saved. The estimated model can be saved and used for predictions on new data with the STATS PROPOR REGRPRED extension command.

Requirements: Version 18 or higher, the corresponding IBM SPSS Statistics - Integration Plug-in for R, and the R betareg package.

Note: For users with IBM SPSS Statistics version 23 or higher, the STATS PROPOR REGR extension bundle and the R betareg package are installed as part of IBM SPSS Statistics - Essentials for R.

STATS PROPOR REGRPRED

STATS PROPOR REGRPRED takes a model estimated by the STATS PROPOR REGR extension command and uses it to calculate predicted values of various types for new data.

Requirements: Version 18 or higher, the corresponding IBM SPSS Statistics - Integration Plug-in for R, and the R betareg package.

Note: For users with IBM SPSS Statistics version 23 or higher, the STATS PROPOR REGRPRED extension bundle and the R betareg package are installed as part of IBM SPSS Statistics - Essentials for R.

STATS QUOTE SQLTEXT

STATS QUOTE SQLTEXT takes as input a file of SQL text that was not generated by the Database Wizard and adds quotes as necessary for it to be used in the GET DATA command. Wizard-generated text is already in this format, but SQL that is generated by an external tool or that is handwritten needs to be properly quoted before use.



STATS RDD

STATS RDD performs regression discontinuity analysis. It estimates the effects of a treatment on an output in a non-experimental context. The treatment is applied based on whether an observed assignment variable (also known as a running variable) exceeds a known threshold.

Requirements: Version 18 or higher, the corresponding IBM SPSS Statistics - Integration Plug-in for R, and the R rdd package.

Note: For users with IBM SPSS Statistics version 23 or higher, the STATS RDD extension bundle and the R rdd package are installed as part of IBM SPSS Statistics - 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 20 or higher, and the corresponding IBM SPSS Statistics - Integration Plug-in for Python.

Note: For users with IBM SPSS Statistics version 21 or higher, the STATS REGRESS PLOT extension bundle is installed as part of IBM SPSS Statistics - 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 IBM SPSS Statistics - Integration Plug-in for R and the R relaimpo package.

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

STATS SOUND

STATS SOUND, which is only available for Windows, plays the specified sound or sound file. It can be useful when a long running job needs an audible notification at a selected point. The specific sound definitions are taken from settings in the Windows Control Panel.


Note: For users with IBM SPSS Statistics version 23 or higher, the STATS SOUND extension bundle is installed as part of IBM SPSS Statistics - Essentials for Python.

STATS STRUC CHANGE

STATS STRUC CHANGE calculates a linear model for specified variables and provides plots and test statistics for structural change. Cases should be time series data or ordered by another variable.



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 or higher, and the corresponding IBM SPSS Statistics - Integration Plug-in for Python.

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

STATS SURVREG

STATS SURVREG calculates survival regressions for left, right, interval, or counting censoring and for Weibull, exponential, gaussian, logistic, and other distributions. Residuals and predicted values of various types can be saved as datasets. The package can do Tobit regression as a special case.

Requirements: Version 18 or higher, the corresponding IBM SPSS Statistics - Integration Plug-in for R, and the R survival package.

STATS SVM

STATS SVM constructs a support vector machine and uses it for predictions. The procedure supports linear or nonlinear classifiers and regression with four choices of kernels. A grid search over the parameter space can be carried out.

Requirements: Version 18 or higher, the corresponding IBM SPSS Statistics - Integration Plug-in for R, and the R e1071 package.

Note: For users with IBM SPSS Statistics version 23 or higher, the STATS SVM extension bundle and the R e1071 package are installed as part of IBM SPSS Statistics - Essentials for R.

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.

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

Note: For users with IBM SPSS Statistics version 22 or higher, the STATS TABLE CALC extension bundle is installed as part of IBM SPSS Statistics - Essentials for Python.

STATS TSTESTS

STATS TSTESTS performs cointegration and stationarity tests for time series data. Specifically it performs one or more of the Phillips-Ouliaris cointegration test, the Phillips-Perron unit root test, the augmented Dickey-Fuller test, and the Kwiatkowski-Phillips_Schmidt-Shinn test.

Requirements: Version 18 or higher, the corresponding IBM SPSS Statistics - Integration Plug-in for R, and the R tseries package.


STATS VALLBLS FROMDATA

STATS VALLBLS FROMDATA creates value labels for selected variables using other variables as the source of the labels. The variables to label and the label source variables can be listed or selected using patterns in the names. The labels can be applied directly by this command, but the VALUE LABEL syntax can also be written to a file.


Note: For users with IBM SPSS Statistics version 23 or higher, the STATS VALLBLS FROMDATA extension bundle is installed as part of IBM SPSS Statistics - Essentials for Python.

STATS WEIBULL PLOT

STATS WEIBULL PLOT creates a Weibull probability plot for data on item failures and suspensions.

Requirements: Version 20, with Fixpack1, or higher and the corresponding IBM SPSS Statistics Integration Plug-in for Python. It also requires the Graphboard template weibull.viztemplate, which must be installed separately (use Graphics >Graphboard Template Chooser> Manage). STATS WEIBULL PLOT can be used with version 19 if the hotfix for the cloglog scale is installed.

Note: For users with IBM SPSS Statistics version 23 or higher, the STATS WEIBULL PLOT extension bundle is installed as part of IBM SPSS Statistics - Essentials for Python.

STATS WEIGHTED KAPPA

STATS WEIGHTED KAPPA provides the weighted version of Cohen's kappa for two raters, using either linear or quadratic weights, as well as confidence interval and test statistic.


STATS ZEROINFL

STATS ZEROINFL estimates and predicts a zero-inflated count model. It estimates mixture models consisting of a Poisson or negative binomial count model and a point mass at zero. The predictors can be different for the two models. The estimated model can be saved and used for predictions on new data.

Requirements: Version 18 or higher, the corresponding IBM SPSS Statistics - Integration Plug-in for R, and the R pscl package.

Note: For users with IBM SPSS Statistics version 23 or higher, the STATS ZEROINFL extension bundle and the R pscl package are installed as part of IBM SPSS Statistics - Essentials for R.

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.


Note: For users with IBM SPSS Statistics version 23 or higher, the TEXT extension bundle is installed as part of IBM SPSS Statistics - Essentials for Python.


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