Extension Bundles from IBM SPSS

Transcription

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Contents
Extension bundles from IBM SPSS . . . 1
Listing of extension bundles . . . . . . . . . 2
Trademarks . . . . . . . . . . . . . . 22
Index . . . . . . . . . . . . . . . 23 iii
iv
Extension bundles from IBM SPSS
Rich Cohen (rjcohen@us.ibm.com), 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
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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 within the
command (in a syntax window) and pressing the F1 key. In either case, the command syntax help is not
integrated with the SPSS Statistics Help system and is not included in the Command Syntax Reference.
Note: The F1 mechanism for displaying help is not supported in distributed mode.
To find the location, in the SPSS Statistics menus, of a dialog installed by an extension bundle, navigate to
Utilities>Extension Bundles>View Installed Extension Bundles and click the highlighted text for the
specific bundle in the Summary column on the Installed Extension Bundles dialog. The menu location is
provided on the associated Extension Bundle Details dialog.
Note: Extension bundles from non-IBM authors may also be available from the SPSS community.
Listing of extension bundles
Following is an alphabetical listing of the extension bundles that are authored by IBM SPSS and available
from the SPSS community.
FormatCorrelations
FormatCorrelations provides options to improve the formatting of correlation tables produced by
CORRELATIONS and NONPAR CORR. It can suppress various rows, display only the lower triangle, blank
insignificant correlations, and highlight large ones. It requires that the SPSSINC MODIFY TABLES extension
bundle is installed.
Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for
Python.
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.
Python.
Note: For users with IBM SPSS Statistics version 19 or higher, the FUZZY extension bundle is installed as
part of IBM SPSS Statistics - Essentials for Python.
GATHERMD
GATHERMD builds a dataset containing the variable names, labels, and optionally selected custom variable
attributes from one or more IBM SPSS Statistics, SAS, or Stata data files.
Python
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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.
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
PROPOR
PROPOR calculates confidence intervals for proportions and differences in proportions.
Python.
PSM
PSM does propensity score matching for cases and controls.
Python.
Note: For users with IBM SPSS Statistics version 22 or higher, the PSM extension bundle is installed as
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.
Python.
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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 IBM SPSS Statistics.
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.
Python.
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.
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.
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Python.
Note: For users with IBM SPSS Statistics version 22 or higher, the SPSSINC CENSOR TABLES extension
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.
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.
Python.
Note: For users with IBM SPSS Statistics version 19 or higher, the SPSSINC CREATE DUMMIES extension
SPSSINC GETURI DATA
SPSSINC GETURI DATA opens data files stored on the Internet. You can open files in SPSS, Excel, SAS, and
Stata formats.
Python.
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.
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.
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SPSSINC MERGE TABLES
SPSSINC MERGE TABLES merges the contents of one pivot table in the Viewer into another.
Python.
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.
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.
Python.
Note: For users with IBM SPSS Statistics versions 19, 20, or 21, the SPSSINC MODIFY OUTPUT extension
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.
Python.
Note: For users with IBM SPSS Statistics versions 19, 20, or 21, the SPSSINC MODIFY TABLES extension
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
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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.
Python.
Note: For users with IBM SPSS Statistics version 22 or higher, the SSPSSINC PROCESS FILES extension
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.
Python.
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.
Python.
Note: For users with IBM SPSS Statistics version 23 or higher, the SPSSINC PROGRAM extension bundle is
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.
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.
and the R quantreg package.
Note: The SPSSINC QUANTREG extension bundle and the R quantreg package are installed as part of IBM
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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
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.
and the R randomForest package.
SPSSINC RANPRED
SPSSINC RANPRED calculates predictions from classification or regression trees generated from the SPSSINC
RANFOR extension command.
and the R randomForest package.
SPSSINC RASCH
SPSSINC RASCH estimates the parameters of the Rasch model for item response data.
and the R ltm package.
Note: The SPSSINC RASCH extension bundle and the R ltm package are installed as part of IBM SPSS
Statistics - 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.
Python.
SPSSINC ROBUST REGR
SPSSINC ROBUST REGR estimates a linear regression model, robustly, using an M estimator.
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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.
Python.
Note: For users with IBM SPSS Statistics version 23 or higher, the SPSSINC SELECT VARIABLES extension
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.
Python.
Note: For users with IBM SPSS Statistics version 22 or higher, the SPSSINC SPLIT DATASET extension
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.
Python.
Note: For users with IBM SPSS Statistics version 23 or higher, the SPSSINC SUMMARY TTEST extension
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.
and the R AER package.
Note: The SPSSINC TOBIT REGR extension bundle and the R AER package are installed as part of IBM
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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.
Python.
Note: For users with IBM SPSS Statistics version 19 or higher, the SPSSINC TRANS extension bundle is
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).
Python.
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.
Python.
Note: For users with IBM SPSS Statistics version 19 or higher, the SPSSINC TURF extension bundle is
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.
Python.
Note: For users with IBM SPSS Statistics version 23 or higher, the STATS ADJUST WIDTHS extension bundle
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.
and the R aplpack package.
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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.
Python.
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.
Python.
Note: For users with IBM SPSS Statistics version 23 or higher, the STATS CANCORR extension bundle is
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.
Python.
Note: For users with IBM SPSS Statistics version 23 or higher, the STATS CARTPROD extension bundle is
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.
Python.
Note: For users with IBM SPSS Statistics version 23 or higher, the STATS CATEGORY ORDER extension
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.
Python.
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Note: For users with IBM SPSS Statistics version 23 or higher, the STATS CLUS CIL extension bundle is
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.
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.
Python.
Note: For users with IBM SPSS Statistics version 23 or higher, the STATS DATA DATE extension bundle is
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.
Python.
Note: For users with IBM SPSS Statistics version 23 or higher, the STATS DATASET extension bundle is
Note: For users with IBM SPSS Statistics version 23 or higher, the STATS DATASET extension bundle is
STATS DBPRED
STATS DBPRED uses the results of the companion procedure, STATS DBSCAN, to predict cluster values for
new cases.
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.
and the R fpc package.
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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.
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.
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.
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.
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.
Python.
STATS FIRTHLOG
STATS FIRTHLOG calculates the Firth logistic regression model, which can address the separation issues
that can arise in standard logistic regression.
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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.
Python.
STATS GARCH
STATS GARCH calculates a variety of univariate GARCH models. The models can be used for forecasting.
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.
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.
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.
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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.
Python.
Note: For users with IBM SPSS Statistics version 23 or higher, the STATS GET TRIPLES extension bundle is
STATS GRM
STATS GRM fits the Graded Response model for ordinal polytomous data via an IRT approach.
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.
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.
Python.
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.
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.
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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.
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.
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.
Python.
16
Note: For users with IBM SPSS Statistics version 23 or higher, the STATS MCSET CONVERT extension bundle
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.
Python.
Note: For users with IBM SPSS Statistics version 23 or higher, the STATS OPEN PROJECT extension bundle
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.
Python.
STATS OPTIMAL DESIGNMC
STATS OPTIMAL DESIGNMC generates an optimal experimental design dataset using Monte Carlo methods
and the Federov algorithm.
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.
Python.
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).
17
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.
Python.
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.
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.
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.
Python.
18
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.
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
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.
Python.
Note: For users with IBM SPSS Statistics version 23 or higher, the STATS SOUND extension bundle is
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.
19
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.
Python.
Note: For users with IBM SPSS Statistics version 21 or higher, the STATS SUBGROUP PLOTS extension
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.
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.
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.
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
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.
and the R tseries package.
20
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.
Python.
Note: For users with IBM SPSS Statistics version 23 or higher, the STATS VALLBLS FROMDATA extension
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
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.
Python.
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.
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.
Python.
Note: For users with IBM SPSS Statistics version 23 or higher, the TEXT extension bundle is installed as
21
Trademarks
IBM, the IBM logo, and ibm.com are trademarks of International Business Machines Corp., registered in
many jurisdictions worldwide. Other product and service names might be trademarks of IBM or other
companies. A current list of IBM trademarks is available on the Web at "Copyright and trademark
information" at http://www.ibm.com/legal/copytrade.shmtl.
22
Index
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Extension Bundles from IBM SPSS

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