Extension Bundles From IBM SPSS

March 19, 2018 | Author: asih359 | Category: Spss, R (Programming Language), Statistics, Regression Analysis, Python (Programming Language)
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���ExtensionBundlesfromIBMSPSS Contents Extension bundles from IBM SPSS . . . 1 Index . . . . . . . . . . . . . . . 17 Listing of extension bundles . . . . . . . . . 2 Trademarks . . . . . . . . . . . . . . 15 iii iv Extension Bundles from IBM SPSS Extension bundles from IBM SPSS Rich Cohen ([email protected]), Advisory Software Engineer IBM Corp. 9 August 2013 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. Anumber 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. 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. © Copyright IBM Corporation 1989, 2013  1 The command syntax help is not, however, integrated with the SPSS Statistics Help system and is not included in the Command Syntax Reference. The Syntax Editor, however, will color code the command syntax just as for built-in commands. 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. 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. Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for 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. Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for Python 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. 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 Extension Bundles from IBM SPSS 2 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. Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for Python. PSM PSM does propensity score matching for cases and controls. Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for Python. 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. Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for Python. 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 SPSSINCANON SPSSINC ANON provides a procedure for anonymizing variable values and names. It is useful, for example, when data need to be obscured for privacy or other reasons. Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for Python Extension bundles from IBM SPSS 3 SPSSINCAPRIORI 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. Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for Python. 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. Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for Python. 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. Extension Bundles from IBM SPSS 4 SPSSINC GETURI DATA SPSSINC GETURI DATA opens data files stored on the Internet. You can open files in SPSS, Excel, SAS, and Stata formats. Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in 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. Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for 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 MFPGLM 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 MODIFYOUTPUT 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. 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 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. Extension bundles from IBM SPSS 5 SPSSINC MODIFYTABLES 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. 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 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. Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for Python. 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. Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for 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. Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for Python. Extension Bundles from IBM SPSS 6 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 theAdvanced 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. Extension bundles from IBM SPSS 7 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 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. Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for Python. 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. Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for Python. SPSSINC SPLIT DATASET SPSSINC SPLIT DATASET creates a set of SAV files by splitting the active dataset according to the values of a splitting variable, leaving the active file unchanged. Output from SPSSINC SPLIT DATASET can be used with the SPSSINC PROCESS FILES extension command to apply a block of transformation and/or procedure syntax to each split group. Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for Python. 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 SUMMARYTTEST SPSSINC SUMMARY TTEST calculates a t test from just the summary information of the two samples: means, standard deviations, and case counts. Extension Bundles from IBM SPSS 8 Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in 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 RAER package. Note: The SPSSINC TOBIT REGR extension bundle and the RAER 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. Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for Python. 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.Adetailed 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). Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for 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. Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for Python. 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. STATSADJUST 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. Extension bundles from IBM SPSS 9 Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for Python. 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. Requirements: Version 19 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for 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. Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for Python. STATS CATEGORYORDER 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. Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in 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. Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for Python. STATS DATA DATE STATS DATA DATE defines the date structure of the dataset based on the first value of an SPSS Statistics date variable. Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in 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. 10 Extension Bundles from IBM SPSS 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 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 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. Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for Python. 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. Extension bundles from IBM SPSS 11 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 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 KERNELDENSITY 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. Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for R. 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 poLCApackage. Note: For users with IBM SPSS Statistics version 21 or higher, the STATS LATENT CLASS extension bundle and the R poLCApackage 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. Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for R. 12 Extension Bundles from IBM SPSS 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. Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for Python. STATS OUTPUTATTRS 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 PageAttributes 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. Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for 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). STATS PMMLDISPLAY STATS PMML DISPLAY reads one or more PMMLfiles and displays summary information about the models. The output varies according to the type of model, and some PMMLfiles might not be able to be processed with this procedure. The procedure may be useful when a PMMLmodel is being used, for example, for scoring but the original model estimation information is not available. Requirements: Version 20 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for Python. STATS QUOTE SQLTEXT STATS QUOTE SQLTEXT takes as input a file of SQLtext 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 SQLthat is generated by an external tool or that is handwritten needs to be properly quoted before use. Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for Python. Extension bundles from IBM SPSS 13 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. Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in 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. Requirements: Version 19 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for R. STATS SUBGROUPPLOTS 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. 14 Extension Bundles from IBM SPSS 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 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 WEIBULLPLOT 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. 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. Requirements: Version 18 or higher and the corresponding IBM SPSS Statistics - Integration Plug-in for Python. 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