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Getting Started with SAS/STAT® 9.2

SAS/STAT 9.2 brings substantial new statistical methodology to you. To get you started with this new release, we have compiled this page which provides an overview of new functionality and contains links to additional information.


Overview

You can start with a summary of these enhancements and find a more detailed list of what's new in the documentation. For a paper that includes several examples, see You Can't Stop Statistics: SAS/STAT Software Keeps Rolling Along.


ODS Statistical Graphics

ODS Statistical Graphics is major new functionality for creating statistical graphics that is available in a number of SAS software products, including SAS/STAT®, SAS/ETS®, SAS/QC®, and SAS/GRAPH®. With the production release of ODS Statistical Graphics in SAS 9.2, over forty statistical procedures have been modified to use this functionality, and they now produce graphs as automatically as they produce tables. In addition, new procedures in SAS/GRAPH use this functionality to produce plots for exploratory data analysis and for customized statistical displays. SAS 9.2 also introduces the ODS Graphics Editor, a point-and-click interface with which you can customize titles, annotate points, and make other changes.The following resources are your guide to harnessing the power of ODS Statistical Graphics:

Essential information for the statistician is contained in the chapter on Statistical Graphics Using ODS included in the SAS/STAT documentation.


Power and Sample Size

There are several noteworthy additions and enhancements to the SAS/STAT power and sample size capabilities. The POWER procedure now provides power for additional analyses including the following:

The Power and Sample Size Application, previously available as a Web application, has been rewritten as a Java client and includes new tasks that correspond to the new features in PROC POWER. See Updates to SAS® Power and Sample Size Software in SAS/STAT® 9.2 for highlights of the new features.

You might also be interested in an autocall macro called %POWTABLE. This macro renders the output of the POWER and GLMPOWER procedures in rectangular form, and it optionally produces simplified results by using weighted means across chosen variables.


Bayesian Analysis

SAS introduced Bayesian capabilities in SAS/STAT 9.1 with experimental web downloads for three procedures: GENMOD, LIFEREG, and PHREG. With the release of SAS 9.2, these capabilities become production. In addition, SAS 9.2 includes a new experimental MCMC procedure. PROC MCMC is a flexible simulation-based procedure that is suitable for fitting a wide range of Bayesian models. You specify a likelihood function for the data and a prior distribution for the parameters, and PROC MCMC obtains samples from the corresponding posterior distributions. It also produces summary and diagnostic statistics.

The following resources will help you explore the new Bayesian capabilities of SAS/STAT software:


Group Sequential Analysis

The experimental SEQDESIGN procedure designs interim analyses for clinical trials. PROC SEQDESIGN computes the boundary values and required sample sizes for the trial. The experimental SEQTEST procedure is used in conjunction with the SEQDESIGN procedure to carry out interim analyses for clinical trials. At each stage, you analyze the data with a statistical procedure and compute test statistics. You then use the SEQTEST procedure to compare the test statistic with the corresponding boundary values computed by the SEQDESIGN procedure. A great place to start learning about these new procedures is Group Sequential Analysis Using the New SEQDESIGN and SEQTEST Procedures.


Generalized Linear Mixed Models

SAS/STAT 9.2 offers an enhanced version of the GLIMMIX procedure, which is now production. The GLIMMIX procedure fits statistical models to data with correlations or nonconstant variability and where the response is not necessarily normally distributed. These generalized linear mixed models (GLMM), like linear mixed models, assume normal (Gaussian) random effects. Conditional on these random effects, data can have any distribution in the exponential family. See the following for details about GLIMMIX and its enhancements:


High-Performance Mixed Models

SAS/STAT 9.2 introduces the experimental HPMIXED procedure, which uses a number of specialized high-performance techniques to fit large linear mixed models with variance component structure. PROC HPMIXED is specifically designed to cope with estimation problems that involve a large number of fixed effects, a large number of random effects, or a large number of observations. Although the HPMIXED procedure fits only a subset of the models fit by the MIXED procedure and does not provide the breadth of confirmatory inference that is available with the MIXED procedure, it can have considerably better performance in terms of memory requirements and computational speed.

The paper All the Cows in Canada: Massive Mixed Modeling with the HPMIXED Procedure in SAS® 9.2 introduces applications of large mixed models, discusses the specialized techniques of the HPMIXED procedure to handle them, and demonstrates the utility of the procedure with examples from agriculture and genomics.


Model Selection

The GLMSELECT procedure performs large-scale model selection in the framework of general linear models. It includes a number of different model selection, methods, including LAR and LASSO. Selection can be in the tens of thousands of effects.

See the following resources for more information about PROC GLMSELECT:


Structural Equation Modeling

SAS/STAT 9.2 introduces the experimental TCALIS procedure, which updates the CALIS procedure with multiple-group analysis, enhanced mean structure analysis, path-like model specification, support of LISREL-type models, customizable effect analysis, general parametric function testing, and customizable Lagrange multiplier tests. To get started, see Structural Equation Modeling and Path Analysis Using PROC TCALIS in SAS® 9.2, which presents practical examples to illustrate some of the features of PROC TCALIS.


Quantile Regression

The QUANTREG procedure, which was previously available as an experimental web download, becomes production in SAS/STAT 9.2. An Introduction to Quantile Regression and the QUANTREG Procedure describes the QUANTREG procedure.


Generalized Additive Models

The GAM procedure is production with SAS 9.2. PROC GAM now produces graphs, including smoothing component plots and additive component plots. The target for an additive logistic model no longer has to be numeric; PROC GAM offers the same types of options for response and classification variables that are available in procedures such as PROC LOGISTIC and PROC GENMOD. The ANODEV=NOREFIT option in the MODEL statement enables a fast approximation analysis of deviance.

See the following resources for details about PROC GAM:


Replication Variance Methods in Survey Data Analysis

The SURVEYFREQ, SURVEYMEANS, SURVEYLOGISTIC, and SURVEYREG procedures now provide variance estimation by balanced repeated replication (BRR) and jackknife methods, in addition to the Taylor series method. You can provide replicate weights for the new replication methods with a REPWEIGHTS statement, or the procedures can construct the replicate weights. For further details and an example, see the following resources:


TTEST Enhancements

The TTEST procedure now performs TOST equivalence analyses, analyses of treatment and period in AB/BA crossover designs, weighted Satterthwaite tests and confidence intervals, analyses of ratios, and one-sided analyses. It supports both normal and lognormal data. Sasabuchi tests and Fieller confidence intervals are computed for normal ratios. PROC TTEST now provides graphs, including histograms, densities, box plots, profiles, agreement plots, Q-Q plots, and interval plots. Like Wine, the TTEST Procedure Improves with Age describes the new features of PROC TTEST in SAS 9.2 and illustrates their use in a number of examples.


SAS/IML Studio

SAS/STAT users will be interested in SAS/IML Studio, a highly flexible programming environment in which you can run SAS/STAT or SAS/IML analyses and display the results with dynamically linked graphics and data tables. SAS/IML Studio is intended for data analysts who use SAS statistical procedures but need more versatility for data exploration and model building. The programming language in SAS/IML Studio, which is called IMLPlus, is an enhanced version of the IML programming language. IMLPlus extends IML to provide new language features, including the ability to create and manipulate statistical graphics, call SAS procedures as functions, and call computational programs written in C, C++, Java, and Fortran. Stat Studio runs on a PC in the Microsoft Windows operating environment.

SAS/IML Studio is distributed with the SAS/IML product and was formerly known as SAS Stat Studio. To learn more about SAS/IML Studio, see the following: