SAS/STAT^{®}
SAS/STAT 14.2
SAS/STAT 14.2 introduces two new procedures and adds new features to many existing analyses. This release is available with the fourth maintenance release for Base SAS® 9.4.

 ACECLUS
Obtains approximate estimates of the pooled withincluster covariance matrix when the clusters are assumed to be multivariate normal with equal covariance matrices  ADAPTIVEREG
Fits multivariate adaptive regression splines  ANOVA
Performs analysis of variance for balanced data  BCHOICE
Performs Bayesian analysis for discrete choice models  BOXPLOT
Creates sidebyside boxandwhiskers plots of measurements organized in groups  CALIS
Fits structural equation models  CANCORR
Performs canonical correlation, partial canonical correlation, and canonical redundancy analysis  CANDISC
Performs a canonical discriminant analysis, computes squared Mahalanobis distances between class means, and performs both univariate and multivariate oneway analyses of variance  CATMOD
Performs categorical data modeling of data that can be represented by a contingency table  CAUSALTRT
Estimates the average causal effect of a binary treatment, T, on a continuous or discrete outcome, Y.  CLUSTER
Hierarchically clusters the observations in a SAS data  CORR
Computes Pearson correlation coefficients, nonparametric measures of association, and the probabilities associated with these statistics  CORRESP
Performs simple correspondence analysis and multiple correspondence analysis (MCA)  DISCRIM
Develops a discriminant criterion to classify each observation into groups  DISTANCE
Computes various measures of distance, dissimilarity, or similarity between the observations (rows) of a SAS data set. Proximity measures are stored as a lower triangular matrix or a square matrix in an output data set that can then be used as input to the CLUSTER, MDS, and MODECLUS procedures.  FACTOR
Performs a variety of common factor and component analyses and rotations  FASTCLUS
Performs a disjoint cluster analysis on the basis of distances computed from one or more quantitative variables  FMM
Fits finite mixture models  FREQ
Produces oneway to nway frequency and contingency (crosstabulation) tables  GAM
Fits generalized additive models  GAMPL
Fits generalized additive models that are based on lowrank regression splines  GEE
Fits generalized linear models for longitudinal data  GENMOD
Fits generalized linear models  GLIMMIX
Fits generalized linear mixed models  GLM
Fits general linear models  GLMMOD
Constructs the design matrix for a general linear model; it essentially constitutes the modelbuilding front end for the GLM procedure  GLMPOWER
Performs prospective power and sample size analysis for linear models  GLMSELECT
Performs effect selection in the framework of general linear models  HPGENSELECT
Model fitting and model building for generalized linear models  HPLOGISTIC
Fits logistic regression models and performs model selection for binary, binomial, and multinomial data  HPMIXED
Fits linear mixed models with simple covariance component structures by sparsematrix techniques  HPREG
Fits and performs model selection for ordinary linear least squares models  ICLIFETEST
Performs nonparametric survival analysis for intervalcensored data  ICPHREG
Fits proportional hazards regression models to intervalcensored data  INBREED
Calculates the covariance or inbreeding coefficients for a pedigree  IRT
Fits item response models  KDE
Performs univariate and bivariate kernel density estimation  KRIGE2D
Performs ordinary kriging in two dimensions  LATTICE
Analysis of variance and analysis of simple covariance for data from experiments with lattice designs  LIFEREG
Fits parametric models to failure time data that can be uncensored, right censored, left censored, or interval censored  LIFETEST
Compute nonparametric estimates of the survivor function either by the productlimit method (also called the KaplanMeier method) or by the lifetable method (also called the actuarial method)  LOESS
Implements a nonparametric method for estimating regression surfaces  LOGISTIC
Fits models with binary, ordinal, or nominal dependent variables  MCMC
A general purpose Markov chain Monte Carlo (MCMC) simulation procedure that is designed to fit Bayesian models  MDS
Fits two and threeway, metric and nonmetric multidimensional scaling models  MI
Performs multiple imputation of missing data  MIANALYZE
Combines the results of the analyses of imputations and generates valid statistical inferences  MIXED
Fits general linear models with fixed and random effects  MODECLUS
Clusters observations in a SAS data set by using any of several algorithms based on nonparametric density estimates  MULTTEST
Addresses the multiple testing problem by adjusting the pvalues from a family of hypothesis tests  NESTED
Performs randomeffects analysis of variance for data from an experiment with a nested (hierarchical) structure and classification effects  NLIN
Fits nonlinear regression models  NLMIXED
Fits mixed models in which the fixed or random effects enter nonlinearly  NPAR1WAY
Performs nonparametric tests for location and scale differences across a oneway classification  ORTHOREG
Fits general linear models by the method of least squares  PHREG
Performs regression analysis of survival data based on the Cox proportional hazards model  PLAN
Constructs designs and randomizes plans for factorial experiments  PLM
Performs postfitting statistical analyses  PLS
Performs principal components regression  POWER
Performs prospective power and sample size analyses for a variety of statistical analyses  PRINCOMP
Performs principal component analysis.  PRINQUAL
Performs principal component analysis (PCA) of qualitative, quantitative, or mixed data  PROBIT
Calculates maximum likelihood estimates of regression parameters and the natural (or threshold) response rate for quantal response data from biological assays or other discrete event data  PSMATCH
Provides a variety of tools for performing propensity score analysis.  QUANTLIFE
Fits quantile regression models for survival data  QUANTREG
Fits quantile regression models  QUANTSELECT
Performs effect selection for linear quantile regression models  REG
General purpose procedure for ordinary least squares regression  ROBUSTREG
Provides resistant (stable) results for linear regression models in the presence of outliers  RSREG
Fits quadratic response surface regression models  SCORE
Multiplies values from two SAS data sets, one containing coefficients and the other containing raw data to be scored using the coefficients from the first data set  SEQDESIGN
Design interim analyses for group sequential clinical trials  SEQTEST
Perform interim analyses for group sequential clinical trials  SIM2D
Produces a spatial simulation for a Gaussian random field with a specified mean and covariance structure in two dimensions by using an LU decomposition technique  SIMNORMAL
Performs conditional and unconditional simulation for a set of correlated normal or Gaussian random variables  SPP
Analyzes spatial point patterns  STDIZE
Standardizes one or more numeric variables in a SAS data set by subtracting a location measure and dividing by a scale measure  STDRATE
Computes directly and indirectly standardized rates and risks for study populations  STEPDISC
Given a classification variable and several quantitative variables, the procedure performs a stepwise discriminant analysis to select a subset of the quantitative variables for use in discriminating among the classes  SURVEYFREQ
Produces oneway to nway frequency and crosstabulation tables from complex multistage survey designs with stratification, clustering, and unequal weighting  SURVEYIMPUTE
Imputes missing values of an item in a data set by replacing them with observed values from the same item and computes replicate weights (such as jackknife weights) that account for the imputation  SURVEYLOGISTIC
Fits models with binary, ordinal, or nominal dependent variables and incorporates complex survey designs  SURVEYMEANS
Estimate statistics such as means, totals, proportions, quantiles, and ratios from complex multistage survey designs with stratification, clustering, and unequal weighting  SURVEYPHREG
Performs regression analysis of survival data based on the Cox proportional hazards model for complex survey sample designs  SURVEYREG
Performs linear regression analysis for complex survey sample designs  SURVEYSELECT
Selects simple random samples or selects samples according to a complex multistage sample design that includes stratification, clustering, and unequal probabilities of selection  TPSPLINE
Provides penalized least squares estimates  TRANSREG
Fits linear models with optimal nonlinear transformations of variables  TREE
Produces a tree diagram, also known as a dendrogram or phenogram, from a data set created by the CLUSTER or VARCLUS procedure that contains the results of hierarchical clustering as a tree structure  TTEST
Performs t tests and computes confidence limits for one sample, paired observations, two independent samples, and the AB/BA crossover design. Twosided, TOST (two onesided test) equivalence, and upper and lower onesided hypotheses are supported for means, mean differences, and mean ratios for either normal or lognormal data  VARCLUS
Divides a set of numeric variables into disjoint or hierarchical clusters  VARCOMP
Fits general linear models with random effects (with the option of specifying certain effects as fixed)  VARIOGRAM
Computes empirical measures of spatial continuity for twodimensional spatial data
Topics
The following presents overviews of SAS/STAT software procedures arranged by topic.
SAS/STAT Documentation Examples
For examples for SAS/STAT in the documentation, go to SAS/STAT software documentation examples.
For examples for SAS/STAT HighPerformance Procedures, go to SAS/STAT HighPerformance Procedures software documentation examples.
SAS/STAT Software Examples
The following SAS/STAT software examples are grouped according to the type of statistical analysis that is being performed. These examples are not included in the SAS/STAT documentation and are available only on the Web.
Bayesian Analysis
 Bayesian ZeroInflated Poisson Regression
 Bayesian Exponential Mixture Model
 Bayesian Linear Regression with Standardized Covariates
 Bayesian Hierarchical Modeling for MetaAnalysis
 Bayesian Hierarchical Poisson Regression Model for Overdispersed Count Data Using SAS/STAT 9.2
 Bayesian Hierarchical Poisson Regression Model for Overdispersed Count Data Using SAS/STAT 9.3
 Bayesian Binomial Model with Power Prior Using the MCMC Procedure
 Bayesian Multivariate Prior for Multiple Linear Regression Using SAS/STAT 9.2
 Bayesian Multivariate Prior for Multiple Linear Regression Using SAS/STAT 9.3
 Bayesian Multinomial Model for Ordinal Data Using SAS/STAT 9.2
 Bayesian Multinomial Model for Ordinal Data Using SAS/STAT 9.3
 Bayesian Quantile Regression
 Bayesian LASSO
 Stochastic Search Variable Selection with PROC MCMC
 Bayesian IRT Models: Unidimensional Binary Models
 Bayesian Unidimensional IRT Models: Graded Response Model
 Bayesian Autoregressive and TimeVarying Coefficients Time Series Models
Generalized Linear Models
 Fitting ZeroInflated Count Data Models by Using PROC GENMOD
 HighPerformance Variable Selection for Generalized Linear Models: PROC HPGENSELECT
 Fitting Tweedie's Compound PoissonGamma Mixture Model by Using PROC HPGENSELECT
Cluster Analysis
Spatial Analysis
Survey Sampling and Analysis
 Using Bootstrap Replicate Weights with SAS/STAT Survey Procedures
 Estimating Geometric Means Using Data from a Complex Survey Sampling Design
 Estimating the Variance of a Variable in a Finite Population
 Estimating the Standard Deviation of a Variable in a Finite Population
 Poststratification with PROC SURVEYMEANS
 Poisson Regressions for Complex Surveys
Videos
SAS/STAT Video Portal.2016 Technical Papers
 Fitting Multilevel Hierarchical Mixed Models Using PROC NLMIXED
Kurada, Raghavendra Rao; SAS Institute, Inc. 2016The NLMIXED procedure’s ability to fit linear and nonlinear models with standard or general distributions enables you to fit a wide range of such models. SAS/STAT® 13.2 enhanced PROC NLMIXED to support multiple RANDOM statements, enabling you to fit nested multilevel mixed models. This paper uses an example to illustrate the new functionality.
 Fitting Your Favorite Mixed Models with PROC MCMC
Chen, Fang; Brown, Gordon; Stokes, Maura; SAS Institute, Inc. 2016This paper describes how to use the MCMC procedure to fit Bayesian mixed models and compares the Bayesian approach to how the classical models would be fit with the familiar mixed modeling procedures.
 Statistical Model Building for Large, Complex Data: Five New Directions in SAS/STAT Software
Rodriguez, Robert N.; SAS Institute, Inc. 2016This paper provides a highlevel tour of five modern approaches to model building that are available in recent releases of SAS/STAT: building sparse regression models with the GLMSELECT procedure, building generalized linear models with the HPGENSELECT procedure, building quantile regression models with the QUANTSELECT procedure, fitting generalized additive models with the GAMPL procedure, and building classification and regression trees with the HPSPLIT procedure
 Survey Data Imputation with PROC SURVEYIMPUTE
Mukhopadhyay, Pushpal; SAS Institute, Inc. 2016This paper discusses different approaches for handling nonresponse in surveys, introduces PROC SURVEYIMPUTE, and demonstrates its use with realworld applications. It also discusses connections with other ways of handling missing values in SAS/STAT.
 Tips and Strategies for Mixed Modeling with SAS/STAT Procedures
Tao, Jill; Kiernan, Kathleen; Gibbs, Phil; SAS Institute, Inc. 2016This paper provides recommendations for circumventing memory problems and reducing execution times for your mixedmodeling analyses. It also shows how the new HPMIXED procedure can be beneficial for certain situations, as with large sparse mixed models. Lastly, it focuses on the best way to interpret and address common notes, warnings, and error messages that can occur with the estimation of mixed models in SAS software.
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