SAS/STAT 15.2 Highlights SAS/STAT 15.2 is a maintenance release that addresses reported issues in SAS/STAT software.

• ACECLUS
  Obtains approximate estimates of the pooled within-cluster 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
• BGLIMM  New Procedure!
  Provides full Bayesian inference for generalized linear mixed models (GLMMs)
• BOXPLOT
  Creates side-by-side box-and-whiskers 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 one-way analyses of variance
• CATMOD
  Performs categorical data modeling of data that can be represented by a contingency table
• CAUSALGRAPH  New Procedure!
  Examines the structure of graphical causal models
• CAUSALMED
  estimates causal mediation effects from observational data.
• 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
• 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 one-way to n-way frequency and contingency (crosstabulation) tables
• GAM
  Fits generalized additive models
• GAMPL
  Fits generalized additive models that are based on low-rank 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 model-building 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
• HPCANDISC
  performs canonical discriminant analysis and is a high-performance version of the CANDISC procedure in SAS/STAT software.
• HPFMM
  Fits statistical models to data for which the distribution of the response is a finite mixture of univariate distributions-that is, each response comes from one of several random univariate distributions that have unknown probabilities.
• HPGENSELECT
  Model fitting and model building for generalized linear models
• HPLMIXED
  Fits a variety of mixed linear models to data and enables use of these fitted models to make statistical inferences about the data
• 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 sparse-matrix techniques
• HPNLMOD
  Uses either nonlinear least squares or maximum likelihood to fit nonlinear regression models.
• HPPLS
  Fits models by using any one of a number of linear predictive methods, including partial least squares (PLS).
• HPPRINCOMP
  Conducts principal components analysis and provides iterative methods to calculate the principal components.
• HPQUANTSELECT
  Fits and performs effect selection for quantile regression analysis.
• HPREG
  Fits and performs model selection for ordinary linear least squares models
• HPSPLIT
  Builds tree-based statistical models for classification and regression
• ICLIFETEST
  Performs nonparametric survival analysis for interval-censored data
• ICPHREG
  Fits proportional hazards regression models to interval-censored 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 product-limit method (also called the Kaplan-Meier 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 three-way, 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 p-values from a family of hypothesis tests
• NESTED
  Performs random-effects 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 one-way 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
• RMSTREG  New Procedure!
  analyzes time-to-event data by using regression with respect to the restricted mean survival time (RMST)
• 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 one-way to n-way 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. Two-sided, TOST (two one-sided test) equivalence, and upper and lower one-sided 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 two-dimensional spatial data

Topics

The following presents overviews of SAS/STAT software procedures arranged by topic.

Analysis of Variance Bayesian Analysis Categorical Data Analysis Causal Analysis Cluster Analysis Descriptive Statistics Discriminant Analysis Distribution Analysis Exact Inference Finite Mixture Models Group Sequential Design and Analysis

Longitudinal Data Analysis Market Research Missing Data Analysis Mixed Models Model Selection Multivariate Analysis Nonlinear Regression Nonparametric Analysis Nonparametric Regression Post Processing Power and Sample Size

Predictive Modeling Psychometric Analysis Quantile Regression Regression Robust Regression Spatial Analysis Standardization Structural Equations Models Survey Sampling and Analysis Survival Analysis

SAS/STAT Documentation Examples

For examples for SAS/STAT in the documentation, go to SAS/STAT software documentation examples.

For examples for SAS/STAT High-Performance Procedures, go to SAS/STAT High-Performance Procedures software documentation examples.

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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 Zero-Inflated Poisson Regression
• Bayesian Exponential Mixture Model
• Bayesian Linear Regression with Standardized Covariates
• Bayesian Hierarchical Modeling for Meta-Analysis
• 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 Time-Varying Coefficients Time Series Models

Generalized Linear Models

• Fitting Zero-Inflated Count Data Models by Using PROC GENMOD
• High-Performance Variable Selection for Generalized Linear Models: PROC HPGENSELECT
• Fitting Tweedie's Compound Poisson-Gamma Mixture Model by Using PROC HPGENSELECT

Cluster Analysis

• Assessing the Accuracy of Cluster Allocations Obtained from Finite Mixture Models

Spatial Analysis

• Handling Spatial Data in Spherical Coordinates

Survey Sampling and Analysis

• Estimating the Variance of a Variable in a Finite Population
• Estimating the Standard Deviation of a Variable in a Finite Population
• Fractional Hot-Deck Imputation for Mixed Variables
• Poststratification with PROC SURVEYMEANS
• Poisson Regressions for Complex Surveys

Videos

Tools for Assessing a Propensity Score Model in SAS/STAT By Michael Lamm Air date: October 26, 2020	Building and Using DAGs for Causal Inference By Clay Thompson Air date: August 28, 2020	What's New in SAS/STAT 15.1 By G. Gordon Brown Air date: December 18, 2019	Scoring Predictive Models with SAS/STAT Software By Phil Gibbs Air date: December 18, 2019
Introducing the BGLIMM Procedure for Bayesian Generalized Linear Mixed Models By Amy Shi Air date: November 4, 2019	PROC GENSELECT and PROC LOGSELECT: Old Friends, New Clothes! By Dave Kessler Air date: October 21, 2019	Model-Based Clustering with PROC MBC By Dave Kessler Air date: October 21, 2019	Handling Excess Zeros with FMM Procedure (PROC FMM) By Dave Kessler Air date: October 10, 2019
CAUSALGRAPH Procedure for Graphical Causal Model Analysis By Clay Thompson Air date: September 19, 2019	Performing Restricted Mean Survival Time Analysis Using SAS/STAT By Changbin Guo Air date: September 19, 2019	Partial Proportional Odds Modeling with the LOGISTIC Procedure By Bob Derr Air date: August 2, 2019	What's New in SAS/STAT 14.3 By G. Gordon Brown Air date: November 29, 2018

For all SAS/STAT videos, go to the SAS/STAT Video Portal.

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2017 Papers

• Advanced Hierarchical Modeling with the MCMC Procedure
  Chen, Fang; Stokes, Maura; SAS Institute, Inc. 2017

  This paper shows how you can use PROC MCMC to fit hierarchical models that have varying degrees of complexity, from frequently encountered conditional independent models to more involved cases of modeling intricate interdependence.

• Estimating Causal Effects from Observational Data with the CAUSALTRT Procedure
  Lamm, Michael; Yung, Yiu-Fai; SAS Institute, Inc. 2017

  This paper reviews the statistical methods that are implemented in the CAUSALTRT procedure and includes examples of how you can use this procedure to estimate causal effects from observational data.

• Evaluating Predictive Accuracy of Survival Models with PROC PHREG
  Guo, Changbin; So, Ying; Woosung, Jang; SAS Institute, Inc. 2017

  This paper reviews the existing features in PROC LOGISTIC for C-statistic and ROC curves, presents the new features in PROC PHREG, and illustrates their applications in examples. Key differences between PROC PHREG and PROC LOGISTIC are also examined.

• Five Things You Should Know about Quantile Regression
  Rodriguez, Robert N.; Yao, Yonggang; SAS Institute, Inc. 2017

  This paper explains the concepts and benefits of quantile regression, and it introduces you to the appropriate procedures in SAS/STAT software.

• Propensity Score Methods for Causal Inference with the PSMATCH Procedure
  Yuan, Yang; Yung, Yiu-Fai; Stokes, Maura; SAS Institute, Inc. 2017

  This paper reviews propensity score methods for causal inference and introduces the PSMATCH procedure, which is new in SAS/STAT 14.2.

• Step Up Your Statistical Practice with Today’s SAS/STAT Software
  Rodriguez, Robert N.; Gibbs, Phil; Tobias, Randy; SAS Institute, Inc. 2017

  This paper will increase your awareness of modern tools in SAS/STAT by providing high-level comparisons with well-established tools and explaining the benefits of enhancements and new procedures.

For all SAS/STAT papers, go to Statistics Papers and Presentations

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