# SAS/STAT^{®}

### SAS/STAT 14.3

- 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 - 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 - 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 - 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 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. - HPREG

Fits and performs model selection for ordinary linear least squares models - 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 - 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.

### 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.

### 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

### Spatial Analysis

### Survey Sampling and Analysis

### Videos

SAS/STAT Video Portal.### 2017 Papers

**Advanced Hierarchical Modeling with the MCMC Procedure**

Chen, Fang; Stokes, Maura; SAS Institute, Inc. 2017This 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. 2017This 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. 2017This 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. 2017This 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. 2017This 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. 2017This 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.

Today's competitive landscape makes the critical link between decision-making and an organization's success more important than ever. From corporations to government agencies to research institutes to universities, organizations are increasingly turning to statistical analysis to help them make their critical decisions. Using the right statistical technique can provide the information that improves processes, drives new development, and maintains a satisfied customer base.

While there are many statistical analysis tools on the market today, only one gives you complete, comprehensive tools for data analysis–SAS/STAT software from SAS Institute. SAS/STAT, a fully integrated component of the SAS System, provides extensive statistical capabilities that meet the needs of an entire organization.

Better information. Better analysis. Better decisions.

With the SAS System, you can easily access data from any source, perform data management, carry out statistical analysis, and then present your findings in a variety of reports and graphs-all within a single software environment. SAS/STAT software enables you to evaluate data from a variety of sources, including clinical trials, marketing databases, health surveys, customer preference studies, stock market research, and so on. SAS/STAT software provides statistical techniques for applications that span every industry:

**Manufacturing**: Identify key factors in a semiconductor manufacturing process**Telecommunications**: Perform market research to determine customer preferences**Government**: Employ statistical sampling techniques to produce public opinion polls**Environmental Research**: Describe air pollution patterns with the use of spatial statistics**Biotechnology**: Evaluate early research findings on a new variety of seed**Retail**: Model customer behavior to establish potential target markets for a new e-commerce endeavor

From regression to exact methods to statistical visualization techniques, SAS/STAT software provides powerful tools for both specialized and enterprise-wide analytical needs. And since SAS Institute remains committed to its long tradition of constantly enriching its statistical offerings, you know that you will have access to the most up-to-date statistical techniques not just today, but well into the future.