SAS/STAT Procedures A-Z

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
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
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
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
HPMIXED — Fits linear mixed models with simple covariance component structures by sparse-matrix techniques
INBREED — Calculates the covariance or inbreeding coefficients for a pedigree
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
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
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
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

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