SAS/STAT^®

SAS/STAT 15.1 User's Guide - Procedures

• The ACECLUS Procedure   PDF   |   HTML
  Obtains approximate estimates of the pooled within-cluster covariance matrix when the clusters are assumed to be multivariate normal with equal covariance matrices.
• The ADAPTIVEREG Procedure    PDF   |   HTML
  Fits multivariate adaptive regression splines.
• The ANOVA Procedure    PDF   |   HTML
  Performs analysis of variance for balanced data.
• The BCHOICE Procedure   PDF   |   HTML
  Performs Bayesian analysis for discrete choice models.
• The BGLIMM Procedure   PDF   |   HTML  New Procedure!
  High-performance, sampling-based procedure that provides Bayesian inference for generalized linear mixed models (GLMMs).
• The BOXPLOT Procedure   PDF   |   HTML
  Creates side-by-side box-and-whiskers plots of measurements organized in groups.
• The CALIS Procedure    PDF   |   HTML
  Fits structural equation models.
• The CANCORR Procedure    PDF   |   HTML
  Performs canonical correlation, partial canonical correlation, and canonical redundancy analysis.
• The CANDISC Procedure    PDF   |   HTML
  Performs a canonical discriminant analysis, computes squared Mahalanobis distances between class means, and performs both univariate and multivariate one-way analyses of variance.
• The CATMOD Procedure    PDF   |   HTML
  Performs categorical data modeling of data that can be represented by a contingency table.
• The CAUSALGRAPH Procedure    PDF   |   HTML  New Procedure!
  Examines the structure of graphical causal models and suggests statistical strategies or steps that enable researchers to estimate causal effects that have valid causal interpretations.
• The CAUSALMED Procedure   PDF   |   HTML
  Estimates causal mediation effects from observational data.
• The CAUSALTRT Procedure   PDF   |   HTML
  Implements causal inference methods that are designed primarily for use with data from nonrandomized trials or observational studies
• The CLUSTER Procedure    PDF   |   HTML
  Hierarchically clusters the observations in a SAS data set.
• The CORRESP Procedure    PDF   |   HTML
  Performs simple correspondence analysis and multiple correspondence analysis (MCA).
• The DISCRIM Procedure    PDF   |   HTML
  Develops a discriminant criterion to classify each observation into groups.
• The DISTANCE Procedure    PDF   |   HTML
  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.
• The FACTOR Procedure    PDF   |   HTML
  Performs a variety of common factor and component analyses and rotations.
• The FASTCLUS Procedure    PDF   |   HTML
  Performs a disjoint cluster analysis on the basis of distances computed from one or more quantitative variables.
• The FMM Procedure   PDF   |   HTML
  Fits finite mixture models.
• The FREQ Procedure    PDF   |   HTML
  Produces one-way to n-way frequency and contingency (crosstabulation) tables and performs table analysis.
• The GAM Procedure    PDF   |   HTML
  Fits generalized additive models.
• The GAMPL Procedure  PDF   |   HTML
  Fits generalized additive models that are based on low-rank regression splines.
• The GEE Procedure   PDF   |   HTML
  Provides weighted generalized estimating equations.
• The GENMOD Procedure    PDF   |   HTML
  Fits generalized linear models.
• The GLIMMIX Procedure   PDF   |   HTML
  Fits generalized linear mixed models.
• The GLM Procedure   PDF   |   HTML
  Fits general linear models.
• The GLMMOD Procedure    PDF   |   HTML
  Constructs the design matrix for a general linear model; it essentially constitutes the model-building front end for the GLM procedure.
• The GLMPOWER Procedure    PDF   |   HTML
  Performs prospective power and sample size analysis for linear models.
• The GLMSELECT Procedure    PDF   |   HTML
  Performs effect selection in the framework of general linear models.
• The HPCANDISC Procedure    PDF   |   HTML
  Performs canonical discriminant analysis.
• The HPFMM Procedure    PDF   |   HTML
  Fits statistical models to data for which the distribution of the response is a finite mixture of univariate distributions.
• The HPGENSELECT Procedure    PDF   |   HTML
  Provides model fitting and model building for generalized linear models.
• The HPLMIXED Procedure   PDF   |   HTML
  Fits a variety of mixed linear models to data and enables use of these fitted models to make statistical inferences about the data.
• The HPLOGISTIC Procedure    PDF   |   HTML
  Fits logistic regression models for binary, binomial, and multinomial data on the SAS appliance.
• The HPMIXED Procedure    PDF   |   HTML
  Fits linear mixed models with simple covariance component structures by sparse-matrix techniques.
• The HPNLMOD Procedure    PDF   |   HTML
  Uses either nonlinear least squares or maximum likelihood to fit nonlinear regression models.
• The HPPLS Procedure    PDF   |   HTML
  Fits models by using any one of a number of linear predictive methods, including partial least squares (PLS).
• The HPPRINCOMP Procedure    PDF   |   HTML
  Performs principal component analysis.
• The HPQUANTSELECT Procedure    PDF   |   HTML
  Fits and performs effect selection for quantile regression analysis.
• The HPREG Procedure    PDF   |   HTML
  Fits and performs model selection for ordinary linear least squares models.
• The HPSPLIT Procedure   PDF   |   HTML
  Builds tree-based statistical models for classification and regression.
• The ICLIFETEST Procedure   PDF   |   HTML
  Performs nonparametric survival analysis for interval-censored data.
• The ICPHREG Procedure    PDF   |   HTML
  Fits proportional hazards regression models to interval-censored data and can also fit proportional hazards regression models to failure time data that are uncensored, right censored, or left censored.
• The INBREED Procedure    PDF   |   HTML
  Calculates the covariance or inbreeding coefficients for a pedigree.
• The IRT Procedure   PDF   |   HTML
  Fits item response models.
• The KDE Procedure    PDF   |   HTML
  Performs univariate and bivariate kernel density estimation.
• The KRIGE2D Procedure    PDF   |   HTML
  Performs ordinary kriging for point-referenced spatial data.
• The LATTICE Procedure    PDF   |   HTML
  Performs analysis of variance and analysis of simple covariance for data from experiments with lattice designs.
• The LIFEREG Procedure    PDF   |   HTML
  Fits parametric models to failure time data that can be uncensored, right censored, left censored, or interval censored.
• The LIFETEST Procedure    PDF   |   HTML
  Computes 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).
• The LOESS Procedure    PDF   |   HTML
  Implements a nonparametric method for estimating regression surfaces.
• The LOGISTIC Procedure    PDF   |   HTML
  Fits regression models with binary, ordinal, or nominal dependent variables.
• The MCMC Procedure    PDF   |   HTML
  Performs general-purpose Markov chain Monte Carlo (MCMC) simulation designed to fit Bayesian models.
• The MDS Procedure    PDF   |   HTML
  Fits two- and three-way, metric and nonmetric multidimensional scaling models.
• The MI Procedure    PDF   |   HTML
  Performs multiple imputation of missing data.
• The MIANALYZE Procedure    PDF   |   HTML
  Combines the results of the analyses of imputations and generates valid statistical inferences.
• The MIXED Procedure    PDF   |   HTML
  Fits general linear models with fixed and random effects.
• The MODECLUS Procedure    PDF   |   HTML
  Clusters observations in a SAS data set by using any of several algorithms based on nonparametric density estimates.
• The MULTTEST Procedure    PDF   |   HTML
  Addresses the multiple testing problem by adjusting the p-values from a family of hypothesis tests.
• The NESTED Procedure    PDF   |   HTML
  Performs random-effects analysis of variance for data from an experiment with a nested (hierarchical) structure and classification effects.
• The NLIN Procedure    PDF   |   HTML
  Fits nonlinear regression models.
• The NLMIXED Procedure   PDF   |   HTML
  Fits mixed models in which the fixed or random effects enter nonlinearly.
• The NPAR1WAY Procedure    PDF   |   HTML
  Performs nonparametric tests for location and scale differences across a one-way classification.
• The ORTHOREG Procedure    PDF   |   HTML
  Fits general linear models by the method of least squares and provides more accurate estimates than other procedures when the data are ill-conditioned.
• The PHREG Procedure    PDF   |   HTML
  Performs regression analysis of survival data based on the Cox proportional hazards model.
• The PLAN Procedure    PDF   |   HTML
  Constructs designs and randomizes plans for factorial experiments.
• The PLM Procedure   PDF   |   HTML
  Performs postfitting statistical analyses.
• The PLS Procedure    PDF   |   HTML
  Fits models by using any one of a number of linear predictive methods, including partial least squares (PLS).
• The POWER Procedure    PDF   |   HTML
  Performs prospective power and sample size analyses for a variety of statistical analyses.
• The Power and Sample Size Application  PDF   |   HTML
  The functionality of the Power and Sample Size application has been replaced by tasks in SAS Studio.
• The PRINCOMP Procedure    PDF   |   HTML
  Performs principal component analysis.
• The PRINQUAL Procedure    PDF   |   HTML
  Performs principal component analysis (PCA) of qualitative, quantitative, or mixed data.
• The PROBIT Procedure    PDF   |   HTML
  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.
• The PSMATCH Procedure   PDF   |   HTML
  Provides a variety of tools for propensity score analysis.
• The QUANTLIFE Procedure   PDF   |   HTML
  Performs quantile regression analysis for survival data.
• The QUANTREG Procedure    PDF   |   HTML
  Fits quantile regression models.
• The QUANTSELECT Procedure    PDF   |   HTML
  Performs effect selection in the framework of quantile regression.
• The REG Procedure    PDF   |   HTML
  General-purpose procedure for ordinary least squares regression.
• The RMSTREG Procedure   PDF   |   HTML  New Procedure!
  Analyzes time-to-event data by using regression with respect to the restricted mean survival time (RMST), using specialized methods such as those developed by Andersen, Hansen, and Klein and Tian, Zhao, and Wei.
• The ROBUSTREG Procedure    PDF   |   HTML
  Provides resistant (stable) results for linear regression models in the presence of outliers.
• The RSREG Procedure    PDF   |   HTML
  Fits quadratic response surface regression models.
• The SCORE Procedure    PDF   |   HTML
  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.
• The SEQDESIGN Procedure    PDF   |   HTML
  Designs interim analyses for group sequential clinical trials.
• The SEQTEST Procedure    PDF   |   HTML
  Performs interim analyses for group sequential clinical trials.
• The SIM2D Procedure   PDF   |   HTML
  Simulates a spatial Gaussian random field that uses a specified mean and covariance structure.
• The SIMNORMAL Procedure    PDF   |   HTML
  Performs conditional and unconditional simulation for a set of correlated normal or Gaussian random variables.
• The SPP Procedure   PDF (8.03MB)  |   HTML
  Analyzes spatial point pattern data and fits spatial process models.
• The STDIZE Procedure    PDF   |   HTML
  Standardizes one or more numeric variables in a SAS data set by subtracting a location measure and dividing by a scale measure.
• The STDRATE Procedure    PDF   |   HTML
  Computes directly standardized rates and risks for study populations.
• The STEPDISC Procedure    PDF   |   HTML
  Performs a stepwise discriminant analysis of a specified classification variable and several quantitative variables to select a subset of the quantitative variables for use in discriminating among the classes.
• The SURVEYFREQ Procedure    PDF   |   HTML
  Produces one-way to n-way frequency and crosstabulation tables from complex multistage survey designs with stratification, clustering, and unequal weighting.
• The SURVEYIMPUTE Procedure   PDF   |   HTML
  Imputes missing values of an item in a data set by replacing them with observed values from the same item.
• The SURVEYLOGISTIC Procedure    PDF   |   HTML
  Fits models with binary, ordinal, or nominal dependent variables and incorporates complex survey designs.
• The SURVEYMEANS Procedure    PDF   |   HTML
  Estimates statistics such as means, totals, proportions, quantiles, and ratios from complex multistage survey designs with stratification, clustering, and unequal weighting.
• The SURVEYPHREG Procedure    PDF   |   HTML
  Performs regression analysis of survival data based on the Cox proportional hazards model for complex survey sample designs.
• The SURVEYREG Procedure    PDF   |   HTML
  Performs linear regression analysis for complex survey sample designs.
• The SURVEYSELECT Procedure    PDF   |   HTML
  Selects simple random samples or selects samples according to a complex multistage sample design that includes stratification, clustering, and unequal probabilities of selection.
• The TPSPLINE Procedure    PDF   |   HTML
  Provides penalized least squares estimates.
• The TRANSREG Procedure    PDF   |   HTML
  Fits linear models with optimal nonlinear transformations of variables.
• The TREE Procedure    PDF   |   HTML
  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.
• The TTEST Procedure    PDF   |   HTML
  Performs t tests and computes confidence limits for one sample, paired observations, two independent samples, and the AB/BA crossover design.
• The VARCLUS Procedure    PDF   |   HTML
  Divides a set of numeric variables into disjoint or hierarchical clusters.
• The VARCOMP Procedure    PDF   |   HTML
  Fits general linear models with random effects (with the option of specifying certain effects as fixed).
• The VARIOGRAM Procedure    PDF   |   HTML
  Fits and selects spatial covariance models for point-referenced spatial data.

SAS/STAT 14.3 User's Guide - Procedures

• The ACECLUS Procedure   PDF   |   HTML
  Obtains approximate estimates of the pooled within-cluster covariance matrix when the clusters are assumed to be multivariate normal with equal covariance matrices.
• The ADAPTIVEREG Procedure    PDF   |   HTML
  Fits multivariate adaptive regression splines.
• The ANOVA Procedure    PDF   |   HTML
  Performs analysis of variance for balanced data.
• The BCHOICE Procedure   PDF   |   HTML
  Performs Bayesian analysis for discrete choice models.
• The BOXPLOT Procedure   PDF   |   HTML
  Creates side-by-side box-and-whiskers plots of measurements organized in groups.
• The CALIS Procedure    PDF   |   HTML
  Fits structural equation models.
• The CANCORR Procedure    PDF   |   HTML
  Performs canonical correlation, partial canonical correlation, and canonical redundancy analysis.
• The CANDISC Procedure    PDF   |   HTML
  Performs a canonical discriminant analysis, computes squared Mahalanobis distances between class means, and performs both univariate and multivariate one-way analyses of variance.
• The CATMOD Procedure    PDF   |   HTML
  Performs categorical data modeling of data that can be represented by a contingency table.
• The CAUSALMED Procedure   PDF   |   HTML  New Procedure!
  Estimates causal mediation effects from observational data.
• The CAUSALTRT Procedure   PDF   |   HTML
  Implements causal inference methods that are designed primarily for use with data from nonrandomized trials or observational studies
• The CLUSTER Procedure    PDF   |   HTML
  Hierarchically clusters the observations in a SAS data.
• The CORRESP Procedure    PDF   |   HTML
  Performs simple correspondence analysis and multiple correspondence analysis (MCA).
• The DISCRIM Procedure    PDF   |   HTML
  Develops a discriminant criterion to classify each observation into groups.
• The DISTANCE Procedure    PDF   |   HTML
  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.
• The FACTOR Procedure    PDF   |   HTML
  Performs a variety of common factor and component analyses and rotations.
• The FASTCLUS Procedure    PDF   |   HTML
  Performs a disjoint cluster analysis on the basis of distances computed from one or more quantitative variables.
• The FMM Procedure   PDF   |   HTML
  Fits finite mixture models.
• The FREQ Procedure    PDF   |   HTML
  Produces one-way to n-way frequency and contingency (crosstabulation) tables and performs table analysis.
• The GAM Procedure    PDF   |   HTML
  Fits generalized additive models.
• The GAMPL Procedure  PDF   |   HTML
  Fits generalized additive models that are based on low-rank regression splines.
• The GEE Procedure   PDF   |   HTML
  Provides weighted generalized estimating equations.
• The GENMOD Procedure    PDF   |   HTML
  Fits generalized linear models.
• The GLIMMIX Procedure   PDF   |   HTML
  Fits generalized linear mixed models.
• The GLM Procedure   PDF   |   HTML
  Fits general linear models.
• The GLMMOD Procedure    PDF   |   HTML
  Constructs the design matrix for a general linear model; it essentially constitutes the model-building front end for the GLM procedure.
• The GLMPOWER Procedure    PDF   |   HTML
  Performs prospective power and sample size analysis for linear models.
• The GLMSELECT Procedure    PDF   |   HTML
  Performs effect selection in the framework of general linear models.
• The HPCANDISC Procedure    PDF   |   HTML
  Performs canonical discriminant analysis.
• The HPFMM Procedure    PDF   |   HTML
  Fits statistical models to data for which the distribution of the response is a finite mixture of univariate distributions.
• The HPGENSELECT Procedure    PDF   |   HTML
  Provides model fitting and model building for generalized linear models.
• The HPLMIXED Procedure   PDF   |   HTML
  Fits a variety of mixed linear models to data and enables use of these fitted models to make statistical inferences about the data.
• The HPLOGISTIC Procedure    PDF   |   HTML
  Fits logistic regression models for binary, binomial, and multinomial data on the SAS appliance.
• The HPMIXED Procedure    PDF   |   HTML
  Fits linear mixed models with simple covariance component structures by sparse-matrix techniques.
• The HPNLMOD Procedure    PDF   |   HTML
  Uses either nonlinear least squares or maximum likelihood to fit nonlinear regression models.
• The HPPLS Procedure    PDF   |   HTML
  Fits models by using any one of a number of linear predictive methods, including partial least squares (PLS).
• The HPPRINCOMP Procedure    PDF   |   HTML
  Performs principal component analysis.
• The HPQUANTSELECT Procedure    PDF   |   HTML
  Fits and performs effect selection for quantile regression analysis.
• The HPREG Procedure    PDF   |   HTML
  Fits and performs model selection for ordinary linear least squares models.
• The HPSPLIT Procedure   PDF   |   HTML
  Builds tree-based statistical models for classification and regression.
• The ICLIFETEST Procedure   PDF   |   HTML
  Performs nonparametric survival analysis for interval-censored data.
• The ICPHREG Procedure    PDF   |   HTML
  Designed to fit proportional hazards regression models to interval-censored data. Can also fit proportional hazards regression models to failure time data that are uncensored, right censored, or left censored.
• The INBREED Procedure    PDF   |   HTML
  Calculates the covariance or inbreeding coefficients for a pedigree.
• The IRT Procedure   PDF   |   HTML
  Fits item response models.
• The KDE Procedure    PDF   |   HTML
  Performs univariate and bivariate kernel density estimation.
• The KRIGE2D Procedure    PDF   |   HTML
  Performs ordinary kriging for point-referenced spatial data.
• The LATTICE Procedure    PDF   |   HTML
  Performs analysis of variance and analysis of simple covariance for data from experiments with lattice designs.
• The LIFEREG Procedure    PDF   |   HTML
  Fits parametric models to failure time data that can be uncensored, right censored, left censored, or interval censored.
• The LIFETEST Procedure    PDF   |   HTML
  Computes 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).
• The LOESS Procedure    PDF   |   HTML
  Implements a nonparametric method for estimating regression surfaces.
• The LOGISTIC Procedure    PDF   |   HTML
  Fits regression models with binary, ordinal, or nominal dependent variables.
• The MCMC Procedure    PDF   |   HTML
  Performs general-purpose Markov chain Monte Carlo (MCMC) simulation designed to fit Bayesian models.
• The MDS Procedure    PDF   |   HTML
  Fits two- and three-way, metric and nonmetric multidimensional scaling models.
• The MI Procedure    PDF   |   HTML
  Performs multiple imputation of missing data.
• The MIANALYZE Procedure    PDF   |   HTML
  Combines the results of the analyses of imputations and generates valid statistical inferences.
• The MIXED Procedure    PDF   |   HTML
  Fits general linear models with fixed and random effects.
• The MODECLUS Procedure    PDF   |   HTML
  Clusters observations in a SAS data set by using any of several algorithms based on nonparametric density estimates.
• The MULTTEST Procedure    PDF   |   HTML
  Addresses the multiple testing problem by adjusting the p-values from a family of hypothesis tests.
• The NESTED Procedure    PDF   |   HTML
  Performs random-effects analysis of variance for data from an experiment with a nested (hierarchical) structure and classification effects.
• The NLIN Procedure    PDF   |   HTML
  Fits nonlinear regression models.
• The NLMIXED Procedure   PDF   |   HTML
  Fits mixed models in which the fixed or random effects enter nonlinearly.
• The NPAR1WAY Procedure    PDF   |   HTML
  Performs nonparametric tests for location and scale differences across a one-way classification.
• The ORTHOREG Procedure    PDF   |   HTML
  Fits general linear models by the method of least squares and provides more accurate estimates than other procedures when your data are ill-conditioned.
• The PHREG Procedure    PDF   |   HTML
  Performs regression analysis of survival data based on the Cox proportional hazards model.
• The PLAN Procedure    PDF   |   HTML
  Constructs designs and randomizes plans for factorial experiments.
• The PLM Procedure   PDF   |   HTML
  Performs postfitting statistical analyses.
• The PLS Procedure    PDF   |   HTML
  Fits models by using any one of a number of linear predictive methods, including partial least squares (PLS).
• The POWER Procedure    PDF   |   HTML
  Performs prospective power and sample size analyses for a variety of statistical analyses.
• The Power and Sample Size Application  PDF   |   HTML
  The functionality of the Power and Sample Size application has been replaced by tasks in SAS Studio.
• The PRINCOMP Procedure    PDF   |   HTML
  Performs principal component analysis.
• The PRINQUAL Procedure    PDF   |   HTML
  Performs principal component analysis (PCA) of qualitative, quantitative, or mixed data.
• The PROBIT Procedure    PDF   |   HTML
  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.
• The PSMATCH Procedure   PDF   |   HTML
  Provides a variety of tools for propensity score analysis.
• The QUANTLIFE Procedure   PDF   |   HTML
  Performs quantile regression analysis for survival data.
• The QUANTREG Procedure    PDF   |   HTML
  Fits quantile regression models.
• The QUANTSELECT Procedure    PDF   |   HTML
  Performs effect selection in the framework of quantile regression.
• The REG Procedure    PDF   |   HTML
  General-purpose procedure for ordinary least squares regression.
• The ROBUSTREG Procedure    PDF   |   HTML
  Provides resistant (stable) results for linear regression models in the presence of outliers.
• The RSREG Procedure    PDF   |   HTML
  Fits quadratic response surface regression models.
• The SCORE Procedure    PDF   |   HTML
  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.
• The SEQDESIGN Procedure    PDF   |   HTML
  Designs interim analyses for group sequential clinical trials.
• The SEQTEST Procedure    PDF   |   HTML
  Performs interim analyses for group sequential clinical trials.
• The SIM2D Procedure   PDF   |   HTML
  Simulates a spatial Gaussian random field that uses a specified mean and covariance structure.
• The SIMNORMAL Procedure    PDF   |   HTML
  Performs conditional and unconditional simulation for a set of correlated normal or Gaussian random variables.
• The SPP Procedure   PDF (8.03MB)  |   HTML
  Analyzes spatial point pattern data and fits spatial process models.
• The STDIZE Procedure    PDF   |   HTML
  Standardizes one or more numeric variables in a SAS data set by subtracting a location measure and dividing by a scale measure.
• The STDRATE Procedure    PDF   |   HTML
  Computes directly standardized rates and risks for study populations.
• The STEPDISC Procedure    PDF   |   HTML
  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.
• The SURVEYFREQ Procedure    PDF   |   HTML
  Produces one-way to n-way frequency and crosstabulation tables from complex multistage survey designs with stratification, clustering, and unequal weighting.
• The SURVEYIMPUTE Procedure   PDF   |   HTML
  Imputes missing values of an item in a data set by replacing them with observed values from the same item.
• The SURVEYLOGISTIC Procedure    PDF   |   HTML
  Fits models with binary, ordinal, or nominal dependent variables and incorporates complex survey designs.
• The SURVEYMEANS Procedure    PDF   |   HTML
  Estimate statistics such as means, totals, proportions, quantiles, and ratios from complex multistage survey designs with stratification, clustering, and unequal weighting.
• The SURVEYPHREG Procedure    PDF   |   HTML
  Performs regression analysis of survival data based on the Cox proportional hazards model for complex survey sample designs.
• The SURVEYREG Procedure    PDF   |   HTML
  Performs linear regression analysis for complex survey sample designs.
• The SURVEYSELECT Procedure    PDF   |   HTML
  Selects simple random samples or selects samples according to a complex multistage sample design that includes stratification, clustering, and unequal probabilities of selection.
• The TPSPLINE Procedure    PDF   |   HTML
  Provides penalized least squares estimates.
• The TRANSREG Procedure    PDF   |   HTML
  Fits linear models with optimal nonlinear transformations of variables.
• The TREE Procedure    PDF   |   HTML
  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.
• The TTEST Procedure    PDF   |   HTML
  Performs t tests and computes confidence limits for one sample, paired observations, two independent samples, and the AB/BA crossover design.
• The VARCLUS Procedure    PDF   |   HTML
  Divides a set of numeric variables into disjoint or hierarchical clusters.
• The VARCOMP Procedure    PDF   |   HTML
  Fits general linear models with random effects (with the option of specifying certain effects as fixed).
• The VARIOGRAM Procedure    PDF   |   HTML
  Fits and selects spatial covariance models for point-referenced spatial data.

SAS/STAT 14.2 User's Guide - Procedures

• The ACECLUS Procedure
  Obtains approximate estimates of the pooled within-cluster covariance matrix when the clusters are assumed to be multivariate normal with equal covariance matrices.
  PDF   |   HTML
• The ADAPTIVEREG Procedure
  Fits multivariate adaptive regression splines.
  PDF   |   HTML
• The ANOVA Procedure
  Performs analysis of variance for balanced data.
  PDF   |   HTML
• The BCHOICE Procedure
  Performs Bayesian analysis for discrete choice models.
  PDF   |   HTML
• The BOXPLOT Procedure
  Creates side-by-side box-and-whiskers plots of measurements organized in groups.
  PDF   |   HTML
• The CALIS Procedure
  Fits structural equation models.
  PDF   |   HTML
• The CANCORR Procedure
  Performs canonical correlation, partial canonical correlation, and canonical redundancy analysis.
  PDF   |   HTML
• The CANDISC Procedure
  Performs a canonical discriminant analysis, computes squared Mahalanobis distances between class means, and performs both univariate and multivariate one-way analyses of variance.
  PDF   |   HTML
• The CATMOD Procedure
  Performs categorical data modeling of data that can be represented by a contingency table.
  PDF   |   HTML
• The CAUSALTRT Procedure New Procedure!
  Implements causal inference methods that are designed primarily for use with data from nonrandomized trials or observational studies
  PDF   |   HTML
• The CLUSTER Procedure
  Hierarchically clusters the observations in a SAS data.
  PDF   |   HTML
• The CORRESP Procedure
  Performs simple correspondence analysis and multiple correspondence analysis (MCA).
  PDF   |   HTML
• The DISCRIM Procedure
  Develops a discriminant criterion to classify each observation into groups.
  PDF   |   HTML
• The DISTANCE Procedure
  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.
  PDF   |   HTML
• The FACTOR Procedure
  Performs a variety of common factor and component analyses and rotations.
  PDF   |   HTML
• The FASTCLUS Procedure
  Performs a disjoint cluster analysis on the basis of distances computed from one or more quantitative variables.
  PDF   |   HTML
• The FMM Procedure
  Fits finite mixture models.
  PDF   |   HTML
• The FREQ Procedure
  Produces one-way to n-way frequency and contingency (crosstabulation) tables and performs table analysis.
  PDF   |   HTML
• The GAM Procedure
  Fits generalized additive models.
  PDF   |   HTML
• The GAMPL Procedure
  Fits generalized additive models that are based on low-rank regression splines.
  PDF   |   HTML
• The GEE Procedure
  Provides weighted generalized estimating equations.
  PDF   |   HTML
• The GENMOD Procedure
  Fits generalized linear models.
  PDF   |   HTML
• The GLIMMIX Procedure
  Fits generalized linear mixed models.
  PDF   |   HTML
• The GLM Procedure
  Fits general linear models.
  PDF   |   HTML
• The GLMMOD Procedure
  Constructs the design matrix for a general linear model; it essentially constitutes the model-building front end for the GLM procedure.
  PDF   |   HTML
• The GLMPOWER Procedure
  Performs prospective power and sample size analysis for linear models.
  PDF   |   HTML
• The GLMSELECT Procedure
  Performs effect selection in the framework of general linear models.
  PDF   |   HTML
• The HPCANDISC Procedure
  Performs canonical discriminant analysis.
  PDF   |   HTML
• The HPFMM Procedure
  Fits statistical models to data for which the distribution of the response is a finite mixture of univariate distributions.
  PDF   |   HTML
• The HPGENSELECT Procedure
  Provides model fitting and model building for generalized linear models.
  PDF   |   HTML
• The HPLMIXED Procedure
  Fits a variety of mixed linear models to data and enables use of these fitted models to make statistical inferences about the data.
  PDF   |   HTML
• The HPLOGISTIC Procedure
  Fits logistic regression models for binary, binomial, and multinomial data on the SAS appliance.
  PDF   |   HTML
• The HPMIXED Procedure
  Fits linear mixed models with simple covariance component structures by sparse-matrix techniques.
  PDF   |   HTML
• The HPNLMOD Procedure
  Uses either nonlinear least squares or maximum likelihood to fit nonlinear regression models.
  PDF   |   HTML
• The HPPLS Procedure
  Fits models by using any one of a number of linear predictive methods, including partial least squares (PLS).
  PDF   |   HTML
• The HPPRINCOMP Procedure
  Performs principal component analysis.
  PDF   |   HTML
• The HPQUANTSELECT Procedure
  Fits and performs effect selection for quantile regression analysis.
  PDF   |   HTML
• The HPREG Procedure
  Fits and performs model selection for ordinary linear least squares models.
  PDF   |   HTML
• The HPSPLIT Procedure
  Builds tree-based statistical models for classification and regression.
  PDF   |   HTML
• The ICLIFETEST Procedure
  Performs nonparametric survival analysis for interval-censored data.
  PDF   |   HTML
• The ICPHREG Procedure
  Designed to fit proportional hazards regression models to interval-censored data. Can also fit proportional hazards regression models to failure time data that are uncensored, right censored, or left censored.
  PDF   |   HTML
• The INBREED Procedure
  Calculates the covariance or inbreeding coefficients for a pedigree.
  PDF   |   HTML
• The IRT Procedure
  Fits item response models.
  PDF   |   HTML
• The KDE Procedure
  Performs univariate and bivariate kernel density estimation.
  PDF   |   HTML
• The KRIGE2D Procedure
  Performs ordinary kriging for point-referenced spatial data.
  PDF   |   HTML
• The LATTICE Procedure
  Performs analysis of variance and analysis of simple covariance for data from experiments with lattice designs.
  PDF   |   HTML
• The LIFEREG Procedure
  Fits parametric models to failure time data that can be uncensored, right censored, left censored, or interval censored.
  PDF   |   HTML
• The LIFETEST Procedure
  Computes 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).
  PDF   |   HTML
• The LOESS Procedure
  Implements a nonparametric method for estimating regression surfaces.
  PDF   |   HTML
• The LOGISTIC Procedure
  Fits regression models with binary, ordinal, or nominal dependent variables.
  PDF   |   HTML
• The MCMC Procedure
  Performs general-purpose Markov chain Monte Carlo (MCMC) simulation designed to fit Bayesian models.
  PDF   |   HTML
• The MDS Procedure
  Fits two- and three-way, metric and nonmetric multidimensional scaling models.
  PDF   |   HTML
• The MI Procedure
  Performs multiple imputation of missing data.
  PDF   |   HTML
• The MIANALYZE Procedure
  Combines the results of the analyses of imputations and generates valid statistical inferences.
  PDF   |   HTML
• The MIXED Procedure
  Fits general linear models with fixed and random effects.
  PDF   |   HTML
• The MODECLUS Procedure
  Clusters observations in a SAS data set by using any of several algorithms based on nonparametric density estimates.
  PDF   |   HTML
• The MULTTEST Procedure
  Addresses the multiple testing problem by adjusting the p-values from a family of hypothesis tests.
  PDF   |   HTML
• The NESTED Procedure
  Performs random-effects analysis of variance for data from an experiment with a nested (hierarchical) structure and classification effects.
  PDF   |   HTML
• The NLIN Procedure
  Fits nonlinear regression models.
  PDF   |   HTML
• The NLMIXED Procedure
  Fits mixed models in which the fixed or random effects enter nonlinearly.
  PDF   |   HTML
• The NPAR1WAY Procedure
  Performs nonparametric tests for location and scale differences across a one-way classification.
  PDF   |   HTML
• The ORTHOREG Procedure
  Fits general linear models by the method of least squares and provides more accurate estimates than other procedures when your data are ill-conditioned.
  PDF   |   HTML
• The PHREG Procedure
  Performs regression analysis of survival data based on the Cox proportional hazards model.
  PDF   |   HTML
• The PLAN Procedure
  Constructs designs and randomizes plans for factorial experiments.
  PDF   |   HTML
• The PLM Procedure
  Performs postfitting statistical analyses.
  PDF   |   HTML
• The PLS Procedure
  Fits models by using any one of a number of linear predictive methods, including partial least squares (PLS).
  PDF   |   HTML
• The POWER Procedure
  Performs prospective power and sample size analyses for a variety of statistical analyses.
  PDF   |   HTML
• The Power and Sample Size Application
  PDF   |   HTML
• The PRINCOMP Procedure
  Performs principal component analysis.
  PDF   |   HTML
• The PRINQUAL Procedure
  Performs principal component analysis (PCA) of qualitative, quantitative, or mixed data.
  PDF   |   HTML
• The PROBIT Procedure
  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.
  PDF   |   HTML
• The PSMATCH Procedure New Procedure!
  Provides a variety of tools for propensity score analysis.
  PDF   |   HTML
• The QUANTLIFE Procedure
  Performs quantile regression analysis for survival data.
  PDF   |   HTML
• The QUANTREG Procedure
  Fits quantile regression models.
  PDF   |   HTML
• The QUANTSELECT Procedure
  Performs effect selection in the framework of quantile regression.
  PDF   |   HTML
• The REG Procedure
  General-purpose procedure for ordinary least squares regression.
  PDF   |   HTML
• The ROBUSTREG Procedure
  Provides resistant (stable) results for linear regression models in the presence of outliers.
  PDF   |   HTML
• The RSREG Procedure
  Fits quadratic response surface regression models.
  PDF   |   HTML
• The SCORE Procedure
  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.
  PDF   |   HTML
• The SEQDESIGN Procedure
  Designs interim analyses for group sequential clinical trials.
  PDF   |   HTML
• The SEQTEST Procedure
  Performs interim analyses for group sequential clinical trials.
  PDF   |   HTML
• The SIM2D Procedure
  Simulates a spatial Gaussian random field that uses a specified mean and covariance structure.
  PDF   |   HTML
• The SIMNORMAL Procedure
  Performs conditional and unconditional simulation for a set of correlated normal or Gaussian random variables.
  PDF   |   HTML
• The SPP Procedure
  Analyzes spatial point pattern data and fits spatial process models.
  PDF (8.03MB)  |   HTML
• The STDIZE Procedure
  Standardizes one or more numeric variables in a SAS data set by subtracting a location measure and dividing by a scale measure.
  PDF   |   HTML
• The STDRATE Procedure
  Computes directly standardized rates and risks for study populations.
  PDF   |   HTML
• The STEPDISC Procedure
  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.
  PDF   |   HTML
• The SURVEYFREQ Procedure
  Produces one-way to n-way frequency and crosstabulation tables from complex multistage survey designs with stratification, clustering, and unequal weighting.
  PDF   |   HTML
• The SURVEYIMPUTE Procedure
  Imputes missing values of an item in a data set by replacing them with observed values from the same item.
  PDF   |   HTML
• The SURVEYLOGISTIC Procedure
  Fits models with binary, ordinal, or nominal dependent variables and incorporates complex survey designs.
  PDF   |   HTML
• The SURVEYMEANS Procedure
  Estimate statistics such as means, totals, proportions, quantiles, and ratios from complex multistage survey designs with stratification, clustering, and unequal weighting.
  PDF   |   HTML
• The SURVEYPHREG Procedure
  Performs regression analysis of survival data based on the Cox proportional hazards model for complex survey sample designs.
  PDF   |   HTML
• The SURVEYREG Procedure
  Performs linear regression analysis for complex survey sample designs.
  PDF   |   HTML
• The SURVEYSELECT Procedure
  Selects simple random samples or selects samples according to a complex multistage sample design that includes stratification, clustering, and unequal probabilities of selection.
  PDF   |   HTML
• The TPSPLINE Procedure
  Provides penalized least squares estimates.
  PDF   |   HTML
• The TRANSREG Procedure
  Fits linear models with optimal nonlinear transformations of variables.
  PDF   |   HTML
• The TREE Procedure
  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.
  PDF   |   HTML
• The TTEST Procedure
  Performs t tests and computes confidence limits for one sample, paired observations, two independent samples, and the AB/BA crossover design.
  PDF   |   HTML
• The VARCLUS Procedure
  Divides a set of numeric variables into disjoint or hierarchical clusters.
  PDF   |   HTML
• The VARCOMP Procedure
  Fits general linear models with random effects (with the option of specifying certain effects as fixed).
  PDF   |   HTML
• The VARIOGRAM Procedure
  Fits and selects spatial covariance models for point-referenced spatial data.
  PDF   |   HTML

SAS/STAT 14.1 User's Guide - Procedures

• The ACECLUS Procedure
  Obtains approximate estimates of the pooled within-cluster covariance matrix when the clusters are assumed to be multivariate normal with equal covariance matrices.
  PDF   |   HTML
• The ADAPTIVEREG Procedure
  Fits multivariate adaptive regression splines.
  PDF   |   HTML
• The ANOVA Procedure
  Performs analysis of variance for balanced data.
  PDF   |   HTML
• The BCHOICE Procedure
  Performs Bayesian analysis for discrete choice models.
  PDF   |   HTML
• The BOXPLOT Procedure
  Creates side-by-side box-and-whiskers plots of measurements organized in groups.
  PDF   |   HTML
• The CALIS Procedure
  Fits structural equation models.
  PDF   |   HTML
• The CANCORR Procedure
  Performs canonical correlation, partial canonical correlation, and canonical redundancy analysis.
  PDF   |   HTML
• The CANDISC Procedure
  Performs a canonical discriminant analysis, computes squared Mahalanobis distances between class means, and performs both univariate and multivariate one-way analyses of variance.
  PDF   |   HTML
• The CATMOD Procedure
  Performs categorical data modeling of data that can be represented by a contingency table.
  PDF   |   HTML
• The CLUSTER Procedure
  Hierarchically clusters the observations in a SAS data.
  PDF   |   HTML
• The CORRESP Procedure
  Performs simple correspondence analysis and multiple correspondence analysis (MCA).
  PDF   |   HTML
• The DISCRIM Procedure
  Develops a discriminant criterion to classify each observation into groups.
  PDF   |   HTML
• The DISTANCE Procedure
  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.
  PDF   |   HTML
• The FACTOR Procedure
  Performs a variety of common factor and component analyses and rotations.
  PDF   |   HTML
• The FASTCLUS Procedure
  Performs a disjoint cluster analysis on the basis of distances computed from one or more quantitative variables.
  PDF   |   HTML
• The FMM Procedure
  Fits finite mixture models.
  PDF   |   HTML
• The FREQ Procedure
  Produces one-way to n-way frequency and contingency (crosstabulation) tables and performs table analysis.
  PDF   |   HTML
• The GAM Procedure
  Fits generalized additive models.
  PDF   |   HTML
• The GAMPL Procedure New Procedure!
  Fits generalized additive models that are based on low-rank regression splines.
  PDF   |   HTML
• The GEE Procedure
  Provides weighted generalized estimating equations.
  PDF   |   HTML
• The GENMOD Procedure
  Fits generalized linear models.
  PDF   |   HTML
• The GLIMMIX Procedure
  Fits generalized linear mixed models.
  PDF   |   HTML
• The GLM Procedure
  Fits general linear models.
  PDF   |   HTML
• The GLMMOD Procedure
  Constructs the design matrix for a general linear model; it essentially constitutes the model-building front end for the GLM procedure.
  PDF   |   HTML
• The GLMPOWER Procedure
  Performs prospective power and sample size analysis for linear models.
  PDF   |   HTML
• The GLMSELECT Procedure
  Performs effect selection in the framework of general linear models.
  PDF   |   HTML
• The HPCANDISC Procedure
  Performs canonical discriminant analysis.
  PDF   |   HTML
• The HPFMM Procedure
  Fits statistical models to data for which the distribution of the response is a finite mixture of univariate distributions.
  PDF   |   HTML
• The HPGENSELECT Procedure
  Provides model fitting and model building for generalized linear models.
  PDF   |   HTML
• The HPLMIXED Procedure
  Fits a variety of mixed linear models to data and enables use of these fitted models to make statistical inferences about the data.
  PDF   |   HTML
• The HPLOGISTIC Procedure
  Fits logistic regression models for binary, binomial, and multinomial data on the SAS appliance.
  PDF   |   HTML
• The HPMIXED Procedure
  Fits linear mixed models with simple covariance component structures by sparse-matrix techniques.
  PDF   |   HTML
• The HPNLMOD Procedure
  Uses either nonlinear least squares or maximum likelihood to fit nonlinear regression models.
  PDF   |   HTML
• The HPPLS Procedure
  Fits models by using any one of a number of linear predictive methods, including partial least squares (PLS).
  PDF   |   HTML
• The HPPRINCOMP Procedure
  Performs principal component analysis.
  PDF   |   HTML
• The HPQUANTSELECT Procedure
  Fits and performs effect selection for quantile regression analysis.
  PDF   |   HTML
• The HPREG Procedure
  Fits and performs model selection for ordinary linear least squares models.
  PDF   |   HTML
• The HPSPLIT Procedure
  Builds tree-based statistical models for classification and regression.
  PDF   |   HTML
• The ICLIFETEST Procedure
  Performs nonparametric survival analysis for interval-censored data.
  PDF   |   HTML
• The ICPHREG Procedure
  Designed to fit proportional hazards regression models to interval-censored data. Can also fit proportional hazards regression models to failure time data that are uncensored, right censored, or left censored.
  PDF   |   HTML
• The INBREED Procedure
  Calculates the covariance or inbreeding coefficients for a pedigree.
  PDF   |   HTML
• The IRT Procedure
  Fits item response models.
  PDF   |   HTML
• The KDE Procedure
  Performs univariate and bivariate kernel density estimation.
  PDF   |   HTML
• The KRIGE2D Procedure
  Performs ordinary kriging for point-referenced spatial data.
  PDF   |   HTML
• The LATTICE Procedure
  Performs analysis of variance and analysis of simple covariance for data from experiments with lattice designs.
  PDF   |   HTML
• The LIFEREG Procedure
  Fits parametric models to failure time data that can be uncensored, right censored, left censored, or interval censored.
  PDF   |   HTML
• The LIFETEST Procedure
  Computes 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).
  PDF   |   HTML
• The LOESS Procedure
  Implements a nonparametric method for estimating regression surfaces.
  PDF   |   HTML
• The LOGISTIC Procedure
  Fits regression models with binary, ordinal, or nominal dependent variables.
  PDF   |   HTML
• The MCMC Procedure
  Performs general-purpose Markov chain Monte Carlo (MCMC) simulation designed to fit Bayesian models.
  PDF   |   HTML
• The MDS Procedure
  Fits two- and three-way, metric and nonmetric multidimensional scaling models.
  PDF   |   HTML
• The MI Procedure
  Performs multiple imputation of missing data.
  PDF   |   HTML
• The MIANALYZE Procedure
  Combines the results of the analyses of imputations and generates valid statistical inferences.
  PDF   |   HTML
• The MIXED Procedure
  Fits general linear models with fixed and random effects.
  PDF   |   HTML
• The MODECLUS Procedure
  Clusters observations in a SAS data set by using any of several algorithms based on nonparametric density estimates.
  PDF   |   HTML
• The MULTTEST Procedure
  Addresses the multiple testing problem by adjusting the p-values from a family of hypothesis tests.
  PDF   |   HTML
• The NESTED Procedure
  Performs random-effects analysis of variance for data from an experiment with a nested (hierarchical) structure and classification effects.
  PDF   |   HTML
• The NLIN Procedure
  Fits nonlinear regression models.
  PDF   |   HTML
• The NLMIXED Procedure
  Fits mixed models in which the fixed or random effects enter nonlinearly.
  PDF   |   HTML
• The NPAR1WAY Procedure
  Performs nonparametric tests for location and scale differences across a one-way classification.
  PDF   |   HTML
• The ORTHOREG Procedure
  Fits general linear models by the method of least squares and provides more accurate estimates than other procedures when your data are ill-conditioned.
  PDF   |   HTML
• The PHREG Procedure
  Performs regression analysis of survival data based on the Cox proportional hazards model.
  PDF   |   HTML
• The PLAN Procedure
  Constructs designs and randomizes plans for factorial experiments.
  PDF   |   HTML
• The PLM Procedure
  Performs postfitting statistical analyses.
  PDF   |   HTML
• The PLS Procedure
  Fits models by using any one of a number of linear predictive methods, including partial least squares (PLS).
  PDF   |   HTML
• The POWER Procedure
  Performs prospective power and sample size analyses for a variety of statistical analyses.
  PDF   |   HTML
• The Power and Sample Size Application
  PDF   |   HTML
• The PRINCOMP Procedure
  Performs principal component analysis.
  PDF   |   HTML
• The PRINQUAL Procedure
  Performs principal component analysis (PCA) of qualitative, quantitative, or mixed data.
  PDF   |   HTML
• The PROBIT Procedure
  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.
  PDF   |   HTML
• The QUANTLIFE Procedure
  Performs quantile regression analysis for survival data.
  PDF   |   HTML
• The QUANTREG Procedure
  Fits quantile regression models.
  PDF   |   HTML
• The QUANTSELECT Procedure
  Performs effect selection in the framework of quantile regression.
  PDF   |   HTML
• The REG Procedure
  General-purpose procedure for ordinary least squares regression.
  PDF   |   HTML
• The ROBUSTREG Procedure
  Provides resistant (stable) results for linear regression models in the presence of outliers.
  PDF   |   HTML
• The RSREG Procedure
  Fits quadratic response surface regression models.
  PDF   |   HTML
• The SCORE Procedure
  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.
  PDF   |   HTML
• The SEQDESIGN Procedure
  Designs interim analyses for group sequential clinical trials.
  PDF   |   HTML
• The SEQTEST Procedure
  Performs interim analyses for group sequential clinical trials.
  PDF   |   HTML
• The SIM2D Procedure
  Simulates a spatial Gaussian random field that uses a specified mean and covariance structure.
  PDF   |   HTML
• The SIMNORMAL Procedure
  Performs conditional and unconditional simulation for a set of correlated normal or Gaussian random variables.
  PDF   |   HTML
• The SPP Procedure
  Analyzes spatial point pattern data and fits spatial process models.
  PDF (8.03MB)  |   HTML
• The STDIZE Procedure
  Standardizes one or more numeric variables in a SAS data set by subtracting a location measure and dividing by a scale measure.
  PDF   |   HTML
• The STDRATE Procedure
  Computes directly standardized rates and risks for study populations.
  PDF   |   HTML
• The STEPDISC Procedure
  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.
  PDF   |   HTML
• The SURVEYFREQ Procedure
  Produces one-way to n-way frequency and crosstabulation tables from complex multistage survey designs with stratification, clustering, and unequal weighting.
  PDF   |   HTML
• The SURVEYIMPUTE Procedure New Procedure!
  Imputes missing values of an item in a data set by replacing them with observed values from the same item.
  PDF   |   HTML
• The SURVEYLOGISTIC Procedure
  Fits models with binary, ordinal, or nominal dependent variables and incorporates complex survey designs.
  PDF   |   HTML
• The SURVEYMEANS Procedure
  Estimate statistics such as means, totals, proportions, quantiles, and ratios from complex multistage survey designs with stratification, clustering, and unequal weighting.
  PDF   |   HTML
• The SURVEYPHREG Procedure
  Performs regression analysis of survival data based on the Cox proportional hazards model for complex survey sample designs.
  PDF   |   HTML
• The SURVEYREG Procedure
  Performs linear regression analysis for complex survey sample designs.
  PDF   |   HTML
• The SURVEYSELECT Procedure
  Selects simple random samples or selects samples according to a complex multistage sample design that includes stratification, clustering, and unequal probabilities of selection.
  PDF   |   HTML
• The TPSPLINE Procedure
  Provides penalized least squares estimates.
  PDF   |   HTML
• The TRANSREG Procedure
  Fits linear models with optimal nonlinear transformations of variables.
  PDF   |   HTML
• The TREE Procedure
  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.
  PDF   |   HTML
• The TTEST Procedure
  Performs t tests and computes confidence limits for one sample, paired observations, two independent samples, and the AB/BA crossover design.
  PDF   |   HTML
• The VARCLUS Procedure
  Divides a set of numeric variables into disjoint or hierarchical clusters.
  PDF   |   HTML
• The VARCOMP Procedure
  Fits general linear models with random effects (with the option of specifying certain effects as fixed).
  PDF   |   HTML
• The VARIOGRAM Procedure
  Fits and selects spatial covariance models for point-referenced spatial data.
  PDF   |   HTML

SAS/STAT 13.2 User's Guide - Procedures

• The ACECLUS Procedure
  Obtains approximate estimates of the pooled within-cluster covariance matrix when the clusters are assumed to be multivariate normal with equal covariance matrices.
  PDF (4.28MB)  |   HTML
• The ADAPTIVEREG Procedure
  Fits multivariate adaptive regression splines.
  PDF (6.10MB)  |   HTML
• The ANOVA Procedure
  Performs analysis of variance for balanced data.
  PDF (6.15MB)  |   HTML
• The BCHOICE Procedure
  Performs Bayesian analysis for discrete choice models.
  PDF (4.79MB)  |   HTML
• The BOXPLOT Procedure
  Creates side-by-side box-and-whiskers plots of measurements organized in groups.
  PDF (2.83MB)  |   HTML
• The CALIS Procedure
  Fits structural equation models.
  PDF (33.9MB)  |   HTML
• The CANCORR Procedure
  Performs canonical correlation, partial canonical correlation, and canonical redundancy analysis.
  PDF (3.6MB)  |   HTML
• The CANDISC Procedure
  Performs a canonical discriminant analysis, computes squared Mahalanobis distances between class means, and performs both univariate and multivariate one-way analyses of variance.
  PDF (3.93MB)  |   HTML
• The CATMOD Procedure
  Performs categorical data modeling of data that can be represented by a contingency table.
  PDF (10.4MB)  |   HTML
• The CLUSTER Procedure
  Hierarchically clusters the observations in a SAS data.
  PDF (9.07MB)  |   HTML
• The CORRESP Procedure
  Performs simple correspondence analysis and multiple correspondence analysis (MCA).
  PDF (6.04MB)  |   HTML
• The DISCRIM Procedure
  Develops a discriminant criterion to classify each observation into groups.
  PDF (12.6MB)  |   HTML
• The DISTANCE Procedure
  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.
  PDF (3.75MB)  |   HTML
• The FACTOR Procedure
  Performs a variety of common factor and component analyses and rotations.
  PDF (10.1MB)  |   HTML
• The FASTCLUS Procedure
  Performs a disjoint cluster analysis on the basis of distances computed from one or more quantitative variables.
  PDF (8.37MB)  |   HTML
• The FMM Procedure
  Fits finite mixture models.
  PDF (10MB)  |   HTML
• The FREQ Procedure
  Produces one-way to n-way frequency and contingency (crosstabulation) tables and performs table analysis.
  PDF (7.5MB)  |   HTML
• The GAM Procedure
  Fits generalized additive models.
  PDF (4.35MB)  |   HTML
• The GEE Procedure (Experimental) New Procedure!
  Provides weighted generalized estimating equations.
  PDF (3.61MB)  |   HTML
• The GENMOD Procedure
  Fits generalized linear models.
  PDF (10.5MB)  |   HTML
• The GLIMMIX Procedure
  Fits generalized linear mixed models.
  PDF (19.8MB)  |   HTML
• The GLM Procedure
  Fits general linear models.
  PDF (16.1MB)  |   HTML
• The GLMMOD Procedure
  Constructs the design matrix for a general linear model; it essentially constitutes the model-building front end for the GLM procedure.
  PDF (3.43MB)  |   HTML
• The GLMPOWER Procedure
  Performs prospective power and sample size analysis for linear models.
  PDF (4.23MB)  |   HTML
• The GLMSELECT Procedure
  Performs effect selection in the framework of general linear models.
  PDF (12.2MB)  |   HTML
• The HPMIXED Procedure
  Fits linear mixed models with simple covariance component structures by sparse-matrix techniques.
  PDF (4.6MB)  |   HTML
• The ICLIFETEST Procedure New Procedure!
  Performs nonparametric survival analysis for interval-censored data.
  PDF (3.09MB)  |   HTML
• The ICPHREG Procedure
  Designed to fit proportional hazards regression models to interval-censored data. Can also fit proportional hazards regression models to failure time data that are uncensored, right censored, or left censored.
  PDF (3.82MB)  |   HTML
• The INBREED Procedure
  Calculates the covariance or inbreeding coefficients for a pedigree.
  PDF (3.73MB)  |   HTML
• The IRT Procedure
  Fits item response models.
  PDF (5.81MB)  |   HTML
• The KDE Procedure
  Performs univariate and bivariate kernel density estimation.
  PDF (4.6MB)  |   HTML
• The KRIGE2D Procedure
  Performs ordinary kriging for point-referenced spatial data.
  PDF (8.18MB)  |   HTML
• The LATTICE Procedure
  Performs analysis of variance and analysis of simple covariance for data from experiments with lattice designs.
  PDF (2.42MB)  |   HTML
• The LIFEREG Procedure
  Fits parametric models to failure time data that can be uncensored, right censored, left censored, or interval censored.
  PDF (7.91MB)  |   HTML
• The LIFETEST Procedure
  Computes 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).
  PDF (5.95MB)  |   HTML
• The LOESS Procedure
  Implements a nonparametric method for estimating regression surfaces.
  PDF (5.28MB)  |   HTML
• The LOGISTIC Procedure
  Fits regression models with binary, ordinal, or nominal dependent variables.
  PDF (20.7MB)  |   HTML
• The MCMC Procedure
  Performs general-purpose Markov chain Monte Carlo (MCMC) simulation designed to fit Bayesian models.
  PDF (11.7MB)  |   HTML
• The MDS Procedure
  Fits two- and three-way, metric and nonmetric multidimensional scaling models.
  PDF (3.6MB)  |   HTML
• The MI Procedure
  Performs multiple imputation of missing data.
  PDF (10.9MB)  |   HTML
• The MIANALYZE Procedure
  Combines the results of the analyses of imputations and generates valid statistical inferences.
  PDF (5.99MB)  |   HTML
• The MIXED Procedure
  Fits general linear models with fixed and random effects.
  PDF (13.9MB)  |   HTML
• The MODECLUS Procedure
  Clusters observations in a SAS data set by using any of several algorithms based on nonparametric density estimates.
  PDF (9.66MB)  |   HTML
• The MULTTEST Procedure
  Addresses the multiple testing problem by adjusting the p-values from a family of hypothesis tests.
  PDF (4.92MB)  |   HTML
• The NESTED Procedure
  Performs random-effects analysis of variance for data from an experiment with a nested (hierarchical) structure and classification effects.
  PDF (2.46MB)  |   HTML
• The NLIN Procedure
  Fits nonlinear regression models.
  PDF (7.12MB)  |   HTML
• The NLMIXED Procedure
  Fits mixed models in which the fixed or random effects enter nonlinearly.
  PDF (7.5MB)  |   HTML
• The NPAR1WAY Procedure
  Performs nonparametric tests for location and scale differences across a one-way classification.
  PDF (5.86MB)  |   HTML
• The ORTHOREG Procedure
  Fits general linear models by the method of least squares and provides more accurate estimates than other procedures when your data are ill-conditioned.
  PDF (3.5MB)  |   HTML
• The PHREG Procedure
  Performs regression analysis of survival data based on the Cox proportional hazards model.
  PDF (14.3MB)  |   HTML
• The PLAN Procedure
  Constructs designs and randomizes plans for factorial experiments.
  PDF (5.29MB)  |   HTML
• The PLM Procedure
  Performs postfitting statistical analyses.
  PDF (5.23MB)  |   HTML
• The PLS Procedure
  Performs principal component regression.
  PDF (6.62MB)  |   HTML
• The POWER Procedure
  Performs prospective power and sample size analyses for a variety of statistical analyses.
  PDF (10MB)  |   HTML
• The Power and Sample Size Application
  PDF (4.26MB)  |   HTML
• The PRINCOMP Procedure
  Performs principal component analysis.
  PDF (5.4MB)  |   HTML
• The PRINQUAL Procedure
  Performs principal component analysis (PCA) of qualitative, quantitative, or mixed data.
  PDF (4.39MB)  |   HTML
• The PROBIT Procedure
  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.
  PDF (6.87MB)  |   HTML
• The QUANTLIFE Procedure
  Performs quantile regression analysis for survival data.
  PDF (3.62MB)  |   HTML
• The QUANTREG Procedure
  Fits quantile regression models.
  PDF (6.79MB)  |   HTML
• The QUANTSELECT Procedure
  Performs effect selection in the framework of quantile regression.
  PDF (6.36MB)  |   HTML
• The REG Procedure
  General-purpose procedure for ordinary least squares regression.
  PDF (14.9MB)  |   HTML
• The ROBUSTREG Procedure
  Provides resistant (stable) results for linear regression models in the presence of outliers.
  PDF (9.69MB)  |   HTML
• The RSREG Procedure
  Fits quadratic response surface regression models.
  PDF (4.47MB)  |   HTML
• The SCORE Procedure
  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.
  PDF (3.97MB)  |   HTML
• The SEQDESIGN Procedure
  Designs interim analyses for group sequential clinical trials.
  PDF (15.3MB)  |   HTML
• The SEQTEST Procedure
  Performs interim analyses for group sequential clinical trials.
  PDF (15.5MB)  |   HTML
• The SIM2D Procedure
  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.
  PDF (5.4MB)  |   HTML
• The SIMNORMAL Procedure
  Performs conditional and unconditional simulation for a set of correlated normal or Gaussian random variables.
  PDF (3.33MB)  |   HTML
• The SPP Procedure New Procedure!
  Analyzes spatial point pattern data and fits spatial process models.
  PDF (8.03MB)  |   HTML
• The STDIZE Procedure
  Standardizes one or more numeric variables in a SAS data set by subtracting a location measure and dividing by a scale measure.
  PDF (3.39MB)  |   HTML
• The STDRATE Procedure
  Computes directly standardized rates and risks for study populations.
  PDF (5.15MB)  |   HTML
• The STEPDISC Procedure
  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.
  PDF (5.05MB)  |   HTML
• The SURVEYFREQ Procedure
  Produces one-way to n-way frequency and crosstabulation tables from complex multistage survey designs with stratification, clustering, and unequal weighting.
  PDF (3.8MB)  |   HTML
• The SURVEYLOGISTIC Procedure
  Fits models with binary, ordinal, or nominal dependent variables and incorporates complex survey designs.
  PDF (4.47MB)  |   HTML
• The SURVEYMEANS Procedure
  Estimate statistics such as means, totals, proportions, quantiles, and ratios from complex multistage survey designs with stratification, clustering, and unequal weighting.
  PDF (4.48MB)  |   HTML
• The SURVEYPHREG Procedure
  Performs regression analysis of survival data based on the Cox proportional hazards model for complex survey sample designs.
  PDF (6.14MB)  |   HTML
• The SURVEYREG Procedure
  Performs linear regression analysis for complex survey sample designs.
  PDF (7.57MB)  |   HTML
• The SURVEYSELECT Procedure
  Selects simple random samples or selects samples according to a complex multistage sample design that includes stratification, clustering, and unequal probabilities of selection.
  PDF (4.06MB)  |   HTML
• The TPSPLINE Procedure
  Provides penalized least squares estimates.
  PDF (6.85MB)  |   HTML
• The TRANSREG Procedure
  Fits linear models with optimal nonlinear transformations of variables.
  PDF (27.4MB)  |   HTML
• The TREE Procedure
  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.
  PDF (3.35MB)  |   HTML
• The TTEST Procedure
  Performs t tests and computes confidence limits for one sample, paired observations, two independent samples, and the AB/BA crossover design.
  PDF (6.02MB)  |   HTML
• The VARCLUS Procedure
  Divides a set of numeric variables into disjoint or hierarchical clusters.
  PDF (4.55MB)  |   HTML
• The VARCOMP Procedure
  Fits general linear models with random effects (with the option of specifying certain effects as fixed).
  PDF (3.97MB)  |   HTML
• The VARIOGRAM Procedure
  Fits and selects spatial covariance models for point-referenced spatial data.
  PDF (7.76MB)  |   HTML

SAS/STAT 13.1 User's Guide - Procedures

• The ACECLUS Procedure
  Obtains approximate estimates of the pooled within-cluster covariance matrix when the clusters are assumed to be multivariate normal with equal covariance matrices.
  PDF (2.39MB)  |   HTML
• The ADAPTIVEREG Procedure
  Fits multivariate adaptive regression splines.
  PDF (4.42MB)  |   HTML
• The ANOVA Procedure
  Performs analysis of variance for balanced data.
  PDF (2.26MB)  |   HTML
• The BCHOICE Procedure (Experimental)
  Performs Bayesian analysis for discrete choice models.
  PDF (2.71MB)  |   HTML
• The BOXPLOT Procedure
  Creates side-by-side box-and-whiskers plots of measurements organized in groups.
  PDF (2.72MB)  |   HTML
• The CALIS Procedure
  Fits structural equation models.
  PDF (7.62MB)  |   HTML
• The CANCORR Procedure
  Performs canonical correlation, partial canonical correlation, and canonical redundancy analysis.
  PDF (2.11MB)  |   HTML
• The CANDISC Procedure
  Performs a canonical discriminant analysis, computes squared Mahalanobis distances between class means, and performs both univariate and multivariate one-way analyses of variance.
  PDF (2.23MB)  |   HTML
• The CATMOD Procedure
  Performs categorical data modeling of data that can be represented by a contingency table.
  PDF (2.45MB)  |   HTML
• The CLUSTER Procedure
  Hierarchically clusters the observations in a SAS data.
  PDF (4.44MB)  |   HTML
• The CORRESP Procedure
  Performs simple correspondence analysis and multiple correspondence analysis (MCA).
  PDF (2.57MB)  |   HTML
• The DISCRIM Procedure
  Develops a discriminant criterion to classify each observation into groups.
  PDF (3.91MB)  |   HTML
• The DISTANCE Procedure
  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.
  PDF (2.59MB)  |   HTML
• The FACTOR Procedure
  Performs a variety of common factor and component analyses and rotations.
  PDF (2.78MB)  |   HTML
• The FASTCLUS Procedure
  Performs a disjoint cluster analysis on the basis of distances computed from one or more quantitative variables.
  PDF (2.66MB)  |   HTML
• The FMM Procedure
  Fits finite mixture models.
  PDF (3.74MB)  |   HTML
• The FREQ Procedure
  Produces one-way to n-way frequency and contingency (crosstabulation) tables and performs table analysis.
  PDF (3.33MB)  |   HTML
• The GAM Procedure
  Fits generalized additive models.
  PDF (3.01MB)  |   HTML
• The GENMOD Procedure
  Fits generalized linear models.
  PDF (4.42MB)  |   HTML
• The GLIMMIX Procedure
  Fits generalized linear mixed models.
  PDF (6.11MB)  |   HTML
• The GLM Procedure
  Fits general linear models.
  PDF (3.75MB)  |   HTML
• The GLMMOD Procedure
  Constructs the design matrix for a general linear model; it essentially constitutes the model-building front end for the GLM procedure.
  PDF (2.03MB)  |   HTML
• The GLMPOWER Procedure
  Performs prospective power and sample size analysis for linear models.
  PDF (2.29MB)  |   HTML
• The GLMSELECT Procedure
  Performs effect selection in the framework of general linear models.
  PDF (5.91MB)  |   HTML
• The HPMIXED Procedure
  Fits linear mixed models with simple covariance component structures by sparse-matrix techniques.
  PDF (2.58MB)  |   HTML
• The ICLIFETEST Procedure
  Performs nonparametric survival analysis for interval-censored data.
  PDF (2.75MB)  |   HTML
• The INBREED Procedure
  Calculates the covariance or inbreeding coefficients for a pedigree.
  PDF (2.12MB)  |   HTML
• The IRT Procedure (Experimental)
  Fits item response models.
  PDF (2.56MB)  |   HTML
• The KDE Procedure
  Performs univariate and bivariate kernel density estimation.
  PDF (3.78MB)  |   HTML
• The KRIGE2D Procedure
  Performs ordinary kriging in two dimensions.
  PDF (7.06MB)  |   HTML
• The LATTICE Procedure
  Performs analysis of variance and analysis of simple covariance for data from experiments with lattice designs.
  PDF (2.0MB)  |   HTML
• The LIFEREG Procedure
  Fits parametric models to failure time data that can be uncensored, right censored, left censored, or interval censored.
  PDF (3.68MB)  |   HTML
• The LIFETEST Procedure
  Computes 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).
  PDF (3.53MB)  |   HTML
• The LOESS Procedure
  Implements a nonparametric method for estimating regression surfaces.
  PDF (4.31MB)  |   HTML
• The LOGISTIC Procedure
  Fits regression models with binary, ordinal, or nominal dependent variables.
  PDF (5.02MB)  |   HTML
• The MCMC Procedure
  Performs general-purpose Markov chain Monte Carlo (MCMC) simulation designed to fit Bayesian models.
  PDF (6.07MB)  |   HTML
• The MDS Procedure
  Fits two- and three-way, metric and nonmetric multidimensional scaling models.
  PDF (3.16MB)  |   HTML
• The MI Procedure
  Performs multiple imputation of missing data.
  PDF (3.21MB)  |   HTML
• The MIANALYZE Procedure
  Combines the results of the analyses of imputations and generates valid statistical inferences.
  PDF (2.23MB)  |   HTML
• The MIXED Procedure
  Fits general linear models with fixed and random effects.
  PDF (4.17MB)  |   HTML
• The MODECLUS Procedure
  Clusters observations in a SAS data set by using any of several algorithms based on nonparametric density estimates.
  PDF (5.95MB)  |   HTML
• The MULTTEST Procedure
  Addresses the multiple testing problem by adjusting the p-values from a family of hypothesis tests.
  PDF (2.97MB)  |   HTML
• The NESTED Procedure
  Performs random-effects analysis of variance for data from an experiment with a nested (hierarchical) structure and classification effects.
  PDF (2.05MB)  |   HTML
• The NLIN Procedure
  Fits nonlinear regression models.
  PDF (3.66MB)  |   HTML
• The NLMIXED Procedure
  Fits mixed models in which the fixed or random effects enter nonlinearly.
  PDF (2.81MB)  |   HTML
• The NPAR1WAY Procedure
  Performs nonparametric tests for location and scale differences across a one-way classification.
  PDF (2.49MB)  |   HTML
• The ORTHOREG Procedure
  Fits general linear models by the method of least squares and provides more accurate estimates than other procedures when your data are ill-conditioned.
  PDF (2.20MB)  |   HTML
• The PHREG Procedure
  Performs regression analysis of survival data based on the Cox proportional hazards model.
  PDF (4.39MB)  |   HTML
• The PLAN Procedure
  Constructs designs and randomizes plans for factorial experiments.
  PDF (2.09MB)  |   HTML
• The PLM Procedure
  Performs postfitting statistical analyses.
  PDF (2.53MB)  |   HTML
• The PLS Procedure
  Performs principal component regression.
  PDF (4.62MB)  |   HTML
• The POWER Procedure
  Performs prospective power and sample size analyses for a variety of statistical analyses.
  PDF (7.03MB)  |   HTML
• The Power and Sample Size Application
  PDF (4.26MB)  |   HTML
• The PRINCOMP Procedure
  Performs principal component analysis.
  PDF (3.79MB)  |   HTML
• The PRINQUAL Procedure
  Performs principal component analysis (PCA) of qualitative, quantitative, or mixed data.
  PDF (3.58MB)  |   HTML
• The PROBIT Procedure
  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.
  PDF (3.22MB)  |   HTML
• The QUANTLIFE Procedure
  Performs quantile regression analysis for survival data.
  PDF (2.78MB)  |   HTML
• The QUANTREG Procedure
  Fits quantile regression models.
  PDF (4.77MB)  |   HTML
• The QUANTSELECT Procedure
  Performs effect selection in the framework of quantile regression.
  PDF (3.17MB)  |   HTML
• The REG Procedure
  General-purpose procedure for ordinary least squares regression.
  PDF (6.92MB)  |   HTML
• The ROBUSTREG Procedure
  Provides resistant (stable) results for linear regression models in the presence of outliers.
  PDF (4.15MB)  |   HTML
• The RSREG Procedure
  Fits quadratic response surface regression models.
  PDF (2.67MB)  |   HTML
• The SCORE Procedure
  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.
  PDF (2.08MB)  |   HTML
• The SEQDESIGN Procedure
  Designs interim analyses for group sequential clinical trials.
  PDF (6.74MB)  |   HTML
• The SEQTEST Procedure
  Performs interim analyses for group sequential clinical trials.
  PDF (5.54MB)  |   HTML
• The SIM2D Procedure
  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.
  PDF (4.13MB)  |   HTML
• The SIMNORMAL Procedure
  Performs conditional and unconditional simulation for a set of correlated normal or Gaussian random variables.
  PDF (2.17MB)  |   HTML
• The STDIZE Procedure
  Standardizes one or more numeric variables in a SAS data set by subtracting a location measure and dividing by a scale measure.
  PDF (2.19MB)  |   HTML
• The STDRATE Procedure
  Computes directly standardized rates and risks for study populations.
  PDF (3.02MB)  |   HTML
• The STEPDISC Procedure
  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.
  PDF (2.07MB)  |   HTML
• The SURVEYFREQ Procedure
  Produces one-way to n-way frequency and crosstabulation tables from complex multistage survey designs with stratification, clustering, and unequal weighting.
  PDF (2.67MB)  |   HTML
• The SURVEYLOGISTIC Procedure
  Fits models with binary, ordinal, or nominal dependent variables and incorporates complex survey designs.
  PDF (2.50MB)  |   HTML
• The SURVEYMEANS Procedure
  Estimate statistics such as means, totals, proportions, quantiles, and ratios from complex multistage survey designs with stratification, clustering, and unequal weighting.
  PDF (2.55MB)  |   HTML
• The SURVEYPHREG Procedure
  Performs regression analysis of survival data based on the Cox proportional hazards model for complex survey sample designs.
  PDF (2.42MB)  |   HTML
• The SURVEYREG Procedure
  Performs linear regression analysis for complex survey sample designs.
  PDF (3.0MB)  |   HTML
• The SURVEYSELECT Procedure
  Selects simple random samples or selects samples according to a complex multistage sample design that includes stratification, clustering, and unequal probabilities of selection.
  PDF (2.35MB)  |   HTML
• The TPSPLINE Procedure
  Provides penalized least squares estimates.
  PDF (4.19MB)  |   HTML
• The TRANSREG Procedure
  Fits linear models with optimal nonlinear transformations of variables.
  PDF (10.0MB)  |   HTML
• The TREE Procedure
  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.
  PDF (2.54MB)  |   HTML
• The TTEST Procedure
  Performs t tests and computes confidence limits for one sample, paired observations, two independent samples, and the AB/BA crossover design.
  PDF (4.04MB)  |   HTML
• The VARCLUS Procedure
  Divides a set of numeric variables into disjoint or hierarchical clusters.
  PDF (2.15MB)  |   HTML
• The VARCOMP Procedure
  Fits general linear models with random effects (with the option of specifying certain effects as fixed).
  PDF (2.16MB)  |   HTML
• The VARIOGRAM Procedure
  Computes empirical measures of spatial continuity for two-dimensional spatial data.
  PDF (5.43MB)  |   HTML

SAS/STAT 12.3 User's Guide - Procedures

• The ACECLUS Procedure
  Obtains approximate estimates of the pooled within-cluster covariance matrix when the clusters are assumed to be multivariate normal with equal covariance matrices.
  PDF (2.35MB)  |   HTML
• The ADAPTIVEREG Procedure (Experimental)
  Fits multivariate adaptive regression splines.
  PDF (4.19MB)  |   HTML
• The ANOVA Procedure
  Performs analysis of variance for balanced data.
  PDF (2.21MB)  |   HTML
• The BOXPLOT Procedure
  Creates side-by-side box-and-whiskers plots of measurements organized in groups.
  PDF (2.66MB)  |   HTML
• The CALIS Procedure
  Fits structural equation models.
  PDF (4.7MB)  |   HTML
• The CANCORR Procedure
  Performs canonical correlation, partial canonical correlation, and canonical redundancy analysis.
  PDF (2.07MB)  |   HTML
• The CANDISC Procedure
  Performs a canonical discriminant analysis, computes squared Mahalanobis distances between class means, and performs both univariate and multivariate one-way analyses of variance.
  PDF (2.22MB)  |   HTML
• The CATMOD Procedure
  Performs categorical data modeling of data that can be represented by a contingency table.
  PDF (2.41MB)  |   HTML
• The CLUSTER Procedure
  Hierarchically clusters the observations in a SAS data.
  PDF (4.41MB)  |   HTML
• The CORRESP Procedure
  Performs simple correspondence analysis and multiple correspondence analysis (MCA).
  PDF (2.53MB)  |   HTML
• The DISCRIM Procedure
  Develops a discriminant criterion to classify each observation into groups.
  PDF (3.86MB)  |   HTML
• The DISTANCE Procedure
  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.
  PDF (2.54MB)  |   HTML
• The FACTOR Procedure
  Performs a variety of common factor and component analyses and rotations.
  PDF (2.74MB)  |   HTML
• The FASTCLUS Procedure
  Performs a disjoint cluster analysis on the basis of distances computed from one or more quantitative variables.
  PDF (2.62MB)  |   HTML
• The FMM Procedure
  Fits finite mixture models.
  PDF (3.22MB)  |   HTML
• The FREQ Procedure
  Produces one-way to n-way frequency and contingency (crosstabulation) tables and performs table analysis.
  PDF (3.24MB)  |   HTML
• The GAM Procedure
  Fits generalized additive models.
  PDF (2.98MB)  |   HTML
• The GENMOD Procedure
  Fits generalized linear models.
  PDF (4.35MB)  |   HTML
• The GLIMMIX Procedure
  Fits generalized linear mixed models.
  PDF (6.04MB)  |   HTML
• The GLM Procedure
  Fits general linear models.
  PDF (3.71MB)  |   HTML
• The GLMMOD Procedure
  Constructs the design matrix for a general linear model; it essentially constitutes the model-building front end for the GLM procedure.
  PDF (1.98MB)  |   HTML
• The GLMPOWER Procedure
  Performs prospective power and sample size analysis for linear models.
  PDF (2.92MB)  |   HTML
• The GLMSELECT Procedure
  Performs effect selection in the framework of general linear models.
  PDF (4.76MB)  |   HTML
• The HPMIXED Procedure
  Fits linear mixed models with simple covariance component structures by sparse-matrix techniques.
  PDF (2.43MB)  |   HTML
• The INBREED Procedure
  Calculates the covariance or inbreeding coefficients for a pedigree.
  PDF (2.09MB)  |   HTML
• The KDE Procedure
  Performs univariate and bivariate kernel density estimation.
  PDF (3.67MB)  |   HTML
• The KRIGE2D Procedure
  Performs ordinary kriging in two dimensions.
  PDF (6.99MB)  |   HTML
• The LATTICE Procedure
  Performs analysis of variance and analysis of simple covariance for data from experiments with lattice designs.
  PDF (1.98MB)  |   HTML
• The LIFEREG Procedure
  Fits parametric models to failure time data that can be uncensored, right censored, left censored, or interval censored.
  PDF (3.6MB)  |   HTML
• The LIFETEST Procedure
  Computes 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).
  PDF (3.49MB)  |   HTML
• The LOESS Procedure
  Implements a nonparametric method for estimating regression surfaces.
  PDF (4.22MB)  |   HTML
• The LOGISTIC Procedure
  Fits regression models with binary, ordinal, or nominal dependent variables.
  PDF (4.82MB)  |   HTML
• The MCMC Procedure
  Performs general-purpose Markov chain Monte Carlo (MCMC) simulation designed to fit Bayesian models.
  PDF (6.02MB)  |   HTML
• The MDS Procedure
  Fits two- and three-way, metric and nonmetric multidimensional scaling models.
  PDF (3.13MB)  |   HTML
• The MI Procedure
  Performs multiple imputation of missing data.
  PDF (2.93MB)  |   HTML
• The MIANALYZE Procedure
  Combines the results of the analyses of imputations and generates valid statistical inferences.
  PDF (2.17MB)  |   HTML
• The MIXED Procedure
  Fits general linear models with fixed and random effects.
  PDF (3.93MB)  |   HTML
• The MODECLUS Procedure
  Clusters observations in a SAS data set by using any of several algorithms based on nonparametric density estimates.
  PDF (5.74MB)  |   HTML
• The MULTTEST Procedure
  Addresses the multiple testing problem by adjusting the p-values from a family of hypothesis tests.
  PDF (2.87MB)  |   HTML
• The NESTED Procedure
  Performs random-effects analysis of variance for data from an experiment with a nested (hierarchical) structure and classification effects.
  PDF (2.01MB)  |   HTML
• The NLIN Procedure
  Fits nonlinear regression models.
  PDF (3.26MB)  |   HTML
• The NLMIXED Procedure
  Fits mixed models in which the fixed or random effects enter nonlinearly.
  PDF (2.54MB)  |   HTML
• The NPAR1WAY Procedure
  Performs nonparametric tests for location and scale differences across a one-way classification.
  PDF (2.45MB)  |   HTML
• The ORTHOREG Procedure
  Fits general linear models by the method of least squares and provides more accurate estimates than other procedures when your data are ill-conditioned.
  PDF (2.16MB)  |   HTML
• The PHREG Procedure
  Performs regression analysis of survival data based on the Cox proportional hazards model.
  PDF (4.2MB)  |   HTML
• The PLAN Procedure
  Constructs designs and randomizes plans for factorial experiments.
  PDF (2.05MB)  |   HTML
• The PLM Procedure
  Performs postfitting statistical analyses.
  PDF (2.47MB)  |   HTML
• The PLS Procedure
  Performs principal component regression.
  PDF (4.47MB)  |   HTML
• The POWER Procedure
  Performs prospective power and sample size analyses for a variety of statistical analyses.
  PDF (6.89MB)  |   HTML
• The Power and Sample Size Application
  PDF (4.23MB)  |   HTML
• The PRINCOMP Procedure
  Performs principal component analysis.
  PDF (3.78MB)  |   HTML
• The PRINQUAL Procedure
  Performs principal component analysis (PCA) of qualitative, quantitative, or mixed data.
  PDF (3.6MB)  |   HTML
• The PROBIT Procedure
  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.
  PDF (3.13MB)  |   HTML
• The QUANTLIFE Procedure (Experimental)
  Performs quantile regression analysis for survival data.
  PDF (2.7MB)  |   HTML
• The QUANTREG Procedure
  Fits quantile regression models.
  PDF (4.27MB)  |   HTML
• The QUANTSELECT Procedure (Experimental)
  Performs effect selection in the framework of quantile regression.
  PDF (2.49MB)  |   HTML
• The REG Procedure
  General-purpose procedure for ordinary least squares regression.
  PDF (7.03MB)  |   HTML
• The ROBUSTREG Procedure
  Provides resistant (stable) results for linear regression models in the presence of outliers.
  PDF (3.92MB)  |   HTML
• The RSREG Procedure
  Fits quadratic response surface regression models.
  PDF (2.6MB)  |   HTML
• The SCORE Procedure
  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.
  PDF (2.05MB)  |   HTML
• The SEQDESIGN Procedure
  Designs interim analyses for group sequential clinical trials.
  PDF (6.75MB)  |   HTML
• The SEQTEST Procedure
  Performs interim analyses for group sequential clinical trials.
  PDF (5.53MB)  |   HTML
• The SIM2D Procedure
  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.
  PDF (4.07MB)  |   HTML
• The SIMNORMAL Procedure
  Performs conditional and unconditional simulation for a set of correlated normal or Gaussian random variables.
  PDF (2.13MB)  |   HTML
• The STDIZE Procedure
  Standardizes one or more numeric variables in a SAS data set by subtracting a location measure and dividing by a scale measure.
  PDF (2.16MB)  |   HTML
• The STDRATE Procedure
  Computes directly standardized rates and risks for study populations.
  PDF (2.92MB)  |   HTML
• The STEPDISC Procedure
  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.
  PDF (2.02MB)  |   HTML
• The SURVEYFREQ Procedure
  Produces one-way to n-way frequency and crosstabulation tables from complex multistage survey designs with stratification, clustering, and unequal weighting.
  PDF (2.6MB)  |   HTML
• The SURVEYLOGISTIC Procedure
  Fits models with binary, ordinal, or nominal dependent variables and incorporates complex survey designs.
  PDF (2.45MB)  |   HTML
• The SURVEYMEANS Procedure
  Estimate statistics such as means, totals, proportions, quantiles, and ratios from complex multistage survey designs with stratification, clustering, and unequal weighting.
  PDF (2.31MB)  |   HTML
• The SURVEYPHREG Procedure
  Performs regression analysis of survival data based on the Cox proportional hazards model for complex survey sample designs.
  PDF (2.38MB)  |   HTML
• The SURVEYREG Procedure
  Performs linear regression analysis for complex survey sample designs.
  PDF (2.43MB)  |   HTML
• The SURVEYSELECT Procedure
  Selects simple random samples or selects samples according to a complex multistage sample design that includes stratification, clustering, and unequal probabilities of selection.
  PDF (2.29MB)  |   HTML
• The TPSPLINE Procedure
  Provides penalized least squares estimates.
  PDF (4.2MB)  |   HTML
• The TRANSREG Procedure
  Fits linear models with optimal nonlinear transformations of variables.
  PDF (9.91MB)  |   HTML
• The TREE Procedure
  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.
  PDF (2.52MB)  |   HTML
• The TTEST Procedure
  Performs t tests and computes confidence limits for one sample, paired observations, two independent samples, and the AB/BA crossover design.
  PDF (4.01MB)  |   HTML
• The VARCLUS Procedure
  Divides a set of numeric variables into disjoint or hierarchical clusters.
  PDF (2.12MB)  |   HTML
• The VARCOMP Procedure
  Fits general linear models with random effects (with the option of specifying certain effects as fixed).
  PDF (2.12MB)  |   HTML
• The VARIOGRAM Procedure
  Computes empirical measures of spatial continuity for two-dimensional spatial data.
  PDF (5.36MB)  |   HTML

SAS/STAT 12.1 User's Guide - Procedures

For the complete SAS/STAT 12.1 User's Guide, go to the SAS/STAT product documentation page.

• The ACECLUS Procedure
  Obtains approximate estimates of the pooled within-cluster covariance matrix when the clusters are assumed to be multivariate normal with equal covariance matrices.
  PDF (10.1MB)  |   HTML
• The ADAPTIVEREG Procedure (Experimental)
  Fits multivariate adaptive regression splines.
  PDF (11.9MB)  |   HTML
• The ANOVA Procedure
  Performs analysis of variance for balanced data.
  PDF (9.99MB)  |   HTML
• The BOXPLOT Procedure
  Creates side-by-side box-and-whiskers plots of measurements organized in groups.
  PDF (10.4MB)  |   HTML
• The CALIS Procedure
  Fits structural equation models.
  PDF (12.2MB)  |   HTML
• The CANCORR Procedure
  Performs canonical correlation, partial canonical correlation, and canonical redundancy analysis.
  PDF (9.86MB)  |   HTML
• The CANDISC Procedure
  Performs a canonical discriminant analysis, computes squared Mahalanobis distances between class means, and performs both univariate and multivariate one-way analyses of variance.
  PDF (10MB)  |   HTML
• The CATMOD Procedure
  Performs categorical data modeling of data that can be represented by a contingency table.
  PDF (10.1MB)  |   HTML
• The CLUSTER Procedure
  Hierarchically clusters the observations in a SAS data.
  PDF (12.1MB)  |   HTML
• The CORRESP Procedure
  Performs simple correspondence analysis and multiple correspondence analysis (MCA).
  PDF (10.3MB)  |   HTML
• The DISCRIM Procedure
  Develops a discriminant criterion to classify each observation into groups.
  PDF (11.6MB)  |   HTML
• The DISTANCE Procedure
  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.
  PDF (10.3MB)  |   HTML
• The FACTOR Procedure
  Performs a variety of common factor and component analyses and rotations.
  PDF (10.5MB)  |   HTML
• The FASTCLUS Procedure
  Performs a disjoint cluster analysis on the basis of distances computed from one or more quantitative variables.
  PDF (10.4MB)  |   HTML
• The FMM Procedure
  Fits finite mixture models.
  PDF (11MB)  |   HTML
• The FREQ Procedure
  Produces one-way to n-way frequency and contingency (crosstabulation) tables and performs table analysis.
  PDF (10.9MB)  |   HTML
• The GAM Procedure
  Fits generalized additive models.
  PDF (10.7MB)  |   HTML
• The GENMOD Procedure
  Fits generalized linear models.
  PDF (12MB)  |   HTML
• The GLIMMIX Procedure
  Fits generalized linear mixed models.
  PDF (13.7MB)  |   HTML
• The GLM Procedure
  Fits general linear models.
  PDF (11.4MB)  |   HTML
• The GLMMOD Procedure
  Constructs the design matrix for a general linear model; it essentially constitutes the model-building front end for the GLM procedure.
  PDF (9.8MB)  |   HTML
• The GLMPOWER Procedure
  Performs prospective power and sample size analysis for linear models.
  PDF (10.7MB)  |   HTML
• The GLMSELECT Procedure
  Performs effect selection in the framework of general linear models.
  PDF (12.5MB)  |   HTML
• The HPMIXED Procedure
  Fits linear mixed models with simple covariance component structures by sparse-matrix techniques.
  PDF (10.2MB)  |   HTML
• The INBREED Procedure
  Calculates the covariance or inbreeding coefficients for a pedigree.
  PDF (9.9MB)  |   HTML
• The KDE Procedure
  Performs univariate and bivariate kernel density estimation.
  PDF (11.4MB)  |   HTML
• The KRIGE2D Procedure
  Performs ordinary kriging in two dimensions.
  PDF (14.7MB)  |   HTML
• The LATTICE Procedure
  Performs analysis of variance and analysis of simple covariance for data from experiments with lattice designs.
  PDF (9.8MB)  |   HTML
• The LIFEREG Procedure
  Fits parametric models to failure time data that can be uncensored, right censored, left censored, or interval censored.
  PDF (11.3MB)  |   HTML
• The LIFETEST Procedure
  Computes 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).
  PDF (11.2MB)  |   HTML
• The LOESS Procedure
  Implements a nonparametric method for estimating regression surfaces.
  PDF (12MB)  |   HTML
• The LOGISTIC Procedure
  Fits regression models with binary, ordinal, or nominal dependent variables.
  PDF (12.5MB)  |   HTML
• The MCMC Procedure
  Performs general-purpose Markov chain Monte Carlo (MCMC) simulation designed to fit Bayesian models.
  PDF (13.7MB)  |   HTML
• The MDS Procedure
  Fits two- and three-way, metric and nonmetric multidimensional scaling models.
  PDF (10.9MB)  |   HTML
• The MI Procedure
  Performs multiple imputation of missing data.
  PDF (10.6MB)  |   HTML
• The MIANALYZE Procedure
  Combines the results of the analyses of imputations and generates valid statistical inferences.
  PDF (10MB)  |   HTML
• The MIXED Procedure
  Fits general linear models with fixed and random effects.
  PDF (11.8MB)  |   HTML
• The MODECLUS Procedure
  Clusters observations in a SAS data set by using any of several algorithms based on nonparametric density estimates.
  PDF (13.5MB)  |   HTML
• The MULTTEST Procedure
  Addresses the multiple testing problem by adjusting the p-values from a family of hypothesis tests.
  PDF (10.6MB)  |   HTML
• The NESTED Procedure
  Performs random-effects analysis of variance for data from an experiment with a nested (hierarchical) structure and classification effects.
  PDF (9.8MB)  |   HTML
• The NLIN Procedure
  Fits nonlinear regression models.
  PDF (11MB)  |   HTML
• The NLMIXED Procedure
  Fits mixed models in which the fixed or random effects enter nonlinearly.
  PDF (10.3MB)  |   HTML
• The NPAR1WAY Procedure
  Performs nonparametric tests for location and scale differences across a one-way classification.
  PDF (10.2MB)  |   HTML
• The ORTHOREG Procedure
  Fits general linear models by the method of least squares and provides more accurate estimates than other procedures when your data are ill-conditioned.
  PDF (10.0MB)  |   HTML
• The PHREG Procedure
  Performs regression analysis of survival data based on the Cox proportional hazards model.
  PDF (11.9MB)  |   HTML
• The PLAN Procedure
  Constructs designs and randomizes plans for factorial experiments.
  PDF (9.8MB)  |   HTML
• The PLM Procedure
  Performs postfitting statistical analyses.
  PDF (10.2MB)  |   HTML
• The PLS Procedure
  Performs principal component regression.
  PDF (12.2MB)  |   HTML
• The POWER Procedure
  Performs prospective power and sample size analyses for a variety of statistical analyses.
  PDF (14.7MB)  |   HTML
• The Power and Sample Size Application
  PDF (12MB)  |   HTML
• The PRINCOMP Procedure
  Performs principal component analysis.
  PDF (11.5MB)  |   HTML
• The PRINQUAL Procedure
  Performs principal component analysis (PCA) of qualitative, quantitative, or mixed data.
  PDF (11.3MB)  |   HTML
• The PROBIT Procedure
  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.
  PDF (10.8MB)  |   HTML
• The QUANTLIFE Procedure (Experimental)
  Performs quantile regression analysis for survival data.
  PDF (10.5MB)  |   HTML
• The QUANTREG Procedure
  Fits quantile regression models.
  PDF (12MB)  |   HTML
• The QUANTSELECT Procedure (Experimental)
  Performs effect selection in the framework of quantile regression.
  PDF (10.2MB)  |   HTML
• The REG Procedure
  General-purpose procedure for ordinary least squares regression.
  PDF (14.7MB)  |   HTML
• The ROBUSTREG Procedure
  Provides resistant (stable) results for linear regression models in the presence of outliers.
  PDF (11.6MB)  |   HTML
• The RSREG Procedure
  Fits quadratic response surface regression models.
  PDF (10.4MB)  |   HTML
• The SCORE Procedure
  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.
  PDF (9.8MB)  |   HTML
• The SEQDESIGN Procedure
  Designs interim analyses for group sequential clinical trials.
  PDF (14.4MB)  |   HTML
• The SEQTEST Procedure
  Performs interim analyses for group sequential clinical trials.
  PDF (13.2MB)  |   HTML
• The SIM2D Procedure
  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.
  PDF (11.8MB)  |   HTML
• The SIMNORMAL Procedure
  Performs conditional and unconditional simulation for a set of correlated normal or Gaussian random variables.
  PDF (9.9MB)  |   HTML
• The STDIZE Procedure
  Standardizes one or more numeric variables in a SAS data set by subtracting a location measure and dividing by a scale measure.
  PDF (9.9MB)  |   HTML
• The STDRATE Procedure
  Computes directly standardized rates and risks for study populations.
  PDF (10.7MB)  |   HTML
• The STEPDISC Procedure
  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.
  PDF (9.8MB)  |   HTML
• The SURVEYFREQ Procedure
  Produces one-way to n-way frequency and crosstabulation tables from complex multistage survey designs with stratification, clustering, and unequal weighting.
  PDF (10.3MB)  |   HTML
• The SURVEYLOGISTIC Procedure
  Fits models with binary, ordinal, or nominal dependent variables and incorporates complex survey designs.
  PDF (10.2MB)  |   HTML
• The SURVEYMEANS Procedure
  Estimate statistics such as means, totals, proportions, quantiles, and ratios from complex multistage survey designs with stratification, clustering, and unequal weighting.
  PDF (10MB)  |   HTML
• The SURVEYPHREG Procedure
  Performs regression analysis of survival data based on the Cox proportional hazards model for complex survey sample designs.
  PDF (10.1MB)  |   HTML
• The SURVEYREG Procedure
  Performs linear regression analysis for complex survey sample designs.
  PDF (10.2MB)  |   HTML
• The SURVEYSELECT Procedure
  Selects simple random samples or selects samples according to a complex multistage sample design that includes stratification, clustering, and unequal probabilities of selection.
  PDF (10MB)  |   HTML
• The TPSPLINE Procedure
  Provides penalized least squares estimates.
  PDF (11.9MB)  |   HTML
• The TRANSREG Procedure
  Fits linear models with optimal nonlinear transformations of variables.
  PDF (17.6MB)  |   HTML
• The TREE Procedure
  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.
  PDF (10.3MB)  |   HTML
• The TTEST Procedure
  Performs t tests and computes confidence limits for one sample, paired observations, two independent samples, and the AB/BA crossover design.
  PDF (11.7MB)  |   HTML
• The VARCLUS Procedure
  Divides a set of numeric variables into disjoint or hierarchical clusters.
  PDF (9.9MB)  |   HTML
• The VARCOMP Procedure
  Fits general linear models with random effects (with the option of specifying certain effects as fixed).
  PDF (9.9MB)  |   HTML
• The VARIOGRAM Procedure
  Computes empirical measures of spatial continuity for two-dimensional spatial data.
  PDF (13.1MB)  |   HTML

SAS/STAT 9.3 User's Guide - Procedures

For the complete SAS/STAT 9.3 User's Guide, go to the SAS/STAT product documentation page.

• The ACECLUS Procedure
  Obtains approximate estimates of the pooled within-cluster covariance matrix when the clusters are assumed to be multivariate normal with equal covariance matrices.
  PDF (10.1MB)  |   HTML
• The ANOVA Procedure
  Performs analysis of variance for balanced data.
  PDF (10.1MB)  |   HTML
• The BOXPLOT Procedure
  Creates side-by-side box-and-whiskers plots of measurements organized in groups.
  PDF (10.7MB)  |   HTML
• The CALIS Procedure
  Fits structural equation models.
  PDF (12.5MB)  |   HTML
• The CANCORR Procedure
  Performs canonical correlation, partial canonical correlation, and canonical redundancy analysis.
  PDF (9.92MB)  |   HTML
• The CANDISC Procedure
  Performs a canonical discriminant analysis, computes squared Mahalanobis distances between class means, and performs both univariate and multivariate one-way analyses of variance.
  PDF (10MB)  |   HTML
• The CATMOD Procedure
  Performs categorical data modeling of data that can be represented by a contingency table.
  PDF (10.3MB)  |   HTML
• The CLUSTER Procedure
  Hierarchically clusters the observations in a SAS data.
  PDF (12.2MB)  |   HTML
• The CORRESP Procedure
  Performs simple correspondence analysis and multiple correspondence analysis (MCA).
  PDF (10.3MB)  |   HTML
• The DISCRIM Procedure
  Develops a discriminant criterion to classify each observation into groups.
  PDF (11.7MB)  |   HTML
• The DISTANCE Procedure
  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.
  PDF (10.4MB)  |   HTML
• The FACTOR Procedure
  Performs a variety of common factor and component analyses and rotations.
  PDF (10.6MB)  |   HTML
• The FASTCLUS Procedure
  Performs a disjoint cluster analysis on the basis of distances computed from one or more quantitative variables.
  PDF (10.4MB)  |   HTML
• The FMM Procedure (Experimental)
  Fits finite mixture models.
  PDF (11.1MB)  |   HTML
• The FREQ Procedure
  Produces one-way to n-way frequency and contingency (crosstabulation) tables and performs table analysis.
  PDF (11.2MB)  |   HTML
• The GAM Procedure
  Fits generalized additive models.
  PDF (10.7MB)  |   HTML
• The GENMOD Procedure
  Fits generalized linear models.
  PDF (12.3MB)  |   HTML
• The GLIMMIX Procedure
  Fits generalized linear mixed models.
  PDF (14.36MB)  |   HTML  |   Purchase chapter
• The GLM Procedure
  Fits general linear models.
  PDF (11.8MB)  |   HTML
• The GLMMOD Procedure
  Constructs the design matrix for a general linear model; it essentially constitutes the model-building front end for the GLM procedure.
  PDF (9.84MB)  |   HTML
• The GLMPOWER Procedure
  Performs prospective power and sample size analysis for linear models.
  PDF (10.8MB)  |   HTML
• The GLMSELECT Procedure
  Performs effect selection in the framework of general linear models.
  PDF (12.6MB)  |   HTML
• The HPMIXED Procedure
  Fits linear mixed models with simple covariance component structures by sparse-matrix techniques.
  PDF (10.3MB)  |   HTML
• The INBREED Procedure
  Calculates the covariance or inbreeding coefficients for a pedigree.
  PDF (9.92B)  |   HTML
• The KDE Procedure
  Performs univariate and bivariate kernel density estimation.
  PDF (11.4MB)  |   HTML
• The KRIGE2D Procedure
  Performs ordinary kriging in two dimensions.
  PDF (14.9MB)  |   HTML
• The LATTICE Procedure
  Performs analysis of variance and analysis of simple covariance for data from experiments with lattice designs.
  PDF (9.8MB)  |   HTML
• The LIFEREG Procedure
  Fits parametric models to failure time data that can be uncensored, right censored, left censored, or interval censored.
  PDF (11.2MB)  |   HTML
• The LIFETEST Procedure
  Computes 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).
  PDF (11.2MB)  |   HTML
• The LOESS Procedure
  Implements a nonparametric method for estimating regression surfaces.
  PDF (12.0MB)  |   HTML
• The LOGISTIC Procedure
  Fits regression models with binary, ordinal, or nominal dependent variables.
  PDF (12.8MB)  |   HTML
• The MCMC Procedure
  Performs general-purpose Markov chain Monte Carlo (MCMC) simulation designed to fit Bayesian models.
  PDF (14.3MB)  |   HTML
• The MDS Procedure
  Fits two- and three-way, metric and nonmetric multidimensional scaling models.
  PDF (11.0MB)  |   HTML
• The MI Procedure
  Performs multiple imputation of missing data.
  PDF (10.7MB)  |   HTML
• The MIANALYZE Procedure
  Combines the results of the analyses of imputations and generates valid statistical inferences.
  PDF (9.98MB)  |   HTML
• The MIXED Procedure
  Fits general linear models with fixed and random effects.
  PDF (12.26MB)  |   HTML  |   Purchase chapter
• The MODECLUS Procedure
  Clusters observations in a SAS data set by using any of several algorithms based on nonparametric density estimates.
  PDF (13.6MB)  |   HTML
• The MULTTEST Procedure
  Addresses the multiple testing problem by adjusting the p-values from a family of hypothesis tests.
  PDF (10.7MB)  |   HTML
• The NESTED Procedure
  Performs random-effects analysis of variance for data from an experiment with a nested (hierarchical) structure and classification effects.
  PDF (9.84MB)  |   HTML
• The NLIN Procedure
  Fits nonlinear regression models.
  PDF (10.8MB)  |   HTML
• The NLMIXED Procedure
  Fits mixed models in which the fixed or random effects enter nonlinearly.
  PDF (10.4MB)  |   HTML
• The NPAR1WAY Procedure
  Performs nonparametric tests for location and scale differences across a one-way classification.
  PDF (10.2MB)  |   HTML
• The ORTHOREG Procedure
  Fits general linear models by the method of least squares and provides more accurate estimates than other procedures when your data are ill-conditioned.
  PDF (10.0MB)  |   HTML
• The PHREG Procedure
  Performs regression analysis of survival data based on the Cox proportional hazards model.
  PDF (12.0MB)  |   HTML
• The PLAN Procedure
  Constructs designs and randomizes plans for factorial experiments.
  PDF (9.91MB)  |   HTML
• The PLM Procedure
  Performs postfitting statistical analyses.
  PDF (10.3MB)  |   HTML
• The PLS Procedure
  Performs principal component regression.
  PDF (12.3MB)  |   HTML
• The POWER Procedure
  Performs prospective power and sample size analyses for a variety of statistical analyses.
  PDF (15.3MB)  |   HTML
• The Power and Sample Size Application
  PDF (12.2MB)  |   HTML
• The PRINCOMP Procedure
  Performs principal component analysis.
  PDF (11.9MB)  |   HTML
• The PRINQUAL Procedure
  Performs principal component analysis (PCA) of qualitative, quantitative, or mixed data.
  PDF (11.2MB)  |   HTML
• The PROBIT Procedure
  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.
  PDF (10.9MB)  |   HTML
• The QUANTREG Procedure
  Fits quantile regression models.
  PDF (12.0MB)  |   HTML
• The REG Procedure
  General purpose procedure for ordinary least squares regression.
  PDF (14.7MB)  |   HTML  |   Purchase chapter
• The ROBUSTREG Procedure
  Provides resistant (stable) results for linear regression models in the presence of outliers.
  PDF (11.7MB)  |   HTML
• The RSREG Procedure
  Fits quadratic response surface regression models.
  PDF (10.4MB)  |   HTML
• The SCORE Procedure
  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.
  PDF (9.88MB)  |   HTML
• The SEQDESIGN Procedure
  Designs interim analyses for group sequential clinical trials.
  PDF (14.5MB)  |   HTML
• The SEQTEST Procedure
  Performs interim analyses for group sequential clinical trials.
  PDF (13.0MB)  |   HTML
• The SIM2D Procedure
  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.
  PDF (11.9MB)  |   HTML
• The SIMNORMAL Procedure
  Performs conditional and unconditional simulation for a set of correlated normal or Gaussian random variables.
  PDF (9.92MB)  |   HTML
• The STDIZE Procedure
  Standardizes one or more numeric variables in a SAS data set by subtracting a location measure and dividing by a scale measure.
  PDF (9.98MB)  |   HTML
• The STEPDISC Procedure
  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.
  PDF (9.87MB)  |   HTML
• The SURVEYFREQ Procedure
  Produces one-way to n-way frequency and crosstabulation tables from complex multistage survey designs with stratification, clustering, and unequal weighting.
  PDF (10.5MB)  |   HTML
• The SURVEYLOGISTIC Procedure
  Fits models with binary, ordinal, or nominal dependent variables and incorporates complex survey designs.
  PDF (10.3MB)  |   HTML
• The SURVEYMEANS Procedure
  Estimate statistics such as means, totals, proportions, quantiles, and ratios from complex multistage survey designs with stratification, clustering, and unequal weighting.
  PDF (10.1MB)  |   HTML
• The SURVEYPHREG Procedure
  Performs regression analysis of survival data based on the Cox proportional hazards model for complex survey sample designs.
  PDF (10.3MB)  |   HTML
• The SURVEYREG Procedure
  Performs linear regression analysis for complex survey sample designs.
  PDF (10.3MB)  |   HTML
• The SURVEYSELECT Procedure
  Selects simple random samples or selects samples according to a complex multistage sample design that includes stratification, clustering, and unequal probabilities of selection.
  PDF (10.2MB)  |   HTML
• The TPSPLINE Procedure
  Provides penalized least squares estimates.
  PDF (12.0MB)  |   HTML
• The TRANSREG Procedure
  Fits linear models with optimal nonlinear transformations of variables.
  PDF (18.1MB)  |   HTML
• The TREE Procedure
  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.
  PDF (10.5MB)  |   HTML
• The TTEST Procedure
  Performs t tests and computes confidence limits for one sample, paired observations, two independent samples, and the AB/BA crossover design.
  PDF (11.9MB)  |   HTML
• The VARCLUS Procedure
  Divides a set of numeric variables into disjoint or hierarchical clusters.
  PDF (9.95MB)  |   HTML
• The VARCOMP Procedure
  Fits general linear models with random effects (with the option of specifying certain effects as fixed).
  PDF (9.95MB)  |   HTML
• The VARIOGRAM Procedure
  Computes empirical measures of spatial continuity for two-dimensional spatial data.
  PDF (3.15MB)  |   HTML

For the complete SAS/STAT User's Guide, go to the SAS/STAT product documentation page.

SAS/STAT 9.22 User's Guide - Procedures

• The ACECLUS Procedure
  PDF (10.2MB)  |   HTML
• The ANOVA Procedure
  PDF (13.0MB)  |   HTML
• The BOXPLOT Procedure
  PDF (10.6MB)  |   HTML
• The CALIS Procedure
  PDF (12.5MB)  |   HTML
• The CANCORR Procedure
  PDF (9.9MB)  |   HTML
• The CANDISC Procedure
  PDF (10.1MB)  |   HTML
• The CATMOD Procedure
  PDF (10.4MB)  |   HTML
• The CLUSTER Procedure
  PDF (12.5MB)  |   HTML
• The CORRESP Procedure
  PDF (10.4MB)  |   HTML
• The DISCRIM Procedure
  PDF (11.7MB)  |   HTML
• The DISTANCE Procedure
  PDF (10.5MB)  |   HTML
• The FACTOR Procedure
  PDF (10.7MB)  |   HTML
• The FASTCLUS Procedure
  PDF (10.4MB)  |   HTML
• The FREQ Procedure
  PDF (11.0MB)  |   HTML
• The GAM Procedure
  PDF (10.8MB)  |   HTML
• The GENMOD Procedure
  PDF (12.3MB)  |   HTML
• The GLIMMIX Procedure
  PDF (14.2MB)  |   HTML
• The GLM Procedure
  PDF (11.8MB)  |   HTML
• The GLMMOD Procedure
  PDF (9.8MB)  |   HTML
• The GLMPOWER Procedure
  PDF (10.5MB)  |   HTML
• The GLMSELECT Procedure
  PDF (12.6MB)  |   HTML
• The HPMIXED Procedure (Experimental)
  PDF (10.3MB)  |   HTML
• The INBREED Procedure
  PDF (9.9MB)  |   HTML
• The KDE Procedure
  PDF (11.4MB)  |   HTML
• The KRIGE2D Procedure
  PDF (14.7MB)  |   HTML
• The LATTICE Procedure
  PDF (9.8MB)  |   HTML
• The LIFEREG Procedure
  PDF (11.2MB)  |   HTML
• The LIFETEST Procedure
  PDF (11.2B)  |   HTML
• The LOESS Procedure
  PDF (11.9MB)  |   HTML
• The LOGISTIC Procedure
  PDF (12.8B)  |   HTML
• The MCMC Procedure
  PDF (14.0MB)  |   HTML
• The MDS Procedure
  PDF (11.0MB)  |   HTML
• The MI Procedure
  PDF (10.4MB)  |   HTML
• The MIANALYZE Procedure
  PDF (10.0MB)  |   HTML
• The MIXED Procedure
  PDF (12.3MB)  |   HTML
• The MODECLUS Procedure
  PDF (13.6MB)  |   HTML
• The MULTTEST Procedure
  PDF (10.7MB)  |   HTML
• The NESTED Procedure
  PDF (9.8MB)  |   HTML
• The NLIN Procedure
  PDF (10.5MB)  |   HTML
• The NLMIXED Procedure
  PDF (10.4MB)  |   HTML
• The NPAR1WAY Procedure
  PDF (10.3MB)  |   HTML
• The ORTHOREG Procedure
  PDF (10.0MB)  |   HTML
• The PHREG Procedure
  PDF (12.0MB)  |   HTML
• The PLAN Procedure
  PDF (9.9MB)  |   HTML
• The PLM Procedure
  PDF (10.3MB)  |   HTML
• The PLS Procedure
  PDF (12.1MB)  |   HTML
• The POWER Procedure
  PDF (14.2MB)  |   HTML
• The Power and Sample Size Application
  PDF (12.2MB)  |   HTML
• The PRINCOMP Procedure
  PDF (11.9B)  |   HTML
• The PRINQUAL Procedure
  PDF (11.3MB)  |   HTML
• The PROBIT Procedure
  PDF (11.3B)  |   HTML
• The QUANTREG Procedure
  PDF (12.0MB)  |   HTML
• The REG Procedure
  PDF (15.1MB)  |   HTML
• The ROBUSTREG Procedure
  PDF (11.6MB)  |   HTML
• The RSREG Procedure
  PDF (10.4MB)  |   HTML
• The SCORE Procedure
  PDF (9.9MB)  |   HTML
• The SEQDESIGN Procedure
  PDF (14.3MB)  |   HTML
• The SEQTEST Procedure
  PDF (13.0MB)  |   HTML
• The SIM2D Procedure
  PDF (11.8MB)  |   HTML
• The SIMNORMAL Procedure
  PDF (9.9MB)  |   HTML
• The STDIZE Procedure
  PDF (10.0MB)  |   HTML
• The STEPDISC Procedure
  PDF (9.9MB)  |   HTML
• The SURVEYFREQ Procedure
  PDF (10.5MB)  |   HTML
• The SURVEYLOGISTIC Procedure
  PDF (10.4MB)  |   HTML
• The SURVEYMEANS Procedure
  PDF (10.2MB)  |   HTML
• The SURVEYPHREG Procedure
  PDF (10.3MB)  |   HTML
• The SURVEYREG Procedure
  PDF (10.4MB)  |   HTML
• The SURVEYSELECT Procedure
  PDF (10.2MB)  |   HTML
• The TPSPLINE Procedure
  PDF (11.8MB)  |   HTML
• The TRANSREG Procedure
  PDF (18.2MB)  |   HTML
• The TREE Procedure
  PDF (10.0MB)  |   HTML
• The TTEST Procedure
  PDF (11.8MB)  |   HTML
• The VARCLUS Procedure
  PDF (10.0MB)  |   HTML
• The VARCOMP Procedure
  PDF (9.9MB)  |   HTML
• The VARIOGRAM Procedure
  PDF (13.4MB)  |   HTML

SAS/STAT 9.2

• The ACECLUS Procedure
  PDF (1.0MB)  |   HTML
• The ANOVA Procedure
  PDF (955KB)  |   HTML
• The BOXPLOT Procedure
  PDF (1.33MB)  |   HTML
• The CALIS Procedure
  PDF (1.62MB)  |   HTML
• The CANCORR Procedure
  PDF (698KB)  |   HTML
• The CANDISC Procedure
  PDF (441KB)  |   HTML
• The CATMOD Procedure
  PDF (1.24MB)  |   HTML
• The CLUSTER Procedure
  PDF (2.70MB)  |   HTML
• The CORRESP Procedure
  PDF (1.19MB)  |   HTML
• The DISCRIM Procedure
  PDF (2.50MB)  |   HTML
• The DISTANCE Procedure
  PDF (937KB)  |   HTML
• The FACTOR Procedure
  PDF (1.42MB)  |   HTML
• The FASTCLUS Procedure
  PDF (1.24MB)  |   HTML
• The FREQ Procedure
  PDF (2.14MB)  |   HTML
• The GAM Procedure
  PDF (1.75MB)  |   HTML
• The GENMOD Procedure
  PDF (3.07MB)  |   HTML
• The GLIMMIX Procedure
  PDF (5.27MB)  |   HTML
• The GLM Procedure
  PDF (2.74MB)  |   HTML
• The GLMMOD Procedure
  PDF (612KB)  |   HTML
• The GLMPOWER Procedure
  PDF (912KB)  |   HTML
• The GLMSELECT Procedure
  PDF (2.72MB)  |   HTML
• The HPMIXED Procedure (Experimental)
  PDF (642KB)  |   HTML
• The INBREED Procedure
  PDF (289KB)  |   HTML
• The KDE Procedure
  PDF (1.79MB)  |   HTML
• The KRIGE2D Procedure
  PDF (2.40MB)  |   HTML
• The LATTICE Procedure
  PDF (160KB)  |   HTML
• The LIFEREG Procedure
  PDF (1.63MB)  |   HTML
• The LIFETEST Procedure
  PDF (1.64MB)  |   HTML
• The LOESS Procedure
  PDF (2.34MB)  |   HTML
• The LOGISTIC Procedure
  PDF (3.14MB)  |   HTML
• The MCMC Procedure
  PDF (5.16MB)  |   HTML
• The MDS Procedure
  PDF (1.36MB)  |   HTML
• The MI Procedure
  PDF (819KB)  |   HTML
• The MIANALYZE Procedure
  PDF (360KB)  |   HTML
• The MIXED Procedure
  PDF (2.75MB)  |   HTML
• The MODECLUS Procedure
  PDF (4.49MB)  |   HTML
• The MULTTEST Procedure
  PDF (520KB)  |   HTML
• The NESTED Procedure
  PDF (209KB)  |   HTML
• The NLIN Procedure
  PDF (923KB)  |   HTML
• The NLMIXED Procedure
  PDF (821KB)  |   HTML
• The NPAR1WAY Procedure
  PDF (688KB)  |   HTML
• The ORTHOREG Procedure
  PDF (209KB)  |   HTML
• The PHREG Procedure
  PDF (2.41MB)  |   HTML
• The PLAN Procedure
  PDF (286KB)  |   HTML
• The PLS Procedure
  PDF (1.70MB)  |   HTML
• The POWER Procedure
  PDF (4.52MB)  |   HTML
• The PRINCOMP Procedure
  PDF (2.28MB)  |   HTML
• The PRINQUAL Procedure
  PDF (1.73MB)  |   HTML
• The PROBIT Procedure
  PDF (1.43MB)  |   HTML
• The QUANTREG Procedure
  PDF (2.49MB)  |   HTML
• The REG Procedure
  PDF (5.70MB)  |   HTML
• The ROBUSTREG Procedure
  PDF (1.26MB)  |   HTML
• The RSREG Procedure
  PDF (826KB)  |   HTML
• The SCORE Procedure
  PDF (241KB)  |   HTML
• The SEQDESIGN Procedure
  PDF (5.42MB)  |   HTML
• The SEQTEST Procedure
  PDF (4.09MB)  |   HTML
• The SIM2D Procedure
  PDF (710KB)  |   HTML
• The SIMNORMAL Procedure
  PDF (276KB)  |   HTML
• The STDIZE Procedure
  PDF (372KB)  |   HTML
• The STEPDISC Procedure
  PDF (251KB)  |   HTML
• The SURVEYFREQ Procedure
  PDF (669KB)  |   HTML
• The SURVEYLOGISTIC Procedure
  PDF (721KB)  |   HTML
• The SURVEYMEANS Procedure
  PDF (553KB)  |   HTML
• The SURVEYREG Procedure
  PDF (587KB)  |   HTML
• The SURVEYSELECT Procedure
  PDF (540KB)  |   HTML
• The TCALIS Procedure (Experimental)
  PDF (2.21MB)  |   HTML
• The TPSPLINE Procedure
  PDF (999KB)  |   HTML
• The TRANSREG Procedure
  PDF (9.26MB)  |   HTML
• The TREE Procedure
  PDF (326KB)  |   HTML
• The TTEST Procedure
  PDF (2.18MB)  |   HTML
• The VARCLUS Procedure
  PDF (295KB)  |   HTML
• The VARCOMP Procedure
  PDF (292KB)  |   HTML
• The VARIOGRAM Procedure
  PDF (3.06MB)  |   HTML