Useful SAS Procedures

Data Related

• PROC IMPORT
  Import an external file to a SAS data set
• PROC SORT
  Order SAS data set observations by the values of one or more character or numeric variables
• PROC CONTENTS
  SAS data Structure Information

  Descriptive Analytics

• PROC FREQ
  Produces one-way to n-way frequency and contingency (crosstabulation) tables
• PROC MEANS
  Compute descriptive statistics for variables
• PROC PRINT
  Print observations in a data set
• PROC SUMMARY
  Compute desctiptive statistics
• PROC TABULATE
  displays descriptive statistics in tabular format
• PROC TTEST
  Performs t tests and computes confidence limits for one sample, paired observations, two independent samples, and the AB/BA crossover design. Two-sided, TOST (two one-sided test) equivalence, and upper and lower one-sided hypotheses are supported for means, mean differences, and mean ratios for either normal or lognormal data
• PROC ANOVA
  Performs analysis of variance for balanced data

Graphs

• PROC SGPLOT
  Performs analysis of variance for balanced data
• SGPLOT Example by Horstman
  Getting Started with the SGPLOT Procedure
• SGPLOT Example by Slaughter and Delwiche
  Getting Started with the SGPLOT Procedure

Classification and Clustering

• PROC CANDISC
  Performs a canonical discriminant analysis, computes squared Mahalanobis distances between class means, and performs both univariate and multivariate one-way analyses of variance
• PROC CLUSTER
  Hierarchically clusters the observations in a SAS data
• PROC TREE
  Produces a tree diagram, also known as a dendrogram or phenogram, from a data set created by the CLUSTER or VARCLUS procedure that contains the results of hierarchical clustering as a tree structure
• PROC DISCRIM
  Develops a discriminant criterion to classify each observation into groups
• PROC DISTANCE
  Computes various measures of distance, dissimilarity, or similarity between the observations (rows) of a SAS data set. Proximity measures are stored as a lower triangular matrix or a square matrix in an output data set that can then be used as input to the CLUSTER, MDS, and MODECLUS procedures.
• PROC FASTCLUS
  Performs a disjoint cluster analysis on the basis of distances computed from one or more quantitative variables

Regression Analysis

• PROC REG
  General purpose procedure for ordinary least squares regression
• PROC LOGISTIC
  Fits models with binary, ordinal, or nominal dependent variables
• PROC GLM
  Fits general linear models
• PROC MCMC
  A general purpose Markov chain Monte Carlo (MCMC) simulation procedure that is designed to fit Bayesian models
• PROC MIXED
  Fits general linear models with fixed and random effects
• PROC MODECLUS
  Clusters observations in a SAS data set by using any of several algorithms based on nonparametric density estimates
• PROC PROBIT
  Calculates maximum likelihood estimates of regression parameters and the natural (or threshold) response rate for quantal response data from biological assays or other discrete event data
• PROC QUANTREG
  Fits quantile regression models
• PROC QUANTSELECT
  Performs effect selection for linear quantile regression models
• PROC ROBUSTREG
  Provides resistant (stable) results for linear regression models in the presence of outliers
• PROC SURVEYSELECT
  Provides a variety of methods for selecting probability-based random samples.