The GLM Procedure
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Overview
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Getting Started
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Syntax
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Details
Statistical Assumptions for Using PROC GLM Specification of Effects Using PROC GLM Interactively Parameterization of PROC GLM Models Hypothesis Testing in PROC GLM Effect Size Measures for F Tests in GLM Absorption Specification of ESTIMATE Expressions Comparing Groups Multivariate Analysis of Variance Repeated Measures Analysis of Variance Random-Effects Analysis Missing Values Computational Resources Computational Method Output Data Sets Displayed Output ODS Table Names ODS Graphics
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Examples
Randomized Complete Blocks with Means Comparisons and Contrasts Regression with Mileage Data Unbalanced ANOVA for Two-Way Design with Interaction Analysis of Covariance Three-Way Analysis of Variance with Contrasts Multivariate Analysis of Variance Repeated Measures Analysis of Variance Mixed Model Analysis of Variance with the RANDOM Statement Analyzing a Doubly Multivariate Repeated Measures Design Testing for Equal Group Variances Analysis of a Screening Design
- References
| PROC GLM Contrasted with Other SAS Procedures |
As described previously, PROC GLM can be used for many different analyses and has many special features not available in other SAS procedures. However, for some types of analyses, other procedures are available. As discussed in the sections PROC GLM for Unbalanced ANOVA and PROC GLM for Quadratic Least Squares Regression, sometimes these other procedures are more efficient than PROC GLM. The following procedures perform some of the same analyses as PROC GLM:
- ANOVA
performs analysis of variance for balanced designs. The ANOVA procedure is generally more efficient than PROC GLM for these designs.
- MIXED
fits mixed linear models by incorporating covariance structures in the model fitting process. Its RANDOM and REPEATED statements are similar to those in PROC GLM but offer different functionalities.
- NESTED
performs analysis of variance and estimates variance components for nested random models. The NESTED procedure is generally more efficient than PROC GLM for these models.
- NPAR1WAY
performs nonparametric one-way analysis of rank scores. This can also be done using the RANK procedure and PROC GLM.
- REG
performs simple linear regression. The REG procedure allows several MODEL statements and gives additional regression diagnostics, especially for detection of collinearity.
- RSREG
performs quadratic response surface regression, and canonical and ridge analysis. The RSREG procedure is generally recommended for data from a response surface experiment.
- TTEST
compares the means of two groups of observations. Also, tests for equality of variances for the two groups are available. The TTEST procedure is usually more efficient than PROC GLM for this type of data.
- VARCOMP