Author Information

George C J Fernandez

Abstract

A user-friendly SAS macro application to perform all possible model selection of fixed effects including quadratic and cross products in the presence of random and repeated measures effects using SAS PROC MIXED is available. This macro application will complement the model selection option currently available in the SAS PROC REG for multiple linear regressions and the experimental SAS procedure GLMSELECT that focuses on the standard independently and identically distributed general linear model for univariate responses. Options are also included in this macro to select the best covariance structure associated with the user-specified fully saturated repeated measures model; to graphically explore and to detect statistical significance of user specified linear, quadratic, interaction terms for fixed effects; and to diagnose multicollinearity, via the VIF statistic for each continuous predictors involved in each model selection step. Two model selection criteria, AICC (corrected Akaike Information Criterion) and MDL (minimal description length) are used in all possible model selection and summaries of the best model selection are compared graphically. The differences in the degree of penalty factors associated with the model dimension between AICC and MDL are investigated. Complete mixed model analysis of final model including data exploration, influential diagnostics, and checking for model violations using the experimental ODS GRAPHICS option available in Version 9.13 is also implemented. Instructions for downloading and running this user-friendly macro application are included.

Keywords

AICC, Delta AICC, MDL, Repeated Measures, GLMSELECT

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Apr 30th, 11:30 AM

ALL POSSIBLE MODEL SELECTION IN PROC MIXED – A SAS MACRO APPLICATION

A user-friendly SAS macro application to perform all possible model selection of fixed effects including quadratic and cross products in the presence of random and repeated measures effects using SAS PROC MIXED is available. This macro application will complement the model selection option currently available in the SAS PROC REG for multiple linear regressions and the experimental SAS procedure GLMSELECT that focuses on the standard independently and identically distributed general linear model for univariate responses. Options are also included in this macro to select the best covariance structure associated with the user-specified fully saturated repeated measures model; to graphically explore and to detect statistical significance of user specified linear, quadratic, interaction terms for fixed effects; and to diagnose multicollinearity, via the VIF statistic for each continuous predictors involved in each model selection step. Two model selection criteria, AICC (corrected Akaike Information Criterion) and MDL (minimal description length) are used in all possible model selection and summaries of the best model selection are compared graphically. The differences in the degree of penalty factors associated with the model dimension between AICC and MDL are investigated. Complete mixed model analysis of final model including data exploration, influential diagnostics, and checking for model violations using the experimental ODS GRAPHICS option available in Version 9.13 is also implemented. Instructions for downloading and running this user-friendly macro application are included.