The GLMSELECT Procedure
-
Overview
- Getting Started
-
Syntax
-
DetailsModel-Selection MethodsModel Selection IssuesCriteria Used in Model Selection MethodsCLASS Variable Parameterization and the SPLIT OptionMacro Variables Containing Selected ModelsUsing the STORE StatementBuilding the SSCP MatrixModel AveragingUsing Validation and Test DataCross ValidationExternal Cross ValidationScreeningDisplayed OutputODS Table NamesODS Graphics
-
Examples
- References
The GLMSELECT procedure performs effect selection in the framework of general linear models. A variety of model selection methods are available, including the LASSO method of Tibshirani (1996) and the related LAR method of Efron et al. (2004). The procedure offers extensive capabilities for customizing the selection with a wide variety of selection and stopping criteria, from traditional and computationally efficient significance-level-based criteria to more computationally intensive validation-based criteria. The procedure also provides graphical summaries of the selection search.
The GLMSELECT procedure compares most closely to REG and GLM. The REG procedure supports a variety of model-selection methods but does not support a CLASS statement. The GLM procedure supports a CLASS statement but does not include effect selection methods. The GLMSELECT procedure fills this gap. GLMSELECT focuses on the standard independently and identically distributed general linear model for univariate responses and offers great flexibility for and insight into the model selection algorithm. GLMSELECT provides results (displayed tables, output data sets, and macro variables) that make it easy to take the selected model and explore it in more detail in a subsequent procedure such as REG or GLM.