![]() The call to PROC PLM creates a sliced fit plot that shows the predicted values versus the systolic blood pressure for males and females in the study. The STORE statement saves the model to an item store named 'GLMModel'. The call to PROC GLM fits a linear regression model that predicts the level of cholesterol from five explanatory variables. The following program creates sample data for 500 patients in a medical study. ) support the STORE statement, which enables you to save a representation of the model in a SAS item store. ![]() Most parametric regression procedures in SAS (GLM, GLIMMIX, MIXED. If a procedure does not support the STORE statement, you can manually create the "slice" of observations and score the model on the slice.If a procedure supports the STORE statement, you can save the model to an item store and then use the EFFECTPLOT statement in PROC PLM to create a sliced fit plot.If you are using another SAS regression procedure, you can still visualize multivariate regression models: The EFFECTPLOT statement isÄirectly supported by the syntax of the GENMOD, LOGISTIC, and ORTHOREG procedures in SAS/STAT. I previously showed an easy way to visualize a regression model that has several continuous explanatory variables: use the SLICEFIT option in the EFFECTPLOT statement in SAS to create a sliced fit plot.
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