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The Most confusion regression question for Statistician
Which model to choose: Glm, Mixed, Genmod, Logistics, Glimmix

Download pdf • Simple Linear Regression Analysis in SAS, Regression Analysis Study Notes 1: Linear Regression

Confusion: If you have been working on regression in SAS, in particular, if you have been exposed to some fancy regression model, say, mixed model, random effect ,
you might easily get confused with a few other regression models? which model to choose: Glm, Mixed, Genmod, Logistics, Glimmix? Answer: The following picture explains the pros/cons for each regression model very clearly:

Each model serves a different purpose, and should be used with different types of data. Here are a few sample code for each model:

PROC GLM DATA=data1;
CLASS CourseLevel expectknownever;
MODEL hours= CourseLevel expectknownever
/SS3
SOLUTION
SINGULAR=1E-07;
LSMEANS CourseLevel / PDIFF=ALL ;
LSMEANS CourseLevel expectknownever / PDIFF=ALL ;


PROC LOGISTIC DATA=data1 desc namelen=100;
CLASS BS (PARAM=EFFECT) workhabits (PARAM=EFFECT);
MODEL CourseLevel=BS workhabits hours /
SELECTION=NONE CLPARM=WALD CLODDS=WALD ALPHA=0.05                                                                                       
  
LINK=PROBIT; /* use CLOGLOG for more than 2 levels */ OUTPUT OUT=out1(LABEL="Logistic regression predictions") PREDPROBS=INDIVIDUAL; PROC MIXED DATA =data1 METHOD=REML ; CLASS CourseLevel Applied Statistics; MODEL hours_modified= Applied CourseLevel Statistics /HTYPE=3 DDFM=CONTAIN /*denominator degrees of freedom */ OUTPM=out2(LABEL="Predicted means") OUTP=out3(LABEL="Predicted values"); RANDOM CourseLevel / G TYPE=VC; LSMEANS Applied CourseLevel Statistics / PDIFF=ALL ; PROC GENMOD DATA=data2; CLASS Applied Statistics workhabits; MODEL hours= Applied Statistics workhabits /LINK=LOG DIST=GAMMA TYPE3 CORRB /*parameter estimate correlation matrix*/ LRCI /* two-sided confidence intervals */ CL /*confidence limits*/ ALPHA=0.05; LSMEANS Applied Statistics workhabits / ALPHA=0.05; OUTPUT OUT=out4 PREDICTED=_predicted1 RESDEV=_resdev1 RESCHI=_reschi1; RUN; QUIT; PROC glimmix DATA = data1; CLASS CourseLevel Applied Statistics; MODEL hours_modified= Applied CourseLevel Statistics /HTYPE=3 DDFM=CONTAIN /*denominator degrees of freedom */ dist=gamma; RANDOM CourseLevel / G TYPE=VC; LSMEANS Applied CourseLevel Statistics / PDIFF=ALL ; RUN; QUIT;
Acknowledgement: The tutorial is based on the popular SAS paper by Dr Cerrito.

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