RANDOM random-effects </ options> ;
The RANDOM statement defines the random effects constituting the vector in the mixed model. It can be used to specify traditional variance component models (as in the VARCOMP procedure) and to specify random coefficients. The random effects can be classification or continuous, and multiple RANDOM statements are possible.
1.If all independent variables are categorical, i.e, specified in CLASS statement, then it is ANOVA; when some of predicators are continuous, called ANCOVA. Both can be done in GLM procedure. PROC ANOVA is specialized on balanced ANOVA, a special case in GLM ANOVA model. Some people prefer Proc ANOVA, even very few, is only because ANOVA procedure is more efficient than GLM procedure somehow.
2.When random effects were involved in a model, we use Mixed model to manage the random effects and thus the correlation caused by the ‘randomness’. Without random effect, we don’t see the difference between GLM and MIXED procedure, even though they are based on different assumptions and estimation methods.
3.The lsd from means has nothing to do with lsmeans statement, which gives ‘means’ by assuming complete cells were observed.
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