Linear mixed models (LMMs) serve as a versatile statistical framework, combining fixed effects that capture the overall trends with random effects that account for variability across subjects ...

In many applications of generalized linear mixed models (GLMMs), there is a hierarchical structure in the effects that needs to be taken into account when performing variable selection. A prime ...

Linear mixed-effects models are frequently used to analyze repeated measures data, because they model flexibly the within-subject correlation often present in this type of data. The most popular ...

Just as PROC GLM is the flagship procedure for fixed-effect linear models, the MIXED procedure is the flagship procedure for random- and mixed-effect linear models. PROC MIXED fits a variety of mixed ...

The MIXED procedure provides easy accessibility to a variety of mixed models useful in many common statistical analyses, including split-plot designs, repeated measures, random coefficients, best ...