Christoff,

Every dataset has its own issues to work around, but first want to make sure you are basing your comments about non-normality based on a residual analysis (such as described in Chapter 10 of "SAS for Mixed Models", 2nd ed.) and not on how the original data look. GLIMMIX has some distribution alternatives that might make an improvement over the normal without computing a transformation,which works much like PROC MIXED.

Robin High UNMC

From: Christoff <14353075@SUN.AC.ZA> To: SAS-L@LISTSERV.UGA.EDU Date: 02/03/2010 08:29 AM Subject: PROC MIXED for non-normal data Sent by: "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>

Hello all,

Can one use PROC MIXED on non-normally distributed data? I have heard it is robust to the assumptions. If so, are there any references in literature that support this?

My dataset consist of body temperatures measured hourly across +-10 sequential days during summer, autumn, winter and spring. I used different study subjects (lizards) during each season, and experimental day therefore is the repeated measure.

Both the number of experimental days and the number of lizards used vary among seasons resulting in an unbalanced design. PROC MIXED is the only model I know of that can handle unbalanced repeated measures data. Does anyone know of non-parametric alternatives? I have tried various transformations yet could not improve normality. Kind regards Christoff