Date:         Wed, 3 Feb 2010 08:51:39 -0600
Reply-To:     Robin R High <rhigh@UNMC.EDU>
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         Robin R High <rhigh@UNMC.EDU>
Subject:      Re: PROC MIXED for non-normal data
Comments: To: Christoff <14353075@SUN.AC.ZA>
In-Reply-To:  <f4f1bb01-a064-4214-8547-df081492deb4@m31g2000yqd.googlegroups.com>
Content-Type: text/plain; charset="US-ASCII"

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