Date:         Wed, 3 Feb 2010 10:47:24 -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:  <979dede3-7f0d-455c-a952-03962fddddd4@g1g2000yqi.googlegroups.com>
Content-Type: text/plain; charset="US-ASCII"

Yes the RANDOM in GLIMMIX looks much like the REPEATED in MIXED, though
you need to specify residual or Rside, something like

in MIXED

REPEATED time / subject=id type=ar(1) R Rcorr;

in GLIMMIX becomes:

RANDOM time / subject=id type=ar(1) v vcorr  residual;   * or add Rside;


Robin High
UNMC






From:
Christoff <14353075@SUN.AC.ZA>
To:
SAS-L@LISTSERV.UGA.EDU
Date:
02/03/2010 10:27 AM
Subject:
Re: PROC MIXED for non-normal data
Sent by:
"SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>



On Feb 3, 4:51 pm, rh...@UNMC.EDU (Robin R High) wrote:
> 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 <14353...@SUN.AC.ZA>
> To:
> SA...@LISTSERV.UGA.EDU
> Date:
> 02/03/2010 08:29 AM
> Subject:
> PROC MIXED for non-normal data
> Sent by:
> "SAS(r) Discussion" <SA...@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

Hi Robin

Yes indeed the residual distributions are non-normal in most of the
datasets.
It seems as though Proc GLIMMIX might do the trick, just a quick
question..I have quickly had a look at PROC GLIMMIX and noticed it has
no repeated statement. Does one simply include the repeated measure in
the RANDOM statement?

Thank you
Christoff