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From: Christoff on 4 Feb 2010 01:37
On Feb 3, 8:25 pm, stringplaye...(a)YAHOO.COM (Dale McLerran) wrote:
> It is correct that in the GLIMMIX procedure the residual covariance
> structures are specified through  a RANDOM statement instead of a
> REPEATED statement.  In trying to match a PROC MIXED REPEATED
> statement, there are two variants of the GLIMMIX RANDOM statement
> that are employed, depending on whether or not your REPEATED
> statement would or would not specify a time/space effect.  That is,
> the PROC MIXED REPEATED statement could be constructed as:
>
>   repeated time / subject=subjID type=AR(1);
>
> or as
>
>   repeated / subject=subjID type=AR(1);
>
> Note that I find the latter to be dangerous for most covariance
> specifications.  It is fine for TYPE=CS, but for an AR(1)
> specification, I always prefer to use an explicit specification
> of the repeated measure effect (TIME in the example above).
>
> Anyway, given these two different constructions of a REPEATED
> statement which one might employ for PROC MIXED, there are
> corresponding RANDOM statements to use in the GLIMMIX procedure
> as follows:
>
>   random time / subject=subjID type=AR(1) residual;
>
> or
>
>   random _residual_ / subject=subjID type=AR(1);
>
> HTH,
>
> Dale
>
> ---------------------------------------
> Dale McLerran
> Fred Hutchinson Cancer Research Center
> mailto: dmclerra(a)NO_SPAMfhcrc.org
> Ph:  (206) 667-2926
> Fax: (206) 667-5977
> ---------------------------------------
>
> --- On Wed, 2/3/10, Robin R High <rh...(a)UNMC.EDU> wrote:
>
>
>
> > From: Robin R High <rh...(a)UNMC.EDU>
> > Subject: Re: PROC MIXED for non-normal data
> > To: SA...(a)LISTSERV.UGA.EDU
> > Date: Wednesday, February 3, 2010, 8:47 AM
> > 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 <14353...(a)SUN.AC.ZA>
> > To:
> > SA...(a)LISTSERV.UGA.EDU
> > Date:
> > 02/03/2010 10:27 AM
> > Subject:
> > Re: PROC MIXED for non-normal data
> > Sent by:
> > "SAS(r) Discussion" <SA...(a)LISTSERV.UGA.EDU>
>
> > On Feb 3, 4:51 pm, rh...(a)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...(a)SUN.AC.ZA>
> > > To:
> > > SA...(a)LISTSERV.UGA.EDU
> > > Date:
> > > 02/03/2010 08:29 AM
> > > Subject:
> > > PROC MIXED for non-normal data
> > > Sent by:
> > > "SAS(r) Discussion" <SA...(a)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- Hide quoted text -
>
> - Show quoted text -

Awesome, thank you Robin and Dale. That is very helpfull.
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