[R-sig-ME] Replicating SAS results in R with unstructured covariance matrix

[Viechtbauer Wolfgang (STAT)]

It's been a while since I used SAS for mixed-effects model, but I think the model:

PROC MIXED data=Comb method=reml;
  CLASS FAC1 TIME SUBJECT FAC2;
  MODEL RESPONSE = BIAS FAC2 FAC1 TIME TIME*FAC1 / ddfm=Residual solution;
  REPEATED TIME / subject=SUBJECT type=un;
RUN;

can be fit with gls() using:

gls(RESPONSE ~ BIAS + factor(FAC2) + factor(TIME)* factor(FAC1), correlation = corSymm(form = ~ TIME | SUBJECT), weights = varIdent(form = ~ 1 | TIME), data=Comb)

TIME needs to be integer valued. SUBJECT should be a factor. ddfm=Residual implies that the fixed effects are tested with t-tests using df = n-rank(X) (where X is the model matrix), while gls() uses the normal distribution to compute those p-values. But this is a minor issue, except in those pesky borderline cases, but then again neither that t-distribution nor that normal distribution is exactly right and one should withhold judgement anyway.

It would be nice if you could provide feedback whether the syntax above did the trick.

Best,
Wolfgang

--   
Wolfgang Viechtbauer, Ph.D., Statistician   
Department of Psychiatry and Psychology   
School for Mental Health and Neuroscience   
Faculty of Health, Medicine, and Life Sciences   
Maastricht University, P.O. Box 616 (VIJV1)   
6200 MD Maastricht, The Netherlands   
+31 (43) 388-4170 | http://www.wvbauer.com   


> -----Original Message-----
> From: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-models-
> bounces at r-project.org] On Behalf Of Charles Determan Jr
> Sent: Wednesday, January 15, 2014 21:27
> To: Tony K.-T.
> Cc: r-sig-mixed-models at r-project.org
> Subject: Re: [R-sig-ME] Replicating SAS results in R with unstructured
> covariance matrix
> 
> Hi Tony,
> 
> Unfortunately I never did solve this problem.  I experimented endlessly
> with all sorts of combinations of correlation structures available with
> lme.  Unless someone reads this will a wonderful solution to replicate the
> unstructure structure analysis from SAS I can't help.  The compound
> symmetry correlation structure was the only that really matched up well.
> I'm sorry I couldn't be more help.  I ended up moving on to using other
> multivariate analyses like PLSDA and Random Forest because of the
> seemingly
> incompatible analyses.  I wanted to make sure anyone could replicate my
> analysis independent of the statistical program.  I poke at it
> occasionally
> but still nothing for the unstructured analysis.  If I ever do I will be
> sure to post it.
> 
> Wish you the best in you analysis,
> Charles
> 
> 
> On Wed, Jan 15, 2014 at 1:44 PM, Tony K.-T. <tkamth at gmail.com> wrote:
> 
> > Dear Charles,
> >
> > I have seen your post regarding replicating the results of SAS (Proc
> > Mixed) using unstructured covariance matrix using R nlme package (gls).
> >  Have you been able to solve this problem?
> >
> > I am also in the process of doing so on a repeated measures dataset to
> no
> > success (not converging identically, estimates are off), if you can
> spare
> > some thoughts on this it'll be really great.
> >
> > The model that I am trying to replicate from SAS to R.
> >
> > PROC MIXED data=Comb method=reml;
> >   CLASS FAC1 TIME SUBJECT FAC2;
> >   MODEL RESPONSE = BIAS FAC2 FAC1 TIME TIME*FAC1 / ddfm=Residual
> solution;
> >   REPEATED  TIME/ subject=SUBJECT type=un;
> > RUN;
> >
> > Cheers,
> >
> > --
> > Tony Kam-Thong, Dr.-Ing.
> > tel.:+41 78 604 5172
> > e-mail:tkamth at gmail.com
> >
> 
> 
> 
> --
> Charles Determan
> Integrated Biosciences PhD Candidate
> University of Minnesota
> 
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> 
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