Re: [R] lme vs. SAS proc mixed. Point estimates and SEs are the same, DFs are different

From: Doran, Harold <HDoran_at_air.org>
Date: Tue, 05 Jun 2007 07:42:25 -0400


In addition to Peter's comments, the following link summarizes the issue as well. This is a direct response to the SAS/lmer DF issue.

https://stat.ethz.ch/pipermail/r-help/2006-May/094765.html

Harold  

> -----Original Message-----
> From: r-help-bounces_at_stat.math.ethz.ch
> [mailto:r-help-bounces_at_stat.math.ethz.ch] On Behalf Of Peter Dalgaard
> Sent: Tuesday, June 05, 2007 4:44 AM
> To: John Sorkin
> Cc: r-help_at_stat.math.ethz.ch
> Subject: Re: [R] lme vs. SAS proc mixed. Point estimates and
> SEs are the same, DFs are different
>
> John Sorkin wrote:
> > R 2.3
> > Windows XP
> >
> > I am trying to understand lme. My aim is to run a random
> effects regression in which the intercept and jweek are
> random effects. I am comparing output from SAS PROC MIXED
> with output from R. The point estimates and the SEs are the
> same, however the DFs and the p values are different. I am
> clearly doing something wrong in my R code. I would
> appreciate any suggestions of how I can change the R code to
> get the same DFs as are provided by SAS.
> >
> This has been hashed over a number of times before. In short:
>
> 1) You're not necessarily doing anything wrong
> 2) SAS PROC MIXED is not necessarily doing it right
> 3) lme() is _definitely_ not doing it right in some cases
> 4) both work reasonably in large sample cases (but beware
> that this is not equivalent to having many observation points)
>
> SAS has an implementation of the method by Kenward and
> Rogers, which could be the most reliable general
> DF-calculation method around (I don't trust their
> Satterthwaite option, though). Getting this or equivalent
> into lme() has been on the wish list for a while, but it is
> not a trivial thing to do.
>
> > SAS code:
> > proc mixed data=lipids2;
> > model ldl=jweek/solution;
> > random int jweek/type=un subject=patient;
> > where lastvisit ge 4;
> > run;
> >
> > SAS output:
> > Solution for Fixed Effects
> >
> > Standard
> > Effect Estimate Error DF t Value Pr > |t|
> >
> > Intercept 113.48 7.4539 25 15.22 <.0001
> > jweek -1.7164 0.5153 24 -3.33 0.0028
> >
> > Type 3 Tests of Fixed Effects
> >
> > Num Den
> > Effect DF DF F Value Pr > F
> > jweek 1 24 11.09 0.0028
> >
> >
> > R code:
> > LesNew3 <- groupedData(LDL~jweek | Patient,
> data=as.data.frame(LesData3), FUN=mean)
> > fit3 <- lme(LDL~jweek,
> data=LesNew3[LesNew3[,"lastvisit"]>=4,], random=~1+jweek)
> > summary(fit3)
> >
> > R output:
> > Random effects:
> > Formula: ~1 + jweek | Patient
> > Structure: General positive-definite, Log-Cholesky parametrization
> >
> >
> > Fixed effects: LDL ~ jweek
> > Value Std.Error DF t-value p-value
> > (Intercept) 113.47957 7.453921 65 15.224144 0.0000
> > jweek -1.71643 0.515361 65 -3.330535 0.0014
> >
> > John Sorkin M.D., Ph.D.
> > Chief, Biostatistics and Informatics
> > University of Maryland School of Medicine Division of Gerontology
> > Baltimore VA Medical Center 10 North Greene Street GRECC (BT/18/GR)
> > Baltimore, MD 21201-1524
> > (Phone) 410-605-7119
> > (Fax) 410-605-7913 (Please call phone number above prior to faxing)
> >
> > Confidentiality Statement:
> > This email message, including any attachments, is for the
> > so...{{dropped}}
> >
> > ______________________________________________
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> > PLEASE do read the posting guide
> > http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
> >
>
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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Tue 05 Jun 2007 - 11:55:38 GMT

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