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

[Doran, Harold]

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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> >
> 
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