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: [EMAIL PROTECTED] > [mailto:[EMAIL PROTECTED] On Behalf Of Peter Dalgaard > Sent: Tuesday, June 05, 2007 4:44 AM > To: John Sorkin > Cc: [email protected] > 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}} > > > > ______________________________________________ > > [email protected] mailing list > > 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. > > > > ______________________________________________ > [email protected] mailing list > 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. > ______________________________________________ [email protected] mailing list 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.
