Dear Thierry, Thanks a lot for pointing me to the right direction! I still have some questions, really appreciate if you could provide any help:
Is there a relationship between the nested mixed model that I used vs. the model that you gave (using biop location as random slope of pid), i.e. lme(y~age + time * trt, random=~1|pid/biopsy.site, data = test, correlation=corAR1()) vs. lme(y~age + time * trt, random=~0 + biopsy.site|pid, data = test, correlation=corAR1()) In my nested mixed model, a variance of pid and a variance of biopsy.site within pid will be estimated. In your mixed model, there is no variance estimated at the pid level , instead variance for each biopsy.site is given. I thought the statistical model was always the same for both mixed models: y_ijk=fixed_effect + b_i + b_ij + e_ijk where b_i is the random effect for pid, and v_ij is the random effect for biopsy.site within pid. I thought that the difference is in my nested mixed model, b_ij is independent of each other and has the same variance, whereas in your mixed model, b_ij is modeled by the pdClasses() chosen. If my thought was correct, then my model should be the same as lme(y~age + time * trt, random=list(pid=pdIdent(~0 + biopsy.site)), data = test, correlation=corAR1()) But they are not the same. What did I misunderstand here? Many thanks John ----- Original Message ----- From: "ONKELINX, Thierry" <thierry.onkel...@inbo.be> To: array chip <arrayprof...@yahoo.com>; "r-sig-mixed-mod...@r-project.org" <r-sig-mixed-mod...@r-project.org> Cc: Sent: Monday, August 8, 2011 1:19 AM Subject: RE: [R] mixed model fitting between R and SAS Please don't cross-post. - corAR1() models the correlation between the residuals of the two time points. - if you want a specific correlation structure for biopsy locations the you must use on of the pdClasses() and use biopsy location as random slope of pid rather than random effect nested in pid. #basic structure = positive definitive symmetrical variance/covariance matrix lme(y~age + time * trt, random=~0 + biopsy.site|pid, data = test, correlation=corAR1(~time)) #no correlation between biopsy location and different variance lme(y~age + time * trt, random=list(pid = pdDiag(~0 + biopsy.site), data = test, correlation=corAR1(~time)) #no correlation between biopsy location and equal variance lme(y~age + time * trt, random=list(pid = pdIdent(~0 + biopsy.site), data = test, correlation=corAR1(~time)) Note that since you have only two biopsy locations there will be no difference between pdSymm (the default) and pdCompSymm Best regards, Thierry ---------------------------------------------------------------------------- ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek team Biometrie & Kwaliteitszorg Gaverstraat 4 9500 Geraardsbergen Belgium Research Institute for Nature and Forest team Biometrics & Quality Assurance Gaverstraat 4 9500 Geraardsbergen Belgium tel. + 32 54/436 185 thierry.onkel...@inbo.be www.inbo.be To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey > -----Oorspronkelijk bericht----- > Van: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] > Namens array chip > Verzonden: maandag 8 augustus 2011 8:48 > Aan: r-sig-mixed-mod...@r-project.org > CC: r-help > Onderwerp: [R] mixed model fitting between R and SAS > > Hi al, > > I have a dataset (see attached), which basically involves 4 treatments for a > chemotherapy drug. Samples were taken from 2 biopsy locations, and biopsy > were taken at 2 time points. So each subject has 4 data points (from 2 biopsy > locations and 2 time points). The objective is to study treatment difference. > > I used lme to fit a mixed model that uses "biopsy.site nested within pid" as a > random term, and used corAR1() as the correlation structure for between the 2 > time points: > > > library(nlme) > > test<-read.table("test.txt",sep='\t',header=T,row.names=1) > fit<-lme(y~age + time * trt, random=~1|pid/biopsy.site, data = test, > correlation=corAR1()) > > First, by above model specification, corAR1() is used for the correlation > between > the 2 time points; what is the correlation structure implicitly used for > between > biopsy locations? How do I specify a particular correlation structure for > between > biopsy locations in this situation? > > Second, does anyone know how to write the above mixed model in SAS? One of > my colleagues wrote the following, but it gave me different results: > > proc mixed data=test; > > class time trt pid biopsysite; > model y=age time trt time*trt; > random biopsysite > repeated pid / type=ar(1) > run; > > Is there anyone familiar with SAS and know if the above SAS code does what the > R code does? > > Many thanks > > John ______________________________________________ R-help@r-project.org 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.
