I am trying to use lme to fit a mixed effects model to get the same results as when using the following SAS code:
proc mixed; class refseqid probeid probeno end; model expression=end logpgc / ddfm=satterth; random probeno probeid / subject=refseqid type=cs; lsmeans end / diff cl; run; There are 3 genes (refseqid) which is the large grouping factor, with 2 probeids nested within each refseqid, and 16 probenos nested within each of the probeids. I have specified in the SAS Proc Mixed procedure that the variance-covariance structure is to be compound symmetric. Therefore, the variance-covariance matrix is a block diagonal matrix of the form, V_1 0 0 0 V_2 0 0 0 V3 where each V_i represents a RefSeqID. Moreover, for each V_i the structure within the block is v_{11} v{12} v_{21} v{22} where v_{11} and v_{22} are different probeids nested within the refseqid, and so are correlated. The structure of both v_{11} and v_{22} are compound symmetric, and v_{12} and v{21} contain a constant for all elements of the matrix. I have tried to reproduce this using lme, but it is unclear from the documentation (and Pinheiro & Bates text) how the pdBlocked and compound symmetric structure can be combined. fit.lme<-lme(expression~End+logpgc,random=list(RefSeqID=pdBlocked(list (~1,~ProbeID-1),pdClass="pdSymm")),data=dataset,correlation=corCompSym m(form=~1|RefSeqID/ProbeID/ProbeNo)) The point estimates are essentially the same comparing R and SAS for the fixed effects, but the 95% confidence intervals are much shorter using lme(). In order to find the difference in the algorithms used by SAS and R I tried to extract the variance-covariance matrix to look at its structure. I used the getVarCov() command, but it tells me that this function is not available for nested structures. Is there another way to extract the variance-covariance structure for nested models? Does anyone know how I could get the var-cov structure above using lme? Kellie J. Archer, Ph.D. Assistant Professor, Department of Biostatistics Fellow, Center for the Study of Biological Complexity Virginia Commonwealth University 1101 East Marshall St., B1-066 Richmond, VA 23298-0032 phone: (804) 827-2039 fax: (804) 828-8900 e-mail: [EMAIL PROTECTED] website: www.people.vcu.edu/~kjarcher ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html