From: Kellie J. Archer, Ph.D. <kjarcher_at_vcu.edu>

Date: Sat 01 Jul 2006 - 05:08:53 EST

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 Received on Sat Jul 01 05:13:01 2006

Date: Sat 01 Jul 2006 - 05:08:53 EST

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: kjarcher@vcu.edu

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 Received on Sat Jul 01 05:13:01 2006

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