From: Rick Bilonick <rab45+_at_pitt.edu>

Date: Thu 29 Jun 2006 - 02:05:48 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 Thu Jun 29 02:17:44 2006

Date: Thu 29 Jun 2006 - 02:05:48 EST

On Wed, 2006-06-28 at 11:04 -0400, harry wills wrote:

*> Thanks for the help Dimitris,
**>
**> However I still have a question, this time I'll be more specific,
**>
**> the following is my SAS code
**>
**>
**>
**> proc mixed data=Reg;
**> class ID;
**> model y=Time Time*x1 Time*x2 Time*x3 /S;
**> random intercept Time /S type=UN subject=ID G GCORR V;
**> repeated /subject = ID R RCORR;
**> run; **
**>
**> (Type =UN for random effects)
**>
**>
**>
**> The eqivalent lme statement I am using is :
**>
**> reglme <- lme(y ~ Time+Time*x1+Time*x2+Time*x3, data=Reg, random = ~ Time |
**> ID)
**>
**>
**>
**> When I compare the results, the values differ by considerable margin; I
**> suppose this is due to the Random effects covariance structure. R output
**> tells me that the structure is
**>
**>
**>
*

> "Structure: General positive-definite, Log-Cholesky parametrization"

*>
**>
**>
**> Hence the problem for me is how to control this structure in R. Any help
**> would appreciated
**>
**> Thanks
**>
**> Harry
*

*>From my understanding of SAS, a*b means the interaction of a and b. But
*

in R, a*b is shorthand for a + b + a:b where a:b is the interaction
term. The way you've written the lme formula, you have time showing up 4
times plus you have additional main effects x1, x2, and x3. Is this what
you want? Maybe I'm wrong but I don't think the SAS code and the R code
represent the same model.

Rick B.

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