Re: [R] lme - Random Effects Struture

From: Rick Bilonick <rab45+_at_pitt.edu>
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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