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. ______________________________________________ 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