proc mixed vs. lme
"Grathwohl,Dominik,LAUSANNE,NRC/NT" <dominik.grathwohl at rdls.nestle.com> writes:
Dear All,
Comparing linear mixed effect models in SAS and R, I found the following
discrepancy:
SAS R
random statement random subj(program); random = ~ 1 |
Subj
-2*loglik 1420.8 1439.363
random effects
variance(Intercept) 9.6033 9.604662
variance(residual) 1.1969 1.187553
the first 3 fixed effects
intercept 83.0952 81.10544
ProgramCont -3.4952 -1.11526
ProgramRI -1.9702 -1.04517
... ... ...
Can somebody explain me this different results?
Different contrasts. Try setting options(contrasts = c(contr.SAS, contr.poly)) and doing the analysis in R again. Note that all of these examples are available in the SASmixed package for R. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._