Hi All, I would like to ask a question on fixed and random effecti in lme. I am fiddlying around Mick Crawley dataset "rats" :
http://www.bio.ic.ac.uk/research/mjcraw/statcomp/data/ The advantage is that most work is already done in Crawley's book (page 361 onwards) so I can check what I am doing. I am tryg to reproduce the nested analysis on page 368: model<-aov(Glycogen~Treatment/Rat/Liver + Error(Treatment/Rat/Liver), rats) using lme. The code: model1<- lme(Glycogen~Treatment, random = ~1|Rat/Liver, data=rats) VarCorr(model1) Variance StdDev Rat = pdLogChol(1) (Intercept) 20.6019981 4.538942 Liver = pdLogChol(1) (Intercept) 0.0540623 0.232513 Residual 42.4362241 6.514309 Does NOT give me the same variance componets I find in Crawley's book (page 371 onwards). The code: model2<- lme(Glycogen~Treatment, random = ~1|Treatment/Rat/Liver, data=rats) VarCorr(model2) Variance StdDev Treatment = pdLogChol(1) (Intercept) 12.54061 3.541272 Rat = pdLogChol(1) (Intercept) 36.07900 6.006580 Liver = pdLogChol(1) (Intercept) 14.17434 3.764883 Residual 21.16227 4.600247 gets me very similar results (I would guess the differences are due to rounding and the fact I am using R 1.6.2 and Crawley used S+). My problem is: as *Treatment* is a fixed factor, why should I put it in both the fixed term side and random terms side of my code to get the right numbers? I fail to get this at all. Any elucidation would be appreciated. Regards, Federico Calboli ========================= Federico C.F. Calboli Department of Biology University College London Room 327 Darwin Building Gower Street London WClE 6BT Tel: (+44) 020 7679 4395 Fax (+44) 020 7679 7096 [EMAIL PROTECTED] ______________________________________________ [EMAIL PROTECTED] mailing list http://www.stat.math.ethz.ch/mailman/listinfo/r-help
