Hi folks, I am new to lme in R, and I have a question regarding to the effect of scale function on the lme. When I use the function to scale and centre the levels of the fixed effects (e.g., X and Y; both have two levels) and write them to new columns: ex: dat$cX<-scale(as.numeric(dat$X),center = TRUE, scale = FALSE) dat$cY<-scale(as.numeric(dat$Y),center = TRUE, scale = FALSE)
and compare the lme of centred model ran on cX and cY with the non-centred model run on X and Y: centred.model <- lmer(quest.ACC~1+cX*cY+(1|Subject)+(1|SetNo),data=dat.Transfer,family='binomial') non.centred.model<- lmer(quest.ACC~1+X*Y+(1|Subject)+(1|SetNo),data=dat.Transfer,family='binomial') I find that the two models give very different results not only for the intercept of the fixed effect effects (which I can understand), but also on the variance of the fixed effect coefficients, leading to the huge differences in some case (interactions emerge). What is going on? Baris -- SB Demiral, PhD. Department of Psychology 7 George Square The University of Edinburgh Edinburgh, EH8 9JZ UK Phone: +44 (0131) 6503063 [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.