sure, thanks again. > summary(m2) Linear mixed-effects model fit by REML Formula: change.wt ~ newwt + (newwt | id) Data: grow AIC BIC logLik MLdeviance REMLdeviance -6203.178 -6164.462 3107.589 -6239.374 -6215.178 Random effects: Groups Name Variance Std.Dev. Corr id (Intercept) 1.0868e-02 0.1042482 newwt 4.7069e-05 0.0068606 -1.000 Residual 1.4236e-02 0.1193136 # of obs: 4688, groups: id, 485
Fixed effects: Estimate Std. Error DF t value Pr(>|t|) (Intercept) 5.5692e-01 6.4189e-03 4686 86.761 < 2.2e-16 *** newwt -3.2382e-02 4.5962e-04 4686 -70.455 < 2.2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Correlation of Fixed Effects: (Intr) newwt -0.954 > Can you provide the summary(m2) results? > >> -----Original Message----- >> From: Simon Pickett [mailto:[EMAIL PROTECTED] >> Sent: Wednesday, August 16, 2006 7:14 AM >> To: Doran, Harold >> Cc: r-help@stat.math.ethz.ch >> Subject: [SPAM] - RE: [R] REML with random slopes and random >> intercepts giving strange results - Bayesian Filter detected spam >> >> Hi again, >> Even stranger is the fact that the coefficeints (the slope) >> and the intercepts are not independent, in fact they are >> directly inversely proportional (r squared = 1). >> This means that that there isnt a random slope and intercept >> for each individual (which is what I wanted), but straight >> line that pivots in the middle and will change from >> individual to individual. Is there a problem with the way I >> have structured the random model or a deeper problem with lmer()? >> here is the code I used >> m2 <- lmer(changewt ~ newwt+(newwt|id), data = grow) >> coef(m2) >> Any suggestions very much appreciated, >> Simon >> >> >> > I don't this is because you are using REML. The BLUPs from a mixed >> > model experience some shrinkage whereas the OLS estimates would not. >> > >> >> -----Original Message----- >> >> From: [EMAIL PROTECTED] >> >> [mailto:[EMAIL PROTECTED] On Behalf Of >> Simon Pickett >> >> Sent: Tuesday, August 15, 2006 11:34 AM >> >> To: r-help@stat.math.ethz.ch >> >> Subject: [R] REML with random slopes and random intercepts giving >> >> strange results >> >> >> >> Hi everyone, >> >> I have been using REML to derive intercepts and >> coeficients for each >> >> individual in a growth study. So the code is >> >> m2 <- lmer(change.wt ~ newwt+(newwt|id), data = grow) >> >> >> >> Calling coef(model.lmer) gives a matrix with this >> information which >> >> is what I want. However, as a test I looked at each >> individual on its >> >> own and used a simple linear regression to obtain the same >> >> information, then I compared the results. It looks like the REML >> >> method doesnt seem to approximate the two parameters as >> well as using >> >> the simple linear regression on each individual >> separately, as judged >> >> by looking at graphs. >> >> Indeed, why do the results differ at all? >> >> Excuse my naivety if this is a silly question. >> >> Thanks to everyone for replying to my previous questions, >> very much >> >> appreciated. >> >> Simon Pickett >> >> PhD student >> >> Centre For Ecology and Conservation >> >> Tremough Campus >> >> University of Exeter in Cornwall >> >> TR109EZ >> >> Tel 01326371852 >> >> >> >> ______________________________________________ >> >> 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 >> >> and provide commented, minimal, self-contained, reproducible code. >> >> >> > >> >> >> Simon Pickett >> PhD student >> Centre For Ecology and Conservation >> Tremough Campus >> University of Exeter in Cornwall >> TR109EZ >> Tel 01326371852 >> >> > Simon Pickett PhD student Centre For Ecology and Conservation Tremough Campus University of Exeter in Cornwall TR109EZ Tel 01326371852 ______________________________________________ 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 and provide commented, minimal, self-contained, reproducible code.