[R] [SPAM] - RE: REML with random slopes and random intercepts giving strange results - Bayesian Filter detected spam

[Doran, Harold]

Can you provide the summary(m2) results?

> -----Original Message-----
> From: Simon Pickett [mailto:S.Pickett at exeter.ac.uk] 
> Sent: Wednesday, August 16, 2006 7:14 AM
> To: Doran, Harold
> Cc: r-help at 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: r-help-bounces at stat.math.ethz.ch 
> >> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of 
> Simon Pickett
> >> Sent: Tuesday, August 15, 2006 11:34 AM
> >> To: r-help at 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 at 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
> 
>