R help - Aug 2006 - [SPAM] - RE: REML with random slopes and random intercepts giving strange results - Bayesian Filter detected spam

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
> 
>

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: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
>>
>>
>

Simon Pickett
PhD student
Centre For Ecology and Conservation
Tremough Campus
University of Exeter in Cornwall
TR109EZ
Tel 01326371852