[SPAM] - Re: Bug in weights in lmer - Bayesian Filter detected spam

-----Original Message-----
From: Luca Borger [mailto:lborger at uoguelph.ca] 
Sent: Wednesday, April 23, 2008 1:20 PM
To: Doran, Harold; Nick Isaac; R-sig-mixed-models at r-project.org
Subject: [SPAM] - Re: [R-sig-ME] Bug in weights in lmer - 
Bayesian Filter detected spam

Hi,

are you sure? Unless I am misunderstanding something, I used 
the latest lme4 development version available on R-forge:

[1] lme4_0.999375-13

which I thought is newer then the CRAN version you used:

[1] lme4_0.99875-9

Please advice me if not.

Cheers,

Luca



----- Original Message -----
From: "Doran, Harold" <HDoran at air.org>
To: "Doran, Harold" <HDoran at air.org>; "Nick Isaac" 
<njbisaac at googlemail.com>; <R-sig-mixed-models at r-project.org>
Sent: Wednesday, April 23, 2008 1:09 PM
Subject: Re: [R-sig-ME] Bug in weights in lmer

It appears you and Luca have older versions. I'm using the 

most recent

version posted on CRAN. Try updating your packages and see 

what happens.

sessionInfo()

R version 2.6.2 (2008-02-08)
i386-pc-mingw32

locale:
LC_COLLATE=English_United States.1252;LC_CTYPE=English_United
States.1252;LC_MONETARY=English_United
States.1252;LC_NUMERIC=C;LC_TIME=English_United States.1252

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base


other attached packages:
[1] lme4_0.99875-9    Matrix_0.999375-7 lattice_0.17-4

loaded via a namespace (and not attached):
[1] grid_2.6.2

-----Original Message-----
From: Doran, Harold
Sent: Wednesday, April 23, 2008 10:16 AM
To: 'Nick Isaac'; 'R-sig-mixed-models at r-project.org'
Subject: RE: [R-sig-ME] Bug in weights in lmer

I'm confused. When I run this, I get the exact same answers
for all three models for all variance components and for all
fixed effects. See my results below. Where is the bug?

w<-rep(1,nrow(sleepstudy))
w

  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1  [75] 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [112] 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 [149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1

(fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) )

Linear mixed-effects model fit by REML
Formula: Reaction ~ Days + (Days | Subject)
   Data: sleepstudy
  AIC  BIC logLik MLdeviance REMLdeviance
 1754 1770 -871.8       1752         1744
Random effects:
 Groups   Name        Variance Std.Dev. Corr
 Subject  (Intercept) 610.835  24.7151
          Days         35.056   5.9208  0.067
 Residual             655.066  25.5943
number of obs: 180, groups: Subject, 18

Fixed effects:
            Estimate Std. Error t value
(Intercept)  251.405      6.820   36.86
Days          10.467      1.546    6.77

Correlation of Fixed Effects:
     (Intr)
Days -0.137

(fm2 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy,

weights =

w) )

Linear mixed-effects model fit by REML
Formula: Reaction ~ Days + (Days | Subject)
   Data: sleepstudy
  AIC  BIC logLik MLdeviance REMLdeviance
 1754 1770 -871.8       1752         1744
Random effects:
 Groups   Name        Variance Std.Dev. Corr
 Subject  (Intercept) 610.835  24.7151
          Days         35.056   5.9208  0.067
 Residual             655.066  25.5943
number of obs: 180, groups: Subject, 18

Fixed effects:
            Estimate Std. Error t value
(Intercept)  251.405      6.820   36.86
Days          10.467      1.546    6.77

Correlation of Fixed Effects:
     (Intr)
Days -0.137

(fm3 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy,

weights =

w/sum(w)) )

Linear mixed-effects model fit by REML
Formula: Reaction ~ Days + (Days | Subject)
   Data: sleepstudy
  AIC  BIC logLik MLdeviance REMLdeviance
 1754 1770 -871.8       1752         1744
Random effects:
 Groups   Name        Variance Std.Dev. Corr
 Subject  (Intercept) 610.835  24.7151
          Days         35.056   5.9208  0.067
 Residual             655.066  25.5943
number of obs: 180, groups: Subject, 18

Fixed effects:
            Estimate Std. Error t value
(Intercept)  251.405      6.820   36.86
Days          10.467      1.546    6.77

Correlation of Fixed Effects:
     (Intr)
Days -0.137

-----Original Message-----
From: r-sig-mixed-models-bounces at r-project.org
[mailto:r-sig-mixed-models-bounces at r-project.org] On 

Behalf Of Nick

Isaac
Sent: Wednesday, April 23, 2008 8:39 AM
To: R-sig-mixed-models at r-project.org
Subject: [R-sig-ME] Bug in weights in lmer

I have unearthed a bug in the way lmer() deals with weights.

Adding weights causes an inflation of the variance estimates.
The phenomenon is easily demonstrated by comparing the following
models, all of which should be identical:

w<-rep(1,nrow(sleepstudy))
(fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) )
(fm2 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy,

weights =

w) )
(fm3 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy,

weights =

w/sum(w)) )

I have tried this with other datasets and models and 

find the same

general pattern. I find that the inflation factor is 

correlated with

sum(w) and is higher for cross-classified models than 

simple nested

ones.

The fixed effect estimates are also changed.

Best wishes, Nick

sessionInfo()

R version 2.6.2 (2008-02-08)
i386-apple-darwin8.10.1

locale:
en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  

methods   base

other attached packages:
[1] lme4_0.999375-13  Matrix_0.999375-7 lattice_0.17-6

loaded via a namespace (and not attached):
[1] grid_2.6.2