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

[Doran, Harold]

No, I am not sure. Doug should clarify. I don't really understand the
difference between versions on R-forge and CRAN. I always download from
CRAN.

> -----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
> >> >
> >> > _______________________________________________
> >> > R-sig-mixed-models at r-project.org mailing list
> >> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >> >
> >
> > _______________________________________________
> > R-sig-mixed-models at r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> > 
> 
>