[R-sig-ME] Size/metric of variance components in lme and lmer

[ONKELINX, Thierry]

Dear Stuart,

I think that the "extra" variance is substracted from the fixed effects.
Which indicates that some of the information in your fixed effects was
due to the levels of tid.

But to make a fair comparison you should run both models with lmer. And
then compare both the random effects variances and the fixed effect
estimates.

HTH,

Thierry

------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek
team Biometrie & Kwaliteitszorg
Gaverstraat 4
9500 Geraardsbergen
Belgium

Research Institute for Nature and Forest
team Biometrics & Quality Assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium

tel. + 32 54/436 185
Thierry.Onkelinx at inbo.be
www.inbo.be

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher

The plural of anecdote is not data.
~ Roger Brinner

The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of
data.
~ John Tukey
  

> -----Oorspronkelijk bericht-----
> Van: r-sig-mixed-models-bounces at r-project.org 
> [mailto:r-sig-mixed-models-bounces at r-project.org] Namens 
> Stuart Luppescu
> Verzonden: dinsdag 9 maart 2010 1:21
> Aan: r-sig-mixed-models at r-project.org
> Onderwerp: [R-sig-ME] Size/metric of variance components in 
> lme and lmer
> 
> Hello, I have run two analyses, each with the same data set 
> and predictors. One is a nested model run with lme; the other 
> is a cross-classified model with lmer. The only difference 
> between the two models is the added random effect. For 
> example, the nested model statement looks like this:
> 
> nested.lm3 <- lme(final.points ~ -1 + gr10 + gr11 + gr12 + 
> per1 + per2 +
> per4 + per5 + per6 + per7 + per8 + per9 + per10 + per11 + per12 +
>                   cblackd + casiand + clatinod + cmale + 
> cssoc + cscon + cold4gr  + cmlatent8 + computer +
>                     ...
>                jourlsm,
>                   data=all.subj, random = ~ 1|sid, na.action=na.omit)
> 
> The cross-classified model looks like this:
> 
> lm4c <- lmer(final.points ~ -1 + gr10 + gr11 + gr12 + per1 + 
> per2 + per4
> + per5 + per6 + per7 + per8 + per9 + per10 + per11 + per12 +
>                   cblackd + casiand + clatinod + cmale + 
> cssoc + cscon + cold4gr  + cmlatent8 + computer +
>                   ...
>                   jourlsm +
>             ( 1 | sid) + (1 | tid), data=all.subj,  REML=F, verbose=T)
> 
> The variance components for the nested model are:
> Random effects:
>  Formula: ~1 | sid
>         (Intercept)  Residual
> StdDev:   0.8826577 0.9259174
> 
> for the cross-classified model:
> 
>  Groups   Name        Variance Std.Dev.
>  sid      (Intercept) 0.75426  0.86848 
>  tid      (Intercept) 0.39601  0.62929 
>  Residual             0.68535  0.82786 
> 
> If we square and sum the variance components for the nested 
> model, the total variance is about 1.64. For the 
> cross-classified model, the total variance is about 1.84. 
> Where did the additional variance come from?
> 
> Should I just interpret the size of the variance components 
> on a relative scale, are the units different, or what?
> 
> --
> Stuart Luppescu -*-*- slu <at> ccsr <dot> uchicago <dot> edu 
> CCSR in UEI at U of C
> 
> _______________________________________________
> R-sig-mixed-models at r-project.org mailing list 
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> 

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