Re: [R] LMER

From: Daniel Malter <daniel_at_umd.edu>
Date: Fri, 15 Feb 2008 09:59:31 -0500


Thanks for your replies. My real problem is that, for my real data, I get basically the same results from r2 and r3 (so to speak), but the coefficient estimates and significance levels for r1 are very different from those of r2 and r3. And therefore, I do not know which of the results to trust and which not (if any).

The session info follows:

R version 2.6.0 (2007-10-03)
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] nlme_3.1-86 mgcv_1.3-29 lme4_0.99875-9 Matrix_0.999375-3 lattice_0.16-5

loaded via a namespace (and not attached): [1] grid_2.6.0

Cheers,
Daniel



cuncta stricte discussurus

-----Ursprüngliche Nachricht-----
Von: dmbates_at_gmail.com [mailto:dmbates_at_gmail.com] Im Auftrag von Douglas Bates
Gesendet: Friday, February 15, 2008 7:29 AM An: Daniel Malter
Cc: r-help_at_stat.math.ethz.ch
Betreff: Re: [R] LMER

Could you send us the output of sessionInfo() please so we can see which version of the lme4 package you are using? In recent versions, especially the development version available as

install.packages("lme4", repos = "http://r-forge.r-project.org")

the PQL algorithm is no longer used. The Laplace approximation is now the default. The adaptive Gauss-Hermite quadrature (AGQ) approximation may be offered in the future.

If the documentation indicates that PQL is the default then that is a documentation error. With the currently available implementation of the direct optimization of the Laplace approximation to the log-likelihood for the model there is no purpose in offering PQL.

On Thu, Feb 14, 2008 at 6:50 PM, Daniel Malter <daniel_at_umd.edu> wrote:
> Hi,
>
> I run the following models:
>
> 1a. lmer(Y~X+(1|Subject),family=binomial(link="logit")) and 1b.
> lmer(Y~X+(1|Subject),family=binomial(link="logit"),method="PQL")
>
> Why does 1b produce results different from 1a? The reason why I am
> asking is that the help states that "PQL" is the default of GLMMs
>
> and
>
> 2. gamm(Y~X,family=binomial(link="logit"),random=list(Subject=~1))
>
> The interesting thing about the example below is, that gamm is also
> supposed to fit by "PQL". Interestingly, however, the GAMM fit yields
> about the coefficient estimates of 1b. But the significance values of
> 1a. Any insight would be greatly appreciated.
>
>
> library(lme4)
> library(mgcv)
>
> Y=c(0,1,1,1,1,0,0,0,0,0,1,1,1,1,0,0,0,1,1,1,1)
> X=c(1,2,3,4,3,1,0,0,2,3,3,2,4,3,2,1,1,3,4,2,3)
> Subject=as.factor(c(1,2,3,4,5,6,7,1,2,3,4,5,6,7,1,2,3,4,5,6,7))
> cbind(Y,X,Subject)
>
> r1=lmer(Y~X+(1|Subject),family=binomial(link="logit"))
> summary(r1)
>
> r2=lmer(Y~X+(1|Subject),family=binomial(link="logit"),method="PQL")
> summary(r2)
>
> r3=gamm(Y~X,family=binomial(link="logit"),random=list(Subject=~1))
> summary(r3$gam)
>
>
>
> -------------------------
> cuncta stricte discussurus
>
> ______________________________________________
> R-help_at_r-project.org mailing list
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> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>



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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. Received on Fri 15 Feb 2008 - 15:33:07 GMT

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