[R] LMER

[Daniel Malter]

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
>
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