[R] LMER

[Abderrahim Oulhaj]

your r2 model  corresponds to method = "Laplace".

r4=lmer(Y~X+(1|Subject),family=binomial(link="logit"),method="Laplace") is 
equivalent to r2.

Bests,

Abderrahim


----- Original Message ----- 
From: "Daniel Malter" <daniel at umd.edu>
To: <r-help at stat.math.ethz.ch>
Sent: Friday, February 15, 2008 12:50 AM
Subject: [R] LMER


> 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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