[R] {spam?} Re: Error: cannot use PQL when using lmer
Martin Henry H. Stevens
HStevens at muohio.edu
Sun Jul 6 14:37:32 CEST 2008
Hi hpdutra,
I do not know what section of which Crawley book you are referring
to, but I assume that Crawley's point is to use a binomial error
distribution (logistic regression) rather than a normal model. It is
generally thought that LaPlace methods are more accurate than PQL
methods.
Hank
On Jul 6, 2008, at 2:55 AM, hpdutra wrote:
>
> In fact I am using Crawley example to fit my data.
> I am running a lmer analysis for binary longitudinal (repeated
> measures)
> data.
> Basically, I have 12 plots, divided in 3 blocks, each block contain
> 4 plots.
> Plots were manipulate for fruits (F) and vegetation (V) that were
> either
> intact(I) or removed(R). Thus, the treatments are
> FIVI
> FIVR
> FRVI
> FRVR
> Within each plot I had 16 track plates. Track plates were checked
> monthly
> for presence or absence of paw prints.
> I am trying to fit lmer model
> track~fruit*vegetation*time*block in which fruit vegetation time
> are fixed
> effects and time is repeated measures and block is a random effect
> here is my code
>> model<-lmer(track~veget*fruit*time*(time|plate)*(1|
>> block),family=binomial)
>> summary(model)
> Generalized linear mixed model fit by the Laplace approximation
> Formula: track ~ veget * fruit * time * (time | plate) * (1 | block)
> AIC BIC logLik deviance
> 933.9 994.5 -454.9 909.9
> Random effects:
> Groups Name Variance Std.Dev. Corr
> plate (Intercept) 0.226747 0.47618
> time 0.054497 0.23345 -1.000
> block (Intercept) 0.615283 0.78440
> Number of obs: 1152, groups: plate, 192; block, 3
>
> Fixed effects:
> Estimate Std.
> Error z value
> Pr(>|z|)
> (Intercept) -1.68645 0.58718
> -2.8721
> 0.00408 **
> vegetremoved -1.39291 0.57742 -2.4123
> 0.01585 *
> fruitremoved -0.54486 0.53765 -1.0134
> 0.31086
> time -0.02091 0.10118
> -0.2067
> 0.83626
> vegetremoved:fruitremoved 0.75130 0.86342 0.8701 0.38422
> vegetremoved:time 0.38229 0.14695 2.6014
> 0.00928 **
> fruitremoved:time 0.17012 0.14227 1.1958
> 0.23178
> vegetremoved:fruitremoved:time -0.47526 0.22134 -2.1473 0.03177 *
>
> According to Crawley PQL is better for fitting binary data like
> this. So
> should I just stick Laplace or try to get the old Lme4? Also, if
> there is an
> interaction of vegetation vs fruit vs time, how can I know which
> months
> fruit had a significant effect?
>
>
>
> =============================
>
> Ben Bolker wrote:
>>
>> <hpdutra <at> yahoo.com> writes:
>>
>>>> library(lme4)
>>>> model1<-lmer(y~trt+(week|ID),family=binomial,method="PQL")
>>> Error in match.arg(method, c("Laplace", "AGQ")) :
>>> 'arg' should be one of “Laplace”, “AGQ”
>>>
>>
>> What is your question?
>> Doug Bates warned a few weeks ago that the newer version
>> of lmer would no longer use PQL for GLMMs (he found that
>> it was unreliable, even as a starting method for Laplace fits).
>> I think you can still get the older version if you want
>> it, or you can use glmmPQL from the MASS package (glmmPQL
>> has some advantages anyway).
>> It might be better to forward further discussion to
>> r-sig-mixed.
>>
>> Ben Bolker
>>
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>>
>>
>
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> use-PQL-when-using-lmer-tp18298149p18299437.html
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>
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