[R] {spam?} Re: Error: cannot use PQL when using lmer

[Martin Henry H. Stevens]

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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