[R] Error: cannot use PQL when using lmer

[hpdutra]

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
> 
> ______________________________________________
> 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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