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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