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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