Dear Mixed-modellers

I've been using R for about a year now to run various generalised linear
mixed models using *lme4*. I've been using the *Anova* function in the
*car* package
to assess my factor effects and it has served me very well thus far.
However, I recently bought a new computer and as a result have moved from
using 32bit R (Ver. 2.13.0) to now using R on a 64bit system. I can't seem
to get the 64bit R to work (a problem for another day...) but I've been
running the 32bit version up to now (Ver. 2.15.1). In almost every way,
it's identical to the 32bit version I was using on my old PC but I noticed
something strange when I tried to use the *Anova* function on a model that
I ran. For some reason, it no longer accepts "Wald" as one of the test
options. When I try to use it, I get the following error message:

Error in match.arg(test.statistic) : 'arg' should be one of “chisq”, “F”

However, the *car* package
materials<http://cran.r-project.org/web/packages/car/car.pdf>state the
following with regard to the
*Anova* function (pg 7): "Wald chi-square tests are provided for fixed
effects in linear and generalized linear mixed-effects models. Wald
chi-square or F tests are provided in the default case." It's as if the
"Wald" option has gone missing all of a sudden! In the past, I've had no
problems with this; I've been able to state "Wald" as my test argument with
no ill effects at all. Weirdly enough, if I take the* *code and run it on
my old machine, there are no problems at all!

Am I missing something here? Why is it not accepting "Wald" as my test
argument? Could it be something to do with running 32bit R on a 64bit
system, and if so, how can I get around this problem? Alternatively, is it
a version thing? Any advice/help would be most appreciated! I'm currently
switching between writing code on my new laptop and then running it on the
old one, which is less than ideal. I've included my code below with a small
section of my data for your enjoyment/pondering.

library(lme4)
library(car)
data<-read.csv("Hetero data.csv",header=T)
attach(data)
names(data)
y<-cbind(Mas,Fem)
model1<-lmer(y~Sex*Treatment*Perfume +
Age+(1|Male)+(1|Female),family=binomial,REML=F)
Anova(model1,type=3,test="Wald")

Subj  Sex Age Treatment Perfume Mas       Fem       Male       Female
181 Female  29      Ctrl       M         0        1 Ponsonby    Mackay
182 Female  29      Ctrl       F         1        0 Tye  Egidio
183 Female  29      Ctrl       U         1        0 Ponsonby    Mackay
184 Female  29      Male       M         1        0 Tye  Egidio
185 Female  29      Male       F         1        0 Ponsonby    Mackay
186 Female  29      Male       U       0        1 Tye  Egidio
187 Female  29    Female       M         1        0 Ponsonby    Mackay
188 Female  29    Female       F         0        1 Tye  Egidio
189 Female  29    Female       U         0        1 Ponsonby    Mackay
190   Male  18      Ctrl       M         4        0      Tye  Egidio
191   Male  18      Ctrl       F         0        4      Ponsonby    Mackay
192   Male  18      Ctrl       U         1        3      Ponsonby    Mackay
193   Male  18      Male       M         1        3      Tye  Egidio
194   Male  18      Male       F         2        2      Ponsonby    Mackay
195   Male  18      Male       U         1        3      Tye  Egidio
196   Male  18    Female       M         2        2      Ponsonby    Mackay
197   Male  18    Female       F         1        3      Tye  Egidio
198   Male  18    Female       U         0        4      Tye  Egidio


Luke Duncan

*Lecturer & Post-doctoral** Fellow*
*School of Animal, Plant and Environmental Sciences*
*University of the Witwatersrand*
*Johannesburg, South Africa*
**
*+27 72 312 0330*
*+27 11 717 6452*

	[[alternative HTML version deleted]]

