Hi. I'm using lmer() from the lme4 package (version 0.8-3) and I can't get
anova() to group variables properly. I'm fitting the mixed model
Response ~ Weight + Experimenter + (1|SUBJECT.NAME) + (1|Date.StudyDay)
where Weight is numeric and Experimenter is a factor, ie,
str(data.df)
`data.frame': 4266 obs. of 5 variables:
$ SUBJECT.NAME : Factor w/ 2133 levels "A0480","A0..",..: 1 1 2 2 ...
$ Response : num -0.490 0.145 1.992 -0.391 0.917 ...
$ Date.StudyDay: Factor w/ 161 levels "16","22","24",..: 24 24 ...
$ Experimenter : Factor w/ 7 levels "amyt","carmena",..: 5 5 1 1 3 3 ...
$ Weight : num 25.3 25.3 16.7 16.7 28.2 28.2 27.1 27.1 ...
The model fits fine but when I call anova() to see the effect of the
Experimenter factor, I get a sequential anova table where factor
levels are fit one at a time, ie,
anova(lmer(Response ~ Weight + Experimenter
+ + (1 | SUBJECT.NAME) + (1 | Date.StudyDay),
+ data=data.df))
Analysis of Variance Table
Df Sum Sq Mean Sq Denom F value Pr(>F)
Weight 1 10.8 10.8 4242.0 14.4457 0.0001463 ***
Experimentercarmena 1 0.5 0.5 4242.0 0.6708 0.4128136
Experimentercnunez 1 4.8 4.8 4242.0 6.5042 0.0107969 *
Experimenterlsolberg 1 0.4 0.4 4242.0 0.5910 0.4420783
Experimenterpeterb 1 0.01430 0.01430 4242.0 0.0192 0.8898029
Experimenterpolinka 1 0.2 0.2 4242.0 0.2290 0.6322775
Experimenterstuart 1 0.9 0.9 4242.0 1.2505 0.2635116
... rather than what I want, which is a sequential anova table where the
levels are grouped, eg [using lme()],
anova(lme(Response ~ Weight + Experimenter, random = ~ 1 | SUBJECT.NAME,
+ data=data.df))
Analysis of Variance Table
Df Sum Sq Mean Sq Denom F value Pr(>F)
Weight 1 9.5 9.5 4242.0 12.7213 0.0003655 ***
Experimenter 6 8.5 1.4 4242.0 1.9067 0.0759496 .
Does anyone know how to make anova() group properly with lmer objects?
Many thanks,
William
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
Dr William Valdar ++44 (0)1865 287 717
Wellcome Trust Centre [EMAIL PROTECTED]
for Human Genetics, Oxford www.well.ox.ac.uk/~valdar
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