Date: Fri, 29 Aug 2014 13:31:03 +0200
From: Christian Brauner <christianvanbrauner at gmail.com>
To: r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] Random effects in clmm() of package ordinal
Hello,
fitting linear mixed models it is often suggested that testing for random
effects is not the best idea; mainly because the value of the random
effects parameters lie at the boundary of the parameter space. Hence, it
is preferred to not test for random effects and rather judge the inclusion
of a random effect by the design of the experiment. Or if one really wants
to do this use computation intensive methods like parametric bootstraps
etc. I have adapted the strategy of not testing for random effects with
linear mixed models.
Now I'm in a situation were I need to analyse ordinal data in a repeated
measures design. The package I decided would best suit this purpose is the
ordinal package (suggestions of alternatives are of course welcome). And
this got me wondering about random effects again. I was testing a random
effect (in fact by accidence as I did a faulty automated regexp
substitution) and it got a p of 0.99. More precisely I was testing for the
significance of a random slope in contrast to only including a random
intercept. As the boundary-of-parameter-space argument is about maximum
likelihood estimation in general it also applies to the proportional odds
cummulative mixed model. But, and here is were I'm unsure what to do in
this particular case the inclusion of a random slope in the clmm will turn
a p of 0.004 into 0.1 for my main effect. In contrast all other methods
(e.g. treating my response not as an ordered factor but as a continuous
variable and using a repeated measures anova) will give me a p of 0.004.
This is the only reason why I'm concerned about this. This difference
worries me and I'm unsure of what to do. Is it advisable to test here for
a random effect?
Best,
Christian