[R-sig-ME] Random effects in clmm() of package ordinal

[Malcolm Fairbrother]

Hi Christian,

The ordinal package (which is otherwise very handy) does not allow for
random slopes (only random intercepts), so I don't think you can have
tested what you think you tested using that package.

You could try the MCMCglmm package instead, which allows for ordinal models
*and* random slopes.

Regarding random slopes more generally, Barr et al.'s (2013) paper "Random
effects structure for confirmatory hypothesis testing: Keep it maximal"
shows pretty definitively that not allowing for random slopes can often be
anticonservative. So if there's a gap between the p values you get with and
without random slopes, I'd be more inclined to trust the value *with*
random slopes.

Hope that's useful.

Cheers,
Malcolm




> 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
>

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