Random effects in clmm() of package ordinal

Hi Malcolm,

Interesting. I just read the reference manual for the ordinal package (v.
July 2, 2014) and indeed at the beginning it states "random slopes (random
coefficient models) are not yet implemented" (p. 3). If this i indeed true
then I'm puzzled by some things:
(a) later on he explains the usage of the extractor function VarCorr() for
class "clmm" and states "[...] a model can contain a random intercept (one
column) or a random intercept and a random slope (two columns) for the
same grouping factor" (p. 52).
(b) at first glance this seems inconsistent with the output of clmm():

library(ordinal)
fm2 <- clmm(rating ~ temp + contact + (temp | judge),
            data = wine,
            Hess = TRUE)

Now look at

summary(fm2)

Random effects:
 Groups Name        Variance Std.Dev. Corr
 judge  (Intercept) 1.15608  1.0752
        tempwarm    0.02801  0.1674   0.649
Number of groups:  judge 9

and

ranef(fm2)

ranef(fm2)

$judge
  (Intercept)    tempwarm
1  1.61166859  0.20664909
2 -0.49269459 -0.06317359
3  0.93254466  0.11957142
4 -0.08571746 -0.01099074
5  0.13893821  0.01781474
6  0.44710565  0.05732815
7 -1.77974875 -0.22820043
8 -0.26589478 -0.03409318
9 -0.51044493 -0.06544955

that looks like a random intercept and random slope model.

What I accidently did in my models (illustrating here with his example) is
not to replace "temp" in the fixed part but in the ranom part:
fm2 <- clmm(rating ~ temp + contact + (temp | judge),
            data = wine,
            Hess = TRUE)
fm2a <- clmm(rating ~ temp + contact + (1 | judge),
             data = wine,
             Hess = TRUE)
anova(fm2a, fm2)

The anova output is fairly similar to mine; the p is extremely high as
well.

All the standard stuff works without a warning, e.g.:
fm2b <- clmm(rating ~ temp + contact + (1 | judge) + (temp + 0 | judge),
             data = wine,
             Hess = TRUE)

So what is happening here? Anybody got an idea?

Best,
Christian



On Fri, Aug 29, 2014 at 09:41:26AM -0700, Malcolm Fairbrother wrote:

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