[R-sig-ME] Random effects in multinomial regression in R?

Souheyla GHEBGHOUB @ouhey|@@ghebghoub @end|ng |rom gm@||@com
Wed Mar 20 18:39:36 CET 2019

Hi Philip,

Thank you for the clarification. But I might have not make it clear in my

I don't have Time in my data at all because I chose to predict change
rather than having posttest and pretest responses as DV and Time as fixed

I chose this way because I have groups of subjects who were tested on
words, and I was not too sure whether, a simple regression with Responses
as DV and Time (Pretest/Posttest) as IV , will take into account
differences between Pretest and Posttest at the level of each word. That
is, I don't know whether it will sum the overall pretest score of each
subject then compare it to its posttest, while I want it to compare each
subject score of each word from pretest to posttest then base its analysis
on these score changes.

That's why I did not want to risk it and chose *score change* as the DV
instead. But I was faced with another problem which is absence of Time
effect by which subjects differ for my random slopes?


On Wed, 20 Mar 2019 at 17:02, Phillip Alday <phillip.alday using mpi.nl> wrote:

> Generally speaking for the parameterization of mixed-effects models in
> lme4/brms/the usual packages, it doesn't make sense to have a varying
> slope (e.g. Time|Subject) without the corresponding fixed effect. This
> is because the varying slopes are calculated as offsets from the group
> mean, i.e from the fixed effect estimate. Not doing including the fixed
> effect is equivalent to assuming the group mean is zero, which is
> usually not the assumption you want to make.
> If you fit models with random slopes without the corresponding fixed
> effects, then there are two main problems:
> 1. The corresponding variance parameter will be mis-estimated because it
> will be the average squared distance to zero and not the average squared
> distance to the mean (and average squared distance to the mean is the
> definition of variance).
> 2. The model may not converge because the numerics are set up under the
> "zero mean" assumption. For lme4/nlme, this is the case, but I believe
> that brms may do some internal reparameterization that may avoid these
> difficulties. (And a model fit with MCMC (brms) may not have the same
> numerical issues as a model fit with MLE (lme4)).
> In brief: just add time as a fixed effect.
> Also: why not fit your model as a continuous model with pre vs. post as
> a contrast in the model rather reducing a continuous variable to a
> category? You can still apply a categorical distinction afterwards if
> you so desire, but in my experience, it's best to defer making things
> categorical until as late as possible (see also Frank Harrel's comments
> on prediction vs. classification:
> http://www.fharrell.com/post/classification/). Moreover, it's a lot
> easier to fit a continuous model than a multinomial one ....
> Best,
> Phillip
> On 18/3/19 7:11 pm, Souheyla GHEBGHOUB wrote:
> > I have *Change* from Pretest to Posttest (gain, no_gain, decline) as the
> > DV. Also *Pretest* and *Group* as covariates. This called for a
> multinomial
> > regression:
> >
> > mod0 <- brm(Change ~ Pretest + Group)
> >
> > *Question: *I'd like to add random effects of *Subject* and *Word*, which
> > may differ by time, but I don't have effect of *Time* to do:
> >
> > mod1 <- brm(Change ~ Pretest + Group + (Time|Subject) + (Time|Word))
> >
> > So I thought of this:
> >
> > mod2 <- brm(Change ~ Pretest + Group + (1|Subject) + (1|Word))
> >
> > but this also seems wrong to me. What do you think is the best way to
> treat
> > random effects in this situation, please?
> >
> > Thank you
> >
> > Souheyla Ghebghoub
> >
> >       [[alternative HTML version deleted]]
> >
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> >

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