[R-sig-ME] Collinearity diagnostics for (mixed) multinomial models

Juho Kristian Ruohonen juho@kr|@t|@n@ruohonen @end|ng |rom gm@||@com
Sat Feb 26 21:45:04 CET 2022


Dear John W,

Thank you very much for the tip-off! Apologies for not responding earlier
(gmail apparently decided to direct your email right into the junk folder).
I am very pleased to note that the package you mention does indeed work
with *brms* multinomial models! Thanks again!

Best,

Juho

pe 25. helmik. 2022 klo 19.23 John Willoughby (johnwillec using gmail.com)
kirjoitti:

> Have you tried the check_collinearity() function in the performance
> package? It's supposed to work on brms models, but whether it will work on
> a multinomial model I don't know.  It works well on mixed models generated
> by glmmTMB().
>
> John Willoughby
>
>
> On Fri, Feb 25, 2022 at 3:01 AM <r-sig-mixed-models-request using r-project.org>
> wrote:
>
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> >    1. Collinearity diagnostics for (mixed) multinomial models
> >       (Juho Kristian Ruohonen)
> >
> > ----------------------------------------------------------------------
> >
> > Message: 1
> > Date: Fri, 25 Feb 2022 10:23:25 +0200
> > From: Juho Kristian Ruohonen <juho.kristian.ruohonen using gmail.com>
> > To: John Fox <jfox using mcmaster.ca>
> > Cc: "r-sig-mixed-models using r-project.org"
> >         <r-sig-mixed-models using r-project.org>
> > Subject: [R-sig-ME] Collinearity diagnostics for (mixed) multinomial
> >         models
> > Message-ID:
> >         <
> > CAG_dBVfZr1-P7Q3kbE8TGPm-_2sJixdGCHCtWM9Q9PEnd8ftZw using mail.gmail.com>
> > Content-Type: text/plain; charset="utf-8"
> >
> > Dear John (and anyone else qualified to comment),
> >
> > I fit lots of mixed-effects multinomial models in my research, and I
> would
> > like to see some (multi)collinearity diagnostics on the fixed effects, of
> > which there are over 30. My models are fit using the Bayesian *brms*
> > package because I know of no frequentist packages with multinomial GLMM
> > compatibility.
> >
> > With continuous or dichotomous outcomes, my go-to function for
> calculating
> > multicollinearity diagnostics is of course *vif()* from the *car*
> package.
> > As expected, however, this function does not report sensible diagnostics
> > for multinomial models -- not even for standard ones fit by the *nnet*
> > package's *multinom()* function. The reason, I presume, is because a
> > multinomial model is not really one but C-1 regression models  (where C
> is
> > the number of response categories) and the *vif()* function is not
> designed
> > to deal with this scenario.
> >
> > Therefore, in order to obtain meaningful collinearity metrics, my present
> > plan is to write a simple helper function that uses *vif() *to calculate
> > and present (generalized) variance inflation metrics for the C-1
> > sub-datasets to which the C-1 component binomial models of the overall
> > multinomial model are fit. In other words, it will partition the data
> into
> > those C-1 subsets, and then apply *vif()* to as many linear regressions
> > using a made-up continuous response and the fixed effects of interest.
> >
> > Does this seem like a sensible approach?
> >
> > Best,
> >
> > Juho
> >
> >
> >
>
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