Collinearity diagnostics for (mixed) multinomial models

2 messages · John Willoughby, Juho Kristian Ruohonen

Original

John Willoughby

Fri, Feb 25, 2022 9:22 AM #

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 at r-project.org>
wrote:

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Today's Topics:

   1. Collinearity diagnostics for (mixed) multinomial models
      (Juho Kristian Ruohonen)

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Message: 1
Date: Fri, 25 Feb 2022 10:23:25 +0200
From: Juho Kristian Ruohonen <juho.kristian.ruohonen at gmail.com>
To: John Fox <jfox at mcmaster.ca>
Cc: "r-sig-mixed-models at r-project.org"
        <r-sig-mixed-models at r-project.org>
Subject: [R-sig-ME] Collinearity diagnostics for (mixed) multinomial
        models
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CAG_dBVfZr1-P7Q3kbE8TGPm-_2sJixdGCHCtWM9Q9PEnd8ftZw at mail.gmail.com>
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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

1 day later

Juho Kristian Ruohonen

Sat, Feb 26, 2022 12:45 PM #

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 at gmail.com)
kirjoitti:

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Today's Topics:

   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 at gmail.com>
To: John Fox <jfox at mcmaster.ca>
Cc: "r-sig-mixed-models at r-project.org"
        <r-sig-mixed-models at r-project.org>
Subject: [R-sig-ME] Collinearity diagnostics for (mixed) multinomial
        models
Message-ID:
        <
CAG_dBVfZr1-P7Q3kbE8TGPm-_2sJixdGCHCtWM9Q9PEnd8ftZw at 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

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