[R-sig-ME] Collinearity diagnostics for (mixed) multinomial models
johnw|||ec @end|ng |rom gm@||@com
Fri Feb 25 18:22:29 CET 2022
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
On Fri, Feb 25, 2022 at 3:01 AM <r-sig-mixed-models-request using r-project.org>
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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
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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
> 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?
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