[R-sig-ME] Variable selection for varying dispersion beta glmm using glmmTMB package

Tahsin Ferdous t@h@|n|erdou@uo|c @end|ng |rom gm@||@com
Tue Jun 1 19:07:10 CEST 2021

Hi John,

Thanks for your clarification. Are you suggesting doing the Breusch-Pagan
Test without the random effects for glmm?



On Fri, May 28, 2021 at 4:13 PM John Maindonald <john.maindonald using anu.edu.au>

> The Breusch-Pagan Test, as implemented in lmtest, is designed for
> lm models with independent normal errors.   You have a random
> effects term — surely that invalidates use of this test.  Additionally,
> I doubt that a normal distribution is a good enough approximation
> to beta that, even without the random effects term, results from
> lmtest() are valid.
> John Maindonald             email: john.maindonald using anu.edu.au
> <john.maindonald using anu.edu.au>
> On 27/05/2021, at 13:01, Tahsin Ferdous <tahsinferdousuofc using gmail.com>
> wrote:
> I am struggling with the varying dispersion beta regression using glmmTMB.
> I did the Breusch-Pagan Test for checking heteroscedasticity for my model.
> As, the p-value is smaller than 0.05, so heterodasticity is present. So, I
> have to use beta glmm for varying dispersion. Further, I need to know which
> variable I should include for a varying dispersion model. To know this, I
> followed a procedure. For example, my response variable is y, independent
> variable is x1,x2 and x3 and there is random effect for study id. At first,
> I ran beta glmm for varying dispersion only for y and x1. Then, I did the
> Breusch-Pagan Test for checking heteroscedasticity. If the p value is
> smaller than 0.05, there is heteroscadsticity. In this case, I added x1
> variable in my dispersion model. Similarly, I run beta glmm for y and x2,
> and then perform the Breusch-Pagan test. If the result shows
> homoscedasticity, then I didn't include x2 covariate for the dispersion
> model. Again, I did the same thing for y and x3. If the result implies
> heteroscedasticity, then I added x3 covariate for my dispersion model.
> Finally, this will be like :
> m1.f <- glmmTMB(y~ x1+x2+x3+(1|study_id), data=mydata, ziformula=
> ~1,dispformula = ~x1+x3, family=beta_family() )
> summary(m1.f)
> Is my procedure correct?
> Should we comment on only conditional mean model?
> Thanks.
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