[R-sig-ME] Variable selection for varying dispersion beta glmm using glmmTMB package
John Maindonald
john@m@|ndon@|d @end|ng |rom @nu@edu@@u
Tue Jun 1 23:11:15 CEST 2021
No, I was not suggesting that. I’d stick with the checks done
using simulateResiduals() and plotResiduals() from DHARMa.
The parameter `form` allows you to specify an explanatory
variable against whose values you can plot the simulated
residuals.
John Maindonald email: john.maindonald using anu.edu.a<mailto:john.maindonald using anu.edu.a>
On 2/06/2021, at 05:07, Tahsin Ferdous <tahsinferdousuofc using gmail.com<mailto:tahsinferdousuofc using gmail.com>> wrote:
Hi John,
Thanks for your clarification. Are you suggesting doing the Breusch-Pagan Test without the random effects for glmm?
Best,
Tahsin
On Fri, May 28, 2021 at 4:13 PM John Maindonald <john.maindonald using anu.edu.au<mailto:john.maindonald using anu.edu.au>> wrote:
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<mailto:john.maindonald using anu.edu.au>
On 27/05/2021, at 13:01, Tahsin Ferdous <tahsinferdousuofc using gmail.com<mailto: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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