[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
Thu Jun 3 00:33:20 CEST 2021
Hi all,
I am struggling to interpret the residual plots from the Dharma package. If
we find a red line in residual plot,does it mean there is
heteroscedasticity in the model for the predictor variables? If the solid
line matches with the dashed line, can we say there is no
heteroscedasticity? I have attached three residual plots here to understand
heteroscedasticity of the model. In the first plot, quantile deviationare
detected by the red line, so there is heteroscedasticity in the model. This
is for the model which includes all covariates. Then I created the residual
plot for one by one covariate to know which predictors are responsible for
variable dispersion. The 2nd and 3rd plots are for just one predictor. In
the 2nd plot, three solid lines are red and there exhibits a clear
deviation from the dashed line. So, there is heteroscedasticity in the
model for that predictor. The 3rd plot is box plot.The distribution for
each factor level should be uniformly distributed, so the box should go
from 0.25 to 0.75, with the median line at 0.5 (within-group ). As the two
box plots are red and it shows deviation of median line from 0.5, so there
is heteroscedasticity in the model for the predictor. The 4th plot shows
less deviation. Can we say this is better? I need your expert suggestions
and also please refer me to any article where I find a clear explanation of
heteroscedasticity checking by residual plot using DHARMA.Many thanks.
Kindest regards,
Tahsin
On Tue, Jun 1, 2021 at 4:14 PM Tahsin Ferdous <tahsinferdousuofc using gmail.com>
wrote:
> Thanks John.
>
> On Tue, Jun 1, 2021 at 3:11 PM John Maindonald <john.maindonald using anu.edu.au>
> wrote:
>
>> 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
>> <john.maindonald using anu.edu.a>
>>
>>
>> On 2/06/2021, at 05:07, Tahsin Ferdous <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> 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
>>> <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.
>>>
>>> [[alternative HTML version deleted]]
>>>
>>> _______________________________________________
>>> R-sig-mixed-models using r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>
>>>
>>>
>>
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