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

[Tahsin Ferdous]

Thanks a lot John for your valuable suggestions.

Kindest regards,

Tahsin

On Wed, Jun 2, 2021 at 8:09 PM John Maindonald <john.maindonald using anu.edu.au>
wrote:

>
> Look first in the help pages (?DHARMa etc) and vignettes for
> the DHARMa package.  After that, I am not sure what to suggest.
> Others may have suggestions.
>
> You will be lucky to get a perfect fit.  At the end of the day, the
> question is whether such differences as are apparent matter,
> for the purpose for which you intend to use the model.  A useful
> tack is to simulate from the fitted model, fit to that model, and
> check what difference it makes for the purpose for which the
> model is used.  If there is little difference, the deviations from
> the model probably do not much matter.  Maybe, repeat several
> times.
>
> Maybe you need to include degree 2 term(s) in your dispformula.
> Try, maybe, a degree 2 normal spline (this may give less wiggle
> at the extremes, and more flexibility of shape in the midrange
> region) or a degree 2 or even 3 orthogonal polynomial [use poly()].
>
> John Maindonald             email: john.maindonald using anu.edu.au
> <john.maindonald using anu.edu.au>
>
>
> On 3/06/2021, at 10:33, Tahsin Ferdous <tahsinferdousuofc using gmail.com>
> wrote:
>
> 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
>>>>
>>>>
>>>>
>>> <Rplot1.png><Rplot 2.png><Rplot 3.png><Rplot 4.png>
>
>
>

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