[R-sig-ME] glmmTMB dispformula models

John H Maindonald jhm@|ndon@|d @end|ng |rom gm@||@com
Mon Jun 20 22:58:04 CEST 2022

You are, as I understand the matter, modifying the variance as specified by
the Gamma family.  See 
  vignette('timeMortality', package='qra’)
where it is the betabinomial variance that is modified. 
John Maindonald.

> On 20/06/2022, at 21:54, Djenifer Kappel <KappelD using cardiff.ac.uk> wrote:
> Hi all,
> I am using glmmTMB to model the effects of genetic factors on longitudinal data of blood tests.
> We are interested in the effect of those factors on the mean blood test outcome (using a gamma distribution), but also if they affect the variance of the outcome (dispersion).
> This is what I am testing at the moment:
>  model <- glmmTMB(outcome ~ gene_sets + covar1+ covar2+covar3+ (1|Individual),
>                    dispformula = ~gene_sets,
>                    data = mydata,
>                    family = Gamma(link=log),
>                    na.action = na.exclude,
>                    control = glmmTMBControl(profile = TRUE))
> After reading the glmmTMB vignette it is not clear to me what variance is being modelled when we specify a dispformula.
> Is this the residual variance of the model on the full dataset or the intra-individual variability (on the repeated measures).
> I supposed it is the latter given that we cannot specify a random effect on dispformula, but I would appreciate if anyone could help me understand this.
> Many thanks!
> Djenifer
> --
> Djenifer Kappel, Ph.D
> Research Associate
> MRC Centre for Neuropsychiatric Genetics and Genomics
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> Cardiff University School of Medicine
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> Djenifer Kappel, Ph.D
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