[R-sig-ME] Choice of distribution for random effects

[J.D. Haltigan]

Thanks, I will inspect the BLUPS.

Re: choice of distribution, what I meant was, for example, if my fixed
effects family is estimated using a Poisson model, does that inform the
choice for random effects as well? (i.e., why would one invoke a gaussian
distribution for random effects if the response variable is, say,
categorical, or a count?)

On Wed, Jul 20, 2022 at 3:40 AM Andrew Robinson <apro using unimelb.edu.au> wrote:

> You can use a qq-plot of the BLUPS to guide that decision.
>
> The difference might also be due to something else, though.  Have you
> tried the call to hglm2 with a gaussian random effects family?  Does it
> give the same output as the glmer?
>
> For: does the choice of distribution for the fixed effects portion of the
> model inform the choice for the random effects?  I’m not sure what you mean
> - do you mean the exponential family for the response variable?
>
>
> Cheers,
>
> Andrew
>
> --
> Andrew Robinson
> Chief Executive Officer, CEBRA and Professor of Biosecurity,
> School/s of BioSciences and Mathematics & Statistics
> University of Melbourne, VIC 3010 Australia
> Tel: (+61) 0403 138 955
> Email: apro using unimelb.edu.au
> Website: https://researchers.ms.unimelb.edu.au/~apro@unimelb/
>
> I acknowledge the Traditional Owners of the land I inhabit, and pay my
> respects to their Elders.
> On 20 Jul 2022, 2:27 PM +1000, J.D. Haltigan <jhaltiga using gmail.com>, wrote:
>
> Hi:
>
> Is there clear best practice or guidance when it comes to choosing the
> distribution of random effects where multiple choices exist (e.g.,
> gaussian, gamma, etc.)? I ask in the context of extending some analyses
> from an RCT in which the outcome is symptomatic seropositivity (so a
> count). The random effects I am modeling are village cluster [union] (it's
> a cluster randomized trial). I get different results (significance-wise)
> depending on whether I choose a normal or gamma distribution for the random
> effects.
>
> The basic model (proportional outcome) is:
>
> lme4_5_B = glmer(posXsymp~ treatment+proper_mask_base+prop_resp_ill_base_2
> + pairID + (1 | union), family = "poisson", nAGQ=0, data = bdata.raw3)#lme4
> package using glmer
>
>
> HGLM2_5_A = hglm2(posXsymp~ treatment+proper_mask_base+prop_resp_ill_base_2
> + pairID + (1 | union), family =poisson (link = log), rand.family
> =Gamma(link=log),
> data = bdata.raw3)#HGLM package using hglm2
>
> Does, for example, the choice of distribution for the fixed effects portion
> of the model inform the choice for the random effects?
>
> Thank you for any insights.
>
> Best regards,
> J.D.
>
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