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

[Andrew Robinson]

You should really also verify that the hglm is doing what you expect by fitting with Gaussian random effects.

The random effects in glmer are Gaussian. The effects enter the model via the linear predictor, which is then translated to the mean function of the chosen distribution of the response variable via the link function (NB this is a conceptual explanation, not an algorithmic one).

Cheers,

Andrew
On 20 Jul 2022, 6:40 PM +1000, J.D. Haltigan <jhaltiga using gmail.com>, wrote:
External email: Please exercise caution

________________________________
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<mailto: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<mailto: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<mailto: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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