Behaviour of weights in GLMMadaptive about glmmadaptive HOT 2 CLOSED

Hi Florian,

a) The likelihood in a multilevel model is defined at the higher-level units. E.g., say we have pupils in schools, then the likelihood contribution is at the school level, not the pupil level. Hence, to my view, this works as documented. See also here: https://drizopoulos.github.io/GLMMadaptive/articles/GLMMadaptive.html#generalized-linear-mixed-models-theory

b) Yes, they are ignored. According to (a), the idea is in the estimation of the model to give different weights to different observations. But when we want to simulate from the model, I don't see why or how the weights should be used. The idea is to simulate new observations from the model using the correctly estimated parameters.

I close this for now as I believe it works as documented.

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Hi Dimitris,

many thanks for the explanations.

About the warning: the reason is that a model fitted with weights will estimate parameters will not necessarily produce data that resembles the observed data (as we don't optimize any more the likelihood of the assumed data-generating process). As this may create problems for people that use the simulations for inferential calculations (e.g. DHARMa, but also if I would do a simulated LRT), I would prefer a warning.

In any case, I have implemented a warning in DHARMa already https://github.com/florianhartig/DHARMa/blob/90ceb371ee0f55748f606435111343e7ad9132f7/DHARMa/R/compatibility.R#L458

Cheers,
Florian

from glmmadaptive.