[R-sig-ME] How to obtain a posterior predictive distribution in MCMCglmm, and constrain it to non-negative values?

[Jarrod Hadfield]

HI,

You can use simulate(model) in exactly the same way as predict(model) to generate posterior predictive distributions with various random effects marginalised using the argument marginal. The only constraints that can be imposed are those that arise from the particular distribution fitted (e.g. if family=�poisson� the outcome is constrained to be non-negative integers) and so arbitrarily imposing a positive constraint is not possible.

Cheers,

Jarrod


From: R-sig-mixed-models <r-sig-mixed-models-bounces using r-project.org> on behalf of Fiona Scarff <fiona.scarff.4 using gmail.com>
Date: Wednesday, 20 September 2023 at 13:31
To: r-sig-mixed-models using r-project.org <r-sig-mixed-models using r-project.org>
Subject: [R-sig-ME] How to obtain a posterior predictive distribution in MCMCglmm, and constrain it to non-negative values?
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In MCMCglmm, how can I obtain a posterior predictive distribution? I
have two random effects; individuals from which observations have been
obtained, and a measurement error. I would like to marginalise only
over the individual random effect, and supply a single trivially small
measurement error for the prediction, so as to get the predicted
distribution of the true (rather than measured) response in any
unspecified individual.

Can I further specify this in such a way as to constrain the
prediction to non-negative values? Naively, I could impose a
distribution like log-normal or poisson when fitting the glmm. But the
model fit needs to be able to handle negative values in the response,
which arise purely due to measurement error.

Many thanks for your time and any suggestions!

Fiona Scarff
Murdoch University

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