Hi Troy,
Sorry for the late follow-up, but I have been traveling the past few days.
Thanks for the explanation. However, I feel the point about incorrect/correct priors in terms of variable selection is more of a statistical question than a software question, so that will be up to you to figure out.
However, I can clarify what happens with the reversible jump in nimble (more in section 7.10 of the
User manual )
The configureRJ function modifies the MCMC configuration to:
(1) assign samplers that turn on and off variables in the model. This can happen in 2 possible ways depending on whether or not indicator variables are written explicitly in the model. The control argument allows specifying the mean and scale for the proposal distribution. This will be the proposal distribution that is used only "when trying to bring a dead node into the model". Notice that here one can potentially provide different proposal distributions for each coefficient; this is achieved by providing vectors for the mean and scale of the size of the vector of coefficients.
(2) modify the existing samplers for the regression coefficients to use ‘toggled’ versions. This is mainly to avoid sampling values for the coefficient parameters when they are out of the model (a.k.a. dead nodes); when the coefficients are in the model, it will use the same samplers that are assigned to the parameters before calling configureRJ.
Finally, if you think there may be something wrong with the algorithm while experimenting with this, please send over a reproducible example so we can look into that.
Hope this helps!
Sally