computeModels: Compute model information for a given list of model...

In glmBfp: Bayesian Fractional Polynomials for GLMs

Description

If we want to compute the marginal likelihood and information necessary for generating posterior samples for new models not encountered in the model search done by glmBayesMfp, this function can be used: Provide it with the models configurations to be interpreted in the context of the object of class GlmBayesMfp. The result is again of the latter class, but contains only the new models (similarly as the whole model space would consist of these and an exhaustive search would have been conducted).

Usage

computeModels(
  configurations,
  object,
  verbose = length(configurations) > 100L,
  debug = FALSE
)

Arguments

configurations	list of the model configurations
object	the GlmBayesMfp object
verbose	be verbose? (default: only for more than 100 configurations)
debug	be even more verbose and echo debug-level information? (not by default)

Value

The GlmBayesMfp object with the new models. This can directly be used as input for sampleGlm.

Author(s)

Daniel Sabanes Bove [e-mail]

glmBfp documentation built on July 2, 2020, 2:30 a.m.