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

Description Usage Arguments Value Author(s)

View source: R/computeModels.R

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

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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 daniel.sabanesbove@ifspm.uzh.ch


glmBfp documentation built on June 7, 2017, 3:01 p.m.

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