If we want to compute the marginal likelihood and information necessary for
generating posterior samples for new models not encountered in the model search
glmBayesMfp, this function can be used: Provide it with the
configurations to be interpreted in the context of the
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).
list of the model configurations
be verbose? (default: only for more than 100 configurations)
be even more verbose and echo debug-level information? (not by default)
GlmBayesMfp object with the new models. This can directly
be used as input for
Daniel Sabanes Bove firstname.lastname@example.org
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.