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).
computeModels(
configurations,
object,
verbose = length(configurations) > 100L,
debug = FALSE
)
list of the model configurations
the GlmBayesMfp
object
be verbose? (default: only for more than 100 configurations)
be even more verbose and echo debug-level information? (not by default)
The GlmBayesMfp
object with the new models. This can directly
be used as input for sampleGlm
.