Based on the result list from glmBayesMfp
, for the first model
in the list MCMC samples are produced. In parallel to the sampling of
coefficients and FP curve points, optionally the marginal likelihood of the
model is estimated with MCMC samples. This provides a check of the
integrated Laplace approximation used in the model sampling. If TBF
methodology is used, then no MCMC is necessary, instead ordinary Monte Carlo
samples from an approximate posterior distribution are obtained.
sampleGlm(
object,
mcmc = McmcOptions(),
estimateMargLik = TRUE,
gridList = list(),
gridSize = 203L,
newdata = NULL,
fixedZ = NULL,
marginalZApprox = NULL,
verbose = TRUE,
debug = FALSE,
useOpenMP = TRUE,
correctedCenter = FALSE
)
the GlmBayesMfp
object, from which only the first model
will be processed (at least for now …)
MCMC options object with class '>McmcOptions
.
If TBF is used, each sample is accepted, and the number of samples is given
by sampleSize
(mcmc
).
shall the marginal likelihood be estimated in parallel? (default) Only has an effect if full Bayes and not TBF is used.
optional list of appropriately named grid vectors for FP
evaluation. Default is length (gridSize
- 2) grid per covariate
additional to the observed values (two are at the endpoints)
see above (default: 203)
new covariate data.frame with exactly the names (and
preferably ranges) as before (default: no new covariate data) Note that
there is no option for offsets for new data at the moment. Just add the
offsets to the predictions
slot of samples
in the return list
yourself.
either NULL
(default) or a (single) fixed z value to
be used, in order to sample from the conditional posterior given this z.
If object
was constructed by the empirical Bayes machinery,
this will default to the estimated z with maximum conditional marginal
likelihood. If object
was constructed with the option fixedg
,
then the fixed value will be used by default.
method for approximating the marginal density of the
log covariance factor z, see getMarginalZ
for the details
(default: same preference list as in getMarginalZ
)
If TBF are used in conjunction with incomplete inverse gamma hyperprior on
g = exp(z), then the posterior distribution of g is again of this form.
Therefore this option does not have any effect in that case, because the
samples are directly obtained from that posterior distribution.
should information on computation progress be given? (default)
print debugging information? (not default)
shall OpenMP be used to accelerate the computations? (default)
If TRUE predict new data based on the centering of the original data.
Returns a list with the following elements:
samples of all original coefficients in the model (nCoefs x nSamples)
proportion of accepted Metropolis-Hastings proposals
if estimateMargLik
is TRUE
, this list is
included: it contains the elements numeratorTerms
and
denominatorTerms
for the numerator and denominator samples of the
Chib Jeliazkov marginal likelihood estimate,
highDensityPointLogUnPosterior
is the log unnormalized posterior
density at the fixed parameter and the resulting estimate
and
standardError
.