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
)
```

object

the `GlmBayesMfp`

object, from which only the first model
will be processed (at least for now …)

mcmc

MCMC options object with class `'>McmcOptions`

.
If TBF is used, each sample is accepted, and the number of samples is given
by `sampleSize`

(`mcmc`

).

estimateMargLik

shall the marginal likelihood be estimated in parallel? (default) Only has an effect if full Bayes and not TBF is used.

gridList

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)

gridSize

see above (default: 203)

newdata

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.

fixedZ

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.

marginalZApprox

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.

verbose

should information on computation progress be given? (default)

debug

print debugging information? (not default)

useOpenMP

shall OpenMP be used to accelerate the computations? (default)

correctedCenter

If TRUE predict new data based on the centering of the original data.

Returns a list with the following elements:

- samples
- coefficients
samples of all original coefficients in the model (nCoefs x nSamples)

- acceptanceRatio
proportion of accepted Metropolis-Hastings proposals

- logMargLik
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`

.