The function computes the average the log-likelihood, log-posterior, the deviance
information criterion and estimated degrees of freedom from samples of, e.g., a
`bamlss`

object.

samplestats(samples, x = NULL, y = NULL,
family = NULL, logLik = FALSE, ...)

## Arguments

samples |
An object of class `"mcmc.list"` or `"bamlss"` which contains MCMC
samples. |

x |
The `x` list as returned by function `bamlss.frame` . |

y |
The model response, as returned by function `bamlss.frame` |

family |
A `bamlss.family` object. |

logLik |
Logical, should the log-likelihood be computed, may take some time! |

… |
Currently not used. |

## Details

If the log-likelihood is not available in the samples, the function tries to compute
the information. Depending on the complexity of the model, this may take some time.
Computations are based on the `$d()`

or `$loglik()`

function of the
`bamlss.family`

object.

If a `bamlss.family`

object contains a function `$p2d()`

or `$p2loglik()`

,
which computes the log-likelihood from parameters, these functions are used for computation.

## Value

A list with the following entries (if available):

logLikThe average log-likelihood.

logPostThe average log-posterior.

DICThe deviance information criterion.

pdThe estimated degrees of freedom.

## See also

## Examples

# NOT RUN {
## Generate some data.
d <- GAMart()
## Estimate model without sampling statistics
b <- bamlss(num ~ s(x1) + s(x2) + s(x3) + te(lon,lat),
data = d, samplestats = FALSE)
## Note: needs the $d() or $loglik() function in the family!
names(family(b))
## Compute sampling statistics.
samplestats(b)
# }