For models fit using MCMC, the `log_lik`

method returns the
\(S\) by \(N\) pointwise log-likelihood matrix, where \(S\) is the size
of the posterior sample and \(N\) is the number of data points.

# S3 method for stapreg
log_lik(object, newsubjdata = NULL,
newdistdata = NULL, newtimedata = NULL, offset = NULL, ...)

## Arguments

object |
A fitted model object returned by one of the
rstap modeling functions. See `stapreg-objects` . |

newsubjdata |
Optionally, a data frame of the subject-specific data
in which to look for variables with which to predict.
If omitted, the original datasets are used. If `newsubjdata`
is provided and any variables were transformed (e.g. rescaled) in the data
used to fit the model, then these variables must also be transformed in
`newsubjdata` . Also see the Note
section below for a note about using the `newsubjdata` argument with with
binomial models. |

newdistdata |
If newsubjdata is provided a data frame of the subject-distance
must also be given for models with a spatial component - can be the same as original distance_dataframe |

newtimedata |
If newsubjdata is provided, a data frame of the subject-time data
must also be given for models with a temporal component |

offset |
A vector of offsets. Only required if `newsubjdata` is
specified and an `offset` was specified when fitting the model. |

... |
Currently ignored. |

## Value

A \(S\) by \(N\) matrix, where \(S\) is the size of the posterior
sample and \(N\) is the number of data points.