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, ...)A fitted model object returned by one of the
rstap modeling functions. See stapreg-objects.
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.
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
If newsubjdata is provided, a data frame of the subject-time data must also be given for models with a temporal component
A vector of offsets. Only required if newsubjdata is
specified and an offset was specified when fitting the model.
Currently ignored.
A \(S\) by \(N\) matrix, where \(S\) is the size of the posterior sample and \(N\) is the number of data points.