The elpd() methods for arrays and matrices can compute the expected log
pointwise predictive density for a new dataset or the log pointwise
predictive density of the observed data (an overestimate of the elpd).
Usage
elpd(x, ...)
# S3 method for array
elpd(x, ...)
# S3 method for matrix
elpd(x, ...)
Arguments
x
A log-likelihood array or matrix. The Methods (by class)
section, below, has detailed descriptions of how to specify the inputs for
each method.
...
Currently ignored.
Methods (by class)
elpd(array): An \(I\) by \(C\) by \(N\) array, where \(I\)
is the number of MCMC iterations per chain, \(C\) is the number of
chains, and \(N\) is the number of data points.
elpd(matrix): An \(S\) by \(N\) matrix, where \(S\) is the size
of the posterior sample (with all chains merged) and \(N\) is the number
of data points.
Details
The elpd() function is an S3 generic and methods are provided for
3-D pointwise log-likelihood arrays and matrices.
See Also
The vignette Holdout validation and K-fold cross-validation of Stan
programs with the loo package for demonstrations of using the elpd()
methods.