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).
elpd(x, ...) # S3 method for array elpd(x, ...) # S3 method for matrix elpd(x, ...)
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. |
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... | Currently ignored. |
The elpd()
function is an S3 generic and methods are provided for
3-D pointwise log-likelihood arrays and matrices.
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.
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.
The vignette Holdout validation and K-fold cross-validation of Stan
programs with the loo package for demonstrations of using the elpd()
methods.
#> #> Computed from 1000 by 32 log-likelihood matrix using the generic elpd function #> #> Estimate SE #> elpd -80.3 3.2 #> ic 160.5 6.5