Generic (expected) log-predictive density
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 class 'array' elpd(x, ...) ## S3 method for class '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. |
... |
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.
# Calculate the lpd of the observed data LLarr <- example_loglik_array() elpd(LLarr)
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