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, ...)

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.

Details

The elpd() function is an S3 generic and methods are provided for 3-D pointwise log-likelihood arrays and matrices.

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.

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.

Examples

# Calculate the lpd of the observed data
LLarr <- example_loglik_array()
elpd(LLarr)
#> 
#> 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