loo.hsstan {hsstan} | R Documentation |
Predictive information criteria for Bayesian models
Description
Compute an efficient approximate leave-one-out cross-validation using Pareto smoothed importance sampling (PSIS-LOO), or the widely applicable information criterion (WAIC), also known as the Watanabe-Akaike information criterion.
Usage
## S3 method for class 'hsstan'
loo(x, cores = getOption("mc.cores"), ...)
## S3 method for class 'hsstan'
waic(x, cores = getOption("mc.cores"), ...)
Arguments
x |
An object of class |
cores |
Number of cores used for parallelisation (the value of
|
... |
Currently ignored. |
Value
A loo
object.
Examples
# continued from ?hsstan
loo(hs.biom)
waic(hs.biom)
[Package hsstan version 0.8.2 Index]