psislw: Pareto smoothed importance sampling (PSIS)

Description

Pareto smoothed importance sampling (PSIS)

Usage

psislw(lw, wcp = 0.2, wtrunc = 3/4, cores = getOption("loo.cores", parallel::detectCores()), llfun = NULL, llargs = NULL, ...)

Arguments

A matrix or vector of log weights. For computing LOO,

lw =
-log_lik

(see extract_log_lik) and is an $S$ by $N$ matrix where $S$ is the number of simulations and $N$ is the number of data points. (If lw is a vector it will be coerced to a one-column matrix.)

wcp

The proportion of importance weights to use for the generalized Pareto fit. The 100*wcp% largest weights are used as the sample from which to estimate the parameters of the generalized Pareto distribution.

wtrunc

For truncating very large weights to $S$^wtrunc. Set to zero for no truncation.

cores

The number of cores to use for parallelization. This can be set for an entire R session by options(loo.cores = NUMBER). The default is detectCores().

llfun, llargs

See loo.function.

...

Ignored when psislw is called directly. The ... is only used internally when psislw is called by the loo function.

Value

A named list with components lw_smooth (modified log weights) and pareto_k (estimated generalized Pareto shape parameter(s) $k$).

Details

See the 'PSIS-LOO' section in loo-package.

References

Vehtari, A., Gelman, A., and Gabry, J. (2016a). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing. Advance online publication. doi:10.1007/s11222-016-9696-4. arXiv preprint: http://arxiv.org/abs/1507.04544/