As of version 2.0.0 this function is deprecated. Please use the psis() function for the new PSIS algorithm.

psislw(
lw,
wcp = 0.2,
wtrunc = 3/4,
cores = getOption("mc.cores", 1),
llfun = NULL,
llargs = NULL,
...
)

## Arguments

lw A matrix or vector of log weights. For computing LOO, lw = -log_lik, the negative of an $$S$$ (simulations) by $$N$$ (data points) pointwise log-likelihood matrix. The proportion of importance weights to use for the generalized Pareto fit. The 100*wcp\ from which to estimate the parameters of the generalized Pareto distribution. For truncating very large weights to $$S$$^wtrunc. Set to zero for no truncation. The number of cores to use for parallelization. This defaults to the option mc.cores which can be set for an entire R session by options(mc.cores = NUMBER), the old option loo.cores is now deprecated but will be given precedence over mc.cores until it is removed. As of version 2.0.0, the default is now 1 core if mc.cores is not set, but we recommend using as many (or close to as many) cores as possible. 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).

## References

Vehtari, A., Gelman, A., and Gabry, J. (2017a). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing. 27(5), 1413--1432. doi:10.1007/s11222-016-9696-4 (journal version, preprint arXiv:1507.04544).

Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2019). Pareto smoothed importance sampling. preprint arXiv:1507.02646