psislw(lw, wcp = 0.2, wtrunc = 3/4, cores = getOption("loo.cores", parallel::detectCores()), llfun = NULL, llargs = NULL, ...)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.)100*wcp% largest weights are used as the sample
from which to estimate the parameters of the generalized Pareto
distribution.wtrunc. Set
to zero for no truncation.options(loo.cores = NUMBER). The default
is detectCores().loo.function.psislw is called directly. The ... is
only used internally when psislw is called by the loo
function.lw_smooth (modified log weights)
and pareto_k (estimated generalized Pareto shape parameter(s)
$k$).
loo-package.