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,
...
)A named list with components lw_smooth (modified log weights) and
pareto_k (estimated generalized Pareto shape parameter(s) k).
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.
Vehtari, A., Gelman, A., and Gabry, J. (2017). 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. (2024). Pareto smoothed importance sampling. Journal of Machine Learning Research, 25(72):1-58. PDF
pareto-k-diagnostic for PSIS diagnostics.