psislw: Pareto smoothed importance sampling (deprecated, old version)

Description Usage Arguments Value References See Also

View source: R/psislw.R


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 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% largest weights are used as the sample 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.

llfun, llargs

See loo.function.


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


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


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. (published version, arXiv preprint).

Vehtari, A., Gelman, A., and Gabry, J. (2017b). Pareto smoothed importance sampling. arXiv preprint:

See Also

pareto-k-diagnostic for PSIS diagnostics.

loo documentation built on April 11, 2018, 5:04 p.m.