parallel_psis_list: Parallel psis list computations

In jgabry/loo: Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian Models

Parallel psis list computations

Description

Parallel psis list computations

Usage

parallel_psis_list(
  N,
  .loo_i,
  .llfun,
  data,
  draws,
  r_eff,
  save_psis,
  cores,
  ...
)

parallel_importance_sampling_list(
  N,
  .loo_i,
  .llfun,
  data,
  draws,
  r_eff,
  save_psis,
  cores,
  method,
  ...
)

Arguments

N	The total number of observations (i.e. nrow(data)).
.loo_i	The function used to compute individual loo contributions.
.llfun	See llfun in loo.function().
data, draws, ...	For the loo.function() method and the loo_i() function, these are the data, posterior draws, and other arguments to pass to the log-likelihood function. See the Methods (by class) section below for details on how to specify these arguments.
r_eff	Vector of relative effective sample size estimates for the likelihood (exp(log_lik)) of each observation. This is related to the relative efficiency of estimating the normalizing term in self-normalized importance sampling when using posterior draws obtained with MCMC. If MCMC draws are used and r_eff is not provided then the reported PSIS effective sample sizes and Monte Carlo error estimates can be over-optimistic. If the posterior draws are (near) independent then r_eff=1 can be used. r_eff has to be a scalar (same value is used for all observations) or a vector with length equal to the number of observations. The default value is 1. See the relative_eff() helper functions for help computing r_eff.
save_psis	Should the psis object created internally by loo() be saved in the returned object? The loo() function calls psis() internally but by default discards the (potentially large) psis object after using it to compute the LOO-CV summaries. Setting save_psis=TRUE will add a psis_object component to the list returned by loo. This is useful if you plan to use the E_loo() function to compute weighted expectations after running loo. Several functions in the bayesplot package also accept psis objects.
cores	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 loo.cores is removed in a future release. 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. Note for Windows 10 users: it is strongly recommended to avoid using the .Rprofile file to set mc.cores (using the cores argument or setting mc.cores interactively or in a script is fine).
method	See is_method for loo()

Details

Refactored function to handle parallel computations for psis_list

jgabry/loo documentation built on Nov. 26, 2024, 5:29 p.m.