parallel_psis_list: Parallel psis list computations
In loo: Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian Models

parallel_psis_list

R Documentation

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-normalizing 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 will be over-optimistic. If the posterior draws are independent then `r_eff=1` and can be omitted. The warning message thrown when `r_eff` is not specified can be disabled by setting `r_eff` to `NA`. See the `relative_eff()` helper functions for 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`. Currently this is only needed if you plan to use the `E_loo()` function to compute weighted expectations after running `loo`.
`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()`