Parallel psis list computations
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,
...
)
The total number of observations (i.e. nrow(data)).
The function used to compute individual loo contributions.
See llfun in loo.function().
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.
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.
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.
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).
See is_method for loo()
Refactored function to handle parallel computations for psis_list