Efficient approximate leave-one-out cross-validation (LOO)

A loo::loo() method that is customized for measrfit objects. This is a simple wrapper around loo::loo.array(). See the loo package vignettes for details.

Usage

# S3 method for measrfit
loo(x, ..., r_eff = NA, force = FALSE)

Arguments

x

A measrfit object.

...

Additional arguments passed to loo::loo.array().

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.

force

If the LOO criterion has already been added to the model object with add_criterion(), should it be recalculated. Default is FALSE.

Value

The object returned by loo::loo.array().