loo-waic: Relative fit for Bayesian models

In measr: Bayesian Psychometric Measurement Using 'Stan'

Relative fit for Bayesian models

Description

For models estimated with MCMC, relative model fit comparisons can be made using the LOO-CV or WAIC indicates (Vehtari et al., 2017). These functions are wrappers for the loo package. See the loo package vignettes for details on the implementation.

Usage

## S3 method for class ''measr::measrdcm''
loo(x, ..., r_eff = NA, force = FALSE)

## S3 method for class ''measr::measrdcm''
waic(x, ..., force = FALSE)

## S3 method for class ''measr::measrdcm''
loo_compare(x, ..., criterion = c("loo", "waic"), model_names = NULL)

Arguments

x	A measrdcm object.
...	For loo() and waic(), additional arguments passed to loo::loo.array() or loo::waic.array(), respectively. For loo_compare(), additional measrdcm objects to be compared to x.
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.
force	If the LOO criterion has already been added to the model object with add_criterion(), should it be recalculated. Default is FALSE.
criterion	The name of the criterion to be extracted from the x for comparison.
model_names	Names given to each provided model in the comparison output. If NULL (the default), the names will be parsed from the names of the objects passed for comparison.

Value

For loo() and waic(), the information criteria returned by loo::loo.array() or loo::waic.array(), respectively.

For loo_compare(), the criterion comparison returned by loo::loo_compare().

References

Vehtari, A., Gelman, A., & Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing, 27(5), 1413-1432. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s11222-016-9696-4")}

measr documentation built on Jan. 14, 2026, 5:08 p.m.