The $loo() method computes approximate LOO-CV using the loo package. In order to use this method you must compute and save the pointwise log-likelihood in your Stan program. See loo::loo.array() and the loo package vignettes for details.

loo(variables = "log_lik", r_eff = TRUE, moment_match = FALSE, ...)



(character vector) The name(s) of the variable(s) in the Stan program containing the pointwise log-likelihood. The default is to look for "log_lik". This argument is passed to the $draws() method.


(multiple options) How to handle the r_eff argument for loo():

  • TRUE (the default) will automatically call loo::relative_eff.array() to compute the r_eff argument to pass to loo::loo.array().

  • FALSE or NULL will avoid computing r_eff (which can sometimes be slow) but will result in a warning from the loo package.

  • If r_eff is anything else, that object will be passed as the r_eff argument to loo::loo.array().


(logical) Whether to use a moment-matching correction for problematic observations. The default is FALSE. Using moment_match=TRUE will result in compiling the additional methods described in fit-method-init_model_methods. This allows CmdStanR to automatically supply the functions for the log_lik_i, unconstrain_pars, log_prob_upars, and log_lik_i_upars arguments to loo::loo_moment_match().


Other arguments (e.g., cores, save_psis, etc.) passed to loo::loo.array() or loo::loo_moment_match.default() (if moment_match = TRUE is set).


The object returned by loo::loo.array() or loo::loo_moment_match.default().

See also

The loo package website with documentation and vignettes.


# \dontrun{
# the "logistic" example model has "log_lik" in generated quantities
fit <- cmdstanr_example("logistic")
loo_result <- fit$loo(cores = 2)
#> Computed from 4000 by 100 log-likelihood matrix
#>          Estimate  SE
#> elpd_loo    -63.7 4.1
#> p_loo         3.9 0.5
#> looic       127.4 8.2
#> ------
#> Monte Carlo SE of elpd_loo is 0.0.
#> All Pareto k estimates are good (k < 0.5).
#> See help('pareto-k-diagnostic') for details.
# }