brms (version 2.14.0)

loo_moment_match.brmsfit: Moment matching for efficient approximate leave-one-out cross-validation

Description

Moment matching for efficient approximate leave-one-out cross-validation (LOO-CV). See loo_moment_match for more details.

Usage

# S3 method for brmsfit
loo_moment_match(
  x,
  loo,
  k_threshold = 0.7,
  newdata = NULL,
  resp = NULL,
  check = TRUE,
  ...
)

Arguments

x

An object of class brmsfit.

loo

An object of class loo originally created from x.

k_threshold

The threshold at which Pareto \(k\) estimates are treated as problematic. Defaults to 0.7. See pareto_k_ids for more details.

newdata

An optional data.frame for which to evaluate predictions. If NULL (default), the original data of the model is used. NA values within factors are interpreted as if all dummy variables of this factor are zero. This allows, for instance, to make predictions of the grand mean when using sum coding.

resp

Optional names of response variables. If specified, predictions are performed only for the specified response variables.

check

Logical; If TRUE (the default), some checks check are performed if the loo object was generated from the brmsfit object passed to argument fit.

...

Further arguments passed to the underlying methods. Additional arguments initially passed to loo, for example, newdata or resp need to be passed again to loo_moment_match in order for the latter to work correctly.

Value

An updated object of class loo.

Details

The moment matching algorithm requires samples of all variables defined in Stan's parameters block to be saved. Otherwise loo_moment_match cannot be computed. Thus, please set save_all_pars = TRUE in the call to brm, if you are planning to apply loo_moment_match to your models.

References

Paananen, T., Piironen, J., Buerkner, P.-C., Vehtari, A. (2020). Implicitly Adaptive Importance Sampling. preprint arXiv:1906.08850

Examples

# NOT RUN {
fit1 <- brm(count ~ zAge + zBase * Trt + (1|patient),
            data = epilepsy, family = poisson(),
            save_all_pars = TRUE)
            
# throws warning about some pareto k estimates being too high
(loo1 <- loo(fit1))
(mmloo1 <- loo_moment_match(fit1, loo = loo1))
# }
# NOT RUN {
# }