brms: loo_moment_match.brmsfit – R documentation

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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 class '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.

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_pars = save_pars(all = TRUE) in the call to brm, if you are planning to apply loo_moment_match to your models.

Value

An updated object of class loo.

References

Paananen, T., Piironen, J., Buerkner, P.-C., Vehtari, A. (2021). Implicitly Adaptive Importance Sampling. Statistics and Computing.

Examples

## Not run: 
fit1 <- brm(count ~ zAge + zBase * Trt + (1|patient),
            data = epilepsy, family = poisson(),
            save_pars = save_pars(all = TRUE))
            
# throws warning about some pareto k estimates being too high
(loo1 <- loo(fit1))
(mmloo1 <- loo_moment_match(fit1, loo = loo1))

## End(Not run)

brms

Bayesian Regression Models using 'Stan'

v2.15.0

GPL-2

Authors

Paul-Christian Bürkner [aut, cre], Jonah Gabry [ctb], Sebastian Weber [ctb], Andrew Johnson [ctb], Martin Modrak [ctb]

Initial release

2021-03-10