R: Moment matching for efficient approximate leave-one-out...

loo_moment_match.brmsfit {brms} | R Documentation

## 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 draws 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)

[Package brms version 2.17.0 Index]