loo_predict.stanreg: Compute weighted expectations using LOO

In rstanarm: Bayesian Applied Regression Modeling via Stan

Compute weighted expectations using LOO

Description

These functions are wrappers around the E_loo function (loo package) that provide compatibility for rstanarm models.

Usage

## S3 method for class 'stanreg'
loo_predict(
  object,
  type = c("mean", "var", "quantile"),
  probs = 0.5,
  ...,
  psis_object = NULL
)

## S3 method for class 'stanreg'
loo_linpred(
  object,
  type = c("mean", "var", "quantile"),
  probs = 0.5,
  transform = FALSE,
  ...,
  psis_object = NULL
)

## S3 method for class 'stanreg'
loo_predictive_interval(object, prob = 0.9, ..., psis_object = NULL)

Arguments

object	A fitted model object returned by one of the rstanarm modeling functions. See stanreg-objects.
type	The type of expectation to compute. The options are "mean", "variance", and "quantile".
probs	For computing quantiles, a vector of probabilities.
...	Currently unused.
psis_object	An object returned by psis. If missing then psis will be run internally, which may be time consuming for models fit to very large datasets.
transform	Passed to posterior_linpred.
prob	For loo_predictive_interval, a scalar in (0,1) indicating the desired probability mass to include in the intervals. The default is prob=0.9 (90% intervals).

Value

A list with elements value and pareto_k.

For loo_predict and loo_linpred the value component is a vector with one element per observation.

For loo_predictive_interval the value component is a matrix with one row per observation and two columns (like predictive_interval). loo_predictive_interval(..., prob = p) is equivalent to loo_predict(..., type = "quantile", probs = c(a, 1-a)) with a = (1 - p)/2, except it transposes the result and adds informative column names.

See E_loo and pareto-k-diagnostic for details on the pareto_k diagnostic.

References

Vehtari, A., Gelman, A., and Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing. 27(5), 1413–1432. doi:10.1007/s11222-016-9696-4. arXiv preprint: https://arxiv.org/abs/1507.04544

Yao, Y., Vehtari, A., Simpson, D., and Gelman, A. (2018) Using stacking to average Bayesian predictive distributions. Bayesian Analysis, advance publication, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/17-BA1091")}.

Gabry, J. , Simpson, D. , Vehtari, A. , Betancourt, M. and Gelman, A. (2019), Visualization in Bayesian workflow. J. R. Stat. Soc. A, 182: 389-402. doi:10.1111/rssa.12378, arXiv preprint, code on GitHub)

Examples

if (.Platform$OS.type != "windows" || .Platform$r_arch != "i386") {
## Not run: 
if (!exists("example_model")) example(example_model)

# optionally, log-weights can be pre-computed and reused
psis_result <- loo::psis(log_ratios = -log_lik(example_model))

loo_probs <- loo_linpred(example_model, type = "mean", transform = TRUE, psis_object = psis_result)
str(loo_probs)

loo_pred_var <- loo_predict(example_model, type = "var", psis_object = psis_result)
str(loo_pred_var)

loo_pred_ints <- loo_predictive_interval(example_model, prob = 0.8, psis_object = psis_result)
str(loo_pred_ints)

## End(Not run)
}

rstanarm documentation built on Sept. 14, 2023, 1:07 a.m.