Compute weighted expectations using LOO
These functions are wrappers around the E_loo function
(loo package) that provide compatibility for rstanarm models.
## 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)object |
A fitted model object returned by one of the
rstanarm modeling functions. See |
type |
The type of expectation to compute. The options are
|
probs |
For computing quantiles, a vector of probabilities. |
... |
Currently unused. |
psis_object |
An object returned by |
transform |
Passed to |
prob |
For |
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.
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: http://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, doi:10.1214/17-BA1091. (online).
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, (journal version, arXiv preprint, code on GitHub)
## 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.