psis_approximate_posterior: Diagnostics for Laplace and ADVI approximations and...
In loo: Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian Models

View source: R/psis_approximate_posterior.R

psis_approximate_posterior

R Documentation

Diagnostics for Laplace and ADVI approximations and Laplace-loo and ADVI-loo

Description

Diagnostics for Laplace and ADVI approximations and Laplace-loo and ADVI-loo

Usage

psis_approximate_posterior(
  log_p = NULL,
  log_g = NULL,
  log_liks = NULL,
  cores,
  save_psis,
  ...,
  log_q = NULL
)

Arguments

`log_p`	The log-posterior (target) evaluated at S samples from the proposal distribution (g). A vector of length S.
`log_g`	The log-density (proposal) evaluated at S samples from the proposal distribution (g). A vector of length S.
`log_liks`	A log-likelihood matrix of size S * N, where N is the number of observations and S is the number of samples from q. See `loo.matrix()` for details. Default is `NULL`. Then only the posterior is evaluated using the k_hat diagnostic.
`cores`	The number of cores to use for parallelization. This defaults to the option `mc.cores` which can be set for an entire R session by `options(mc.cores = NUMBER)`. The old option `loo.cores` is now deprecated but will be given precedence over `mc.cores` until `loo.cores` is removed in a future release. As of version 2.0.0 the default is now 1 core if `mc.cores` is not set, but we recommend using as many (or close to as many) cores as possible. Note for Windows 10 users: it is strongly recommended to avoid using the `.Rprofile` file to set `mc.cores` (using the `cores` argument or setting `mc.cores` interactively or in a script is fine).
`save_psis`	Should the `"psis"` object created internally by `loo()` be saved in the returned object? The `loo()` function calls `psis()` internally but by default discards the (potentially large) `"psis"` object after using it to compute the LOO-CV summaries. Setting `save_psis=TRUE` will add a `psis_object` component to the list returned by `loo`. Currently this is only needed if you plan to use the `E_loo()` function to compute weighted expectations after running `loo`.
`log_q`	Deprecated argument name (the same as log_g).

Value

If log likelihoods are supplied, the function returns a "loo" object, otherwise the function returns a "psis" object.

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 (journal version, preprint arXiv:1507.04544).

Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2019). Pareto smoothed importance sampling. preprint arXiv:1507.02646