psis.brmsfit | R Documentation |
Implementation of Pareto smoothed importance sampling (PSIS), a method for stabilizing importance ratios. The version of PSIS implemented here corresponds to the algorithm presented in Vehtari, Simpson, Gelman, Yao, and Gabry (2024). For PSIS diagnostics see the pareto-k-diagnostic page.
## S3 method for class 'brmsfit'
psis(log_ratios, newdata = NULL, resp = NULL, model_name = NULL, ...)
log_ratios |
A fitted model object of class |
newdata |
An optional data.frame for which to evaluate predictions. If
|
resp |
Optional names of response variables. If specified, predictions are performed only for the specified response variables. |
model_name |
Currently ignored. |
... |
Further arguments passed to |
The psis()
methods return an object of class "psis"
,
which is a named list with the following components:
log_weights
Vector or matrix of smoothed (and truncated) but unnormalized log
weights. To get normalized weights use the
weights()
method provided for objects of
class "psis"
.
diagnostics
A named list containing two vectors:
pareto_k
: Estimates of the shape parameter k
of the
generalized Pareto distribution. See the pareto-k-diagnostic
page for details.
n_eff
: PSIS effective sample size estimates.
Objects of class "psis"
also have the following attributes:
norm_const_log
Vector of precomputed values of colLogSumExps(log_weights)
that are
used internally by the weights
method to normalize the log weights.
tail_len
Vector of tail lengths used for fitting the generalized Pareto distribution.
r_eff
If specified, the user's r_eff
argument.
dims
Integer vector of length 2 containing S
(posterior sample size)
and N
(number of observations).
method
Method used for importance sampling, here psis
.
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. (2024). Pareto smoothed importance sampling. Journal of Machine Learning Research, 25(72):1-58. PDF
## Not run:
fit <- brm(rating ~ treat + period + carry, data = inhaler)
psis(fit)
## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.