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 (2022). For PSIS diagnostics see the pareto-k-diagnostic page.

```
# S3 method for brmsfit
psis(log_ratios, newdata = NULL, resp = NULL, model_name = NULL, ...)
```

- log_ratios
A fitted model object of class

`brmsfit`

. Argument is named "log_ratios" to match the argument name of the`loo::psis`

generic function.- 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.

- model_name
Currently ignored.

- ...

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. (2022). Pareto smoothed importance sampling. preprint arXiv:1507.02646

```
if (FALSE) {
fit <- brm(rating ~ treat + period + carry, data = inhaler)
psis(fit)
}
```