Add weights to `draws`

objects, with one weight per draw, for use in
subsequent weighting operations. For reasons of numerical accuracy, weights
are stored in the form of unnormalized log-weights (in a variable called
`.log_weight`

). See `weights.draws()`

for details how to extract weights from
`draws`

objects.

```
weight_draws(x, weights, ...)
# S3 method for draws_matrix
weight_draws(x, weights, log = FALSE, pareto_smooth = FALSE, ...)
# S3 method for draws_array
weight_draws(x, weights, log = FALSE, pareto_smooth = FALSE, ...)
# S3 method for draws_df
weight_draws(x, weights, log = FALSE, pareto_smooth = FALSE, ...)
# S3 method for draws_list
weight_draws(x, weights, log = FALSE, pareto_smooth = FALSE, ...)
# S3 method for draws_rvars
weight_draws(x, weights, log = FALSE, pareto_smooth = FALSE, ...)
```

- x
(draws) A

`draws`

object or another R object for which the method is defined.- weights
(numeric vector) A vector of weights of length

`ndraws(x)`

. Weights will be internally stored on the log scale (in a variable called`.log_weight`

) and will not be normalized, but normalized (non-log) weights can be returned via the`weights.draws()`

method later.- ...
Arguments passed to individual methods (if applicable).

- log
(logical) Are the weights passed already on the log scale? The default is

`FALSE`

, that is, expecting`weights`

to be on the standard (non-log) scale.- pareto_smooth
(logical) Should the weights be Pareto-smoothed? The default is

`FALSE`

.

A `draws`

object of the same class as `x`

.

```
x <- example_draws()
# sample some random weights for illustration
wts <- rexp(ndraws(x))
head(wts)
#> [1] 0.07943442 1.20975078 0.09003050 0.22634413 0.47107425 0.22054226
# add weights
x <- weight_draws(x, weights = wts)
# extract weights
head(weights(x)) # defaults to normalized weights
#> [1] 0.0002133569 0.0032493306 0.0002418175 0.0006079491 0.0012652821
#> [6] 0.0005923656
head(weights(x, normalize=FALSE)) # recover original weights
#> [1] 0.07943442 1.20975078 0.09003050 0.22634413 0.47107425 0.22054226
head(weights(x, log=TRUE)) # get normalized log-weights
#> [1] -8.452544 -5.729306 -8.327327 -7.405419 -6.672460 -7.431387
# add weights which are already on the log scale
log_wts <- log(wts)
head(log_wts)
#> [1] -2.5328235 0.1904144 -2.4076068 -1.4856988 -0.7527396 -1.5116659
x <- weight_draws(x, weights = log_wts, log = TRUE)
# extract weights
head(weights(x))
#> [1] 0.0002133569 0.0032493306 0.0002418175 0.0006079491 0.0012652821
#> [6] 0.0005923656
head(weights(x, log=TRUE, normalize = FALSE)) # recover original log_wts
#> [1] -2.5328235 0.1904144 -2.4076068 -1.4856988 -0.7527396 -1.5116659
# add weights on log scale and Pareto smooth them
x <- weight_draws(x, weights = log_wts, log = TRUE, pareto_smooth = TRUE)
#> Pareto k-hat = 0.04.
```