Uses weights computed in `adjust_weights`

to compute posterior
summary statistics. These statistics can be compared against their reference
values to quantify the sensitivity of the model to aspects of its
specification.

# S3 method for adjustr_weighted
summarise(.data, ..., .resampling = FALSE, .model_data = NULL)
# S3 method for adjustr_weighted
summarize(.data, ..., .resampling = FALSE, .model_data = NULL)

## Arguments

.data |
An `adjustr_weighted` object. |

... |
Name-value pairs of expressions. The name of each argument will be
the name of a new variable, and the value will be computed for the
posterior distribution of eight alternative specification. For example,
a value of `mean(theta)` will compute the posterior mean of
`theta` for each alternative specification.
Also supported is the custom function `wasserstein` , which computes
the Wasserstein-p distance between the posterior distribution of the
provided expression under the new model and under the original model, with
`p=1` the default. Lower the `spacing` parameter from the
default of 0.005 to compute a finer (but slower) approximation.
The arguments in `...` are automatically quoted and evaluated in the
context of `.data` . They support unquoting and splicing. |

.resampling |
Whether to compute summary statistics by first resampling
the data according to the weights. Defaults to `FALSE` , but will be
used for any summary statistic that is not `mean` , `var` or
`sd` . |

.model_data |
Stan model data, if not provided in the earlier call to
`adjust_weights` . |

## Value

An `adjustr_weighted`

object, wth the new columns specified in
`...`

added.

## See also

## Examples