Tidy summarizes information about the components of a model.
A model component might be a single term in a regression, a single
hypothesis, a cluster, or a class. Exactly what tidy considers to be a
model component varies cross models but is usually self-evident.
If a model has several distinct types of components, you will need to
specify which components to return.

# S3 method for boot
tidy(
x,
conf.int = FALSE,
conf.level = 0.95,
conf.method = c("perc", "bca", "basic", "norm"),
...
)

## Arguments

x |
A `boot::boot()` object. |

conf.int |
Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to `FALSE` . |

conf.level |
The confidence level to use for the confidence interval
if `conf.int = TRUE` . Must be strictly greater than 0 and less than 1.
Defaults to 0.95, which corresponds to a 95 percent confidence interval. |

conf.method |
Passed to the `type` argument of `boot::boot.ci()` .
Defaults to `"perc"` . The allowed types are `"perc"` , `"basic"` ,
`"bca"` , and `"norm"` . Does not support `"stud"` or `"all"` . |

... |
Additional arguments. Not used. Needed to match generic
signature only. **Cautionary note:** Misspelled arguments will be
absorbed in `...` , where they will be ignored. If the misspelled
argument has a default value, the default value will be used.
For example, if you pass `conf.lvel = 0.9` , all computation will
proceed using `conf.level = 0.95` . Additionally, if you pass
`newdata = my_tibble` to an `augment()` method that does not
accept a `newdata` argument, it will use the default value for
the `data` argument. |

## Details

If weights were provided to the `boot`

function, an `estimate`

column is included showing the weighted bootstrap estimate, and the
standard error is of that estimate.

If there are no original statistics in the "boot" object, such as with a
call to `tsboot`

with `orig.t = FALSE`

, the `original`

and `statistic`

columns are omitted, and only `estimate`

and
`std.error`

columns shown.

## See also

## Value

A `tibble::tibble()`

with columns:

biasBias of the statistic.

std.errorThe standard error of the regression term.

termThe name of the regression term.

statisticOriginal value of the statistic.

## Examples

#>
#> Attaching package: ‘boot’

#> The following object is masked from ‘package:speedglm’:
#>
#> control

#> The following object is masked from ‘package:robustbase’:
#>
#> salinity

#> The following object is masked from ‘package:car’:
#>
#> logit

#> The following object is masked from ‘package:survival’:
#>
#> aml

#> # A tibble: 2 x 7
#> term estimate std.error statistic p.value conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) -0.0239 0.00133 -18.0 0.000000400 -0.0265 -0.0213
#> 2 log(u) 0.0236 0.000577 40.9 0.00000000136 0.0225 0.0247

#> # A tibble: 2 x 6
#> term statistic bias std.error conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) -0.0239 -0.00185 0.00322 -0.0328 -0.0222
#> 2 log(u) 0.0236 0.000557 0.00103 0.0227 0.0265