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 across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.

## 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"`

.- exponentiate
Logical indicating whether or not to exponentiate the the coefficient estimates. This is typical for logistic and multinomial regressions, but a bad idea if there is no log or logit link. Defaults to

`FALSE`

.- ...
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`

. Two exceptions here are:

## 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.

## Value

A `tibble::tibble()`

with columns:

- bias
Bias of the statistic.

- std.error
The standard error of the regression term.

- term
The name of the regression term.

- statistic
Original value of the statistic.

## Examples

```
# load modeling library
library(boot)
#>
#> 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
clotting <- data.frame(
u = c(5, 10, 15, 20, 30, 40, 60, 80, 100),
lot1 = c(118, 58, 42, 35, 27, 25, 21, 19, 18),
lot2 = c(69, 35, 26, 21, 18, 16, 13, 12, 12)
)
# fit models
g1 <- glm(lot2 ~ log(u), data = clotting, family = Gamma)
bootfun <- function(d, i) {
coef(update(g1, data = d[i, ]))
}
bootres <- boot(clotting, bootfun, R = 999)
# summarize model fits with tidiers
tidy(g1, conf.int = TRUE)
#> # A tibble: 2 × 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 4.00e-7 -0.0265 -0.0213
#> 2 log(u) 0.0236 0.000577 40.9 1.36e-9 0.0225 0.0247
tidy(bootres, conf.int = TRUE)
#> # A tibble: 2 × 6
#> term statistic bias std.error conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) -0.0239 -0.00171 0.00336 -0.0328 -0.0222
#> 2 log(u) 0.0236 0.000504 0.00107 0.0227 0.0265
```