Tidy a(n) boot object

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.

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

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:

• tidy() methods will warn when supplied an exponentiate argument if it will be ignored.

• augment() methods will warn when supplied a newdata argument if it will be ignored.

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

tidy(), boot::boot(), boot::tsboot(), boot::boot.ci(), rsample::bootstraps()

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 0.000000400    -0.0265   -0.0213
#> 2 log(u)        0.0236  0.000577      40.9 0.00000000136   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