Bootstrap Empirical Data

Takes an input vector of numeric data and produces a bootstrapped nested tibble by simulation number.

Usage

tidy_bootstrap(
  .x,
  .num_sims = 2000,
  .proportion = 0.8,
  .distribution_type = "continuous"
)

Arguments

.x

The vector of data being passed to the function. Must be a numeric vector.

.num_sims

The default is 2000, can be set to anything desired. A warning will pass to the console if the value is less than 2000.

.proportion

How much of the original data do you want to pass through to the sampling function. The default is 0.80 (80%)

.distribution_type

This can either be 'continuous' or 'discrete'

Value

A nested tibble

Details

This function will take in a numeric input vector and produce a tibble of bootstrapped values in a list. The table that is output will have two columns: sim_number and bootstrap_samples

The sim_number corresponds to how many times you want the data to be resampled, and the bootstrap_samples column contains a list of the boostrapped resampled data.

See also

Other Bootstrap: bootstrap_density_augment(), bootstrap_p_augment(), bootstrap_p_vec(), bootstrap_q_augment(), bootstrap_q_vec(), bootstrap_stat_plot(), bootstrap_unnest_tbl()

Author

Steven P. Sanderson II, MPH

Examples

x <- mtcars$mpg
tidy_bootstrap(x)
#> # A tibble: 2,000 × 2
#>    sim_number bootstrap_samples
#>    <fct>      <list>           
#>  1 1          <dbl [25]>       
#>  2 2          <dbl [25]>       
#>  3 3          <dbl [25]>       
#>  4 4          <dbl [25]>       
#>  5 5          <dbl [25]>       
#>  6 6          <dbl [25]>       
#>  7 7          <dbl [25]>       
#>  8 8          <dbl [25]>       
#>  9 9          <dbl [25]>       
#> 10 10         <dbl [25]>       
#> # ℹ 1,990 more rows