R rsample int_pctl 自舉置信區間

int_pctl(.data, ...)

# S3 method for bootstraps
int_pctl(.data, statistics, alpha = 0.05, ...)

int_t(.data, ...)

# S3 method for bootstraps
int_t(.data, statistics, alpha = 0.05, ...)

int_bca(.data, ...)

# S3 method for bootstraps
int_bca(.data, statistics, alpha = 0.05, .fn, ...)

reg_intervals()

# \donttest{
library(broom)
library(dplyr)
library(purrr)
library(tibble)

lm_est <- function(split, ...) {
  lm(mpg ~ disp + hp, data = analysis(split)) %>%
    tidy()
}

set.seed(52156)
car_rs <-
  bootstraps(mtcars, 500, apparent = TRUE) %>%
  mutate(results = map(splits, lm_est))

int_pctl(car_rs, results)
#> Warning: Recommend at least 1000 non-missing bootstrap resamples for terms: `(Intercept)`, `disp`, `hp`.
#> # A tibble: 3 × 6
#>   term         .lower .estimate   .upper .alpha .method   
#>   <chr>         <dbl>     <dbl>    <dbl>  <dbl> <chr>     
#> 1 (Intercept) 27.5      30.7    33.6       0.05 percentile
#> 2 disp        -0.0440   -0.0300 -0.0162    0.05 percentile
#> 3 hp          -0.0572   -0.0260 -0.00840   0.05 percentile
int_t(car_rs, results)
#> # A tibble: 3 × 6
#>   term         .lower .estimate   .upper .alpha .method  
#>   <chr>         <dbl>     <dbl>    <dbl>  <dbl> <chr>    
#> 1 (Intercept) 28.1      30.7    34.6       0.05 student-t
#> 2 disp        -0.0446   -0.0300 -0.0170    0.05 student-t
#> 3 hp          -0.0449   -0.0260 -0.00337   0.05 student-t
int_bca(car_rs, results, .fn = lm_est)
#> Warning: Recommend at least 1000 non-missing bootstrap resamples for terms: `(Intercept)`, `disp`, `hp`.
#> # A tibble: 3 × 6
#>   term         .lower .estimate   .upper .alpha .method
#>   <chr>         <dbl>     <dbl>    <dbl>  <dbl> <chr>  
#> 1 (Intercept) 27.7      30.7    33.7       0.05 BCa    
#> 2 disp        -0.0446   -0.0300 -0.0172    0.05 BCa    
#> 3 hp          -0.0576   -0.0260 -0.00843   0.05 BCa    

# putting results into a tidy format
rank_corr <- function(split) {
  dat <- analysis(split)
  tibble(
    term = "corr",
    estimate = cor(dat$sqft, dat$price, method = "spearman"),
    # don't know the analytical std.err so no t-intervals
    std.err = NA_real_
  )
}

set.seed(69325)
data(Sacramento, package = "modeldata")
bootstraps(Sacramento, 1000, apparent = TRUE) %>%
  mutate(correlations = map(splits, rank_corr)) %>%
  int_pctl(correlations)
#> # A tibble: 1 × 6
#>   term  .lower .estimate .upper .alpha .method   
#>   <chr>  <dbl>     <dbl>  <dbl>  <dbl> <chr>     
#> 1 corr   0.737     0.768  0.796   0.05 percentile
# }