Calculate bootstrap confidence intervals using various methods.
Usage
int_pctl(.data, ...)
# S3 method for class 'bootstraps'
int_pctl(.data, statistics, alpha = 0.05, ...)
int_t(.data, ...)
# S3 method for class 'bootstraps'
int_t(.data, statistics, alpha = 0.05, ...)
int_bca(.data, ...)
# S3 method for class 'bootstraps'
int_bca(.data, statistics, alpha = 0.05, .fn, ...)Arguments
- .data
A data frame containing the bootstrap resamples created using
bootstraps(). For t- and BCa-intervals, theapparentargument should be set toTRUE. Even if theapparentargument is set toTRUEfor the percentile method, the apparent data is never used in calculating the percentile confidence interval.- ...
Arguments to pass to
.fn(int_bca()only).- statistics
An unquoted column name or
dplyrselector that identifies a single column in the data set containing the individual bootstrap estimates. This must be a list column of tidy tibbles (with columnstermandestimate). Optionally, users can include columns whose names begin with a period and the intervals will be created for each combination of these variables and thetermcolumn. For t-intervals, a standard tidy column (usually calledstd.error) is required. See the examples below.- alpha
Level of significance.
- .fn
A function to calculate statistic of interest. The function should take an
rsplitas the first argument and the...are required.
Value
Each function returns a tibble with columns .lower,
.estimate, .upper, .alpha, .method, and term.
.method is the type of interval (eg. "percentile",
"student-t", or "BCa"). term is the name of the estimate. Note
the .estimate returned from int_pctl()
is the mean of the estimates from the bootstrap resamples
and not the estimate from the apparent model.
Details
Percentile intervals are the standard method of obtaining confidence intervals but require thousands of resamples to be accurate. T-intervals may need fewer resamples but require a corresponding variance estimate. Bias-corrected and accelerated intervals require the original function that was used to create the statistics of interest and are computationally taxing.
References
https://rsample.tidymodels.org/articles/Applications/Intervals.html
Davison, A., & Hinkley, D. (1997). Bootstrap Methods and their Application. Cambridge: Cambridge University Press. doi:10.1017/CBO9780511802843
Examples
# \donttest{
library(broom)
library(dplyr)
library(purrr)
library(tibble)
library(tidyr)
# ------------------------------------------------------------------------------
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`, and `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`, and `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.error so no t-intervals
std.error = 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
# ------------------------------------------------------------------------------
# An example of computing the interval for each value of a custom grouping
# factor (type of house in this example)
# Get regression estimates for each house type
lm_est <- function(split, ...) {
analysis(split) %>%
tidyr::nest(.by = c(type)) %>%
# Compute regression estimates for each house type
mutate(
betas = purrr::map(data, ~ lm(log10(price) ~ sqft, data = .x) %>% tidy())
) %>%
# Convert the column name to begin with a period
rename(.type = type) %>%
select(.type, betas) %>%
unnest(cols = betas)
}
set.seed(52156)
house_rs <-
bootstraps(Sacramento, 1000, apparent = TRUE) %>%
mutate(results = map(splits, lm_est))
int_pctl(house_rs, results)
#> # A tibble: 6 × 7
#> term .type .lower .estimate .upper .alpha .method
#> <chr> <fct> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 (Intercept) Condo 4.45 4.59 4.72 0.05 percentile
#> 2 (Intercept) Multi_Family 4.74 5.25 5.71 0.05 percentile
#> 3 (Intercept) Residential 4.93 4.96 4.99 0.05 percentile
#> 4 sqft Condo 0.000412 0.000520 0.000659 0.05 percentile
#> 5 sqft Multi_Family -0.000197 0.0000344 0.000277 0.05 percentile
#> 6 sqft Residential 0.000211 0.000225 0.000240 0.05 percentile
# }