boot_ci: Standard Error and Confidence Intervals for bootstrapped estimates

Description

Compute bootstrap standard error, confidence intervals and p-value for a vector of bootstrap replicate estimates.

Usage

boot_ci(data, ...)
boot_se(data, ...)
boot_p(data, ...)

Arguments

data

A data frame that containts the vector with bootstrapped estimates, or directly the vector (see 'Examples').

...

Optional, names of the variables with bootstrapped estimates. Required, if either data is a data frame and no vector, or if only selected variables from data should be used in the function.

Value

A data_frame with either bootstrap standard error, the lower and upper confidence intervals or the p-value for all bootstrapped estimates.

Details

This method requires one or more vectors of bootstrap replicate estimates as input. The function then computes the bootstrap standard error by calculating the standard deviation of the input vector. The mean value of the input vector is used to calculate the lower and upper confidence interval, assuming a t-distribution of bootstrap estimate replicates.

Examples

Run this code

data(efc)
bs <- bootstrap(efc, 100)

# now run models for each bootstrapped sample
bs$models <- lapply(bs$strap, function(x) lm(neg_c_7 ~ e42dep + c161sex, data = x))

# extract coefficient "dependency" and "gender" from each model
bs$dependency <- unlist(lapply(bs$models, function(x) coef(x)[2]))
bs$gender <- unlist(lapply(bs$models, function(x) coef(x)[3]))

# get bootstrapped confidence intervals
boot_ci(bs$dependency)

# compare with model fit
fit <- lm(neg_c_7 ~ e42dep + c161sex, data = efc)
confint(fit)[2, ]

# alternative function calls.
boot_ci(bs$dependency)
boot_ci(bs, dependency)
boot_ci(bs, dependency, gender)


# compare coefficients
mean(bs$dependency)
coef(fit)[2]

# bootstrap() and boot_ci() work fine within pipe-chains
library(dplyr)
efc %>%
  bootstrap(100) %>%
  mutate(models = lapply(.$strap, function(x) {
    lm(neg_c_7 ~ e42dep + c161sex, data = x)
  })) %>%
  mutate(dependency = unlist(lapply(.$models, function(x) coef(x)[2]))) %>%
  boot_ci(dependency)


# check p-value
boot_p(bs$gender)
summary(fit)$coefficients[3, ]