sjstats (version 0.3.0)

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, x)
boot_se(data, x)
boot_p(data, x)

Arguments

data

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

x

Name of the variable with bootstrapped estimates. Required, if data is a data frame and no vector.

Value

The bootstrap standard error, the lower and upper confidence intervals or the p-value of the bootstrapped estimates.

Details

This method requires a vector 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.

See Also

bootstrap to genearte bootstrap samples.

Examples

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" from each model
bs$dependency <- unlist(lapply(bs$models, function(x) coef(x)[2]))

# get bootstrapped confidence intervals
boot_ci(bs$dependency)

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

# 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)


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

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