coxed (version 0.3.3)

bca: Bias-corrected and accelerated confidence intervals

Description

This function uses the method proposed by DiCiccio and Efron (1996) to generate confidence intervals that produce more accurate coverage rates when the distribution of bootstrap draws is non-normal. This code is adapted from the BC.CI() function within the mediate function in the mediation package.

Usage

bca(theta, conf.level = 0.95)

Arguments

theta

a vector that contains draws of a quantity of interest using bootstrap samples. The length of theta is equal to the number of iterations in the previously-run bootstrap simulation.

conf.level

the level of the desired confidence interval, as a proportion. Defaults to .95 which returns the 95 percent confidence interval.

Value

returns a vector of length 2 in which the first element is the lower bound and the second element is the upper bound

Details

\(BC_a\) confidence intervals are typically calculated using influence statistics from jackknife simulations. For our purposes, however, running jackknife simulation in addition to ordinary bootstrapping is too computationally expensive. This function follows the procedure outlined by DiCiccio and Efron (1996, p. 201) to calculate the bias-correction and acceleration parameters using only the draws from ordinary bootstrapping.

References

DiCiccio, T. J. and B. Efron. (1996). Bootstrap Confidence Intervals. Statistical Science. 11(3): 189<U+2013>212. https://doi.org/10.1214/ss/1032280214

See Also

coxed, bootcov, mediate

Examples

# NOT RUN {
theta <- rnorm(1000, mean=3, sd=4)
bca(theta, conf.level = .95)
# }