jackknife {jackknifeR}

Delete-d Jackknife for Estimates

Description

This function creates jackknife samples from the data by sequentially removing d observations from the data, and calculates the estimates by the specified function and its bias, standard error, and confidence intervals.

Usage

jackknife(statistic, d = 1, data, conf = 0.95, numCores = detectCores())

Arguments

statistic

a function returning a vector of estimates to be passed to jackknife

d

Number of observations to be deleted from data to make jackknife samples. The default is 1 (for delete-1 jackknife).

data

Data frame with dependent and independent independent variables specified in the formula

conf

Confidence level, a positive number < 1. The default is 0.95.

numCores

Number of processors to be used

Value

A list containing a summary data frame of jackknife estimates with bias, standard error. t-statistics, and confidence intervals, estimate for the original sample and a data frame with estimates for jackknife samples.

References

Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353-360. doi:10.2307/2332914

Tukey, J. W. (1958). Bias and Confidence in Not-quite Large Samples. Annals of Mathematical Statistics, 29(2), 614-623. doi:10.1214/aoms/1177706647

Shi, X. (1988). A note on the delete-d jackknife variance estimators. Statistics & Probability Letters, 6(5), 341-347. doi:10.1016/0167-7152(88)90011-9

See Also

jackknife.lm() which is used for jackknifing in linear regression.

Examples

## library(jackknifeR)
fn <- function(data){
   mod <- lm(speed~dist, data = data)
   return(coef(mod))}
jkn <- jackknife(statistic = fn, d = 2, data = cars, numCores= 2)
jkn