jackknife.lm {jackknifeR}

Delete-d Jackknife Estimate for Linear Regression

Description

This function creates jackknife samples from the data by sequentially removing d observations from the data, fits models linear regression model using the jackknife samples as specified in the formula and estimates the jackknife coefficients bias standard error, standard error and confidence intervals.

Usage

jackknife.lm(formula, d = 1, data, conf = 0.95, numCores = detectCores())

Arguments

formula

Simple or multiple linear regression formula with dependent and independent variables

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, linear regression model of original data and a data frame with coefficient estimates of 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

lm() which is used for linear regression.

Examples

## library(jackknifeR)
j.lm <- jackknife.lm(dist~speed, d = 2, data = cars, numCores = 2)
summary(j.lm)