Package 'jackknifeR'

Title: Delete-d Jackknife for Point and Interval Estimation
Description: This function creates jackknife samples from the data by sequentially removing d observations from the data, performs estimation using the jackknife samples and calculates the jackknife coefficients, bias, standard error and confidence intervals based on the methodology discussed by Quenouille (1956) <doi:10.2307/2332914>, Tukey (1958) <doi:10.1214/aoms/1177706647> and Shi (1988) <doi:10.1016/0167-7152(88)90011-9>.
Authors: S. Mohanasundaram [aut, cre] (ORCID = 0000-0003-4639-9419)
Maintainer: S. Mohanasundaram <[email protected]>
License: GPL (>= 3)
Version: 1.2.0
Built: 2025-02-24 03:01:06 UTC
Source: https://github.com/mohanasundarams/jackknifer

Help Index


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

Delete-d Jackknife Estimate for Correlation between Two Variables

Description

This function creates jackknife samples from the data by sequentially removing d observations from the data, calculates correlation between the two variables using the jackknife samples and estimates the jackknife correlation coefficients, bias standard error, standard error and confidence intervals.

Usage

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

Arguments

data

A data frame with two columns of numerical values for which the jackknife estimate of correlation needs to be found. estimated

d

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

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 correlation coefficient estimates with bias, standard error. t-statistics, and confidence intervals,correlation estimate of original data and a data frame with correlation estimates of individual 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

cor() which is used to estimate correlation coefficient.

Examples

## library(jackknifeR)
j.cor <- jackknife.cor(cars, d = 2, numCores = 2)
summary(j.cor)

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)