jackknife.cor {jackknifeR}

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)