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.
jackknife.cor(data, d = 1, conf = 0.95, numCores = detectCores())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.
A data frame with two columns of numerical values for which the jackknife estimate of correlation needs to be found. estimated
Number of observations to be deleted from data to make jackknife samples. The default is 1 (for delete-1 jackknife).
Confidence level, a positive number < 1. The default is 0.95.
Number of processors to be used
Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353-360. tools:::Rd_expr_doi("10.2307/2332914")
Tukey, J. W. (1958). Bias and Confidence in Not-quite Large Samples. Annals of Mathematical Statistics, 29(2), 614-623. tools:::Rd_expr_doi("10.1214/aoms/1177706647")
Shi, X. (1988). A note on the delete-d jackknife variance estimators. Statistics & Probability Letters, 6(5), 341-347. tools:::Rd_expr_doi("10.1016/0167-7152(88)90011-9")
cor() which is used to estimate correlation coefficient.
## library(jackknifeR)
j.cor <- jackknife.cor(cars, d = 2, numCores = 2)
summary(j.cor)
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