dKendall: The distribution of Kendall's tau.
In andrie/SuppDists: Supplementary Distributions

Description Usage Arguments Details Value Author(s) References Examples

Density, distribution function, quantile function, random generator and summary function for Kendall's tau.

dKendall(x, N, log = FALSE)

pKendall(q, N, lower.tail = TRUE, log.p = FALSE)

qKendall(p, N, lower.tail = TRUE, log.p = FALSE)

rKendall(n, N)

sKendall(N)

`x, q`	vector of non-negative quantities
`N`	vector number of treatments
`log, log.p`	logical vector; if TRUE, probabilities p are given as log(p)
`lower.tail`	logical vector; if TRUE (default), probabilities are P[X <= x], otherwise, P[X > x]
`p`	vector of probabilities
`n`	number of values to generate. If n is a vector, length(n) values will be generated

There are two categories with N treatments each. The treatments are ranked for each category, and then sorted according to the ranks for the first category. This produces a 2 by N array in which the numbers in the first row are increasing from 1 to N. The array is scanned, and every time two adjacent ranks in the second row are not in order, they are exchanged. The scanning is repeated until the second row is in increasing order. Let s denote the number of exchanges, then Kendall's tau is given by

tau=1-4 s/(N (N-1))

This too is a product-moment correlation coefficient. See Kendall (1975), Chapter 2. Other methods for calculating the statistic are also discussed there.

The calculated values are exact for N < 13, thereafter an Edgeworth expansion is used.

The output values conform to the output from other such functions in dKendall() gives the density, pKendall() the distribution function and qKendall() its inverse. rKendall() generates random numbers. sKendall() produces a list containing parameters corresponding to the arguments – mean, median, mode, variance, sd, third cental moment, fourth central moment, Pearson's skewness, skewness, and kurtosis.

Bob Wheeler bwheelerg@gmail.com

Kendall, M. (1975). Rank Correlation Methods. Griffin, London.

pKendall(0, N=10)
pKendall(c(-.42, 0.02, .42), N=10) ## approximately 5% 50% and 95% 
qKendall(.95, N=c(10, 20))
sKendall(N=10)
plot(function(x) dKendall(x, N=10), -0.5, 0.5)