TauStar (version 1.1.7)

TauStar-package: Efficient Computation and Testing of the t* Statistic of Bergsma and Dassios

Description

Computes the t* statistic corresponding to the tau star population coefficient introduced by Bergsma and Dassios (2014) <DOI:10.3150/13-BEJ514> and does so in O(n^2*log(n)) time following the algorithm of Weihs, Drton, and Leung (2016) <DOI:10.1007/s00180-015-0639-x>. Also allows for independence testing using the asymptotic distribution of t* as described by Nandy, Weihs, and Drton (2016) <http://arxiv.org/abs/1602.04387>. To directly compute the t* statistic see the function tStar. If otherwise interested in performing tests of independence then see the function tauStarTest.

Author

Maintainer: Julian D. Karch (ORCID)

Authors:

• Luca Weihs

Other contributors:

• Emin Martinian (Created the red-black tree library included in package.) [contributor]

References

Bergsma, Wicher; Dassios, Angelos. A consistent test of independence based on a sign covariance related to Kendall's tau. Bernoulli 20 (2014), no. 2, 1006--1028.

Luca Weihs, Mathias Drton, and Dennis Leung. Efficient Computation of the Bergsma-Dassios Sign Covariance. Computational Statistics, x:x-x, 2016. to appear.

Preetam Nandy, Luca Weihs, and Mathias Drton. Large-Sample Theory for the Bergsma-Dassios Sign Covariance. arXiv preprint arXiv:1602.04387. 2016.

Examples

library(TauStar)

# Compute t* for a concordant quadruple
tStar(c(1, 2, 3, 4), c(1, 2, 3, 4)) # == 2/3

# Compute t* for a discordant quadruple
tStar(c(1, 2, 3, 4), c(1, -1, 1, -1)) # == -1/3

# Compute t* on random normal iid normal data
set.seed(23421)
tStar(rnorm(4000), rnorm(4000)) # near 0

# Compute t* as a v-statistic
set.seed(923)
tStar(rnorm(100), rnorm(100), vStatistic = TRUE)

# Compute an approximation of tau* via resampling
set.seed(9492)
tStar(rnorm(10000), rnorm(10000),
    resample = TRUE, sampleSize = 30, numResamples = 5000
)

# Perform a test of independence using continuous data
set.seed(123)
x <- rnorm(100)
y <- rnorm(100)
testResults <- tauStarTest(x, y)
print(testResults$pVal) # big p-value

# Now make x and y correlated so we expect a small p-value
y <- y + x
testResults <- tauStarTest(x, y)
print(testResults$pVal) # small p-value