WH.normal: Wilson-Hilferty transformation for chi-squared variates

In fastmatrix: Fast Computation of some Matrices Useful in Statistics

Wilson-Hilferty transformation for chi-squared variates

Description

Returns the Wilson-Hilferty transformation of random variables with chi-squared distribution.

Usage

  WH.normal(x)

Arguments

x	vector or matrix of data with, say, p columns.

Details

Let T = D^2/p be a random variable, where D^2 denotes the squared Mahalanobis distance defined as

D^2 = (\bold{x} - \bold{\mu})^T \bold{\Sigma}^{-1} (\bold{x} - \bold{\mu})

Thus the Wilson-Hilferty transformation is given by

z = \frac{T^{1/3} - (1 - \frac{2}{9p})}{(\frac{2}{9p})^{1/2}}

and z is approximately distributed as a standard normal distribution. This is useful, for instance, in the construction of QQ-plots.

References

Wilson, E.B., and Hilferty, M.M. (1931). The distribution of chi-square. Proceedings of the National Academy of Sciences of the United States of America 17, 684-688.

See Also

Mahalanobis

Examples

x <- iris[,1:4]
z <- WH.normal(x)
par(pty = "s")
qqnorm(z, main = "Transformed distances QQ-plot")
abline(c(0,1), col = "red", lwd = 2, lty = 2)

fastmatrix documentation built on Sept. 11, 2024, 7:22 p.m.