robustCorr: Calculate a robust correlation value

Description

This function is intended to calculate robust correlation values between pairs of rows of numerical matrix or between two numerical vectors.

Usage

robustCorr(x, y=NULL)

Arguments

numerical matrix or vector. If a matrix the method calculates the robust correlations between all pairs of rows. If x is a vector, y must be specified as another vector of same length as x and the robust correlation between them is calculate.

optional numeric vector, must be specified if x is a vector.

Value

If x is a matrix, the method return a list with two square matrices, the first one containing the robust correlation values between all pairs of rows from x and the second containing the index of the point removed from calculation. If x is a vector, y must be specified and the function return a list with the robust correlation value between them and the index of the point removed.

Details

This function calculates a robust correlation value in a procedure similar to the leave-one-out used for cross-validation of classification results. The algorithm removes one point at a time and calculates a usual Pearson correlation value. Them, with a vector r of correlation values that has the same length as the columns of x (or vectors x and y), the algorithm decides by the min(r) or max(r), according with that one that is more distant from the median value.

Examples

Run this code

x <- runif(50, 0, 1)
y <- rbeta(50, 1, 2)
robustCorr(x, y)

z <- matrix(rnorm(100, 0, 1), 4, 25)
robustCorr(z)