robustCorr {maigesPack} | R Documentation

## 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

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

### 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.

### 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.

### Author(s)

Gustavo H. Esteves <gesteves@vision.ime.usp.br>

### See Also

`cor` from package stats.

### Examples

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

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

[Package maigesPack version 1.56.0 Index]