R: Robust correlation coefficients.

pbcor {WRS2} | R Documentation

## Robust correlation coefficients.

### Description

The `pbcor` function computes the percentage bend correlation coefficient, `wincor` the Winsorized correlation, `pball` the percentage bend correlation matrix, `winall` the Winsorized correlation matrix.

### Usage

    pbcor(x, y = NULL, beta = 0.2)
    pball(x, beta = 0.2)
    wincor(x, y = NULL, tr = 0.2)
    winall(x, tr = 0.2)

### Arguments

`x` | a numeric vector, a matrix or a data frame.
`y` | a second numeric vector (for correlation functions).
`beta` | bending constant.
`tr` | amount of Winsorization.

### Details

It tested is whether the correlation coefficient equals 0 (null hypothesis) or not. Missing values are deleted pairwise. The tests are sensitive to heteroscedasticity. The test statistic H in `pball` tests the hypothesis that all correlations are equal to zero.

### Value

`pbcor` and `wincor` return an object of class `"pbcor"` containing:

`cor` | robust correlation coefficient
`test` | value of the test statistic
`p.value` | p-value
`n` | number of effective observations
`call` | function call

`pball` and `winall` return an object of class `"pball"` containing:

`pbcorm` | robust correlation matrix
`p.values` | p-values
`H` | H-statistic
`H.p.value` | p-value H-statistic
`cov` | variance-covariance matrix

### References

Wilcox, R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Elsevier.

### See Also

`twocor`

### Examples

    x1 <- subset(hangover, subset = (group == "control" & time == 1))$symptoms
    x2 <- subset(hangover, subset = (group == "control" & time == 2))$symptoms

    pbcor(x1, x2)
    pbcor(x1, x2, beta = 0.1)

    wincor(x1, x2)
    wincor(x1, x2, tr = 0.1)

    require(reshape)
    hanglong <- subset(hangover, subset = group == "control")
    hangwide <- cast(hanglong, id ~ time, value = "symptoms")[,-1]

    pball(hangwide)
    winall(hangwide)

[Package WRS2 version 0.9-2 Index]