WRS2 (version 1.0-0)

pbcor: 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.

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

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.

References

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

See Also

twocor

Examples

# NOT RUN {
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)
# }