Confidence intervals for two-sided tests on correlation coefficients.

Description

The twopcor function tests whether the difference between two Pearson correlations is 0. The twocor function performs the same test on a robust correlation coefficient (percentage bend correlation or Winsorized correlation).

Usage

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twopcor(x1, y1, x2, y2, nboot = 599)
twocor(x1, y1, x2, y2, corfun = "pbcor", nboot = 599, tr = 0.2, beta = 0.2)

Arguments

x1

a numeric vector.

y1

a numeric vector.

x2

a numeric vector.

y2

a numeric vector.

nboot

number of bootstrap samples.

corfun

Either "pbcor" for percentage based correlation or "wincor" for Winsorized correlation.

tr

amount of Winsorization.

beta

bending constant.

Details

It is tested whether the first correlation coefficient (based on x1 and y1) equals to the second correlation coefficient (based on x2 and y2). Both approaches return percentile bootstrap CIs.

Value

twopcor and twocor return an object of class "twocor" containing:

r1

robust correlation coefficient

r2

value of the test statistic

ci

confidence interval

p.value

p-value

call

function call

References

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

See Also

pbcor, wincor

Examples

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ct1 <- subset(hangover, subset = (group == "control" & time == 1))$symptoms
ct2 <- subset(hangover, subset = (group == "control" & time == 2))$symptoms
at1 <- subset(hangover, subset = (group == "alcoholic" & time == 1))$symptoms
at2 <- subset(hangover, subset = (group == "alcoholic" & time == 2))$symptoms

set.seed(111)
twopcor(ct1, ct2, at1, at2)
set.seed(123)
twocor(ct1, ct2, at1, at2, corfun = "pbcor", beta = 0.15)
set.seed(224)
twocor(ct1, ct2, at1, at2, corfun = "wincor", tr = 0.15, nboot = 50)

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