test.diff.cor: Test for difference in correlation

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

This functions tests the hypothesis of no difference in correlations. It uses the Fisher Z transform (atanh) to test the null hypothesis of no difference in correlations. See details.

Usage

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## S3 method for class 'diff.cor'
test(X1, X2, cor1 = cor(X1), cor2 = cor(X2),
  n1 = nrow(X1), n2 = nrow(X2), alternative = c("two.sided", "less",
  "greater"), conf.level = 0.95, null = 0)

Arguments

X1

A numeric matrix of observations.

X2

A numeric matrix of observations.

cor1

A numeric matrix of correlation coefficients in the first group. May be omitted if X1 is provided.

cor2

A numeric matrix of correlation coefficients in the second group. May be omitted if X2 is provided.

n1

integer of length 1. The number of samples in group 1.

n2

integer of length 1. The number of samples in group 2.

alternative

The alternative hypothesis.

conf.level

The confidence level used in the computed confidence intervals.

null

A matrix of number giving the difference in correlation under the null hypothesis.

Details

The function uses the Fisher Z transform (atanh) of correlations to test that the hypotheses of no difference in correlation. The computed Z-score is

(Z_1 - Z_2)/ sqrt(1/(n_1 - 3) + 1/(n_2 - 3))

where Z_1 and Z_2 are the Fisher transformed correlations. It performs the test for all correlations in the correlation matrix.

Value

A list of matrices or vector containing:

LCL

The lower confidence interval limit.

UCL

The upper confidence interval limit.

z

A numeric matrix of Z-scores for the hypothesis.

p.val

A numeric matrix of the P-values.

with an attribute giving the alternative hypothesis.

Author(s)

Anders Ellern Bilgrau <anders.ellern.bilgrau (at) gmail.com>

References

http://core.ecu.edu/psyc/wuenschk/docs30/CompareCorrCoeff.pdf

See Also

Similar usage to cor.test (but NOT the same thing).
This is a vectorised version of test.diff.cor.single.

Examples

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n1 <- 8
n2 <- 10
X1 <- createData(n = n1, m = 5)
X2 <- createData(n = n2, m = 5)

print(cor1 <- cor(X1))
print(cor2 <- cor(X2))

test.diff.cor(X1, X2)

# Directly supplied correlation matrices
test.diff.cor(cor1 = cor1, cor2 = cor2, n1 = n1, n2 = n2)

test.diff.cor(X1, X2, alternative = "less")

AEBilgrau/correlateR documentation built on June 1, 2019, 9:11 p.m.