cortest: Correlation significance testing using Fisher's...

In pchc: Bayesian Network Learning with the PCHC and Related Algorithms

Correlation significance testing using Fisher's z-transformation

Description

Correlation significance testing using Fisher's z-transformation.

Usage

cortest(y, x, rho = 0, a = 0.05 )

Arguments

y	A numerical vector.
x	A numerical vector.
rho	The value of the hypothesised correlation to be used in the hypothesis testing.
a	The significance level used for the confidence intervals.

Details

The function uses the built-in function "cor" which is very fast, then computes a confidence interval and produces a p-value for the hypothesis test.

Value

A vector with 5 numbers; the correlation, the p-value for the hypothesis test that each of them is equal to "rho", the test statistic and the $a/2%$ lower and upper confidence limits.

Author(s)

Michail Tsagris.

R implementation and documentation: Michail Tsagris [e-mail].

References

Tsagris M. (2021). A new scalable Bayesian network learning algorithm with applications to economics. Computational Economics, 57(1): 341–367.

See Also

pcor, correls, corpairs

Examples

x <- rcauchy(60)
y <- rnorm(60)
cortest(y, x)

pchc documentation built on April 4, 2025, 1:11 a.m.