cat.tests: Chi-square and G-square tests of (unconditional) indepdence

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

Chi-square and G-square tests of (unconditional) indepdence

Description

Chi-square and G-square tests of (unconditional) indepdence.

Usage

cat.tests(x, y, logged = FALSE)

Arguments

x	A numerical vector or a factor variable with data. The data must be consecutive numbers.
y	A numerical vector or a factor variable with data. The data must be consecutive numbers.
logged	Should the p-values be returned (FALSE) or their logarithm (TRUE)?

Details

The function calculates the test statistic of the X^2 and the G^2 tests of unconditional independence between x and y. x and y need not be numerical vectors like in g2test. This function is more close to the spirit of MASS' loglm()" function which calculates both statistics using Poisson log-linear models (Tsagris, 2017).

Value

A matrix with two rows. In each row the X^2 or G^2 test statistic, its p-value and the degrees of freedom are returned.

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.

Tsagris M. (2017). Conditional independence test for categorical data using Poisson log-linear model. Journal of Data Science, 15(2): 347–356.

See Also

g2test, correls, pchc.skel

Examples

x <- rbinom(100, 3, 0.5)
y <- rbinom(100, 2, 0.5)
cat.tests(x, y)

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