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

Chi-square and G-square tests of (unconditional) indepdence {Rfast} | R Documentation

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

### Description

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

### Usage

    gchi2Test(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 χ^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 X2 or G2 test statistic, its p-value and the degrees of freedom are returned.

### Author(s)

Manos Papadakis and Michail Tsagris

R implementation and documentation: Manos Papadakis <papadakm95@gmail.com> and Michail Tsagris <mtsagris@uoc.gr>.

### References

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_univariate, g2Test_univariate_perm, g2Test`

### Examples

    nvalues <- 3
    nvars <- 2
    nsamples <- 5000
    data <- matrix( sample( 0:(nvalues - 1), nvars * nsamples, replace = TRUE ), nsamples, nvars )

    res<-gchi2Test(data[, 1], data[, 2])
    res<-g2Test_univariate( data, rep(3, 2) )  ## G^2 test
    res<-chisq.test(data[, 1], data[, 2])  ## X^2 test from R

    data<-NULL

[Package Rfast version 2.0.4 Index]