# gchi2Test: Chi-square and G-square tests of (unconditional) indepdence In Rfast: A Collection of Efficient and Extremely Fast R Functions

## Description

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

## Usage

 `1` ```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.

## References

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

``` g2Test_univariate, g2Test_univariate_perm, g2Test ```

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10``` ```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 ```

### Example output

```Loading required package: Rcpp