Chi-square and G-square tests of (unconditional) indepdence
Chi-square and G-square tests of (unconditional) indepdence.
gchi2Test(x, y, logged = FALSE)
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)? |
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).
A matrix with two rows. In each row the X2 or G2 test statistic, its p-value and the degrees of freedom are returned.
Manos Papadakis and Michail Tsagris
R implementation and documentation: Manos Papadakis <papadakm95@gmail.com> and Michail Tsagris <mtsagris@yahoo.gr>.
Tsagris M. (2017). Conditional independence test for categorical data using Poisson log-linear model. Journal of Data Science, 15(2):347-356.
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
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