Given two ranges of observations of jointly distributed variables x, y, tests
the null hypothesis that values in x are independent of the corresponding
values in y. This is done using the Likelihood Ratio G test. Usage is similar
to chiSquareObs. For an otherwise identical test that assumes the data has
already been tabulated into a contingency table, see gTestContingency.
Note: This test can be thought of as a test for nonzero mutual information
between x and y, since the test statistic and P-value are strictly increasing
and strictly decreasing, respectively, in mutual information. Therefore, this
function returns a GTestRes, which is a subtype of TestRes and also gives
the mutual information for use in information theoretic settings.
Given two ranges of observations of jointly distributed variables x, y, tests the null hypothesis that values in x are independent of the corresponding values in y. This is done using the Likelihood Ratio G test. Usage is similar to chiSquareObs. For an otherwise identical test that assumes the data has already been tabulated into a contingency table, see gTestContingency.
Note: This test can be thought of as a test for nonzero mutual information between x and y, since the test statistic and P-value are strictly increasing and strictly decreasing, respectively, in mutual information. Therefore, this function returns a GTestRes, which is a subtype of TestRes and also gives the mutual information for use in information theoretic settings.