Author image Daniel S. T. Hughes


Statistics::Distributions::GTest - Perl implementation of the Log-Likelihood Ratio Test (G-test) of Independence.


This document describes Statistics::Distributions::GTest version 0.1.5.


    use Statistics::Distributions::GTest;

    # Create an GTest object.
    my $gtest = Statistics::Distributions::GTest->new();

    # A 3x3 example. Data is sent to object a reference to a LoL.
    my $a_ref = [
                    [ 458, 537 ,345],
                    [ 385, 457 ,456],
                    [ 332, 376 ,364 ],

    # Feed the object the data by passing reference with named argument 'table'.
    $gtest->read_data ( { table => $a_ref } );

    # Perform the analysis using one of the two methods - see DESCRIPTION.

    # Print a table of the calculated expected values.

    # To access results use results method. The return of this method is context dependent (see METHODS). 
    # To print a report to STDOUT call results in VOID context - may also call in BOOLEAN, NUMERIC and LIST (see METHODS).


The G-test of independence is an alternative to the chi-square test of independence for testing for independence in contingency tables. G-tests are coming into increasing use and as with the chi-square test for independence the G-test for independence is used when you have two nominal variables each with two or more possible values. The null hypothesis is that the relative proportions of one variable are independent of the second variable. This module implements two two equivalent, but marginally different approaches to calculate G scores (that described in and that used by Benchmarking indicates that first approach works about a third faster than the alternative. However, this difference diminishes as the categories increase. See and



Create a new Statistics::Distributions::GTest object.

    my $gtest = Statistics::Distributions::GTest->new();


Used for loading data into object. Data is fed as a reference to a list of lists within an anonymous hash using the named argument 'table'.

    $gtest->read_data ( { table => $LoL_ref } );


To calculate G value. This method implements the calculation described in



To calculate G you may also use this method. This method implements procedure described in This approach does not directly generate a table of expected values.


Prints a table of the calculated expected values to STDOUT. If you used G_alt to calculate G it will first generated the table of excpeted values.


Prints a table of the observation values to STDOUT.



Used to access the results of the G-test calculation. This method is context-dependent and will return a variety of different values depending on its calling context. In VOID context it simply prints the calculated value of G, df and the p_value in a table to STDOUT.


In BOOLEAN context it requires you to pass it a value for the significance level of the test you wish to apply e.g. 0.05. It returns True or False depending on whether the null hypothesis is rejected at that significance level.

    # test if the result is significant at the p = 0.05 level.
    if ($gtest->results( 0.05 )) { print qq{\nthis is significant } } else { print qq{\nthis is not significant} }

In LIST context it simply returns a LIST of the calculated values of G, df and p for the observation data.

    my ($G, $df, $p) = $gtest->results();

In NUMERIC context it returns the calculated value of G.

    print qq{\n\nG in numeric is: }, 0+$gtest->results();


'version' => 0, 'Statistics::Distributions' => '1.02', 'Math::Cephes' => '0.47', 'Carp' => '1.08', 'Contextual::Return' => '0.2.1', 'List::Util' => '1.19', 'Text::SimpleTable' => '2.0',


Daniel S. T. Hughes <>


Copyright (c) 2009, Daniel S. T. Hughes <>. All rights reserved.

This module is free software; you can redistribute it and/or modify it under the same terms as Perl itself. See perlartistic.


Statistics::Descriptive, Statistics::Distributions, Statistics::Distributions::Analyze, Statistics::ANOVA, Statistics::Distributions::Ancova, Statistics::ChiSquare.


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