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https://issues.apache.org/jira/browse/MATH-878?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13475216#comment-13475216
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Ted Dunning commented on MATH-878:
----------------------------------
{quote}
Could you provide pls. some reference data for rootLogLikelihoodRatio test?
{quote}
>From Mahout (with a few extras added just now)
{code}
@Test
public void testRootLogLikelihood() {
// positive where k11 is bigger than expected.
assertTrue(LogLikelihood.rootLogLikelihoodRatio(904, 21060, 1144, 283012) >
0.0);
// negative because k11 is lower than expected
assertTrue(LogLikelihood.rootLogLikelihoodRatio(36, 21928, 60280, 623876) <
0.0);
assertEquals(Math.sqrt(2.772589), LogLikelihood.rootLogLikelihoodRatio(1,
0, 0, 1), 0.000001);
assertEquals(-Math.sqrt(2.772589), LogLikelihood.rootLogLikelihoodRatio(0,
1, 1, 0), 0.000001);
assertEquals(Math.sqrt(27.72589), LogLikelihood.rootLogLikelihoodRatio(10,
0, 0, 10), 0.00001);
assertEquals(Math.sqrt(39.33052), LogLikelihood.rootLogLikelihoodRatio(5,
1995, 0, 100000), 0.00001);
assertEquals(-Math.sqrt(39.33052), LogLikelihood.rootLogLikelihoodRatio(0,
100000, 5, 1995), 0.00001);
assertEquals(Math.sqrt(4730.737),
LogLikelihood.rootLogLikelihoodRatio(1000, 1995, 1000, 100000), 0.001);
assertEquals(-Math.sqrt(4730.737),
LogLikelihood.rootLogLikelihoodRatio(1000, 100000, 1000, 1995), 0.001);
assertEquals(Math.sqrt(5734.343),
LogLikelihood.rootLogLikelihoodRatio(1000, 1000, 1000, 100000), 0.001);
assertEquals(Math.sqrt(5714.932),
LogLikelihood.rootLogLikelihoodRatio(1000, 1000, 1000, 99000), 0.001);
}
{code}
> G-Test (Log-Likelihood ratio - LLR test) in math.stat.inference
> ---------------------------------------------------------------
>
> Key: MATH-878
> URL: https://issues.apache.org/jira/browse/MATH-878
> Project: Commons Math
> Issue Type: New Feature
> Affects Versions: 3.1, 3.2, 4.0
> Environment: Netbeans
> Reporter: Radoslav Tsvetkov
> Labels: features, test
> Fix For: 3.1
>
> Attachments: MATH-878_gTest_12102012.patch, MATH-878_gTest.patch,
> vcs-diff16294.patch
>
> Original Estimate: 24h
> Remaining Estimate: 24h
>
> 1. Implementation of G-Test (Log-Likelihood ratio LLR test for independence
> and goodnes-of-fit)
> 2. Reference: http://en.wikipedia.org/wiki/G-test
> 3. Reasons-Usefulness: G-tests are tests are increasingly being used in
> situations where chi-squared tests were previously recommended.
> The approximation to the theoretical chi-squared distribution for the G-test
> is better than for the Pearson chi-squared tests. In cases where Observed
> >2*Expected for some cell case, the G-test is always better than the
> chi-squared test.
> For testing goodness-of-fit the G-test is infinitely more efficient than the
> chi squared test in the sense of Bahadur, but the two tests are equally
> efficient in the sense of Pitman or in the sense of Hodge and Lehman.
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