GoodmanKruskalGammaTest

GoodmanKruskalGammaTest[v1,v2]

tests whether the vectors v1 and v2 are independent.

GoodmanKruskalGammaTest[…,"property"]

returns the value of "property".

Details and Options

  • GoodmanKruskalGammaTest performs a hypothesis test on v1 and v2 with null hypothesis that the vectors are independent, and alternative hypothesis that they are not.
  • By default, a probability value or -value is returned.
  • A small -value suggests that it is unlikely that is true.
  • The arguments v1 and v2 can be any real-valued vectors of equal length.
  • GoodmanKruskalGammaTest is based on the Goodman–Kruskal gamma coefficient γ, which is computed by GoodmanKruskalGamma[v1,v2].
  • GoodmanKruskalGammaTest[v1,v2,"HypothesisTestData"] returns a HypothesisTestData object htd that can be used to extract additional test results and properties using the form htd["property"].
  • GoodmanKruskalGammaTest[v1,v2,"property"] can be used to directly give the value of "property".
  • Properties related to the reporting of test results include:
  • "PValue"the -value of the test
    "PValueTable"formatted table containing the -value
    "ShortTestConclusion"a short description of the conclusion of the test
    "TestConclusion"a description of the conclusion of the test
    "TestData"a list containing the test statistic and -value
    "TestDataTable"formatted table of the -value and test statistic
    "TestStatistic"the test statistic
    "TestStatisticTable"formatted table containing the test statistic
  • The following options can be used:
  • AlternativeHypothesis "Unequal"the inequality for the alternative hypothesis
    Method Automaticthe method to use for computing -values
    SignificanceLevel 0.05cutoff for diagnostics and reporting
  • For tests of independence, a cutoff is chosen such that is rejected only if . The value of used for the "TestConclusion" and "ShortTestConclusion" properties is controlled by the SignificanceLevel option. By default, is set to 0.05.

Examples

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Basic Examples  (1)

Test whether two vectors are independent:

Scope  (7)

Testing  (4)

Test whether two vectors are independent:

The -values are typically large when the vectors are independent:

The -values are typically small when there are dependencies:

Create a HypothesisTestData object for repeated property extraction:

The properties available for extraction:

Extract some properties from the HypothesisTestData object:

The -value and test statistic from the test:

Extract any number of properties simultaneously:

The -value and test statistic from the test:

Reporting  (3)

Tabulate the results from the test:

A table of the test results:

Retrieve the entries from a test table for customized reporting:

Tabulate the -value or test statistic:

The -value from the table:

The test statistic from the table:

Options  (7)

AlternativeHypothesis  (2)

A two-sided test is performed by default:

Perform a two-sided test or a one-sided alternative:

A two-sided test:

The two one-sided alternatives:

Method  (4)

By default, -values are computed using asymptotic test statistic distributions:

The -value can be obtained using permutation methods:

Set the number of permutations to use:

By default, random permutations are used:

Set the seed used for generating random permutations:

SignificanceLevel  (1)

The significance level is used for "TestConclusion" and "ShortTestConclusion":

Properties & Relations  (3)

For vector to vector comparisons, the test statistic is computed as GoodmanKruskalGamma:

IndependenceTest can be used to select an appropriate test of independence:

GoodmanKruskalGammaTest is one of the available tests:

GoodmanKruskalGammaTest only detects monotonic dependence:

HoeffdingDTest can be used to detect a wider variety of dependence structures:

Neat Examples  (1)

Compute the statistic when the null hypothesis is true:

The test statistic given a particular alternative:

Compare the distributions of the test statistics:

Wolfram Research (2012), GoodmanKruskalGammaTest, Wolfram Language function, https://reference.wolfram.com/language/ref/GoodmanKruskalGammaTest.html.

Text

Wolfram Research (2012), GoodmanKruskalGammaTest, Wolfram Language function, https://reference.wolfram.com/language/ref/GoodmanKruskalGammaTest.html.

CMS

Wolfram Language. 2012. "GoodmanKruskalGammaTest." Wolfram Language & System Documentation Center. Wolfram Research. https://reference.wolfram.com/language/ref/GoodmanKruskalGammaTest.html.

APA

Wolfram Language. (2012). GoodmanKruskalGammaTest. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/GoodmanKruskalGammaTest.html

BibTeX

@misc{reference.wolfram_2024_goodmankruskalgammatest, author="Wolfram Research", title="{GoodmanKruskalGammaTest}", year="2012", howpublished="\url{https://reference.wolfram.com/language/ref/GoodmanKruskalGammaTest.html}", note=[Accessed: 22-November-2024 ]}

BibLaTeX

@online{reference.wolfram_2024_goodmankruskalgammatest, organization={Wolfram Research}, title={GoodmanKruskalGammaTest}, year={2012}, url={https://reference.wolfram.com/language/ref/GoodmanKruskalGammaTest.html}, note=[Accessed: 22-November-2024 ]}