public class ChiSquareIndependenceTest extends HypothesisTest
Modifier and Type | Class and Description |
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static class |
ChiSquareIndependenceTest.Type
the available distributions used for the test
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Constructor and Description |
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ChiSquareIndependenceTest(Matrix sample)
Assess whether the two random variables in the contingency table are independent.
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ChiSquareIndependenceTest(Matrix sample,
int nSims,
ChiSquareIndependenceTest.Type type)
Assess whether the two random variables in the contingency table are independent.
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Modifier and Type | Method and Description |
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String |
getAlternativeHypothesis()
Get the description of the alternative hypothesis.
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static Matrix |
getExpectedContingencyTable(int[] rowSums,
int[] colSums)
Assume the null hypothesis of independence, we compute the expected frequency of each
category.
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String |
getNullHypothesis()
Get a description of the null hypothesis.
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static double |
pearsonStat(Matrix O,
Matrix E,
boolean YatesContinuityCorrection)
Compute the Pearson's cumulative test statistic, which asymptotically approaches a
χ2 distribution.
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double |
pValue()
Get the p-value for the test statistics.
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double |
statistics()
Get the test statistics.
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isNullRejected, nGroups, nObs, oneSidedPvalue
public ChiSquareIndependenceTest(Matrix sample, int nSims, ChiSquareIndependenceTest.Type type)
sample
- a contingency tablenSims
- the number of simulations when EXACT distribution is usedtype
- the type of distributionpublic ChiSquareIndependenceTest(Matrix sample)
sample
- a contingency tablepublic String getNullHypothesis()
HypothesisTest
getNullHypothesis
in class HypothesisTest
public String getAlternativeHypothesis()
HypothesisTest
getAlternativeHypothesis
in class HypothesisTest
public double statistics()
HypothesisTest
statistics
in class HypothesisTest
public double pValue()
HypothesisTest
pValue
in class HypothesisTest
public static Matrix getExpectedContingencyTable(int[] rowSums, int[] colSums)
rowSums
- the row totalscolSums
- the column totalspublic static double pearsonStat(Matrix O, Matrix E, boolean YatesContinuityCorrection)
O
- the observation matrixE
- the expectation matrixYatesContinuityCorrection
- true
if to minus 0.5 for each observation in
the test statisticsCopyright © 2010-2020 NM FinTech Ltd.. All Rights Reserved.