@ConfidenceTestAssumptions(name="Friedman\'s test",alsoKnownAs="",description={"Friedman\'s test determines if the rankings associated with various treatments are equal.","This is a nonparametric rank-based alternative to ANOVA, a multiple comparison generalization similar to the difference between Student\'s t-test and Wilcoxon rank-signed test.","Friedman\'s test tends to have as much power as ANOVA, but without ANOVA\'s parameteric assumptions"},assumptions={"All data came from same distribution, without considering treatment effects.","Measurements are independent and equivalent within a treatment.","All observations are independent."},nullHypothesis="The treatments have no effect on experimental observations.",dataPaired=true,dataSameSize=true,distribution=SnedecorFDistribution.class,reference={@PublicationReference(author="Janez Demsar",title="Statistical Comparisons of Classifiers over Multiple Data Sets",type=Journal,publication="Journal of Machine Learning Research",year=2006,url="http://www.jmlr.org/papers/volume7/demsar06a/demsar06a.pdf"),@PublicationReference(author="Wikipedia",title="Friedman test",type=WebPage,year=2011,url="http://en.wikipedia.org/wiki/Friedman_test",notes="Our test uses the tighter F-statistic rather than the original chi-square statistic")}) public class FriedmanConfidence extends AbstractCloneableSerializable implements BlockExperimentComparison<java.lang.Number>
Modifier and Type | Class and Description |
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static class |
FriedmanConfidence.Statistic
Confidence statistic associated with the Friedman test using the tighter
F-statistic.
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Modifier and Type | Field and Description |
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static FriedmanConfidence |
INSTANCE
Default instance.
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Constructor and Description |
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FriedmanConfidence()
Creates a new instance of FriedmanConfidence
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Modifier and Type | Method and Description |
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static java.util.ArrayList<java.lang.Double> |
computeTreatmentRankMeans(java.util.Collection<? extends java.util.Collection<? extends java.lang.Number>> data)
Computes the mean rank of the treatments
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FriedmanConfidence.Statistic |
evaluateNullHypothesis(java.util.Collection<? extends java.util.Collection<? extends java.lang.Number>> data)
Evaluates the null hypothesis for the given block-design treatments
|
FriedmanConfidence.Statistic |
evaluateNullHypothesis(java.util.Collection<? extends java.lang.Number> data1,
java.util.Collection<? extends java.lang.Number> data2)
Computes the probability that two data were generated by
the same distribution.
|
clone
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
clone
public static final FriedmanConfidence INSTANCE
public FriedmanConfidence()
public FriedmanConfidence.Statistic evaluateNullHypothesis(java.util.Collection<? extends java.lang.Number> data1, java.util.Collection<? extends java.lang.Number> data2)
NullHypothesisEvaluator
evaluateNullHypothesis
in interface NullHypothesisEvaluator<java.util.Collection<? extends java.lang.Number>>
data1
- First dataset to considerdata2
- Second dataset to considerpublic FriedmanConfidence.Statistic evaluateNullHypothesis(java.util.Collection<? extends java.util.Collection<? extends java.lang.Number>> data)
BlockExperimentComparison
evaluateNullHypothesis
in interface BlockExperimentComparison<java.lang.Number>
data
- Collection of treatments for the block-design experiment, where each
treatment containspublic static java.util.ArrayList<java.lang.Double> computeTreatmentRankMeans(java.util.Collection<? extends java.util.Collection<? extends java.lang.Number>> data)
data
- Collection of treatments, where each treatment must have the same number
of subjects in each treatment