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java.lang.Object gov.sandia.cognition.util.AbstractCloneableSerializable gov.sandia.cognition.statistics.method.FriedmanConfidence
@ConfidenceTestAssumptions(name="Friedman\'s test", alsoKnownAs="", description={"Friedman\'s test determines if the rankings associated with various treatments are equal.","This is a nonparametric rankbased alternative to ANOVA, a multiple comparison generalization similar to the difference between Student\'s ttest and Wilcoxon ranksigned 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 Fstatistic rather than the original chisquare statistic")}) public class FriedmanConfidence
The Friedman test determines if the rankings associated with various treatments are equal. This is a nonparametric alternative to ANOVA.
Nested Class Summary  

static class 
FriedmanConfidence.Statistic
Confidence statistic associated with the Friedman test using the tighter Fstatistic. 
Field Summary  

static FriedmanConfidence 
INSTANCE
Default instance. 
Constructor Summary  

FriedmanConfidence()
Creates a new instance of FriedmanConfidence 
Method Summary  

static ArrayList<Double> 
computeTreatmentRankMeans(Collection<? extends Collection<? extends Number>> data)
Computes the mean rank of the treatments 
FriedmanConfidence.Statistic 
evaluateNullHypothesis(Collection<? extends Collection<? extends Number>> data)
Evaluates the null hypothesis for the given blockdesign treatments 
FriedmanConfidence.Statistic 
evaluateNullHypothesis(Collection<? extends Number> data1,
Collection<? extends Number> data2)
Computes the probability that two data were generated by the same distribution. 
Methods inherited from class gov.sandia.cognition.util.AbstractCloneableSerializable 

clone 
Methods inherited from class java.lang.Object 

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait 
Methods inherited from interface gov.sandia.cognition.util.CloneableSerializable 

clone 
Field Detail 

public static final FriedmanConfidence INSTANCE
Constructor Detail 

public FriedmanConfidence()
Method Detail 

public FriedmanConfidence.Statistic evaluateNullHypothesis(Collection<? extends Number> data1, Collection<? extends Number> data2)
NullHypothesisEvaluator
evaluateNullHypothesis
in interface NullHypothesisEvaluator<Collection<? extends Number>>
data1
 First dataset to considerdata2
 Second dataset to consider
public FriedmanConfidence.Statistic evaluateNullHypothesis(Collection<? extends Collection<? extends Number>> data)
BlockExperimentComparison
evaluateNullHypothesis
in interface BlockExperimentComparison<Number>
data
 Collection of treatments for the blockdesign experiment, where each
treatment contains
public static ArrayList<Double> computeTreatmentRankMeans(Collection<? extends Collection<? extends Number>> data)
data
 Collection of treatments, where each treatment must have the same number
of subjects in each treatment


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