public static class NemenyiConfidence.Statistic extends AbstractMultipleHypothesisComparison.Statistic
nullHypothesisProbabilities, testStatistics, treatmentCount, uncompensatedAlpha
Constructor and Description |
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Statistic(double uncompensatedAlpha,
int subjectCount,
java.util.ArrayList<java.lang.Double> treatmentRankMeans,
double standardError)
Creates a new instance of StudentizedMultipleComparisonStatistic
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Modifier and Type | Method and Description |
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boolean |
acceptNullHypothesis(int i,
int j)
Determines if the (i,j) null hypothesis should be accepted (true) or
rejected (false) .
|
NemenyiConfidence.Statistic |
clone()
This makes public the clone method on the
Object class and
removes the exception that it throws. |
protected Matrix |
computeNullHypothesisProbabilities(int subjectCount,
Matrix Z)
Computes null-hypothesis probability for the (i,j) treatment comparison
|
Matrix |
computeTestStatistics(int subjectCount,
java.util.ArrayList<java.lang.Double> treatmentRankMeans,
double standardError)
Computes the test statistic for all treatments
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double |
getStandardError()
Getter for standardError
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int |
getSubjectCount()
Getter for subjectCount
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java.util.ArrayList<java.lang.Double> |
getTreatmentMeans()
Getter for treatmentRankMeans
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getNullHypothesisProbability, getTestStatistic, getTreatmentCount, getUncompensatedAlpha, toString
public Statistic(double uncompensatedAlpha, int subjectCount, java.util.ArrayList<java.lang.Double> treatmentRankMeans, double standardError)
uncompensatedAlpha
- Uncompensated alpha (p-value threshold) for the multiple comparison
testsubjectCount
- Number of subjects in each treatmenttreatmentRankMeans
- Mean for each treatmentstandardError
- Standard error of the entire experimentpublic Matrix computeTestStatistics(int subjectCount, java.util.ArrayList<java.lang.Double> treatmentRankMeans, double standardError)
subjectCount
- Number of subjects in each treatmenttreatmentRankMeans
- Mean for each treatmentstandardError
- Standard error of the entire experimentprotected Matrix computeNullHypothesisProbabilities(int subjectCount, Matrix Z)
subjectCount
- Number of subjects in the experimentZ
- Test statistic for the (i,j) treatment comparisonpublic NemenyiConfidence.Statistic clone()
AbstractCloneableSerializable
Object
class and
removes the exception that it throws. Its default behavior is to
automatically create a clone of the exact type of object that the
clone is called on and to copy all primitives but to keep all references,
which means it is a shallow copy.
Extensions of this class may want to override this method (but call
super.clone()
to implement a "smart copy". That is, to target
the most common use case for creating a copy of the object. Because of
the default behavior being a shallow copy, extending classes only need
to handle fields that need to have a deeper copy (or those that need to
be reset). Some of the methods in ObjectUtil
may be helpful in
implementing a custom clone method.
Note: The contract of this method is that you must use
super.clone()
as the basis for your implementation.clone
in interface CloneableSerializable
clone
in class AbstractMultipleHypothesisComparison.Statistic
public int getSubjectCount()
public double getStandardError()
public java.util.ArrayList<java.lang.Double> getTreatmentMeans()
public boolean acceptNullHypothesis(int i, int j)
MultipleHypothesisComparison.Statistic
i
- First treatment indexj
- Second treatment index