public static class TukeyKramerConfidence.Statistic extends AbstractMultipleHypothesisComparison.Statistic
Modifier and Type | Field and Description |
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protected double |
meanSquaredResiduals
Mean-squared difference over all subjects
|
protected Matrix |
standardErrors
Gets the standard errors in the experiment
|
protected java.util.ArrayList<java.lang.Integer> |
subjectCounts
Number of subjects in each treatment
|
protected java.util.ArrayList<java.lang.Double> |
treatmentMeans
Mean for each treatment
|
nullHypothesisProbabilities, testStatistics, treatmentCount, uncompensatedAlpha
Constructor and Description |
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Statistic(double uncompensatedAlpha,
java.util.ArrayList<java.lang.Integer> subjectCounts,
java.util.ArrayList<java.lang.Double> treatmentMeans,
double meanSquaredResiduals)
Creates a new instance of Statistic
|
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) .
|
TukeyKramerConfidence.Statistic |
clone()
This makes public the clone method on the
Object class and
removes the exception that it throws. |
protected Matrix |
computeNullHypothesisProbabilities(java.util.ArrayList<java.lang.Integer> subjectCounts,
Matrix Z)
Computes null-hypothesis probability for the (i,j) treatment comparison
|
Matrix |
computeTestStatistics(java.util.ArrayList<java.lang.Integer> subjectCounts,
java.util.ArrayList<java.lang.Double> treatmentMeans,
double meanSquaredResiduals)
Computes the test statistic for all treatments
|
double |
getMeanSquaredResiduals()
Getter for meanSquaredResiduals
|
Matrix |
getStandardErrors()
Getter for standardErrors
|
java.util.ArrayList<java.lang.Integer> |
getSubjectCounts()
Getter for subjectCounts
|
java.util.ArrayList<java.lang.Double> |
getTreatmentMeans()
Getter for treatmentMeans
|
getNullHypothesisProbability, getTestStatistic, getTreatmentCount, getUncompensatedAlpha, toString
protected java.util.ArrayList<java.lang.Integer> subjectCounts
protected java.util.ArrayList<java.lang.Double> treatmentMeans
protected double meanSquaredResiduals
protected Matrix standardErrors
public Statistic(double uncompensatedAlpha, java.util.ArrayList<java.lang.Integer> subjectCounts, java.util.ArrayList<java.lang.Double> treatmentMeans, double meanSquaredResiduals)
uncompensatedAlpha
- Uncompensated alpha (p-value threshold) for the multiple comparison
testsubjectCounts
- Number of subjects in each treatmenttreatmentMeans
- Mean for each treatmentmeanSquaredResiduals
- Mean-squared difference over all subjectspublic Matrix computeTestStatistics(java.util.ArrayList<java.lang.Integer> subjectCounts, java.util.ArrayList<java.lang.Double> treatmentMeans, double meanSquaredResiduals)
subjectCounts
- Number of subjects in each treatmenttreatmentMeans
- Mean for each treatmentmeanSquaredResiduals
- Mean-squared difference over all subjectsprotected Matrix computeNullHypothesisProbabilities(java.util.ArrayList<java.lang.Integer> subjectCounts, Matrix Z)
subjectCounts
- Number of subjects in the experimentZ
- Test statistic for the (i,j) treatment comparisonpublic TukeyKramerConfidence.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 java.util.ArrayList<java.lang.Integer> getSubjectCounts()
public java.util.ArrayList<java.lang.Double> getTreatmentMeans()
public double getMeanSquaredResiduals()
public boolean acceptNullHypothesis(int i, int j)
MultipleHypothesisComparison.Statistic
i
- First treatment indexj
- Second treatment indexpublic Matrix getStandardErrors()