org.codehaus.jet.hypothesis.rejection.estimators
Class AbstractRejectionValueEstimator
java.lang.Object
org.codehaus.jet.hypothesis.rejection.estimators.AbstractRejectionValueEstimator
- All Implemented Interfaces:
- RejectionValueEstimator
- Direct Known Subclasses:
- CriticalValueEstimator, PValueEstimator
public abstract class AbstractRejectionValueEstimator
- extends java.lang.Object
- implements RejectionValueEstimator
Abstract base RejectionValueEstimator. Concrete implementations include the
CriticalValueEstimator and PValueEstimator.
- Author:
- Mauro Talevi
- See Also:
CriticalValueEstimator
,
PValueEstimator
Method Summary |
protected int |
calculateMinimizingIndex(double[] values,
double level,
double threshold)
|
protected double |
cumulativeNormal(double level)
|
protected double[] |
extractMiddleColumnPoints(double[][] beta,
int first,
int min,
int np)
|
protected double[] |
extractTailColumnPoints(double[][] beta,
int first,
int np,
boolean upper)
|
protected int |
getNumberOfPoints()
|
protected int |
getTailPoints(int min,
int np,
boolean upper)
|
protected double |
getThreshold()
|
protected double[] |
glsRegression(double[] y,
double[][] x,
double[][] omega)
|
protected double |
inverseCumulativeNormal(double level)
|
protected boolean |
isLower(int min,
int np)
|
protected boolean |
isMiddle(int min,
int np)
|
protected double |
powerSeries(double[] series,
double x,
int order)
|
protected double[][] |
toCovariance(double[] probs,
double[] weights,
int min,
int np)
|
protected double[] |
toCriticalValues(double[][] beta,
int sampleSize,
int[] params)
|
protected double[][] |
toMiddleCovariance(double[] probs,
double[] weights,
int min,
int np)
|
protected int |
toMiddleIndex(int min,
int np,
int i)
|
protected double[][] |
toMiddleXSample(double[] values,
int min,
int np,
int nvar)
|
protected double[] |
toMiddleYSample(double[] values,
int min,
int np)
|
protected double[][] |
toTailCovariance(double[] probs,
double[] weights,
int np,
boolean upper)
|
protected double[][] |
toTailXSample(double[] values,
int np,
int nvar,
boolean upper)
|
protected double[] |
toTailYSample(double[] values,
int np,
boolean upper)
|
protected int |
toUpperIndex(int i)
|
protected double[][] |
toXSample(double[] norms,
int min,
int np,
int nvar)
|
protected double[] |
toYSample(double[] values,
int min,
int np)
|
protected boolean |
tTest(double[] gamma,
int element,
double threshold)
|
protected void |
validateParams(int sampleSize,
double level)
|
Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
distribution
protected org.apache.commons.math.distribution.NormalDistribution distribution
gls
protected org.codehaus.jet.regression.MultipleLinearRegressionEstimator gls
responseSurfaceEvaluator
protected ResponseSurfaceEvaluator responseSurfaceEvaluator
AbstractRejectionValueEstimator
public AbstractRejectionValueEstimator(int numberOfPoints,
double threshold)
- Creates an AbstractRejectionValueEstimator
- Parameters:
numberOfPoints
- threshold
-
AbstractRejectionValueEstimator
public AbstractRejectionValueEstimator(ResponseSurfaceEvaluator responseSurfaceEvaluator,
int numberOfPoints,
double threshold)
- Parameters:
responseSurfaceEvaluator
- numberOfPoints
- threshold
-
validateParams
protected void validateParams(int sampleSize,
double level)
powerSeries
protected double powerSeries(double[] series,
double x,
int order)
calculateMinimizingIndex
protected int calculateMinimizingIndex(double[] values,
double level,
double threshold)
extractMiddleColumnPoints
protected double[] extractMiddleColumnPoints(double[][] beta,
int first,
int min,
int np)
extractTailColumnPoints
protected double[] extractTailColumnPoints(double[][] beta,
int first,
int np,
boolean upper)
isMiddle
protected boolean isMiddle(int min,
int np)
isLower
protected boolean isLower(int min,
int np)
toMiddleIndex
protected int toMiddleIndex(int min,
int np,
int i)
toUpperIndex
protected int toUpperIndex(int i)
getTailPoints
protected int getTailPoints(int min,
int np,
boolean upper)
toXSample
protected double[][] toXSample(double[] norms,
int min,
int np,
int nvar)
toMiddleXSample
protected double[][] toMiddleXSample(double[] values,
int min,
int np,
int nvar)
toTailXSample
protected double[][] toTailXSample(double[] values,
int np,
int nvar,
boolean upper)
toCovariance
protected double[][] toCovariance(double[] probs,
double[] weights,
int min,
int np)
toMiddleCovariance
protected double[][] toMiddleCovariance(double[] probs,
double[] weights,
int min,
int np)
toTailCovariance
protected double[][] toTailCovariance(double[] probs,
double[] weights,
int np,
boolean upper)
toCriticalValues
protected double[] toCriticalValues(double[][] beta,
int sampleSize,
int[] params)
glsRegression
protected double[] glsRegression(double[] y,
double[][] x,
double[][] omega)
tTest
protected boolean tTest(double[] gamma,
int element,
double threshold)
cumulativeNormal
protected double cumulativeNormal(double level)
inverseCumulativeNormal
protected double inverseCumulativeNormal(double level)
getNumberOfPoints
protected int getNumberOfPoints()
getThreshold
protected double getThreshold()
toYSample
protected double[] toYSample(double[] values,
int min,
int np)
toMiddleYSample
protected double[] toMiddleYSample(double[] values,
int min,
int np)
toTailYSample
protected double[] toTailYSample(double[] values,
int np,
boolean upper)
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