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java.lang.Object gov.sandia.cognition.util.AbstractCloneableSerializable gov.sandia.cognition.statistics.method.AbstractConfidenceStatistic gov.sandia.cognition.statistics.method.MannWhitneyUConfidence.Statistic gov.sandia.cognition.statistics.method.ReceiverOperatingCharacteristic.Statistic
public static class ReceiverOperatingCharacteristic.Statistic
Contains useful statistics derived from a ROC curve
Field Summary 

Fields inherited from class gov.sandia.cognition.statistics.method.AbstractConfidenceStatistic 

nullHypothesisProbability 
Constructor Summary  

protected 
ReceiverOperatingCharacteristic.Statistic(ReceiverOperatingCharacteristic roc)
Creates a new instance of Statistic 
Method Summary  

static double 
computeAreaUnderCurve(ReceiverOperatingCharacteristic roc)
Computes the "pessimistic" area under the ROC curve using the topleft rectangle method for numerical integration. 
static double 
computeAreaUnderCurveTopLeft(Collection<ReceiverOperatingCharacteristic.DataPoint> points)
Computes the Area Under Curve for an xaxis sorted Collection of ROC points using the topleft rectangle method for numerical integration. 
static double 
computeAreaUnderCurveTrapezoid(Collection<ReceiverOperatingCharacteristic.DataPoint> points)
Computes the Area Under Curve for an xaxis sorted Collection of ROC points using the topleft rectangle method for numerical integration. 
static double 
computeDPrime(ReceiverOperatingCharacteristic.DataPoint data)
Computes the value of dprime given a datapoint 
static ReceiverOperatingCharacteristic.DataPoint 
computeOptimalThreshold(ReceiverOperatingCharacteristic roc)
Determines the DataPoint, and associated threshold, that simultaneously maximizes the value of Area=TruePositiveRate+TrueNegativeRate, usually the upperleft "knee" on the ROC curve. 
static ReceiverOperatingCharacteristic.DataPoint 
computeOptimalThreshold(ReceiverOperatingCharacteristic roc,
double truePositiveWeight,
double trueNegativeWeight)
Determines the DataPoint, and associated threshold, that simultaneously maximizes the value of Area=TruePositiveRate+TrueNegativeRate, usually the upperleft "knee" on the ROC curve. 
double 
getAreaUnderCurve()
Getter for areaUnderCurve 
double 
getDPrime()
Getter for dPrime 
ReceiverOperatingCharacteristic.DataPoint 
getOptimalThreshold()
Getter for optimalThreshold 
protected void 
setAreaUnderCurve(double areaUnderCurve)
Setter for areaUnderCurve 
protected void 
setDPrime(double dPrime)
Setter for dPrime 
protected void 
setOptimalThreshold(ReceiverOperatingCharacteristic.DataPoint optimalThreshold)
Setter for optimalThreshold 
Methods inherited from class gov.sandia.cognition.statistics.method.MannWhitneyUConfidence.Statistic 

computeNullHypothesisProbability, computeU, computeZ, getN1, getN2, getTestStatistic, getU, getZ, setN1, setN2, setU, setZ 
Methods inherited from class gov.sandia.cognition.statistics.method.AbstractConfidenceStatistic 

getNullHypothesisProbability, setNullHypothesisProbability, toString 
Methods inherited from class gov.sandia.cognition.util.AbstractCloneableSerializable 

clone 
Methods inherited from class java.lang.Object 

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

clone 
Constructor Detail 

protected ReceiverOperatingCharacteristic.Statistic(ReceiverOperatingCharacteristic roc)
roc
 ROC Curve from which to pull statisticsMethod Detail 

public static double computeAreaUnderCurve(ReceiverOperatingCharacteristic roc)
roc
 ROC Curve to compute the area under
@PublicationReference(author="Wikipedia", title="Rectangle method", type=WebPage, year=2011, url="http://en.wikipedia.org/wiki/Rectangle_method") public static double computeAreaUnderCurveTopLeft(Collection<ReceiverOperatingCharacteristic.DataPoint> points)
points
 xaxis sorted collection of xaxis points
@PublicationReference(author="Wikipedia", title="Trapezoidal rule", type=WebPage, year=2011, url="http://en.wikipedia.org/wiki/Trapezoidal_rule") public static double computeAreaUnderCurveTrapezoid(Collection<ReceiverOperatingCharacteristic.DataPoint> points)
points
 xaxis sorted collection of xaxis points
public static ReceiverOperatingCharacteristic.DataPoint computeOptimalThreshold(ReceiverOperatingCharacteristic roc)
roc
 ROC Curve to consider
public static ReceiverOperatingCharacteristic.DataPoint computeOptimalThreshold(ReceiverOperatingCharacteristic roc, double truePositiveWeight, double trueNegativeWeight)
truePositiveWeight
 Amount to weight the TruePositiveRatetrueNegativeWeight
 Amount to weight the TrueNegativeRateroc
 ROC Curve to consider
public static double computeDPrime(ReceiverOperatingCharacteristic.DataPoint data)
data
 Datapoint from which to estimate d'
public double getDPrime()
protected void setDPrime(double dPrime)
dPrime
 Estimated distance between the two classes to be split. Larger
values of d' indicate that the classes are easier to split,
d'=0 means that the classes overlap, and negative values mean
that your classifier is doing worse than chance, chump. This
appears to only be used by psychologists.public double getAreaUnderCurve()
protected void setAreaUnderCurve(double areaUnderCurve)
areaUnderCurve
 Area underneath the ROC curve, on the interval [0,1]. A value of
0.5 means that the classifier is doing no better than chance and
bigger is betterpublic ReceiverOperatingCharacteristic.DataPoint getOptimalThreshold()
protected void setOptimalThreshold(ReceiverOperatingCharacteristic.DataPoint optimalThreshold)
optimalThreshold
 DataPoint, with corresponding threshold, that maximizes the value
of Area=TruePositiveRate*(1FalsePositiveRate), usually the
upperleft "knee" on the ROC curve.


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