Kappa Test for agreement in contingency tables.
Inheritance Hierarchy Namespace: Accord.Statistics.TestingAccord.Statistics (in Accord.Statistics.dll) Version: 3.8.0
Syntax[SerializableAttribute] public class KappaTest : ZTest
<SerializableAttribute> Public Class KappaTest Inherits ZTestRequest Example View Source
The KappaTest type exposes the following members.
Constructors Name Description KappaTest(GeneralConfusionMatrix, OneSampleHypothesis)Creates a new Kappa test.
KappaTest(Double, Double, OneSampleHypothesis)Creates a new Kappa test.
KappaTest(GeneralConfusionMatrix, Double, OneSampleHypothesis)Creates a new Kappa test.
KappaTest(WeightedConfusionMatrix, Double, OneSampleHypothesis)Creates a new Kappa test.
KappaTest(Double, Double, Double, OneSampleHypothesis)Creates a new Kappa test.
Top Properties Methods Name Description AsymptoticKappaVariance(GeneralConfusionMatrix)Computes the asymptotic variance for Fleiss's Kappa variance using the formulae by (Fleiss et al, 1969) when the underlying Kappa is assumed different from zero.
AsymptoticKappaVariance(GeneralConfusionMatrix, Double, Boolean)Computes the asymptotic variance for Fleiss's Kappa variance using the formulae by (Fleiss et al, 1969). If nullHypothesis is set to true, the method will return the variance under the null hypothesis.
AsymptoticKappaVariance(WeightedConfusionMatrix, Double, Boolean)Computes the asymptotic variance for Fleiss's Kappa variance using the formulae by (Fleiss et al, 1969). If nullHypothesis is set to true, the method will return the variance under the null hypothesis.
Compute(Double, OneSampleHypothesis)Computes the Z test.
(Inherited from ZTest.) Compute(Double, Double, Double, OneSampleHypothesis)Computes the Z test.
(Inherited from ZTest.) DeltaMethodKappaVariance(GeneralConfusionMatrix)Compute Cohen's Kappa variance using the large sample approximation given by Congalton, which is common in the remote sensing literature.
DeltaMethodKappaVariance(GeneralConfusionMatrix, Double)Compute Cohen's Kappa variance using the large sample approximation given by Congalton, which is common in the remote sensing literature.
EqualsDetermines whether the specified object is equal to the current object.
(Inherited from Object.) FinalizeAllows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.
(Inherited from Object.) GetConfidenceIntervalGets a confidence interval for the
EstimatedValuestatistic within the given confidence level percentage.
(Inherited from ZTest.) GetHashCodeServes as the default hash function.
(Inherited from Object.) GetTypeGets the Type of the current instance.
(Inherited from Object.) MemberwiseCloneCreates a shallow copy of the current Object.
(Inherited from Object.) OnSizeChangedUpdate event.
(Inherited from ZTest.) PValueToStatistic(Double)Converts a given p-value to a test statistic.
(Inherited from ZTest.) StatisticToPValue(Double)Converts a given test statistic to a p-value.
(Inherited from ZTest.) ToStringConverts the numeric P-Value of this test to its equivalent string representation.
(Inherited from HypothesisTestTDistribution.) ToString(String, IFormatProvider)Converts the numeric P-Value of this test to its equivalent string representation.
(Inherited from HypothesisTestTDistribution.) Top Extension Methods Name Description HasMethodChecks whether an object implements a method with the given name.
(Defined by ExtensionMethods.) IsEqualCompares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices.
(Defined by Matrix.) To(Type) Overloaded.Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.) ToT Overloaded.Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.) Top RemarksThe Kappa test tries to assert whether the Kappa measure of a a contingency table, is significantly different from another hypothesized value.
The computations used by the test are the same found in the 1969 paper by J. L. Fleiss, J. Cohen, B. S. Everitt, in which they presented the finally corrected version of the Kappa's variance formulae. This is contrast to the computations traditionally found in the remote sensing literature. For those variance computations, see the DeltaMethodKappaVariance(GeneralConfusionMatrix) method.
This is a z-test kind of test.
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