KSLCore/ksl.utilities.distributions.fitting/DiscretePMFGoodnessOfFit

DiscretePMFGoodnessOfFit

open class DiscretePMFGoodnessOfFit(data: DoubleArray, val distribution: DiscretePMFInRangeDistributionIfc, val numEstimatedParameters: Int = 1, breakPoints: DoubleArray = PMFModeler.makeZeroToInfinityBreakPoints(data.size, distribution)) : DistributionGOF, DiscreteDistributionGOFIfc

Inheritors

PoissonGoodnessOfFit

Constructors

DiscretePMFGoodnessOfFit

constructor(data: DoubleArray, distribution: DiscretePMFInRangeDistributionIfc, numEstimatedParameters: Int = 1, breakPoints: DoubleArray = PMFModeler.makeZeroToInfinityBreakPoints(data.size, distribution))

Properties

override val binCounts: DoubleArray

binProbabilities

override val binProbabilities: DoubleArray

override val breakPoints: DoubleArray

chiSquaredPValue

override val chiSquaredPValue: Double

chiSquaredTestDOF

override val chiSquaredTestDOF: Int

chiSquaredTestStatistic

override val chiSquaredTestStatistic: Double

override val distribution: DiscretePMFInRangeDistributionIfc

override val expectedCounts: DoubleArray

override val histogram: HistogramIfc

indexOfDispersion

open val indexOfDispersion: Double

numEstimatedParameters

override val numEstimatedParameters: Int = 1

poissonVarianceTestStatistic

open val poissonVarianceTestStatistic: Double

Functions

chiSquaredTestResults

open fun chiSquaredTestResults(type1Error: Double = 0.05): String

open override fun toString(): String