KSLCore/ksl.utilities.statistic/U01Test

U01Test

object U01Test

Functions

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fun approxChiSQValue(df: Int, confidenceLevel: Double): Double

Use for large degrees of freedom

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fun chiSquared1DTestStatistic(u: DoubleArray, k: Int): Double
fun chiSquared1DTestStatistic(rng: RandU01Ifc, n: Long, k: Int): Double

Computes the chi-squared test statistic

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fun chiSquaredSerial2DTestStatistic(u: DoubleArray, k: Int): Double

Computes the 2-D chi-squared serial test. Assumes that the pairs are (u(1), u(2)), (u(3), u(4)) ..., (u(n/2 - 1), u(n/2))

fun chiSquaredSerial2DTestStatistic(rng: RandU01Ifc, n: Long, k: Int): Double

Performs the 2-D chi-squared serial test

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fun chiSquaredSerial3DTestStatistic(u: DoubleArray, k: Int): Double

Computes the 3-D chi-squared serial test Tests triplets (u(i-1), u(i), u(i+1))

fun chiSquaredSerial3DTestStatistic(rng: RandU01Ifc, n: Long, k: Int): Double

Performs the 3-D chi-squared serial test

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fun chiSquaredTestStatistic(u: DoubleArray, k: Int): Double

Computes the chi-squared test statistic

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fun correlationTest(rng: RandU01Ifc, lag: Int, n: Long): Double

Performs the correlation test

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fun recommendedChiSquaredTestStatistic(u: DoubleArray, k: Int = floor(u.size / 5.0).toInt()): Double

Computes the chi-squared test statistic with recommended intervals and array size

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fun recommendedU01BreakPoints(sampleSize: Int, confidenceLevel: Double = 0.95): DoubleArray

Recommends break points over the range (0,1) based on the recommended number of intervals for a chi-squared goodness of fit test.

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fun recommendNumChiSquaredIntervals(sampleSize: Int, confidenceLevel: Double = 0.95): Int

Recommends the number of intervals for a chi-squared goodness of fit test based on the supplied sample size. This does not assume anything about the distribution. An adjustment ensures that there are at least 3 intervals based on other theory.

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fun recommendNumChiSquareU01Intervals(sampleSize: Int, confidenceLevel: Double = 0.95): Int

Recommends the number of intervals for a chi-squared goodness of fit test of possible U(0,1) data based on the supplied sample size. Adjusts the result of Williams approximation to ensure at least 5 or more expected within the intervals and a minimum of at least 3 intervals.

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fun runsUpTestStatistic(doubleArray: DoubleArray): Double

Computes the runs up test statistic for the array of supposedly U(0,1) values.

fun runsUpTestStatistic(rng: RandU01Ifc, n: Long): Double

Performs the runs up test