computeTestKolmogorovWeibull {rotRPackage}R Documentation

Compute the Kolmogorov-Smirnoff test on a Weibull Distribution sample.

Description

This ROT function, called from a Test C++ object, is given a sample, a point, the necessary distribution parameters and optionnaly a test level. It then returns the result of a K-S test against the null hypothesis that the sample has un underlying Weibull distribution of the given parameters and returns a list containing the result and test p-value.

Usage

computeTestKolmogorovWeibull(numericalSample, alpha, beta, gamma,
testLevel = 0.95, estimatedParameters)

Arguments

numericalSample the sample to be tested (numeric vector)
alpha The Weibull distribution alphaParameter.
beta The Weibull distribution betaParameter.
gamma The Weibull distribution gammaParameter.
testLevel the test level. (scalar in [0:1])
estimatedParameters the test level. (scalar in [0:1])

Value

A list is returned, containing :

testResult The result. 1 means H0 is not rejected. (scalar)
threshold The threshold applied to the p-value when deciding the outcome of the test.
pValue The test p-value. (scalar)

Author(s)

Pierre-Matthieu Pair, Softia for EDF.

Examples

# Standard Weibull distribution example. 
sample <- 3.0 * (-log(runif(1000)))^(1 / 5)
print(computeTestKolmogorovWeibull(sample, 3, 5, 0))
print(computeTestKolmogorovWeibull(sample, 2.5, 5, 0))

# Non - Standard Weibull distribution example. 

print(computeTestKolmogorovWeibull(sample + 1, 3, 5, 1))
print(computeTestKolmogorovWeibull(sample + 1, 3, 5, 0.5))

[Package rotRPackage version 1.4.3 Index]