computeTestKolmogorovLogNormal {rotRPackage} | R Documentation |
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 LogNormal distribution of the given parameters and returns a list containing the result and test p-value.
computeTestKolmogorovLogNormal(numericalSample, muLog, sigmaLog, gamma, testLevel = 0.95, estimatedParameters)
numericalSample |
the sample to be tested (numeric vector) |
muLog |
The LogNormal distribution mu. |
sigmaLog |
The LogNormal distribution sigma. |
gamma |
The LogNormal distribution gammaParameter. |
testLevel |
the test level. (scalar in [0:1]) |
estimatedParameters |
the test level. (scalar in [0:1]) |
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) |
Pierre-Matthieu Pair, Softia for EDF.
# Standard LogNormal distribution example. print(computeTestKolmogorovLogNormal(exp(rnorm(1000, 3, 1.5)), 3, 1.5, 0)) print(computeTestKolmogorovLogNormal(exp(rnorm(1000, 2.5, 1.5)), 3, 1.5, 0)) # Non - Standard LogNormal distribution example. print(computeTestKolmogorovLogNormal(exp(rnorm(1000, 3, 1.5)) + 1, 3, 1.5, 1)) print(computeTestKolmogorovLogNormal(exp(rnorm(1000, 3, 1)) + 1, 3, 1.5, 1))