FittingTest_Kolmogorov¶

FittingTest_Kolmogorov
(*args)¶ Perform a Kolmogorov goodnessoffit test for 1d continuous distributions.
Refer to KolmogorovSmirnov fitting test.
Parameters:  sample : 2d sequence of float
Tested sample.
 model :
Distribution
orDistributionFactory
Tested distribution.
 level : float, , optional
This is the risk of committing a Type I error, that is an incorrect rejection of a true null hypothesis.
 n_parameters : int, , optional
The number of parameters in the distribution that have been estimated from the sample. This parameter must not be provided if a
DistributionFactory
was provided as the second argument (it will internally be set to the number of parameters estimated by theDistributionFactory
). It can be specified if aDistribution
was provided as the second argument, but if it is not, it will be set equal to 0.
Returns:  test_result :
TestResult
Test result.
Raises:  TypeError : If the distribution is not continuous or if the sample is
multivariate.
Notes
The present implementation of the Kolmogorov goodnessoffit test is twosided. This uses an external C implementation of the Kolmogorov cumulative distribution function by [Simard2011]. If it is called with a distribution, it is supposed to be fully specified ie no parameter has been estimated from the given sample. Otherwise, the distribution is estimated using the given factory based on the given sample and the distribution of the test statistics is estimated using a Monte Carlo approach (see the FittingTestKolmogorovSamplingSize key in
ResourceMap
).Examples
>>> import openturns as ot >>> ot.RandomGenerator.SetSeed(0) >>> distribution = ot.Normal() >>> sample = distribution.getSample(30) >>> ot.FittingTest.Kolmogorov(sample, ot.NormalFactory(), 0.01) class=TestResult name=Unnamed type=KolmogorovDistribution binaryQualityMeasure=true pvalue threshold=0.01 pvalue=0.7 description=[Normal(mu = 0.0944924, sigma = 0.989808) vs sample Normal]