/usr/include/openturns/HypothesisTest.hxx is in libopenturns-dev 1.9-5.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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/**
* @brief StatTest implements statistical tests
*
* Copyright 2005-2017 Airbus-EDF-IMACS-Phimeca
*
* This library is free software: you can redistribute it and/or modify
* it under the terms of the GNU Lesser General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This library is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* along with this library. If not, see <http://www.gnu.org/licenses/>.
*
*/
#ifndef OPENTURNS_HYPOTHESISTEST_HXX
#define OPENTURNS_HYPOTHESISTEST_HXX
#include "openturns/OTprivate.hxx"
#include "openturns/TestResult.hxx"
#include "openturns/Indices.hxx"
#include "openturns/Sample.hxx"
#include "openturns/LinearModel.hxx"
#include "openturns/Distribution.hxx"
#include "openturns/DistributionFactoryImplementation.hxx"
BEGIN_NAMESPACE_OPENTURNS
/**
* @class HypothesisTest
*
*/
class OT_API HypothesisTest
{
public:
typedef Collection<TestResult> TestResultCollection;
/** Independance ChiSquared test between 2 scalar samples for discrete distributions */
static TestResult ChiSquared(const Sample & firstSample,
const Sample & secondSample,
const Scalar level = 0.95);
/** Independence Pearson test between 2 samples which form a gaussian vector: test the linear relation */
static TestResult Pearson(const Sample & firstSample,
const Sample & secondSample,
const Scalar level = 0.95);
/** Smirnov test if two scalar samples (of sizes not necessarily equal) follow the same distribution (only for continuous distributions) */
static TestResult Smirnov(const Sample & firstSample,
const Sample & secondSample,
const Scalar level = 0.95);
/** Spearman test between 2 scalar samples : test the monotonous relation */
static TestResult Spearman(const Sample & firstSample,
const Sample & secondSample,
const Scalar level = 0.95);
/** Independence Pearson test between 2 samples : firstSample of dimension n and secondSample of dimension 1. If firstSample[i] is the numeriacl sample extracted from firstSample (ith coordinate of each point of the numerical sample), PartialPearson performs the Independence Pearson test simultaneously on firstSample[i] and secondSample, for i in the selection. For all i, it is supposed that the couple (firstSample[i] and secondSample) is issued from a gaussian vector. */
static TestResultCollection PartialPearson(const Sample & firstSample,
const Sample & secondSample,
const Indices & selection,
const Scalar level = 0.95);
/** Regression test between 2 samples : firstSample of dimension n and secondSample of dimension 1. If firstSample[i] is the numerical sample extracted from firstSample (ith coordinate of each point of the numerical sample), PartialRegression performs the Regression test simultaneously on all firstSample[i] and secondSample, for i in the selection. The Regression test tests ifthe regression model between two scalar numerical samples is significant. It is based on the deviation analysis of the regression. The Fisher distribution is used. */
static TestResultCollection PartialRegression(const Sample & firstSample,
const Sample & secondSample,
const Indices & selection,
const Scalar level = 0.95);
/** Spearman test between 2 samples : firstSample of dimension n and secondSample of dimension 1. If firstSample[i] is the numerical sample extracted from firstSample (ith coordinate of each point of the numerical sample), PartialSpearman performs the Independence Spearman test simultaneously on firstSample[i] and secondSample, for i in the selection. */
static TestResultCollection PartialSpearman(const Sample & firstSample,
const Sample & secondSample,
const Indices & selection,
const Scalar level = 0.95);
/** Independence Pearson test between 2 samples : firstSample of dimension n and secondSample of dimension 1. If firstSample[i] is the numerical sample extracted from firstSample (ith coordinate of each point of the numerical sample), FullPearson performs the Independence Pearson test simultaneously on all firstSample[i] and secondSample. For all i, it is supposed that the couple (firstSample[i] and secondSample) is issued from a gaussian vector. */
static TestResultCollection FullPearson(const Sample & firstSample,
const Sample & secondSample,
const Scalar level = 0.95);
/** Regression test between 2 samples : firstSample of dimension n and secondSample of dimension 1. If firstSample[i] is the numerical sample extracted from firstSample (ith coordinate of each point of the numerical sample), FullRegression performs the Regression test simultaneously on all firstSample[i] and secondSample. The Regression test tests ifthe regression model between two scalar numerical samples is significant. It is based on the deviation analysis of the regression. The Fisher distribution is used. */
static TestResultCollection FullRegression(const Sample & firstSample,
const Sample & secondSample,
const Scalar level = 0.95);
/** Spearman test between 2 samples : firstSample of dimension n and secondSample of dimension 1. If firstSample[i] is the numerical sample extracted from firstSample (ith coordinate of each point of the numerical sample), PartialSpearman performs the Independence Spearman test simultaneously on all firstSample[i] and secondSample. */
static TestResultCollection FullSpearman(const Sample & firstSample,
const Sample & secondSample,
const Scalar level = 0.95);
protected:
/** Generic invocation of a R script for executing a partial test between two samples */
static TestResultCollection RunTwoSamplesASelectionRTest(const Sample & firstSample,
const Sample & secondSample,
const Indices & selection,
const Scalar level,
const String & testName);
/** Generic invocation of a R script for executing a test between two 1D samples */
static TestResult RunTwoSamplesRTest(const Sample & firstSample,
const Sample & secondSample,
const Scalar level,
const String & testName);
HypothesisTest();
}; /* class HypothesisTest */
END_NAMESPACE_OPENTURNS
#endif /* OPENTURNS_HYPOTHESISTEST_HXX */
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