ROOT logo
ROOT » MATH » MATHCORE » ROOT::Math::GoFTest

class ROOT::Math::GoFTest

Function Members (Methods)

public:
virtual~GoFTest()
Double_tAndersonDarling2SamplesTest(const Char_t* option = "p") const
voidAndersonDarling2SamplesTest(Double_t& pvalue, Double_t& testStat) const
static voidAndersonDarling2SamplesTest(const ROOT::Fit::BinData& data1, const ROOT::Fit::BinData& data2, Double_t& pvalue, Double_t& testStat)
Double_tAndersonDarlingTest(const Char_t* option = "p") const
voidAndersonDarlingTest(Double_t& pvalue, Double_t& testStat) const
ROOT::Math::GoFTestGoFTest(UInt_t sampleSize, const Double_t* sample, ROOT::Math::GoFTest::EDistribution dist = kUndefined)
ROOT::Math::GoFTestGoFTest(UInt_t sample1Size, const Double_t* sample1, UInt_t sample2Size, const Double_t* sample2)
ROOT::Math::GoFTestGoFTest(UInt_t sampleSize, const Double_t* sample, const ROOT::Math::IGenFunction& dist, ROOT::Math::GoFTest::EUserDistribution userDist = kPDF, Double_t xmin = 1, Double_t xmax = 0)
Double_tKolmogorovSmirnov2SamplesTest(const Char_t* option = "p") const
voidKolmogorovSmirnov2SamplesTest(Double_t& pvalue, Double_t& testStat) const
Double_tKolmogorovSmirnovTest(const Char_t* option = "p") const
voidKolmogorovSmirnovTest(Double_t& pvalue, Double_t& testStat) const
Double_toperator()(ROOT::Math::GoFTest::ETestType test = kAD, const Char_t* option = "p") const
voidoperator()(ROOT::Math::GoFTest::ETestType test, Double_t& pvalue, Double_t& testStat) const
static Double_tPValueADKSamples(UInt_t nsamples, Double_t A2)
voidSetDistribution(ROOT::Math::GoFTest::EDistribution dist)
voidSetUserCDF(const ROOT::Math::IGenFunction& cdf, Double_t xmin = 1, Double_t xmax = 0)
voidSetUserDistribution(const ROOT::Math::IGenFunction& dist, ROOT::Math::GoFTest::EUserDistribution userDist = kPDF, Double_t xmin = 1, Double_t xmax = 0)
voidSetUserPDF(const ROOT::Math::IGenFunction& pdf, Double_t xmin = 1, Double_t xmax = 0)
private:
Double_tExponentialCDF(Double_t x) const
Double_tGaussianCDF(Double_t x) const
static Double_tGetSigmaN(const vector<UInt_t>& ns, UInt_t N)
ROOT::Math::GoFTestGoFTest()
ROOT::Math::GoFTestGoFTest(ROOT::Math::GoFTest& gof)
voidInstantiate(const Double_t* sample, UInt_t sampleSize)
static Double_tInterpolatePValues(int nsamples, Double_t A2)
Double_tLogNormalCDF(Double_t x) const
voidLogSample()
ROOT::Math::GoFTestoperator=(ROOT::Math::GoFTest& gof)
Double_tPValueAD1Sample(Double_t A2) const
voidSetCDF()
voidSetDistributionFunction(const ROOT::Math::IGenFunction& cdf, Bool_t isPDF, Double_t xmin, Double_t xmax)
voidSetParameters()
voidSetSamples(vector<const Double_t*> samples, const vector<UInt_t> samplesSizes)

Data Members

public:
enum EDistribution { kUndefined
kUserDefined
kGaussian
kLogNormal
kExponential
};
enum EUserDistribution { kCDF
kPDF
};
enum ETestType { kAD
kAD2s
kKS
kKS2s
};
private:
auto_ptr<ROOT::Math::IBaseFunctionOneDim>fCDF
vector<Double_t>fCombinedSamples
ROOT::Math::GoFTest::EDistributionfDist
Double_tfMean
vector<std::vector<Double_t> >fSamples
Double_tfSigma
Bool_tfTestSampleFromH0

Class Charts

Inheritance Inherited Members Includes Libraries
Class Charts

Function documentation

GoFTest(UInt_t sample1Size, const Double_t* sample1, UInt_t sample2Size, const Double_t* sample2)
Constructor for using only with 2-samples tests
GoFTest(UInt_t sampleSize, const Double_t* sample, ROOT::Math::GoFTest::EDistribution dist = kUndefined)
Constructor for using only with 1-sample tests with a specified distribution
GoFTest(UInt_t sampleSize, const Double_t* sample, const ROOT::Math::IGenFunction& dist, ROOT::Math::GoFTest::EUserDistribution userDist = kPDF, Double_t xmin = 1, Double_t xmax = 0)
Templated constructor for using only with 1-sample tests with a user specified distribution
Instantiate(const Double_t* sample, UInt_t sampleSize)
GoFTest(UInt_t sampleSize, const Double_t* sample, const ROOT::Math::IGenFunction& dist, ROOT::Math::GoFTest::EUserDistribution userDist = kPDF, Double_t xmin = 1, Double_t xmax = 0)
Specialization using IGenFunction interface
SetUserDistribution(const ROOT::Math::IGenFunction& dist, ROOT::Math::GoFTest::EUserDistribution userDist = kPDF, Double_t xmin = 1, Double_t xmax = 0)
SetDistributionFunction(const ROOT::Math::IGenFunction& cdf, Bool_t isPDF, Double_t xmin, Double_t xmax)
void SetUserPDF(const ROOT::Math::IGenFunction& pdf, Double_t xmin = 1, Double_t xmax = 0)
Sets the user input distribution as a probability density function for 1-sample tests
void SetUserCDF(const ROOT::Math::IGenFunction& cdf, Double_t xmin = 1, Double_t xmax = 0)
 Sets the user input distribution as a cumulative distribution function for 1-sample tests
      The CDF must return zero
void SetDistribution(ROOT::Math::GoFTest::EDistribution dist)
Sets the distribution for the predefined distribution types
virtual ~GoFTest()
Double_t AndersonDarling2SamplesTest(const Char_t* option = "p") const
  The Anderson-Darling K-Sample Test algorithm is described and taken from
  http://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/andeksam.htm
  and described and taken from
   (1) Scholz F.W., Stephens M.A. (1987), K-sample Anderson-Darling Tests, Journal of the American Statistical Association, 82, 918–924. (2-samples variant implemented)
*/ void AndersonDarling2SamplesTest(Double_t& pvalue, Double_t& testStat) const;
Double_t AndersonDarlingTest(const Char_t* option = "p") const
  The Anderson-Darling 1-Sample Test algorithm for a specific distribution is described at
  http://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/andedarl.htm
  and described and taken from (2)
  Marsaglia J.C.W., Marsaglia G. (2004), Evaluating the Anderson-Darling Distribution, Journal of Statistical Software, Volume 09, Issue i02.
  and described and taken from (3)
  Lewis P.A.W. (1961), The Annals of Mathematical Statistics, Distribution of the Anderson-Darling Statistic, Volume 32, Number 4, 1118-1124.
*/ void AndersonDarlingTest(Double_t& pvalue, Double_t& testStat) const;
Double_t KolmogorovSmirnov2SamplesTest(const Char_t* option = "p") const
  The Kolmogorov-Smirnov 2-Samples Test algorithm is described at
  http://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/ks2samp.htm
  and described and taken from
  http://root.cern.ch/root/html/TMath.html#TMath:KolmogorovTest
*/ void KolmogorovSmirnov2SamplesTest(Double_t& pvalue, Double_t& testStat) const;
Double_t KolmogorovSmirnovTest(const Char_t* option = "p") const
  The Kolmogorov-Smirnov 1-Sample Test algorithm for a specific distribution is described at
  http://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/kstest.htm
  and described and taken from (4)
  Press W. H., Teukolsky S.A., Vetterling W.T., Flannery B.P. (2007), Numerical Recipes - The Art of Scientific Computing (Third Edition), Cambridge Univerdity Press
*/ void KolmogorovSmirnovTest(Double_t& pvalue, Double_t& testStat) const;
void operator()(ROOT::Math::GoFTest::ETestType test, Double_t& pvalue, Double_t& testStat) const
 The class's unary functions
Double_t operator()(ROOT::Math::GoFTest::ETestType test = kAD, const Char_t* option = "p") const
 Returns default Anderson Darling 1-Sample Test and default p-value; option "t" returns the test statistic value
 specific to the test type
Double_t PValueADKSamples(UInt_t nsamples, Double_t A2)
 Computation of the K-Sample Anderson-Darling Test's p-value as described in (1)
 given a normalized test statistic. The first variant described in the paper is used
void AndersonDarling2SamplesTest(const ROOT::Fit::BinData& data1, const ROOT::Fit::BinData& data2, Double_t& pvalue, Double_t& testStat)
 Compute The 2-Sample Anderson Darling test for binned data
GoFTest()
GoFTest operator=(ROOT::Math::GoFTest& gof)
void SetCDF()
Double_t LogNormalCDF(Double_t x) const
Double_t GaussianCDF(Double_t x) const
Double_t ExponentialCDF(Double_t x) const
Double_t GetSigmaN(const vector<UInt_t>& ns, UInt_t N)
Double_t InterpolatePValues(int nsamples, Double_t A2)
Double_t PValueAD1Sample(Double_t A2) const
void LogSample()
void SetSamples(vector<const Double_t*> samples, const vector<UInt_t> samplesSizes)
void SetParameters()