Hi Rene and Ben,
> The values given to TMath::KolmogorovProv are discrete due to
> the difference between consecutive bins (small integers).
it is possible, but too suspicious. I did more tests.
1. I repeated Ben test in fortran with HBOOK and got exactly the same
result.
So it is not a result of F77 ==> C++ conversion. Conversion is correct.
2. I have made a test with two flat distrubutions (rndm) without histograms
involved.
So now "The values given to TMath::KolmogorovProb are NOT discrete "
But result is still bad. Again there is no entries between 0.8 and 0.9
Resume: KolmogorovProb is wrong. It is wrong already in HBOOK.
Is it possible to find the author of this function?
Victor
Victor M. Perevoztchikov perev@bnl.gov
Brookhaven National Laboratory MS 510A PO Box 5000 Upton NY 11973-5000
tel office : 631-344-7894; fax 631-344-4206;
----- Original Message -----
From: "Rene Brun" <Rene.Brun@cern.ch>
To: "Ben Kilminster" <bjk@fnal.gov>
Cc: <roottalk@pcroot.cern.ch>
Sent: Sunday, July 27, 2003 10:50 AM
Subject: Re: [ROOT] KolmogorovTest
> Hi Ben,
>
> The fact that there are no entries between 0.8 and 0.9 is simply a
> binning artefact. Increase your number of bins from 10 to 100.
>
> The values given to TMath::KolmogorovProv are discrete due to
> the difference between consecutive bins (small integers).
> Thus introduces an asymmetry in favour of high z values.
>
> Rene Brun
>
> On Thu,
> 24 Jul 2003, Ben Kilminster wrote:
>
> > Hi fellow Rooters,
> >
> > The KS probability is supposed to be a value which is uniformly
> > distributed between zero and one if you are comparing two distributions
> > which come from the same parent distribution.
> >
> > In the following root macro that I run, I see that it is not flat, it is
> > peaked strongly at one, and that there is a big hole where the
probability
> > is not filled.
> >
> > Does anyone have any idea what is wrong ?
> >
> > Cheers,
> > Ben
> >
> > (I am using root v3_05_04d KCC_4_0 Linux+2.4)
> >
> >
> > {
> > TCanvas *c1 = new TCanvas("c1","plots",600,700);
> > c1->Divide(2,2);
> > // Make a gaussian distribution
> > TH1F *HGaussMain = new TH1F("HGaussMain","Gaussian pseudoexperiment
> > ",100,0,10);
> > for (i = 0; i < 10000; i++) {
> > HGaussMain->Fill(gRandom->Gaus(5,1.0));
> > }
> > // Draw the parent distribution
> > TCanvas *c1 = new TCanvas("c1","plots",600,700);
> > c1->Divide(2,2);
> > c1->cd(1);
> > HGaussMain->Draw();
> >
> > // Now loop through choosing 100 event
> > // daughter distributions from the parent distribution
> > // comparing them with KS statistic
> >
> > TH1F *HGauss1 = new TH1F("HGauss1","Random Gaussian
> > pseudoexperiment",100,0,10);
> > TH1F *HGauss2 = new TH1F("HGauss2","Random Gaussian
> > pseudoexperiment",100,0,10);
> > TH1F *HKSValues = new TH1F("HKSValues","KS values",10,0,1.0);
> >
> > for (int j = 0; j < 1000; j++) {
> > HGauss1->Reset();
> > HGauss2->Reset();
> > for (int i = 0; i < 100; i++) {
> > HGauss1->Fill(HGaussMain->GetRandom());
> > HGauss2->Fill(HGaussMain->GetRandom());
> > }
> > double KS_agree = HGauss1->KolmogorovTest(HGauss2);
> > HKSValues->Fill(KS_agree);
> > }
> >
> > c1->cd(2);
> > HGauss1->Draw(); // typical daughter distribution
> > c1->cd(3);
> > HGauss2->Draw(); // another typical daughter distribution
> > c1->cd(4);
> > HKSValues->Draw(); // This is not flat
> >
> > }
> >
> >
>
This archive was generated by hypermail 2b29 : Thu Jan 01 2004 - 17:50:14 MET