| library: libHist #include "TLimit.h" |
| Inheritance Chart: | |||||
|
protected:
static TLimitDataSource* Fluctuate(TLimitDataSource* input, bool init, TRandom*, bool stat = false)
static Double_t LogLikelihood(Double_t s, Double_t b, Double_t d)
public:
TLimit()
TLimit(const TLimit&)
virtual ~TLimit()
static TClass* Class()
static TConfidenceLevel* ComputeLimit(TLimitDataSource* data, Int_t nmc = 50000, bool stat = false, TRandom* generator = NULL, Double_t (*) (Double_t, Double_t,Double_t) statistic = &(TLimit::LogLikelihood))
virtual TClass* IsA() const
TLimit& operator=(const TLimit&)
virtual void ShowMembers(TMemberInspector& insp, char* parent)
virtual void Streamer(TBuffer& b)
void StreamerNVirtual(TBuffer& b)
private:
static TArrayD* fgTable a log table... just to speed up calculation
static TOrdCollection* fgSystNames Collection of systematics names
TLimit Class to compute 95% CL limits adapted from the mclimit code from Tom Junk (CLs method) see http://root.cern.ch/root/doc/TomJunk.pdf see http://cern.ch/thomasj/searchlimits/ecl.html see: Tom Junk,NIM A434, p. 435-443, 1999 see also the following interesting references: Alex Read, "Presentation of search results: the CLs technique" Journal of Physics G: Nucl. Part. Phys. 28 2693-2704 (2002). http://www.iop.org/EJ/abstract/0954-3899/28/10/313 A nice article is also available in the CERN yellow report with the proceeding of the 2000 CERN workshop on confidence intervals. Alex Read, "Modified Frequentist Analysis of Search Results (The CLs Method)" CERN 2000-005 (30 May 2000) see note about: "Should I use TRolke, TFeldmanCousins, TLimit?" in the TRolke class description.
class TLimit ------------ Algorithm to compute 95% C.L. limits using the Likelihood ratio semi-bayesian method. It takes signal, background and data histograms wrapped in a TLimitDataSource as input and runs a set of Monte Carlo experiments in order to compute the limits. If needed, inputs are fluctuated according to systematics. The output is a TConfidenceLevel. class TLimitDataSource ---------------------- Takes the signal, background and data histograms as well as different systematics sources to form the TLimit input. class TConfidenceLevel ---------------------- Final result of the TLimit algorithm. It is created just after the time-consuming part and can be stored in a TFile for further processing. It contains light methods to return CLs, CLb and other interesting quantities. The actual algorithm... From an input (TLimitDataSource) it produces an output TConfidenceLevel. For this, nmc Monte Carlo experiments are performed. As usual, the larger this number, the longer the compute time, but the better the result./*
Supposing that there is a plotfile.root file containing 3 histograms (signal, background and data), you can imagine doing things like:
TFile* infile=new TFile("plotfile.root","READ");
infile->cd();
TH1D* sh=(TH1D*)infile->Get("signal");
TH1D* bh=(TH1D*)infile->Get("background");
TH1D* dh=(TH1D*)infile->Get("data");
TLimitDataSource* mydatasource = new TLimitDataSource(sh,bh,dh);
TConfidenceLevel *myconfidence = TLimit::ComputeLimit(mydatasource,50000);
cout << " CLs : " << myconfidence->CLs() << endl;
cout << " CLsb : " << myconfidence->CLsb() << endl;
cout << " CLb : " << myconfidence->CLb() << endl;
cout << "< CLs > : " << myconfidence->GetExpectedCLs_b() << endl;
cout << "< CLsb > : " << myconfidence->GetExpectedCLsb_b() << endl;
cout << "< CLb > : " << myconfidence->GetExpectedCLb_b() << endl;
delete myconfidence;
delete mydatasource;
infile->Close();
More informations can still be found on this page.
*/initialisation: create a sorted list of all the names of systematics
void ~TLimit()
TLimit TLimit()
Double_t LogLikelihood(Double_t s, Double_t b, Double_t d)
TClass* Class()
TClass* IsA() const
void ShowMembers(TMemberInspector& insp, char* parent)
void Streamer(TBuffer& b)
void StreamerNVirtual(TBuffer& b)
TLimit TLimit(const TLimit&)
TLimit& operator=(const TLimit&)