11#ifndef ROOSTATS_AsymptoticCalculator
12#define ROOSTATS_AsymptoticCalculator
34 bool nominalAsimov =
false
61 static double GetExpectedPValues(
double pnull,
double palt,
double nsigma,
bool usecls,
bool oneSided =
true );
120 &binVolume,
int &ibin);
#define ClassDef(name, id)
RooAbsArg * first() const
RooAbsData is the common abstract base class for binned and unbinned datasets.
RooArgList is a container object that can hold multiple RooAbsArg objects.
RooArgSet is a container object that can hold multiple RooAbsArg objects.
RooCategory is an object to represent discrete states.
RooProdPdf is an efficient implementation of a product of PDFs of the form.
RooRealVar represents a variable that can be changed from the outside.
Hypothesis Test Calculator based on the asymptotic formulae for the profile likelihood ratio.
static RooAbsData * GenerateAsimovDataSinglePdf(const RooAbsPdf &pdf, const RooArgSet &obs, const RooRealVar &weightVar, RooCategory *channelCat=0)
Compute the asimov data set for an observable of a pdf.
virtual HypoTestResult * GetHypoTest() const
re-implement HypoTest computation using the asymptotic
const RooRealVar * GetMuHat() const
return best fit parameter (firs of poi)
static double GetExpectedPValues(double pnull, double palt, double nsigma, bool usecls, bool oneSided=true)
function given the null and the alt p value - return the expected one given the N - sigma value
static RooAbsData * MakeAsimovData(RooAbsData &data, const ModelConfig &model, const RooArgSet &poiValues, RooArgSet &globObs, const RooArgSet *genPoiValues=0)
Make Asimov data.
void SetOneSidedDiscovery(bool on)
set the test statistics for one-sided discovery
static bool SetObsToExpected(RooAbsPdf &pdf, const RooArgSet &obs)
set observed value to the expected one works for Gaussian, Poisson or LogNormal assumes mean paramete...
static void SetPrintLevel(int level)
set print level (static function)
static RooAbsData * GenerateAsimovData(const RooAbsPdf &pdf, const RooArgSet &observables)
generate the asimov data for the observables (not the global ones) need to deal with the case of a si...
static RooAbsData * GenerateCountingAsimovData(RooAbsPdf &pdf, const RooArgSet &obs, const RooRealVar &weightVar, RooCategory *channelCat=0)
Generate counting Asimov data for the case when the pdf cannot be extended.
int fUseQTilde
flag to check if calculator is initialized
bool IsOneSidedDiscovery() const
const RooArgSet & GetBestFitParams() const
return best fit value for all parameters
AsymptoticCalculator(RooAbsData &data, const ModelConfig &altModel, const ModelConfig &nullModel, bool nominalAsimov=false)
constructor for asymptotic calculator from Data set and ModelConfig
virtual void SetNullModel(const ModelConfig &nullModel)
re-implementation of setters since they are needed to re-initialize the calculator
void SetTwoSided()
set the test statistics for two sided (in case of upper limits for discovery does not make really sen...
virtual void SetData(RooAbsData &data)
Set the DataSet.
void SetQTilde(bool on)
set using of qtilde, by default is controlled if RoORealVar is limited or not
virtual void SetAlternateModel(const ModelConfig &altModel)
Set the model for the alternate hypothesis (S+B)
const RooArgSet & GetBestFitPoi() const
return snapshot of the best fit parameter
static void FillBins(const RooAbsPdf &pdf, const RooArgList &obs, RooAbsData &data, int &index, double &binVolume, int &ibin)
fill bins by looping recursively on observables
static double EvaluateNLL(RooAbsPdf &pdf, RooAbsData &data, const RooArgSet *condObs, const RooArgSet *globObs, const RooArgSet *poiSet=0)
void SetOneSided(bool on)
set test statistic for one sided (upper limits)
bool Initialize() const
initialize the calculator by performing a global fit and make the Asimov data set
Common base class for the Hypothesis Test Calculators.
virtual void SetAlternateModel(const ModelConfig &altModel)
Set the model for the alternate hypothesis (S+B)
virtual void SetNullModel(const ModelConfig &nullModel)
virtual void SetData(RooAbsData &data)
Set the DataSet.
HypoTestResult is a base class for results from hypothesis tests.
ModelConfig is a simple class that holds configuration information specifying how a model should be u...
Namespace for the RooStats classes.