ROOT » ROOFIT » ROOSTATS » RooStats::RatioOfProfiledLikelihoodsTestStat

class RooStats::RatioOfProfiledLikelihoodsTestStat: public RooStats::TestStatistic

Function Members (Methods)

public:
virtual~RatioOfProfiledLikelihoodsTestStat()
static TClass*Class()
virtual voidEnableDetailedOutput(bool e = true)
virtual Double_tEvaluate(RooAbsData& data, RooArgSet& nullParamsOfInterest)
virtual const RooArgSet*GetDetailedOutput() const
virtual const TStringGetVarName() const
virtual TClass*IsA() const
RooStats::RatioOfProfiledLikelihoodsTestStat&operator=(const RooStats::RatioOfProfiledLikelihoodsTestStat&)
Double_tProfiledLikelihood(RooAbsData& data, RooArgSet& poi, RooAbsPdf& pdf)
virtual boolRooStats::TestStatistic::PValueIsRightTail() const
RooStats::RatioOfProfiledLikelihoodsTestStatRatioOfProfiledLikelihoodsTestStat()
RooStats::RatioOfProfiledLikelihoodsTestStatRatioOfProfiledLikelihoodsTestStat(const RooStats::RatioOfProfiledLikelihoodsTestStat&)
RooStats::RatioOfProfiledLikelihoodsTestStatRatioOfProfiledLikelihoodsTestStat(RooAbsPdf& nullPdf, RooAbsPdf& altPdf, const RooArgSet* altPOI = 0)
static voidSetAlwaysReuseNLL(Bool_t flag)
virtual voidSetConditionalObservables(const RooArgSet& set)
voidSetMinimizer(const char* minimizer)
voidSetPrintLevel(Int_t printLevel)
voidSetReuseNLL(Bool_t flag)
voidSetStrategy(Int_t strategy)
voidSetSubtractMLE(bool subtract)
voidSetTolerance(Double_t tol)
virtual voidShowMembers(TMemberInspector& insp) const
virtual voidStreamer(TBuffer&)
voidStreamerNVirtual(TBuffer& ClassDef_StreamerNVirtual_b)
RooStats::TestStatisticRooStats::TestStatistic::TestStatistic()
RooStats::TestStatisticRooStats::TestStatistic::TestStatistic(const RooStats::TestStatistic&)

Data Members

private:
RooArgSet*fAltPOI
RooStats::ProfileLikelihoodTestStatfAltProfile
RooArgSet*fDetailedOutput
boolfDetailedOutputEnabled
RooStats::ProfileLikelihoodTestStatfNullProfile
Bool_tfSubtractMLE
static Bool_tfgAlwaysReuseNll

Class Charts

Inheritance Inherited Members Includes Libraries
Class Charts

Function documentation

void SetAlwaysReuseNLL(Bool_t flag)
{ fgAlwaysReuseNll = flag ; }
Double_t ProfiledLikelihood(RooAbsData& data, RooArgSet& poi, RooAbsPdf& pdf)
 returns -logL(poi, conditonal MLE of nuisance params)
 subtract off the global MLE or not depending on the option
 It is the numerator or the denominator of the ratio (depending on the pdf)
L.M. : not sure why this method is needed now
Double_t Evaluate(RooAbsData& data, RooArgSet& nullParamsOfInterest)
 evaluate the ratio of profile likelihood
RatioOfProfiledLikelihoodsTestStat()
 Proof constructor. Don't use.
RatioOfProfiledLikelihoodsTestStat(RooAbsPdf& nullPdf, RooAbsPdf& altPdf, const RooArgSet* altPOI = 0)
         Calculates the ratio of profiled likelihoods.

	 By default the calculation is:

	    Lambda(mu_alt , conditional MLE for alt nuisance)
	log --------------------------------------------
   	    Lambda(mu_null , conditional MLE for null nuisance)

	where Lambda is the profile likeihood ratio, so the
	MLE for the null and alternate are subtracted off.

	If SetSubtractMLE(false) then it calculates:

	    L(mu_alt , conditional MLE for alt nuisance)
	log --------------------------------------------
	    L(mu_null , conditional MLE for null nuisance)


	The values of the parameters of interest for the alternative
	hypothesis are taken at the time of the construction.
	If empty, it treats all free parameters as nuisance parameters.

	The value of the parameters of interest for the null hypotheses
	are given at each call of Evaluate(data,nullPOI).

~RatioOfProfiledLikelihoodsTestStat(void)
void EnableDetailedOutput(bool e = true)
void SetReuseNLL(Bool_t flag)
void SetMinimizer(const char* minimizer)
void SetStrategy(Int_t strategy)
void SetTolerance(Double_t tol)
void SetPrintLevel(Int_t printLevel)
void SetConditionalObservables(const RooArgSet& set)
 set the conditional observables which will be used when creating the NLL
 so the pdf's will not be normalized on the conditional observables when computing the NLL
const RooArgSet* GetDetailedOutput(void)
 Returns detailed output. The value returned by this function is updated after each call to Evaluate().
 The returned RooArgSet contains the following for the alternative and null hypotheses:
 <ul>
 <li> the minimum nll, fitstatus and convergence quality for each fit </li>
 <li> for each fit and for each non-constant parameter, the value, error and pull of the parameter are stored </li>
 </ul>
const TString GetVarName() const
void SetSubtractMLE(bool subtract)
    const bool PValueIsRightTail(void) { return false; } // overwrites default
{fSubtractMLE = subtract;}