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class TFractionFitter: public TObject


 Fits MC fractions to data histogram (a la HMCMLL, see R. Barlow and C. Beeston,
 Comp. Phys. Comm. 77 (1993) 219-228, and http://www.hep.man.ac.uk/~roger/hfrac.f).

 The virtue of this fit is that it takes into account both data and Monte Carlo
 statistical uncertainties. The way in which this is done is through a standard
 likelihood fit using Poisson statistics; however, the template (MC) predictions
 are also varied within statistics, leading to additional contributions to the
 overall likelihood. This leads to many more fit parameters (one per bin per
 template), but the minimisation with respect to these additional parameters is
 done analytically rather than introducing them as formal fit parameters. Some
 special care needs to be taken in the case of bins with zero content. For more
 details please see the original publication cited above.

 An example application of this fit is given below. For a TH1* histogram
 ("data") fitted as the sum of three Monte Carlo sources ("mc"):

 {
   TH1F *data;                              //data histogram
   TH1F *mc0;                               // first MC histogram
   TH1F *mc1;                               // second MC histogram
   TH1F *mc2;                               // third MC histogram
   ....                                     // retrieve histograms
   TObjArray *mc = new TObjArray(3);        // MC histograms are put in this array
   mc->Add(mc0);
   mc->Add(mc1);
   mc->Add(mc2);
   TFractionFitter* fit = new TFractionFitter(data, mc); // initialise
   fit->Constrain(1,0.0,1.0);               // constrain fraction 1 to be between 0 and 1
   fit->SetRangeX(1,15);                    // use only the first 15 bins in the fit
   Int_t status = fit->Fit();               // perform the fit
   cout << "fit status: " << status << endl;
   if (status == 0) {                       // check on fit status
     TH1F* result = (TH1F*) fit->GetPlot();
     data->Draw("Ep");
     result->Draw("same");
   }
 }


 Assumptions

 A few assumptions need to be made for the fit procedure to be carried out:

 (1) The total number of events in each template is not too small
     (so that its Poisson uncertainty can be neglected).
 (2) The number of events in each bin is much smaller than the total
     number of events in each template (so that multinomial
     uncertainties can be replaced with Poisson uncertainties).

 Biased fit uncertainties may result if these conditions are not fulfilled
 (see e.g. arXiv:0803.2711).

 Instantiation

 A fit object is instantiated through
     TFractionFitter* fit = new TFractionFitter(data, mc);
 A number of basic checks (intended to ensure that the template histograms
 represent the same "kind" of distribution as the data one) are carried out.
 The TVirtualFitter object is then addressed and all fit parameters (the
 template fractions) declared (initially unbounded).

 Applying constraints

 Fit parameters can be constrained through
     fit->Constrain(parameter #, lower bound, upper bound);
 Setting lower bound = upper bound = 0 removes the constraint (a la Minuit);
 however, a function
     fit->Unconstrain(parameter #)
 is also provided to simplify this.

 Setting parameter values

 The function
     TVirtualFitter* vFit = fit->GetFitter();
 is provided for direct access to the TVirtualFitter object. This allows to
 set and fix parameter values, and set step sizes directly.

 Restricting the fit range

 The fit range can be restricted through
     fit->SetRangeX(first bin #, last bin #);
 and freed using
     fit->ReleaseRangeX();
 For 2D histograms the Y range can be similarly restricted using
     fit->SetRangeY(first bin #, last bin #);
     fit->ReleaseRangeY();
 and for 3D histograms also
     fit->SetRangeZ(first bin #, last bin #);
     fit->ReleaseRangeZ();

 Weights histograms

 Weights histograms (for a motivation see the above publication) can be specified
 for the individual MC sources through
     fit->SetWeight(parameter #, pointer to weights histogram);
 and unset by specifying a null pointer.

 Obtaining fit results

 The fit is carried out through
     Int_t status = fit->Fit();
 where  status  is the code returned from the "MINIMIZE" command. For fits
 that converged, parameter values and errors can be obtained through
     fit->GetResult(parameter #, value, error);
 and the histogram corresponding to the total Monte Carlo prediction (which
 is not the same as a simple weighted sum of the input Monte Carlo distributions)
 can be obtained by
     TH1* result = fit->GetPlot();

 Using different histograms

 It is possible to change the histogram being fitted through
     fit->SetData(TH1* data);
 and to change the template histogram for a given parameter number through
     fit->SetMC(parameter #, TH1* MC);
 This can speed up code in case of multiple data or template histograms;
 however, it should be done with care as any settings are taken over from
 the previous fit. In addition, neither the dimensionality nor the numbers of
 bins of the histograms should change (in that case it is better to instantiate
 a new TFractionFitter object).

 Errors

 Any serious inconsistency results in an error.


Function Members (Methods)

public:
TFractionFitter()
TFractionFitter(TH1* data, TObjArray* MCs)
virtual~TFractionFitter()
voidTObject::AbstractMethod(const char* method) const
virtual voidTObject::AppendPad(Option_t* option = "")
virtual voidTObject::Browse(TBrowser* b)
static TClass*Class()
static TClass*TObject::Class()
virtual const char*TObject::ClassName() const
virtual voidTObject::Clear(Option_t* = "")
virtual TObject*TObject::Clone(const char* newname = "") const
virtual Int_tTObject::Compare(const TObject* obj) const
voidConstrain(Int_t parm, Double_t low, Double_t high)
virtual voidTObject::Copy(TObject& object) const
virtual voidTObject::Delete(Option_t* option = "")MENU
virtual Int_tTObject::DistancetoPrimitive(Int_t px, Int_t py)
virtual voidTObject::Draw(Option_t* option = "")
virtual voidTObject::DrawClass() constMENU
virtual TObject*TObject::DrawClone(Option_t* option = "") constMENU
virtual voidTObject::Dump() constMENU
virtual voidTObject::Error(const char* method, const char* msgfmt) const
voidErrorAnalysis(Double_t UP)
virtual voidTObject::Execute(const char* method, const char* params, Int_t* error = 0)
virtual voidTObject::Execute(TMethod* method, TObjArray* params, Int_t* error = 0)
virtual voidTObject::ExecuteEvent(Int_t event, Int_t px, Int_t py)
virtual voidTObject::Fatal(const char* method, const char* msgfmt) const
virtual TObject*TObject::FindObject(const char* name) const
virtual TObject*TObject::FindObject(const TObject* obj) const
Int_tFit()
Double_tGetChisquare() const
virtual Option_t*TObject::GetDrawOption() const
static Long_tTObject::GetDtorOnly()
TVirtualFitter*GetFitter() const
virtual const char*TObject::GetIconName() const
TH1*GetMCPrediction(Int_t parm) const
virtual const char*TObject::GetName() const
Int_tGetNDF() const
virtual char*TObject::GetObjectInfo(Int_t px, Int_t py) const
static Bool_tTObject::GetObjectStat()
virtual Option_t*TObject::GetOption() const
TH1*GetPlot()
Double_tGetProb() const
voidGetResult(Int_t parm, Double_t& value, Double_t& error) const
virtual const char*TObject::GetTitle() const
virtual UInt_tTObject::GetUniqueID() const
virtual Bool_tTObject::HandleTimer(TTimer* timer)
virtual ULong_tTObject::Hash() const
virtual voidTObject::Info(const char* method, const char* msgfmt) const
virtual Bool_tTObject::InheritsFrom(const char* classname) const
virtual Bool_tTObject::InheritsFrom(const TClass* cl) const
virtual voidTObject::Inspect() constMENU
voidTObject::InvertBit(UInt_t f)
virtual TClass*IsA() const
virtual TClass*TObject::IsA() const
virtual Bool_tTObject::IsEqual(const TObject* obj) const
virtual Bool_tTObject::IsFolder() const
Bool_tTObject::IsOnHeap() const
virtual Bool_tTObject::IsSortable() const
Bool_tTObject::IsZombie() const
virtual voidTObject::ls(Option_t* option = "") const
voidTObject::MayNotUse(const char* method) const
virtual Bool_tTObject::Notify()
static voidTObject::operator delete(void* ptr)
static voidTObject::operator delete(void* ptr, void* vp)
static voidTObject::operator delete[](void* ptr)
static voidTObject::operator delete[](void* ptr, void* vp)
void*TObject::operator new(size_t sz)
void*TObject::operator new(size_t sz, void* vp)
void*TObject::operator new[](size_t sz)
void*TObject::operator new[](size_t sz, void* vp)
TObject&TObject::operator=(const TObject& rhs)
virtual voidTObject::Paint(Option_t* option = "")
virtual voidTObject::Pop()
virtual voidTObject::Print(Option_t* option = "") const
virtual Int_tTObject::Read(const char* name)
virtual voidTObject::RecursiveRemove(TObject* obj)
voidReleaseRangeX()
voidReleaseRangeY()
voidReleaseRangeZ()
voidTObject::ResetBit(UInt_t f)
virtual voidTObject::SaveAs(const char* filename = "", Option_t* option = "") constMENU
virtual voidTObject::SavePrimitive(basic_ostream<char,char_traits<char> >& out, Option_t* option = "")
voidTObject::SetBit(UInt_t f)
voidTObject::SetBit(UInt_t f, Bool_t set)
voidSetData(TH1* data)
virtual voidTObject::SetDrawOption(Option_t* option = "")MENU
static voidTObject::SetDtorOnly(void* obj)
voidSetMC(Int_t parm, TH1* MC)
static voidTObject::SetObjectStat(Bool_t stat)
voidSetRangeX(Int_t low, Int_t high)
voidSetRangeY(Int_t low, Int_t high)
voidSetRangeZ(Int_t low, Int_t high)
virtual voidTObject::SetUniqueID(UInt_t uid)
voidSetWeight(Int_t parm, TH1* weight)
virtual voidShowMembers(TMemberInspector& insp, char* parent)
virtual voidTObject::ShowMembers(TMemberInspector& insp, char* parent)
virtual voidStreamer(TBuffer& b)
virtual voidTObject::Streamer(TBuffer& b)
voidStreamerNVirtual(TBuffer& b)
voidTObject::StreamerNVirtual(TBuffer& b)
virtual voidTObject::SysError(const char* method, const char* msgfmt) const
Bool_tTObject::TestBit(UInt_t f) const
Int_tTObject::TestBits(UInt_t f) const
voidUnConstrain(Int_t parm)
virtual voidTObject::UseCurrentStyle()
virtual voidTObject::Warning(const char* method, const char* msgfmt) const
virtual Int_tTObject::Write(const char* name = 0, Int_t option = 0, Int_t bufsize = 0)
virtual Int_tTObject::Write(const char* name = 0, Int_t option = 0, Int_t bufsize = 0) const
protected:
virtual voidTObject::DoError(int level, const char* location, const char* fmt, va_list va) const
voidTObject::MakeZombie()
private:
voidCheckConsistency()
voidCheckParNo(Int_t parm) const
voidComputeChisquareLambda()
voidComputeFCN(Int_t& npar, Double_t* gin, Double_t& f, Double_t* par, Int_t flag)
voidFindPrediction(int bin, double* fractions, double& Ti, int& k0, double& Aki) const
voidGetRanges(Int_t& minX, Int_t& maxX, Int_t& minY, Int_t& maxY, Int_t& minZ, Int_t& maxZ) const

Data Members

protected:
TObjArrayfAjiarray of pointers to predictions of real template distributions
Double_tfChisquareTemplate fit chisquare
TH1*fDatapointer to the "data" histogram to be fitted to
Bool_tfFitDoneflags whether a valid fit has been performed
Double_t*fFractionstemplate fractions scaled to the "data" histogram statistics
Int_tfHighLimitXlast bin in X dimension
Int_tfHighLimitYlast bin in Y dimension
Int_tfHighLimitZlast bin in Z dimension
Double_tfIntegralData"data" histogram content integral over allowed fit range
Double_t*fIntegralMCssame for template histograms (weights not taken into account)
Int_tfLowLimitXfirst bin in X dimension
Int_tfLowLimitYfirst bin in Y dimension
Int_tfLowLimitZfirst bin in Z dimension
TObjArrayfMCsarray of pointers to template histograms
Int_tfNDFNumber of degrees of freedom in the fit
Int_tfNparnumber of fit parameters
Int_tfNpfitsNumber of points used in the fit
TH1*fPlotpointer to histogram containing summed template predictions
TObjArrayfWeightsarray of pointers to corresponding weight factors (may be null)

Class Charts

Inheritance Inherited Members Includes Libraries
Class Charts

Function documentation

TFractionFitter()
 TFractionFitter default constructor.
TFractionFitter(TH1* data, TObjArray* MCs)
 TFractionFitter constructor. Does a complete initialisation (including
 consistency checks, default fit range as the whole histogram but without
 under- and overflows, and declaration of the fit parameters). Note that
 the histograms are not copied, only references are used.
 Arguments:
     data: histogram to be fitted
     MCs:  array of TH1* corresponding template distributions
~TFractionFitter()
 TFractionFitter default destructor
void SetData(TH1* data)
 Change the histogram to be fitted to. Notes:
 - Parameter constraints and settings are retained from a possible previous fit.
 - Modifying the dimension or number of bins results in an error (in this case
   rather instantiate a new TFractionFitter object)
void SetMC(Int_t parm, TH1* MC)
 Change the histogram for template number <parm>. Notes:
 - Parameter constraints and settings are retained from a possible previous fit.
 - Modifying the dimension or number of bins results in an error (in this case
   rather instantiate a new TFractionFitter object)
void SetWeight(Int_t parm, TH1* weight)
 Set bin by bin weights for template number <parm> (the parameter numbering
 follows that of the input template vector).
 Weights can be "unset" by passing a null pointer.
 Consistency of the weights histogram with the data histogram is checked at
 this point, and an error in case of problems.
TVirtualFitter* GetFitter() const
 Give direct access to the underlying minimisation class. This can be
 used e.g. to modify parameter values or step sizes.
void CheckParNo(Int_t parm) const
 Function for internal use, checking parameter validity
 An invalid parameter results in an error.
void SetRangeX(Int_t low, Int_t high)
 Set the X range of the histogram to be used in the fit.
 Use ReleaseRangeX() to go back to fitting the full histogram.
 The consistency check ensures that no empty fit range occurs (and also
 recomputes the bin content integrals).
 Arguments:
     low:  lower X bin number
     high: upper X bin number
void ReleaseRangeX()
 Release restrictions on the X range of the histogram to be used in the fit.
void SetRangeY(Int_t low, Int_t high)
 Set the Y range of the histogram to be used in the fit (2D or 3D histograms only).
 Use ReleaseRangeY() to go back to fitting the full histogram.
 The consistency check ensures that no empty fit range occurs (and also
 recomputes the bin content integrals).
 Arguments:
     low:  lower Y bin number
     high: upper Y bin number
void ReleaseRangeY()
 Release restrictions on the Y range of the histogram to be used in the fit.
void SetRangeZ(Int_t low, Int_t high)
 Set the Z range of the histogram to be used in the fit (3D histograms only).
 Use ReleaseRangeY() to go back to fitting the full histogram.
 The consistency check ensures that no empty fit range occurs (and also
 recomputes the bin content integrals).
 Arguments:
     low:  lower Y bin number
     high: upper Y bin number
void ReleaseRangeZ()
 Release restrictions on the Z range of the histogram to be used in the fit.
void Constrain(Int_t parm, Double_t low, Double_t high)
 Constrain the values of parameter number <parm> (the parameter numbering
 follows that of the input template vector).
 Use UnConstrain() to remove this constraint.
void UnConstrain(Int_t parm)
 Remove the constraints on the possible values of parameter <parm>.
void CheckConsistency()
 Function used internally to check the consistency between the
 various histograms. Checks are performed on nonexistent or empty
 histograms, the precise histogram class, and the number of bins.
 In addition, integrals over the "allowed" bin ranges are computed.
 Any inconsistency results in a error.
Int_t Fit()
 Perform the fit with the default UP value.
 The value returned is the minimisation status.
void ErrorAnalysis(Double_t UP)
 Set UP to the given value (see class TMinuit), and perform a MINOS minimisation.
void GetResult(Int_t parm, Double_t& value, Double_t& error) const
 Obtain the fit result for parameter <parm> (the parameter numbering
 follows that of the input template vector).
TH1* GetPlot()
 Return the "template prediction" corresponding to the fit result (this is not
 the same as the weighted sum of template distributions, as template statistical
 uncertainties are taken into account).
 Note that the name of this histogram will simply be the same as that of the
 "data" histogram, prefixed with the string "Fraction fit to hist: ".
void GetRanges(Int_t& minX, Int_t& maxX, Int_t& minY, Int_t& maxY, Int_t& minZ, Int_t& maxZ) const
 Used internally to obtain the bin ranges according to the dimensionality of
 the histogram and the limits set by hand.
void ComputeFCN(Int_t& npar, Double_t* gin, Double_t& f, Double_t* par, Int_t flag)
 Used internally to compute the likelihood value.
void FindPrediction(int bin, double* fractions, double& Ti, int& k0, double& Aki) const
 Function used internally to obtain the template prediction in the individual bins
Double_t GetChisquare() const
 Return the likelihood ratio Chi-squared (chi2) for the fit.
 The value is computed when the fit is executed successfully.
 Chi2 calculation is based on the "likelihood ratio" lambda,
 lambda = L(y;n) / L(m;n),
 where L(y;n) is the likelihood of the fit result <y> describing the data <n>
 and L(m;n) is the likelihood of an unknown "true" underlying distribution
 <m> describing the data <n>. Since <m> is unknown, the data distribution is
 used instead,
 lambda = L(y;n) / L(n;n).
 Note that this ratio is 1 if the fit is perfect. The chi2 value is then
 computed according to
 chi2 = -2*ln(lambda).
 This parameter can be shown to follow a Chi-square distribution. See for
 example S. Baker and R. Cousins, "Clarification of the use of chi-square
 and likelihood functions in fits to histograms", Nucl. Instr. Meth. A221, 
 pp. 437-442 (1984)
Int_t GetNDF() const
 return the number of degrees of freedom in the fit
 the fNDF parameter has been previously computed during a fit.
 The number of degrees of freedom corresponds to the number of points
 used in the fit minus the number of templates.
Double_t GetProb() const
 return the fit probability
void ComputeChisquareLambda()
 Method used internally to compute the likelihood ratio chi2
 See the function GetChisquare() for details
TH1* GetMCPrediction(Int_t parm) const
 Return the adjusted MC template (Aji) for template (parm).
 Note that the (Aji) times fractions only sum to the total prediction
 of the fit if all weights are 1.