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#include <MaxLikelihoodFit.h>
Public Member Functions | |
| virtual void | applyOptions (const boost::program_options::variables_map &vm) |
| MaxLikelihoodFit () | |
| virtual const std::string & | name () const |
| virtual void | setNToys (const int) |
| virtual void | setToyNumber (const int) |
| ~MaxLikelihoodFit () | |
Protected Member Functions | |
| void | createFitResultTrees (const RooStats::ModelConfig &) |
| void | getNormalizations (RooAbsPdf *pdf, const RooArgSet &obs, RooArgSet &out) |
| virtual bool | runSpecific (RooWorkspace *w, RooStats::ModelConfig *mc_s, RooStats::ModelConfig *mc_b, RooAbsData &data, double &limit, double &limitErr, const double *hint) |
| void | setFitResultTrees (const RooArgSet *, double *) |
Protected Attributes | |
| static std::string | backgroundPdfNames_ = "shapeBkg*" |
| int | currentToy_ |
| std::auto_ptr< TFile > | fitOut |
| int | fitStatus_ |
| double * | globalObservables_ |
| double | mu_ |
| double | nll_bonly_ |
| double | nll_nll0_ |
| double | nll_sb_ |
| static bool | noErrors_ = false |
| int | nToys |
| double * | nuisanceParameters_ |
| int | numbadnll_ |
| TTree * | t_fit_b_ |
| TTree * | t_fit_sb_ |
Static Protected Attributes | |
| static bool | justFit_ = false |
| static bool | makePlots_ = false |
| static std::string | minos_ = "poi" |
| static std::string | name_ = "" |
| static std::string | out_ = "." |
| static float | rebinFactor_ = 1.0 |
| static bool | reuseParams_ = false |
| static bool | saveNormalizations_ = false |
| static std::string | signalPdfNames_ = "shapeSig*" |
Do a ML fit of the data with background and signal+background hypothesis and print out diagnostics plots
Definition at line 13 of file MaxLikelihoodFit.h.
| MaxLikelihoodFit::MaxLikelihoodFit | ( | ) |
Definition at line 43 of file MaxLikelihoodFit.cc.
References backgroundPdfNames_, fitStatus_, minos_, mu_, nll_bonly_, nll_nll0_, nll_sb_, nToys, numbadnll_, LimitAlgo::options_, out_, rebinFactor_, and signalPdfNames_.
:
FitterAlgoBase("MaxLikelihoodFit specific options")
{
options_.add_options()
("minos", boost::program_options::value<std::string>(&minos_)->default_value(minos_), "Compute MINOS errors for: 'none', 'poi', 'all'")
("out", boost::program_options::value<std::string>(&out_)->default_value(out_), "Directory to put output in")
("plots", "Make plots")
("rebinFactor", boost::program_options::value<float>(&rebinFactor_)->default_value(rebinFactor_), "Rebin by this factor before plotting (does not affect fitting!)")
("signalPdfNames", boost::program_options::value<std::string>(&signalPdfNames_)->default_value(signalPdfNames_), "Names of signal pdfs in plots (separated by ,)")
("backgroundPdfNames", boost::program_options::value<std::string>(&backgroundPdfNames_)->default_value(backgroundPdfNames_), "Names of background pdfs in plots (separated by ',')")
("saveNormalizations", "Save post-fit normalizations of all components of the pdfs")
("justFit", "Just do the S+B fit, don't do the B-only one, don't save output file")
("noErrors", "Don't compute uncertainties on the best fit value")
("initFromBonly", "Use the vlaues of the nuisance parameters from the background only fit as the starting point for the s+b fit")
;
// setup a few defaults
nToys=0; fitStatus_=0; mu_=0;numbadnll_=-1;nll_nll0_=-1; nll_bonly_=-1;nll_sb_=-1;
}
| MaxLikelihoodFit::~MaxLikelihoodFit | ( | ) |
Definition at line 63 of file MaxLikelihoodFit.cc.
References globalObservables_, and nuisanceParameters_.
{
// delete the Arrays used to fill the trees;
delete globalObservables_;
delete nuisanceParameters_;
}
| void MaxLikelihoodFit::applyOptions | ( | const boost::program_options::variables_map & | vm | ) | [virtual] |
Reimplemented from LimitAlgo.
Definition at line 75 of file MaxLikelihoodFit.cc.
References FitterAlgoBase::applyOptionsBase(), justFit_, makePlots_, name_, noErrors_, out_, reuseParams_, and saveNormalizations_.
{
applyOptionsBase(vm);
makePlots_ = vm.count("plots");
name_ = vm["name"].defaulted() ? std::string() : vm["name"].as<std::string>();
saveNormalizations_ = vm.count("saveNormalizations");
justFit_ = vm.count("justFit");
noErrors_ = vm.count("noErrors");
reuseParams_ = vm.count("initFromBonly");
if (justFit_) { out_ = "none"; makePlots_ = false; saveNormalizations_ = false; reuseParams_ = false;}
// For now default this to true;
}
| void MaxLikelihoodFit::createFitResultTrees | ( | const RooStats::ModelConfig & | mc | ) | [protected] |
Definition at line 402 of file MaxLikelihoodFit.cc.
References a, prof2calltree::count, gather_cfg::cout, fitStatus_, globalObservables_, mu_, name(), nll_bonly_, nll_nll0_, nll_sb_, nuisanceParameters_, numbadnll_, t_fit_b_, and t_fit_sb_.
Referenced by runSpecific().
{
// Initiate the arrays to store parameters
// create TTrees to store fit results:
t_fit_b_ = new TTree("tree_fit_b","tree_fit_b");
t_fit_sb_ = new TTree("tree_fit_sb","tree_fit_sb");
t_fit_b_->Branch("fit_status",&fitStatus_,"fit_status/Int_t");
t_fit_sb_->Branch("fit_status",&fitStatus_,"fit_status/Int_t");
t_fit_b_->Branch("mu",&mu_,"mu/Double_t");
t_fit_sb_->Branch("mu",&mu_,"mu/Double_t");
t_fit_b_->Branch("numbadnll",&numbadnll_,"numbadnll/Int_t");
t_fit_sb_->Branch("numbadnll",&numbadnll_,"numbadnll/Int_t");
t_fit_b_->Branch("nll_min",&nll_bonly_,"nll_min/Double_t");
t_fit_sb_->Branch("nll_min",&nll_sb_,"nll_min/Double_t");
t_fit_sb_->Branch("nll_nll0",&nll_nll0_,"nll_nll0/Double_t");
// fill the maps for the nuisances, and global observables
const RooArgSet *cons = mc.GetGlobalObservables();
const RooArgSet *nuis = mc.GetNuisanceParameters();
globalObservables_ = new double[cons->getSize()];
nuisanceParameters_= new double[nuis->getSize()];
int count=0;
TIterator* iter_c(cons->createIterator());
for (TObject *a = iter_c->Next(); a != 0; a = iter_c->Next()) {
RooRealVar *rrv = dynamic_cast<RooRealVar *>(a);
std::string name = rrv->GetName();
globalObservables_[count]=0;
t_fit_sb_->Branch(name.c_str(),&(globalObservables_[count]),Form("%s/Double_t",name.c_str()));
t_fit_b_->Branch(name.c_str(),&(globalObservables_[count]),Form("%s/Double_t",name.c_str()));
count++;
}
count = 0;
TIterator* iter_n(nuis->createIterator());
for (TObject *a = iter_n->Next(); a != 0; a = iter_n->Next()) {
RooRealVar *rrv = dynamic_cast<RooRealVar *>(a);
std::string name = rrv->GetName();
nuisanceParameters_[count] = 0;
t_fit_sb_->Branch(name.c_str(),&(nuisanceParameters_[count])),Form("%s/Double_t",name.c_str());
t_fit_b_->Branch(name.c_str(),&(nuisanceParameters_[count]),Form("%s/Double_t",name.c_str()));
count++;
}
std::cout << "Created Branches" <<std::endl;
return;
}
| void MaxLikelihoodFit::getNormalizations | ( | RooAbsPdf * | pdf, |
| const RooArgSet & | obs, | ||
| RooArgSet & | out | ||
| ) | [protected] |
Definition at line 355 of file MaxLikelihoodFit.cc.
References Clusterizer1DCommons::add(), i, list(), n, and parseEventContent::prod.
Referenced by runSpecific().
{
RooSimultaneous *sim = dynamic_cast<RooSimultaneous *>(pdf);
if (sim != 0) {
RooAbsCategoryLValue &cat = const_cast<RooAbsCategoryLValue &>(sim->indexCat());
for (int i = 0, n = cat.numBins((const char *)0); i < n; ++i) {
cat.setBin(i);
RooAbsPdf *pdfi = sim->getPdf(cat.getLabel());
if (pdfi) getNormalizations(pdfi, obs, out);
}
return;
}
RooProdPdf *prod = dynamic_cast<RooProdPdf *>(pdf);
if (prod != 0) {
RooArgList list(prod->pdfList());
for (int i = 0, n = list.getSize(); i < n; ++i) {
RooAbsPdf *pdfi = (RooAbsPdf *) list.at(i);
if (pdfi->dependsOn(obs)) getNormalizations(pdfi, obs, out);
}
return;
}
RooAddPdf *add = dynamic_cast<RooAddPdf *>(pdf);
if (add != 0) {
RooArgList list(add->coefList());
for (int i = 0, n = list.getSize(); i < n; ++i) {
RooAbsReal *coeff = (RooAbsReal *) list.at(i);
out.addOwned(*(new RooConstVar(coeff->GetName(), "", coeff->getVal())));
}
return;
}
}
| virtual const std::string& MaxLikelihoodFit::name | ( | void | ) | const [inline, virtual] |
Implements LimitAlgo.
Definition at line 16 of file MaxLikelihoodFit.h.
Referenced by createFitResultTrees(), and setFitResultTrees().
| bool MaxLikelihoodFit::runSpecific | ( | RooWorkspace * | w, |
| RooStats::ModelConfig * | mc_s, | ||
| RooStats::ModelConfig * | mc_b, | ||
| RooAbsData & | data, | ||
| double & | limit, | ||
| double & | limitErr, | ||
| const double * | hint | ||
| ) | [protected, virtual] |
Implements FitterAlgoBase.
Definition at line 88 of file MaxLikelihoodFit.cc.
References backgroundPdfNames_, alignmentValidation::c1, Combine::commitPoint(), corr, gather_cfg::cout, createFitResultTrees(), currentToy_, data, FitterAlgoBase::do95_, FitterAlgoBase::doFit(), fitOut, fitStatus_, getNormalizations(), globalObservables_, justFit_, utils::makeNuisancePdf(), makePlots(), makePlots_, FitterAlgoBase::minimizerStrategy_, minos_, mu_, name_, FitterAlgoBase::nll, nll_bonly_, nll_nll0_, nll_sb_, noErrors_, nToys, nuisanceParameters_, numbadnll_, out_, RecoTauValidation_cfi::plots, FitterAlgoBase::preFitValue_, alignCSCRings::r, rebinFactor_, reuseParams_, saveNormalizations_, setFitResultTrees(), signalPdfNames_, t_fit_b_, t_fit_sb_, plotscripts::tdrStyle, and withSystematics.
{
if (reuseParams_ && minos_!="none"){
std::cout << "Cannot reuse b-only fit params when running minos. Parameters will be reset when running S+B fit"<<std::endl;
reuseParams_=false;
}
if (!justFit_ && out_ != "none"){
if (currentToy_ < 1){
fitOut.reset(TFile::Open((out_+"/mlfit"+name_+".root").c_str(), "RECREATE"));
createFitResultTrees(*mc_s);
}
}
RooRealVar *r = dynamic_cast<RooRealVar *>(mc_s->GetParametersOfInterest()->first());
TCanvas *c1 = 0;
if (makePlots_) {
utils::tdrStyle();
c1 = new TCanvas("c1","c1");
}
// Make pre-plots before the fit
r->setVal(preFitValue_);
if (makePlots_) {
std::vector<RooPlot *> plots = utils::makePlots(*mc_s->GetPdf(), data, signalPdfNames_.c_str(), backgroundPdfNames_.c_str(), rebinFactor_);
for (std::vector<RooPlot *>::iterator it = plots.begin(), ed = plots.end(); it != ed; ++it) {
(*it)->Draw();
c1->Print((out_+"/"+(*it)->GetName()+"_prefit.png").c_str());
if (fitOut.get() && currentToy_< 1) fitOut->WriteTObject(*it, (std::string((*it)->GetName())+"_prefit").c_str());
}
}
// Determine pre-fit values of nuisance parameters
if (currentToy_ < 1){
const RooArgSet *nuis = mc_s->GetNuisanceParameters();
const RooArgSet *globalObs = mc_s->GetGlobalObservables();
if (!justFit_ && nuis && globalObs ) {
std::auto_ptr<RooAbsPdf> nuisancePdf(utils::makeNuisancePdf(*mc_s));
std::auto_ptr<RooDataSet> globalData(new RooDataSet("globalData","globalData", *globalObs));
globalData->add(*globalObs);
RooFitResult *res_prefit = 0;
{
CloseCoutSentry sentry(verbose < 2);
res_prefit = nuisancePdf->fitTo(*globalData,
RooFit::Save(1),
RooFit::Minimizer(ROOT::Math::MinimizerOptions::DefaultMinimizerType().c_str(), ROOT::Math::MinimizerOptions::DefaultMinimizerAlgo().c_str()),
RooFit::Strategy(minimizerStrategy_),
RooFit::Minos(minos_ == "all")
);
}
if (fitOut.get() ) fitOut->WriteTObject(res_prefit, "nuisances_prefit_res");
if (fitOut.get() ) fitOut->WriteTObject(nuis->snapshot(), "nuisances_prefit");
nuisancePdf.reset();
globalData.reset();
delete res_prefit;
} else if (nuis) {
if (fitOut.get() ) fitOut->WriteTObject(nuis->snapshot(), "nuisances_prefit");
}
}
RooFitResult *res_b = 0, *res_s = 0;
const RooCmdArg &constCmdArg_s = withSystematics ? RooFit::Constrain(*mc_s->GetNuisanceParameters()) : RooFit::NumCPU(1); // use something dummy
const RooCmdArg &minosCmdArg = minos_ == "poi" ? RooFit::Minos(*mc_s->GetParametersOfInterest()) : RooFit::Minos(minos_ != "none");
w->loadSnapshot("clean");
r->setVal(0.0); r->setConstant(true);
// Setup Nll before calling fits;
if (currentToy_<1) nll.reset(mc_s->GetPdf()->createNLL(data,constCmdArg_s,RooFit::Extended(mc_s->GetPdf()->canBeExtended())));
// Get the nll value on the prefit
double nll0 = nll->getVal();
if (justFit_) {
// skip b-only fit
} else if (minos_ != "all") {
RooArgList minos;
res_b = doFit(*mc_s->GetPdf(), data, minos, constCmdArg_s, /*hesse=*/true,/*reuseNLL*/ true);
nll_bonly_=nll->getVal()-nll0;
} else {
CloseCoutSentry sentry(verbose < 2);
res_b = mc_s->GetPdf()->fitTo(data,
RooFit::Save(1),
RooFit::Minimizer(ROOT::Math::MinimizerOptions::DefaultMinimizerType().c_str(), ROOT::Math::MinimizerOptions::DefaultMinimizerAlgo().c_str()),
RooFit::Strategy(minimizerStrategy_),
RooFit::Extended(mc_s->GetPdf()->canBeExtended()),
constCmdArg_s, minosCmdArg
);
if (res_b) nll_bonly_ = nll->getVal() - nll0;
}
if (res_b) {
if (verbose > 1) res_b->Print("V");
if (fitOut.get()) {
if (currentToy_< 1) fitOut->WriteTObject(res_b,"fit_b");
setFitResultTrees(mc_s->GetNuisanceParameters(),nuisanceParameters_);
setFitResultTrees(mc_s->GetGlobalObservables(),globalObservables_);
fitStatus_ = res_b->status();
}
numbadnll_=res_b->numInvalidNLL();
if (makePlots_) {
std::vector<RooPlot *> plots = utils::makePlots(*mc_b->GetPdf(), data, signalPdfNames_.c_str(), backgroundPdfNames_.c_str(), rebinFactor_);
for (std::vector<RooPlot *>::iterator it = plots.begin(), ed = plots.end(); it != ed; ++it) {
c1->cd(); (*it)->Draw();
c1->Print((out_+"/"+(*it)->GetName()+"_fit_b.png").c_str());
if (fitOut.get() && currentToy_< 1) fitOut->WriteTObject(*it, (std::string((*it)->GetName())+"_fit_b").c_str());
}
}
if (saveNormalizations_ && currentToy_<1) {
RooArgSet *norms = new RooArgSet();
norms->setName("norm_fit_b");
getNormalizations(mc_s->GetPdf(), *mc_s->GetObservables(), *norms);
if (fitOut.get()) fitOut->WriteTObject(norms, "norm_fit_b");
delete norms;
}
if (makePlots_ && currentToy_<1) {
TH2 *corr = res_b->correlationHist();
c1->SetLeftMargin(0.25); c1->SetBottomMargin(0.25);
corr->SetTitle("Correlation matrix of fit parameters");
gStyle->SetPaintTextFormat(res_b->floatParsFinal().getSize() > 10 ? ".1f" : ".2f");
gStyle->SetOptStat(0);
corr->SetMarkerSize(res_b->floatParsFinal().getSize() > 10 ? 2 : 1);
corr->Draw("COLZ TEXT");
c1->Print((out_+"/covariance_fit_b.png").c_str());
c1->SetLeftMargin(0.16); c1->SetBottomMargin(0.13);
if (fitOut.get()) fitOut->WriteTObject(corr, "covariance_fit_b");
}
}
else {
fitStatus_=-1;
numbadnll_=-1;
}
mu_=r->getVal();
if (t_fit_b_) t_fit_b_->Fill();
// no longer need res_b
delete res_b;
if (!reuseParams_) w->loadSnapshot("clean"); // Reset, also ensures nll_prefit is same in call to doFit for b and s+b
r->setVal(preFitValue_); r->setConstant(false);
if (minos_ != "all") {
RooArgList minos; if (minos_ == "poi") minos.add(*r);
res_s = doFit(*mc_s->GetPdf(), data, minos, constCmdArg_s, /*hesse=*/!noErrors_,/*reuseNLL*/ true);
nll_sb_ = nll->getVal()-nll0;
} else {
CloseCoutSentry sentry(verbose < 2);
res_s = mc_s->GetPdf()->fitTo(data,
RooFit::Save(1),
RooFit::Minimizer(ROOT::Math::MinimizerOptions::DefaultMinimizerType().c_str(), ROOT::Math::MinimizerOptions::DefaultMinimizerAlgo().c_str()),
RooFit::Strategy(minimizerStrategy_),
RooFit::Extended(mc_s->GetPdf()->canBeExtended()),
constCmdArg_s, minosCmdArg
);
if (res_s) nll_sb_= nll->getVal()-nll0;
}
if (res_s) {
limit = r->getVal();
limitErr = r->getError();
if (verbose > 1) res_s->Print("V");
if (fitOut.get()){
if (currentToy_<1) fitOut->WriteTObject(res_s, "fit_s");
setFitResultTrees(mc_s->GetNuisanceParameters(),nuisanceParameters_);
setFitResultTrees(mc_s->GetGlobalObservables(),globalObservables_);
fitStatus_ = res_s->status();
numbadnll_ = res_s->numInvalidNLL();
// Additionally store the nll_sb - nll_bonly (=0.5*q0)
nll_nll0_ = nll_sb_ - nll_bonly_;
}
if (makePlots_) {
std::vector<RooPlot *> plots = utils::makePlots(*mc_s->GetPdf(), data, signalPdfNames_.c_str(), backgroundPdfNames_.c_str(), rebinFactor_);
for (std::vector<RooPlot *>::iterator it = plots.begin(), ed = plots.end(); it != ed; ++it) {
c1->cd(); (*it)->Draw();
c1->Print((out_+"/"+(*it)->GetName()+"_fit_s.png").c_str());
if (fitOut.get() && currentToy_< 1) fitOut->WriteTObject(*it, (std::string((*it)->GetName())+"_fit_s").c_str());
}
}
if (saveNormalizations_&& currentToy_< 1) {
RooArgSet *norms = new RooArgSet();
norms->setName("norm_fit_s");
getNormalizations(mc_s->GetPdf(), *mc_s->GetObservables(), *norms);
if (fitOut.get() ) fitOut->WriteTObject(norms, "norm_fit_s");
delete norms;
}
if (makePlots_&& currentToy_< 1) {
TH2 *corr = res_s->correlationHist();
c1->SetLeftMargin(0.25); c1->SetBottomMargin(0.25);
corr->SetTitle("Correlation matrix of fit parameters");
gStyle->SetPaintTextFormat(res_s->floatParsFinal().getSize() > 10 ? ".1f" : ".2f");
gStyle->SetOptStat(0);
corr->SetMarkerSize(res_s->floatParsFinal().getSize() > 10 ? 2 : 1);
corr->Draw("COLZ TEXT");
c1->Print((out_+"/covariance_fit_s.png").c_str());
c1->SetLeftMargin(0.16); c1->SetBottomMargin(0.13);
if (fitOut.get() ) fitOut->WriteTObject(corr, "covariance_fit_s");
}
} else {
fitStatus_=-1;
numbadnll_=-1;
nll_nll0_ = -1;
}
mu_=r->getVal();
if (t_fit_sb_) t_fit_sb_->Fill();
if (res_s) {
RooRealVar *rf = dynamic_cast<RooRealVar*>(res_s->floatParsFinal().find(r->GetName()));
double bestFitVal = rf->getVal();
double hiErr = +(rf->hasRange("err68") ? rf->getMax("err68") - bestFitVal : rf->getAsymErrorHi());
double loErr = -(rf->hasRange("err68") ? rf->getMin("err68") - bestFitVal : rf->getAsymErrorLo());
double maxError = std::max<double>(std::max<double>(hiErr, loErr), rf->getError());
if (fabs(hiErr) < 0.001*maxError) hiErr = -bestFitVal + rf->getMax();
if (fabs(loErr) < 0.001*maxError) loErr = +bestFitVal - rf->getMin();
double hiErr95 = +(do95_ && rf->hasRange("err95") ? rf->getMax("err95") - bestFitVal : 0);
double loErr95 = -(do95_ && rf->hasRange("err95") ? rf->getMin("err95") - bestFitVal : 0);
limit = bestFitVal; limitErr = 0;
if (!noErrors_) Combine::commitPoint(/*expected=*/true, /*quantile=*/0.5);
limit = bestFitVal - loErr; limitErr = 0;
if (!noErrors_) Combine::commitPoint(/*expected=*/true, /*quantile=*/0.16);
limit = bestFitVal + hiErr; limitErr = 0;
if (!noErrors_) Combine::commitPoint(/*expected=*/true, /*quantile=*/0.84);
if (do95_ && rf->hasRange("err95") && !noErrors_) {
limit = rf->getMax("err95"); Combine::commitPoint(/*expected=*/true, /*quantile=*/0.975);
limit = rf->getMin("err95"); Combine::commitPoint(/*expected=*/true, /*quantile=*/0.025);
}
limit = bestFitVal;
limitErr = maxError;
std::cout << "\n --- MaxLikelihoodFit ---" << std::endl;
std::cout << "Best fit " << r->GetName() << ": " << rf->getVal() << " "<< -loErr << "/+" << +hiErr << " (68% CL)" << std::endl;
if (do95_) {
std::cout << " " << r->GetName() << ": " << rf->getVal() << " "<< -loErr95 << "/+" << +hiErr95 << " (95% CL)" << std::endl;
}
} else {
std::cout << "\n --- MaxLikelihoodFit ---" << std::endl;
std::cout << "Fit failed." << std::endl;
}
if (currentToy_==nToys-1 || nToys==0 ) {
if (fitOut.get()) {
fitOut->cd();
t_fit_sb_->Write(); t_fit_b_->Write();
fitOut.release()->Close();
}
}
bool fitreturn = (res_s!=0);
delete res_s;
std::cout << "nll S+B -> "<<nll_sb_ << " nll B -> " << nll_bonly_ <<std::endl;
return fitreturn;
}
| void MaxLikelihoodFit::setFitResultTrees | ( | const RooArgSet * | args, |
| double * | vals | ||
| ) | [protected] |
Definition at line 387 of file MaxLikelihoodFit.cc.
References a, prof2calltree::count, and name().
Referenced by runSpecific().
| void MaxLikelihoodFit::setNToys | ( | const int | iToy | ) | [virtual] |
Reimplemented from LimitAlgo.
Definition at line 72 of file MaxLikelihoodFit.cc.
References nToys.
{
nToys = iToy;
}
| void MaxLikelihoodFit::setToyNumber | ( | const int | iToy | ) | [virtual] |
Reimplemented from LimitAlgo.
Definition at line 69 of file MaxLikelihoodFit.cc.
References currentToy_.
{
currentToy_ = iToy;
}
std::string MaxLikelihoodFit::backgroundPdfNames_ = "shapeBkg*" [protected] |
Definition at line 36 of file MaxLikelihoodFit.h.
Referenced by MaxLikelihoodFit(), and runSpecific().
int MaxLikelihoodFit::currentToy_ [protected] |
Definition at line 39 of file MaxLikelihoodFit.h.
Referenced by runSpecific(), and setToyNumber().
std::auto_ptr<TFile> MaxLikelihoodFit::fitOut [protected] |
Definition at line 42 of file MaxLikelihoodFit.h.
Referenced by runSpecific().
int MaxLikelihoodFit::fitStatus_ [protected] |
Definition at line 40 of file MaxLikelihoodFit.h.
Referenced by createFitResultTrees(), MaxLikelihoodFit(), and runSpecific().
double* MaxLikelihoodFit::globalObservables_ [protected] |
Definition at line 43 of file MaxLikelihoodFit.h.
Referenced by createFitResultTrees(), runSpecific(), and ~MaxLikelihoodFit().
bool MaxLikelihoodFit::justFit_ = false [static, protected] |
Definition at line 32 of file MaxLikelihoodFit.h.
Referenced by applyOptions(), and runSpecific().
bool MaxLikelihoodFit::makePlots_ = false [static, protected] |
Definition at line 34 of file MaxLikelihoodFit.h.
Referenced by applyOptions(), and runSpecific().
std::string MaxLikelihoodFit::minos_ = "poi" [static, protected] |
Definition at line 30 of file MaxLikelihoodFit.h.
Referenced by MaxLikelihoodFit(), and runSpecific().
double MaxLikelihoodFit::mu_ [protected] |
Definition at line 41 of file MaxLikelihoodFit.h.
Referenced by createFitResultTrees(), MaxLikelihoodFit(), and runSpecific().
std::string MaxLikelihoodFit::name_ = "" [static, protected] |
Definition at line 28 of file MaxLikelihoodFit.h.
Referenced by applyOptions(), and runSpecific().
double MaxLikelihoodFit::nll_bonly_ [protected] |
Definition at line 41 of file MaxLikelihoodFit.h.
Referenced by createFitResultTrees(), MaxLikelihoodFit(), and runSpecific().
double MaxLikelihoodFit::nll_nll0_ [protected] |
Definition at line 41 of file MaxLikelihoodFit.h.
Referenced by createFitResultTrees(), MaxLikelihoodFit(), and runSpecific().
double MaxLikelihoodFit::nll_sb_ [protected] |
Definition at line 41 of file MaxLikelihoodFit.h.
Referenced by createFitResultTrees(), MaxLikelihoodFit(), and runSpecific().
bool MaxLikelihoodFit::noErrors_ = false [protected] |
Definition at line 32 of file MaxLikelihoodFit.h.
Referenced by applyOptions(), and runSpecific().
int MaxLikelihoodFit::nToys [protected] |
Definition at line 39 of file MaxLikelihoodFit.h.
Referenced by MaxLikelihoodFit(), runSpecific(), and setNToys().
double* MaxLikelihoodFit::nuisanceParameters_ [protected] |
Definition at line 44 of file MaxLikelihoodFit.h.
Referenced by createFitResultTrees(), runSpecific(), and ~MaxLikelihoodFit().
int MaxLikelihoodFit::numbadnll_ [protected] |
Definition at line 40 of file MaxLikelihoodFit.h.
Referenced by createFitResultTrees(), MaxLikelihoodFit(), and runSpecific().
std::string MaxLikelihoodFit::out_ = "." [static, protected] |
Definition at line 33 of file MaxLikelihoodFit.h.
Referenced by applyOptions(), MaxLikelihoodFit(), and runSpecific().
float MaxLikelihoodFit::rebinFactor_ = 1.0 [static, protected] |
Definition at line 35 of file MaxLikelihoodFit.h.
Referenced by MaxLikelihoodFit(), and runSpecific().
bool MaxLikelihoodFit::reuseParams_ = false [static, protected] |
Definition at line 38 of file MaxLikelihoodFit.h.
Referenced by applyOptions(), and runSpecific().
bool MaxLikelihoodFit::saveNormalizations_ = false [static, protected] |
Definition at line 37 of file MaxLikelihoodFit.h.
Referenced by applyOptions(), and runSpecific().
std::string MaxLikelihoodFit::signalPdfNames_ = "shapeSig*" [static, protected] |
Definition at line 36 of file MaxLikelihoodFit.h.
Referenced by MaxLikelihoodFit(), and runSpecific().
TTree* MaxLikelihoodFit::t_fit_b_ [protected] |
Definition at line 46 of file MaxLikelihoodFit.h.
Referenced by createFitResultTrees(), and runSpecific().
TTree * MaxLikelihoodFit::t_fit_sb_ [protected] |
Definition at line 46 of file MaxLikelihoodFit.h.
Referenced by createFitResultTrees(), and runSpecific().
1.7.3