This macro will perform a scan of the p-values for computing the interval or limit
0x55d50010f5e0 results/example_combined_GaussExample_model.root
Running HypoTestInverter on the workspace combined
RooWorkspace(combined) combined contents
variables
---------
(Lumi,SigXsecOverSM,alpha_syst1,alpha_syst2,alpha_syst3,channelCat,gamma_stat_channel1_bin_0,gamma_stat_channel1_bin_1,nom_alpha_syst1,nom_alpha_syst2,nom_alpha_syst3,nom_gamma_stat_channel1_bin_0,nom_gamma_stat_channel1_bin_1,nominalLumi,obs_x_channel1)
p.d.f.s
-------
RooGaussian::alpha_syst1Constraint[ x=alpha_syst1 mean=nom_alpha_syst1 sigma=1 ] = 1
RooGaussian::alpha_syst2Constraint[ x=alpha_syst2 mean=nom_alpha_syst2 sigma=1 ] = 1
RooGaussian::alpha_syst3Constraint[ x=alpha_syst3 mean=nom_alpha_syst3 sigma=1 ] = 1
RooRealSumPdf::channel1_model[ signal_channel1_scaleFactors * signal_channel1_shapes + background1_channel1_scaleFactors * background1_channel1_shapes + background2_channel1_scaleFactors * background2_channel1_shapes ] = 240
RooPoisson::gamma_stat_channel1_bin_0_constraint[ x=nom_gamma_stat_channel1_bin_0 mean=gamma_stat_channel1_bin_0_poisMean ] = 0.019943
RooPoisson::gamma_stat_channel1_bin_1_constraint[ x=nom_gamma_stat_channel1_bin_1 mean=gamma_stat_channel1_bin_1_poisMean ] = 0.039861
RooGaussian::lumiConstraint[ x=Lumi mean=nominalLumi sigma=0.1 ] = 1
RooProdPdf::model_channel1[ lumiConstraint * alpha_syst1Constraint * alpha_syst2Constraint * alpha_syst3Constraint * gamma_stat_channel1_bin_0_constraint * gamma_stat_channel1_bin_1_constraint * channel1_model(obs_x_channel1) ] = 0.190787
RooSimultaneous::simPdf[ indexCat=channelCat channel1=model_channel1 ] = 0.190787
functions
--------
RooHistFunc::background1_channel1_Hist_alphanominal[ depList=(obs_x_channel1) depList=(obs_x_channel1) ] = 100
RooStats::HistFactory::FlexibleInterpVar::background1_channel1_epsilon[ paramList=(alpha_syst2) ] = 1
RooProduct::background1_channel1_scaleFactors[ background1_channel1_epsilon * Lumi ] = 1
RooProduct::background1_channel1_shapes[ background1_channel1_Hist_alphanominal * mc_stat_channel1 * channel1_model_binWidth ] = 200
RooHistFunc::background2_channel1_Hist_alphanominal[ depList=(obs_x_channel1) depList=(obs_x_channel1) ] = 0
RooStats::HistFactory::FlexibleInterpVar::background2_channel1_epsilon[ paramList=(alpha_syst3) ] = 1
RooProduct::background2_channel1_scaleFactors[ background2_channel1_epsilon * Lumi ] = 1
RooProduct::background2_channel1_shapes[ background2_channel1_Hist_alphanominal * mc_stat_channel1 * channel1_model_binWidth ] = 0
RooBinWidthFunction::channel1_model_binWidth[ HistFuncForBinWidth=signal_channel1_Hist_alphanominal HistFuncForBinWidth=signal_channel1_Hist_alphanominal ] = 2
RooProduct::gamma_stat_channel1_bin_0_poisMean[ gamma_stat_channel1_bin_0 * gamma_stat_channel1_bin_0_tau ] = 400
RooProduct::gamma_stat_channel1_bin_1_poisMean[ gamma_stat_channel1_bin_1 * gamma_stat_channel1_bin_1_tau ] = 100
ParamHistFunc::mc_stat_channel1[ ] = 1
RooHistFunc::signal_channel1_Hist_alphanominal[ depList=(obs_x_channel1) depList=(obs_x_channel1) ] = 20
RooStats::HistFactory::FlexibleInterpVar::signal_channel1_epsilon[ paramList=(alpha_syst1) ] = 1
RooProduct::signal_channel1_scaleFactors[ signal_channel1_epsilon * SigXsecOverSM * Lumi ] = 1
RooProduct::signal_channel1_shapes[ signal_channel1_Hist_alphanominal * channel1_model_binWidth ] = 40
datasets
--------
RooDataSet::obsData(obs_x_channel1,channelCat)
RooDataSet::asimovData(obs_x_channel1,channelCat)
embedded datasets (in pdfs and functions)
-----------------------------------------
RooDataHist::signal_channel1_Hist_alphanominalDHist(obs_x_channel1)
RooDataHist::background1_channel1_Hist_alphanominalDHist(obs_x_channel1)
RooDataHist::background2_channel1_Hist_alphanominalDHist(obs_x_channel1)
parameter snapshots
-------------------
NominalParamValues = (nominalLumi=1[C],nom_alpha_syst1=0[C],nom_alpha_syst2=0[C],nom_alpha_syst3=0[C],nom_gamma_stat_channel1_bin_0=400[C],nom_gamma_stat_channel1_bin_1=100[C],obs_x_channel1=1.25,Lumi=1 +/- 0.1[C],alpha_syst1=0 +/- 1[C],alpha_syst2=0 +/- 1,alpha_syst3=0 +/- 1,gamma_stat_channel1_bin_0=1 +/- 0.05,gamma_stat_channel1_bin_1=1 +/- 0.1,SigXsecOverSM=1)
named sets
----------
ModelConfig_GlobalObservables:(nominalLumi,nom_alpha_syst1,nom_alpha_syst2,nom_alpha_syst3,nom_gamma_stat_channel1_bin_0,nom_gamma_stat_channel1_bin_1)
ModelConfig_NuisParams:(alpha_syst2,alpha_syst3,gamma_stat_channel1_bin_0,gamma_stat_channel1_bin_1)
ModelConfig_Observables:(obs_x_channel1,channelCat)
ModelConfig_POI:(SigXsecOverSM)
globalObservables:(nominalLumi,nom_alpha_syst1,nom_alpha_syst2,nom_alpha_syst3,nom_gamma_stat_channel1_bin_0,nom_gamma_stat_channel1_bin_1)
observables:(obs_x_channel1,channelCat)
generic objects
---------------
RooStats::ModelConfig::ModelConfig
Using data set obsData
StandardHypoTestInvDemo : POI initial value: SigXsecOverSM = 1
[#1] INFO:InputArguments -- HypoTestInverter ---- Input models:
using as S+B (null) model : ModelConfig
using as B (alternate) model : ModelConfig_with_poi_0
Doing a fixed scan in interval : 0 , 5
[#1] INFO:Eval -- HypoTestInverter::GetInterval - run a fixed scan
[#0] WARNING:InputArguments -- HypoTestInverter::RunFixedScan - xMax > upper bound, using xmax = 3
[#1] INFO:ObjectHandling -- RooWorkspace::saveSnapshot(combined) replacing previous snapshot with name ModelConfig__snapshot
[#0] PROGRESS:Eval -- Running for SigXsecOverSM = 0
=== Using the following for ModelConfig ===
Observables: RooArgSet:: = (obs_x_channel1,channelCat)
Parameters of Interest: RooArgSet:: = (SigXsecOverSM)
Nuisance Parameters: RooArgSet:: = (alpha_syst2,alpha_syst3,gamma_stat_channel1_bin_0,gamma_stat_channel1_bin_1)
Global Observables: RooArgSet:: = (nominalLumi,nom_alpha_syst1,nom_alpha_syst2,nom_alpha_syst3,nom_gamma_stat_channel1_bin_0,nom_gamma_stat_channel1_bin_1)
PDF: RooSimultaneous::simPdf[ indexCat=channelCat channel1=model_channel1 ] = 0.158989
Snapshot:
1) 0x55d50149b960 RooRealVar:: SigXsecOverSM = 0 L(0 - 3) "SigXsecOverSM"
=== Using the following for ModelConfig_with_poi_0 ===
Observables: RooArgSet:: = (obs_x_channel1,channelCat)
Parameters of Interest: RooArgSet:: = (SigXsecOverSM)
Nuisance Parameters: RooArgSet:: = (alpha_syst2,alpha_syst3,gamma_stat_channel1_bin_0,gamma_stat_channel1_bin_1)
Global Observables: RooArgSet:: = (nominalLumi,nom_alpha_syst1,nom_alpha_syst2,nom_alpha_syst3,nom_gamma_stat_channel1_bin_0,nom_gamma_stat_channel1_bin_1)
PDF: RooSimultaneous::simPdf[ indexCat=channelCat channel1=model_channel1 ] = 0.158989
Snapshot:
1) 0x55d50145a130 RooRealVar:: SigXsecOverSM = 0 L(0 - 3) "SigXsecOverSM"
[#0] PROGRESS:Generation -- Test Statistic on data: 0
[#1] INFO:InputArguments -- Profiling conditional MLEs for Null.
[#1] INFO:InputArguments -- Using a ToyMCSampler. Now configuring for Null.
[#0] PROGRESS:Generation -- generated toys: 500 / 1000
[#1] INFO:InputArguments -- Profiling conditional MLEs for Alt.
[#1] INFO:InputArguments -- Using a ToyMCSampler. Now configuring for Alt.
[#0] PROGRESS:Eval -- P values for SigXsecOverSM = 0
CLs = 1 +/- 0
CLb = 1 +/- 0
CLsplusb = 1 +/- 0
[#1] INFO:ObjectHandling -- RooWorkspace::saveSnapshot(combined) replacing previous snapshot with name ModelConfig__snapshot
[#0] PROGRESS:Eval -- Running for SigXsecOverSM = 0.6
=== Using the following for ModelConfig ===
Observables: RooArgSet:: = (obs_x_channel1,channelCat)
Parameters of Interest: RooArgSet:: = (SigXsecOverSM)
Nuisance Parameters: RooArgSet:: = (alpha_syst2,alpha_syst3,gamma_stat_channel1_bin_0,gamma_stat_channel1_bin_1)
Global Observables: RooArgSet:: = (nominalLumi,nom_alpha_syst1,nom_alpha_syst2,nom_alpha_syst3,nom_gamma_stat_channel1_bin_0,nom_gamma_stat_channel1_bin_1)
PDF: RooSimultaneous::simPdf[ indexCat=channelCat channel1=model_channel1 ] = 0.178068
Snapshot:
1) 0x55d501655cd0 RooRealVar:: SigXsecOverSM = 0.6 L(0 - 3) "SigXsecOverSM"
=== Using the following for ModelConfig_with_poi_0 ===
Observables: RooArgSet:: = (obs_x_channel1,channelCat)
Parameters of Interest: RooArgSet:: = (SigXsecOverSM)
Nuisance Parameters: RooArgSet:: = (alpha_syst2,alpha_syst3,gamma_stat_channel1_bin_0,gamma_stat_channel1_bin_1)
Global Observables: RooArgSet:: = (nominalLumi,nom_alpha_syst1,nom_alpha_syst2,nom_alpha_syst3,nom_gamma_stat_channel1_bin_0,nom_gamma_stat_channel1_bin_1)
PDF: RooSimultaneous::simPdf[ indexCat=channelCat channel1=model_channel1 ] = 0.178068
Snapshot:
1) 0x55d501499540 RooRealVar:: SigXsecOverSM = 0 L(0 - 3) "SigXsecOverSM"
[#0] PROGRESS:Generation -- Test Statistic on data: -2.35222
[#1] INFO:InputArguments -- Profiling conditional MLEs for Null.
[#1] INFO:InputArguments -- Using a ToyMCSampler. Now configuring for Null.
[#0] PROGRESS:Generation -- generated toys: 500 / 1000
[#1] INFO:InputArguments -- Profiling conditional MLEs for Alt.
[#1] INFO:InputArguments -- Using a ToyMCSampler. Now configuring for Alt.
[#0] PROGRESS:Eval -- P values for SigXsecOverSM = 0.6
CLs = 0.826039 +/- 0.0187037
CLb = 0.914 +/- 0.0125383
CLsplusb = 0.755 +/- 0.0136006
[#1] INFO:ObjectHandling -- RooWorkspace::saveSnapshot(combined) replacing previous snapshot with name ModelConfig__snapshot
[#0] PROGRESS:Eval -- Running for SigXsecOverSM = 1.2
=== Using the following for ModelConfig ===
Observables: RooArgSet:: = (obs_x_channel1,channelCat)
Parameters of Interest: RooArgSet:: = (SigXsecOverSM)
Nuisance Parameters: RooArgSet:: = (alpha_syst2,alpha_syst3,gamma_stat_channel1_bin_0,gamma_stat_channel1_bin_1)
Global Observables: RooArgSet:: = (nominalLumi,nom_alpha_syst1,nom_alpha_syst2,nom_alpha_syst3,nom_gamma_stat_channel1_bin_0,nom_gamma_stat_channel1_bin_1)
PDF: RooSimultaneous::simPdf[ indexCat=channelCat channel1=model_channel1 ] = 0.197147
Snapshot:
1) 0x55d5015e4140 RooRealVar:: SigXsecOverSM = 1.2 L(0 - 3) "SigXsecOverSM"
=== Using the following for ModelConfig_with_poi_0 ===
Observables: RooArgSet:: = (obs_x_channel1,channelCat)
Parameters of Interest: RooArgSet:: = (SigXsecOverSM)
Nuisance Parameters: RooArgSet:: = (alpha_syst2,alpha_syst3,gamma_stat_channel1_bin_0,gamma_stat_channel1_bin_1)
Global Observables: RooArgSet:: = (nominalLumi,nom_alpha_syst1,nom_alpha_syst2,nom_alpha_syst3,nom_gamma_stat_channel1_bin_0,nom_gamma_stat_channel1_bin_1)
PDF: RooSimultaneous::simPdf[ indexCat=channelCat channel1=model_channel1 ] = 0.197147
Snapshot:
1) 0x55d5015f4a00 RooRealVar:: SigXsecOverSM = 0 L(0 - 3) "SigXsecOverSM"
[#0] PROGRESS:Generation -- Test Statistic on data: -2.9364
[#1] INFO:InputArguments -- Profiling conditional MLEs for Null.
[#1] INFO:InputArguments -- Using a ToyMCSampler. Now configuring for Null.
[#0] PROGRESS:Generation -- generated toys: 500 / 1000
[#1] INFO:InputArguments -- Profiling conditional MLEs for Alt.
[#1] INFO:InputArguments -- Using a ToyMCSampler. Now configuring for Alt.
[#0] PROGRESS:Eval -- P values for SigXsecOverSM = 1.2
CLs = 0.495652 +/- 0.018325
CLb = 0.92 +/- 0.0121326
CLsplusb = 0.456 +/- 0.01575
[#1] INFO:ObjectHandling -- RooWorkspace::saveSnapshot(combined) replacing previous snapshot with name ModelConfig__snapshot
[#0] PROGRESS:Eval -- Running for SigXsecOverSM = 1.8
=== Using the following for ModelConfig ===
Observables: RooArgSet:: = (obs_x_channel1,channelCat)
Parameters of Interest: RooArgSet:: = (SigXsecOverSM)
Nuisance Parameters: RooArgSet:: = (alpha_syst2,alpha_syst3,gamma_stat_channel1_bin_0,gamma_stat_channel1_bin_1)
Global Observables: RooArgSet:: = (nominalLumi,nom_alpha_syst1,nom_alpha_syst2,nom_alpha_syst3,nom_gamma_stat_channel1_bin_0,nom_gamma_stat_channel1_bin_1)
PDF: RooSimultaneous::simPdf[ indexCat=channelCat channel1=model_channel1 ] = 0.216225
Snapshot:
1) 0x55d501497f70 RooRealVar:: SigXsecOverSM = 1.8 L(0 - 3) "SigXsecOverSM"
=== Using the following for ModelConfig_with_poi_0 ===
Observables: RooArgSet:: = (obs_x_channel1,channelCat)
Parameters of Interest: RooArgSet:: = (SigXsecOverSM)
Nuisance Parameters: RooArgSet:: = (alpha_syst2,alpha_syst3,gamma_stat_channel1_bin_0,gamma_stat_channel1_bin_1)
Global Observables: RooArgSet:: = (nominalLumi,nom_alpha_syst1,nom_alpha_syst2,nom_alpha_syst3,nom_gamma_stat_channel1_bin_0,nom_gamma_stat_channel1_bin_1)
PDF: RooSimultaneous::simPdf[ indexCat=channelCat channel1=model_channel1 ] = 0.216225
Snapshot:
1) 0x55d5015b7620 RooRealVar:: SigXsecOverSM = 0 L(0 - 3) "SigXsecOverSM"
[#0] PROGRESS:Generation -- Test Statistic on data: -2.05075
[#1] INFO:InputArguments -- Profiling conditional MLEs for Null.
[#1] INFO:InputArguments -- Using a ToyMCSampler. Now configuring for Null.
[#0] PROGRESS:Generation -- generated toys: 500 / 1000
[#1] INFO:InputArguments -- Profiling conditional MLEs for Alt.
[#1] INFO:InputArguments -- Using a ToyMCSampler. Now configuring for Alt.
[#0] PROGRESS:Eval -- P values for SigXsecOverSM = 1.8
CLs = 0.209052 +/- 0.0137241
CLb = 0.928 +/- 0.0115599
CLsplusb = 0.194 +/- 0.0125046
[#1] INFO:ObjectHandling -- RooWorkspace::saveSnapshot(combined) replacing previous snapshot with name ModelConfig__snapshot
[#0] PROGRESS:Eval -- Running for SigXsecOverSM = 2.4
=== Using the following for ModelConfig ===
Observables: RooArgSet:: = (obs_x_channel1,channelCat)
Parameters of Interest: RooArgSet:: = (SigXsecOverSM)
Nuisance Parameters: RooArgSet:: = (alpha_syst2,alpha_syst3,gamma_stat_channel1_bin_0,gamma_stat_channel1_bin_1)
Global Observables: RooArgSet:: = (nominalLumi,nom_alpha_syst1,nom_alpha_syst2,nom_alpha_syst3,nom_gamma_stat_channel1_bin_0,nom_gamma_stat_channel1_bin_1)
PDF: RooSimultaneous::simPdf[ indexCat=channelCat channel1=model_channel1 ] = 0.235304
Snapshot:
1) 0x55d50175ad80 RooRealVar:: SigXsecOverSM = 2.4 L(0 - 3) "SigXsecOverSM"
=== Using the following for ModelConfig_with_poi_0 ===
Observables: RooArgSet:: = (obs_x_channel1,channelCat)
Parameters of Interest: RooArgSet:: = (SigXsecOverSM)
Nuisance Parameters: RooArgSet:: = (alpha_syst2,alpha_syst3,gamma_stat_channel1_bin_0,gamma_stat_channel1_bin_1)
Global Observables: RooArgSet:: = (nominalLumi,nom_alpha_syst1,nom_alpha_syst2,nom_alpha_syst3,nom_gamma_stat_channel1_bin_0,nom_gamma_stat_channel1_bin_1)
PDF: RooSimultaneous::simPdf[ indexCat=channelCat channel1=model_channel1 ] = 0.235304
Snapshot:
1) 0x55d5015f7440 RooRealVar:: SigXsecOverSM = 0 L(0 - 3) "SigXsecOverSM"
[#0] PROGRESS:Generation -- Test Statistic on data: 0.0783908
[#1] INFO:InputArguments -- Profiling conditional MLEs for Null.
[#1] INFO:InputArguments -- Using a ToyMCSampler. Now configuring for Null.
[#0] PROGRESS:Generation -- generated toys: 500 / 1000
[#1] INFO:InputArguments -- Profiling conditional MLEs for Alt.
[#1] INFO:InputArguments -- Using a ToyMCSampler. Now configuring for Alt.
[#0] PROGRESS:Eval -- P values for SigXsecOverSM = 2.4
CLs = 0.0503282 +/- 0.00728062
CLb = 0.914 +/- 0.0125383
CLsplusb = 0.046 +/- 0.0066245
[#1] INFO:ObjectHandling -- RooWorkspace::saveSnapshot(combined) replacing previous snapshot with name ModelConfig__snapshot
[#0] PROGRESS:Eval -- Running for SigXsecOverSM = 3
=== Using the following for ModelConfig ===
Observables: RooArgSet:: = (obs_x_channel1,channelCat)
Parameters of Interest: RooArgSet:: = (SigXsecOverSM)
Nuisance Parameters: RooArgSet:: = (alpha_syst2,alpha_syst3,gamma_stat_channel1_bin_0,gamma_stat_channel1_bin_1)
Global Observables: RooArgSet:: = (nominalLumi,nom_alpha_syst1,nom_alpha_syst2,nom_alpha_syst3,nom_gamma_stat_channel1_bin_0,nom_gamma_stat_channel1_bin_1)
PDF: RooSimultaneous::simPdf[ indexCat=channelCat channel1=model_channel1 ] = 0.254383
Snapshot:
1) 0x55d5015ef7b0 RooRealVar:: SigXsecOverSM = 3 L(0 - 3) "SigXsecOverSM"
=== Using the following for ModelConfig_with_poi_0 ===
Observables: RooArgSet:: = (obs_x_channel1,channelCat)
Parameters of Interest: RooArgSet:: = (SigXsecOverSM)
Nuisance Parameters: RooArgSet:: = (alpha_syst2,alpha_syst3,gamma_stat_channel1_bin_0,gamma_stat_channel1_bin_1)
Global Observables: RooArgSet:: = (nominalLumi,nom_alpha_syst1,nom_alpha_syst2,nom_alpha_syst3,nom_gamma_stat_channel1_bin_0,nom_gamma_stat_channel1_bin_1)
PDF: RooSimultaneous::simPdf[ indexCat=channelCat channel1=model_channel1 ] = 0.254383
Snapshot:
1) 0x55d5016394f0 RooRealVar:: SigXsecOverSM = 0 L(0 - 3) "SigXsecOverSM"
[#0] PROGRESS:Generation -- Test Statistic on data: 3.27476
[#1] INFO:InputArguments -- Profiling conditional MLEs for Null.
[#1] INFO:InputArguments -- Using a ToyMCSampler. Now configuring for Null.
[#0] PROGRESS:Generation -- generated toys: 500 / 1000
[#1] INFO:InputArguments -- Profiling conditional MLEs for Alt.
[#1] INFO:InputArguments -- Using a ToyMCSampler. Now configuring for Alt.
[#0] PROGRESS:Eval -- P values for SigXsecOverSM = 3
CLs = 0.00534188 +/- 0.0023838
CLb = 0.936 +/- 0.0109457
CLsplusb = 0.005 +/- 0.00223047
Time to perform limit scan
Real time 0:00:10, CP time 10.810
The computed upper limit is: 2.40438 +/- 0.0566658
Expected upper limits, using the B (alternate) model :
expected limit (median) 1.61927
expected limit (-1 sig) 1.09318
expected limit (+1 sig) 2.24198
expected limit (-2 sig) 0.787047
expected limit (+2 sig) 2.86153
[#0] WARNING:Plotting -- Could not determine xmin and xmax of sampling distribution that was given to plot.
[#0] WARNING:Plotting -- Could not determine xmin and xmax of sampling distribution that was given to plot.
#include <cassert>
using std::cout, std::endl;
struct HypoTestInvOptions {
bool plotHypoTestResult = true;
bool writeResult = true;
bool optimize = true;
bool useVectorStore = true;
bool generateBinned = false;
bool noSystematics = false;
double nToysRatio = 2;
double maxPOI = -1;
bool useProof = false;
int nworkers = 0;
bool enableDetailedOutput =
false;
bool rebuild = false;
int nToyToRebuild = 100;
int rebuildParamValues = 0;
int initialFit = -1;
int randomSeed = -1;
int nAsimovBins = 0;
bool reuseAltToys = false;
double confLevel = 0.95;
std::string minimizerType =
"";
std::string massValue = "";
int printLevel = 0;
bool useNLLOffset = false;
};
HypoTestInvOptions optHTInv;
class HypoTestInvTool {
public:
HypoTestInvTool();
~HypoTestInvTool(){};
const char *dataName,
int type,
int testStatType,
bool useCLs,
int npoints,
double poimin, double poimax, int ntoys, bool useNumberCounting = false,
const char *nuisPriorName = nullptr);
const char *fileNameBase = nullptr);
void SetParameter(
const char *
name,
const char *
value);
void SetParameter(
const char *
name,
bool value);
void SetParameter(
const char *
name,
int value);
void SetParameter(
const char *
name,
double value);
private:
bool mPlotHypoTestResult;
bool mWriteResult;
bool mOptimize;
bool mUseVectorStore;
bool mGenerateBinned;
bool mUseProof;
bool mRebuild;
bool mReuseAltToys;
bool mEnableDetOutput;
int mNWorkers;
int mNToyToRebuild;
int mRebuildParamValues;
int mPrintLevel;
int mInitialFit;
int mRandomSeed;
double mNToysRatio;
double mMaxPoi;
int mAsimovBins;
std::string mMassValue;
std::string
mMinimizerType;
};
}
RooStats::HypoTestInvTool::HypoTestInvTool()
: mPlotHypoTestResult(true), mWriteResult(false), mOptimize(true), mUseVectorStore(true), mGenerateBinned(false),
mUseProof(false), mEnableDetOutput(false), mRebuild(false), mReuseAltToys(false), mNWorkers(4),
mNToyToRebuild(100), mRebuildParamValues(0), mPrintLevel(0), mInitialFit(-1), mRandomSeed(-1), mNToysRatio(2),
mMaxPoi(-1), mAsimovBins(0), mMassValue(""), mMinimizerType(""), mResultFileName()
{
}
void RooStats::HypoTestInvTool::SetParameter(
const char *
name,
bool value)
{
std::string s_name(
name);
if (s_name.find("PlotHypoTestResult") != std::string::npos)
mPlotHypoTestResult =
value;
if (s_name.find("WriteResult") != std::string::npos)
if (s_name.find("Optimize") != std::string::npos)
if (s_name.find("UseVectorStore") != std::string::npos)
if (s_name.find("GenerateBinned") != std::string::npos)
if (s_name.find("UseProof") != std::string::npos)
if (s_name.find("EnableDetailedOutput") != std::string::npos)
mEnableDetOutput =
value;
if (s_name.find("Rebuild") != std::string::npos)
if (s_name.find("ReuseAltToys") != std::string::npos)
return;
}
void RooStats::HypoTestInvTool::SetParameter(
const char *
name,
int value)
{
std::string s_name(
name);
if (s_name.find("NWorkers") != std::string::npos)
if (s_name.find("NToyToRebuild") != std::string::npos)
if (s_name.find("RebuildParamValues") != std::string::npos)
mRebuildParamValues =
value;
if (s_name.find("PrintLevel") != std::string::npos)
if (s_name.find("InitialFit") != std::string::npos)
if (s_name.find("RandomSeed") != std::string::npos)
if (s_name.find("AsimovBins") != std::string::npos)
return;
}
void RooStats::HypoTestInvTool::SetParameter(
const char *
name,
double value)
{
std::string s_name(
name);
if (s_name.find("NToysRatio") != std::string::npos)
if (s_name.find("MaxPOI") != std::string::npos)
return;
}
void RooStats::HypoTestInvTool::SetParameter(
const char *
name,
const char *
value)
{
std::string s_name(
name);
if (s_name.find("MassValue") != std::string::npos)
mMassValue.assign(
value);
if (s_name.find("MinimizerType") != std::string::npos)
mMinimizerType.assign(
value);
if (s_name.find("ResultFileName") != std::string::npos)
return;
}
void StandardHypoTestInvDemo(const char *infile = nullptr, const char *wsName = "combined",
const char *modelSBName = "ModelConfig", const char *modelBName = "",
const char *dataName = "obsData", int calculatorType = 0, int testStatType = 0,
bool useCLs = true, int npoints = 6, double poimin = 0, double poimax = 5,
int ntoys = 1000, bool useNumberCounting = false, const char *nuisPriorName = nullptr)
{
filename =
"results/example_combined_GaussExample_model.root";
if (!fileExist) {
cout << "will run standard hist2workspace example" << endl;
gROOT->ProcessLine(
".! prepareHistFactory .");
gROOT->ProcessLine(
".! hist2workspace config/example.xml");
cout << "\n\n---------------------" << endl;
cout << "Done creating example input" << endl;
cout << "---------------------\n\n" << endl;
}
} else
if (!file) {
cout <<
"StandardRooStatsDemoMacro: Input file " <<
filename <<
" is not found" << endl;
return;
}
HypoTestInvTool calc;
calc.SetParameter("PlotHypoTestResult", optHTInv.plotHypoTestResult);
calc.SetParameter("WriteResult", optHTInv.writeResult);
calc.SetParameter("Optimize", optHTInv.optimize);
calc.SetParameter("UseVectorStore", optHTInv.useVectorStore);
calc.SetParameter("GenerateBinned", optHTInv.generateBinned);
calc.SetParameter("NToysRatio", optHTInv.nToysRatio);
calc.SetParameter("MaxPOI", optHTInv.maxPOI);
calc.SetParameter("UseProof", optHTInv.useProof);
calc.SetParameter("EnableDetailedOutput", optHTInv.enableDetailedOutput);
calc.SetParameter("NWorkers", optHTInv.nworkers);
calc.SetParameter("Rebuild", optHTInv.rebuild);
calc.SetParameter("ReuseAltToys", optHTInv.reuseAltToys);
calc.SetParameter("NToyToRebuild", optHTInv.nToyToRebuild);
calc.SetParameter("RebuildParamValues", optHTInv.rebuildParamValues);
calc.SetParameter("MassValue", optHTInv.massValue.c_str());
calc.SetParameter("MinimizerType", optHTInv.minimizerType.c_str());
calc.SetParameter("PrintLevel", optHTInv.printLevel);
calc.SetParameter("InitialFit", optHTInv.initialFit);
calc.SetParameter("ResultFileName", optHTInv.resultFileName);
calc.SetParameter("RandomSeed", optHTInv.randomSeed);
calc.SetParameter("AsimovBins", optHTInv.nAsimovBins);
if (optHTInv.useNLLOffset)
std::cout <<
w <<
"\t" <<
filename << std::endl;
r = calc.RunInverter(
w, modelSBName, modelBName, dataName, calculatorType, testStatType, useCLs, npoints, poimin,
poimax, ntoys, useNumberCounting, nuisPriorName);
std::cerr << "Error running the HypoTestInverter - Exit " << std::endl;
return;
}
} else {
std::cout <<
"Reading an HypoTestInverterResult with name " << wsName <<
" from file " <<
filename << std::endl;
std::cerr <<
"File " <<
filename <<
" does not contain a workspace or an HypoTestInverterResult - Exit "
<< std::endl;
return;
}
}
calc.AnalyzeResult(
r, calculatorType, testStatType, useCLs, npoints, infile);
return;
}
bool useCLs, int npoints, const char *fileNameBase)
{
double lowerLimit = 0;
double llError = 0;
#if defined ROOT_SVN_VERSION && ROOT_SVN_VERSION >= 44126
lowerLimit =
r->LowerLimit();
llError =
r->LowerLimitEstimatedError();
}
#else
lowerLimit =
r->LowerLimit();
llError =
r->LowerLimitEstimatedError();
#endif
double upperLimit =
r->UpperLimit();
double ulError =
r->UpperLimitEstimatedError();
if (lowerLimit < upperLimit * (1. - 1.E-4) && lowerLimit != 0)
std::cout << "The computed lower limit is: " << lowerLimit << " +/- " << llError << std::endl;
std::cout << "The computed upper limit is: " << upperLimit << " +/- " << ulError << std::endl;
std::cout << "Expected upper limits, using the B (alternate) model : " << std::endl;
std::cout <<
" expected limit (median) " <<
r->GetExpectedUpperLimit(0) << std::endl;
std::cout <<
" expected limit (-1 sig) " <<
r->GetExpectedUpperLimit(-1) << std::endl;
std::cout <<
" expected limit (+1 sig) " <<
r->GetExpectedUpperLimit(1) << std::endl;
std::cout <<
" expected limit (-2 sig) " <<
r->GetExpectedUpperLimit(-2) << std::endl;
std::cout <<
" expected limit (+2 sig) " <<
r->GetExpectedUpperLimit(2) << std::endl;
if (mEnableDetOutput) {
mWriteResult = true;
Info(
"StandardHypoTestInvDemo",
"detailed output will be written in output result file");
}
if (
r !=
nullptr && mWriteResult) {
const char *calcType = (calculatorType == 0) ? "Freq" : (calculatorType == 1) ? "Hybr" : "Asym";
const char *limitType = (useCLs) ? "CLs" : "Cls+b";
const char *scanType = (npoints < 0) ? "auto" : "grid";
if (mResultFileName.IsNull()) {
mResultFileName =
TString::Format(
"%s_%s_%s_ts%d_", calcType, limitType, scanType, testStatType);
if (!mMassValue.empty()) {
mResultFileName += mMassValue.c_str();
mResultFileName += "_";
}
}
TString uldistFile =
"RULDist.root";
if (existULDist) {
if (fileULDist)
ulDist = fileULDist->
Get(
"RULDist");
}
if (ulDist)
Info(
"StandardHypoTestInvDemo",
"HypoTestInverterResult has been written in the file %s", mResultFileName.Data());
}
std::string typeName = "";
if (calculatorType == 0)
typeName = "Frequentist";
if (calculatorType == 1)
typeName = "Hybrid";
else if (calculatorType == 2 || calculatorType == 3) {
typeName = "Asymptotic";
mPlotHypoTestResult = false;
}
const char *resultName =
r->GetName();
const int nEntries =
r->ArraySize();
if (mPlotHypoTestResult) {
if (nEntries > 1) {
}
for (int i = 0; i < nEntries; i++) {
if (nEntries > 1)
}
}
}
const char *modelBName,
const char *dataName,
int type,
int testStatType, bool useCLs, int npoints,
double poimin, double poimax, int ntoys,
bool useNumberCounting, const char *nuisPriorName)
{
std::cout <<
"Running HypoTestInverter on the workspace " <<
w->
GetName() << std::endl;
Error(
"StandardHypoTestDemo",
"Not existing data %s", dataName);
return nullptr;
} else
std::cout << "Using data set " << dataName << std::endl;
if (mUseVectorStore) {
data->convertToVectorStore();
}
if (!sbModel) {
Error(
"StandardHypoTestDemo",
"Not existing ModelConfig %s", modelSBName);
return nullptr;
}
Error(
"StandardHypoTestDemo",
"Model %s has no pdf ", modelSBName);
return nullptr;
}
Error(
"StandardHypoTestDemo",
"Model %s has no poi ", modelSBName);
return nullptr;
}
Error(
"StandardHypoTestInvDemo",
"Model %s has no observables ", modelSBName);
return nullptr;
}
Info(
"StandardHypoTestInvDemo",
"Model %s has no snapshot - make one using model poi", modelSBName);
}
if (optHTInv.noSystematics) {
if (nuisPar && nuisPar->
getSize() > 0) {
std::cout << "StandardHypoTestInvDemo"
<< " - Switch off all systematics by setting them constant to their initial values" << std::endl;
}
if (bModel) {
if (bnuisPar)
}
}
if (!bModel || bModel == sbModel) {
Info(
"StandardHypoTestInvDemo",
"The background model %s does not exist", modelBName);
Info(
"StandardHypoTestInvDemo",
"Copy it from ModelConfig %s and set POI to zero", modelSBName);
if (!var)
return nullptr;
double oldval = var->
getVal();
} else {
Info(
"StandardHypoTestInvDemo",
"Model %s has no snapshot - make one using model poi and 0 values ",
modelBName);
if (var) {
double oldval = var->
getVal();
} else {
Error(
"StandardHypoTestInvDemo",
"Model %s has no valid poi", modelBName);
return nullptr;
}
}
}
if (hasNuisParam && !hasGlobalObs) {
if (constrPdf) {
Warning(
"StandardHypoTestInvDemo",
"Model %s has nuisance parameters but no global observables associated",
"\tThe effect of the nuisance parameters will not be treated correctly ");
}
}
}
allParams->snapshot(initialParameters);
std::cout <<
"StandardHypoTestInvDemo : POI initial value: " << poi->
GetName() <<
" = " << poi->
getVal()
<< std::endl;
bool doFit = mInitialFit;
if (testStatType == 0 && mInitialFit == -1)
doFit = false;
if (
type == 3 && mInitialFit == -1)
doFit = false;
double poihat = 0;
if (mMinimizerType.empty())
else
Info(
"StandardHypoTestInvDemo",
"Using %s as minimizer for computing the test statistic",
if (doFit) {
Info(
"StandardHypoTestInvDemo",
" Doing a first fit to the observed data ");
std::unique_ptr<RooFitResult> fitres{sbModel->
GetPdf()->
fitTo(
if (fitres->status() != 0) {
"Fit to the model failed - try with strategy 1 and perform first an Hesse computation");
fitres = std::unique_ptr<RooFitResult>{sbModel->
GetPdf()->
fitTo(
}
if (fitres->status() != 0)
Warning(
"StandardHypoTestInvDemo",
" Fit still failed - continue anyway.....");
std::cout <<
"StandardHypoTestInvDemo - Best Fit value : " << poi->
GetName() <<
" = " << poihat <<
" +/- "
std::cout << "Time for fitting : ";
std::cout <<
"StandardHypoTestInvo: snapshot of S+B Model " << sbModel->
GetName()
<< " is set to the best fit value" << std::endl;
}
if (testStatType == 0) {
if (!doFit)
Info(
"StandardHypoTestInvDemo",
"Using LEP test statistic - an initial fit is not done and the TS will use "
"the nuisances at the model value");
else
Info(
"StandardHypoTestInvDemo",
"Using LEP test statistic - an initial fit has been done and the TS will use "
"the nuisances at the best fit value");
}
slrts.SetNullParameters(nullParams);
slrts.SetAltParameters(altParams);
if (mEnableDetOutput)
slrts.EnableDetailedOutput();
ropl.SetSubtractMLE(false);
if (testStatType == 11)
ropl.SetSubtractMLE(true);
ropl.SetPrintLevel(mPrintLevel);
ropl.SetMinimizer(mMinimizerType.c_str());
if (mEnableDetOutput)
ropl.EnableDetailedOutput();
if (testStatType == 3)
profll.SetOneSided(true);
if (testStatType == 4)
profll.SetSigned(true);
profll.SetMinimizer(mMinimizerType.c_str());
profll.SetPrintLevel(mPrintLevel);
if (mEnableDetOutput)
profll.EnableDetailedOutput();
profll.SetReuseNLL(mOptimize);
slrts.SetReuseNLL(mOptimize);
ropl.SetReuseNLL(mOptimize);
if (mOptimize) {
profll.SetStrategy(0);
ropl.SetStrategy(0);
}
if (mMaxPoi > 0)
AsymptoticCalculator::SetPrintLevel(mPrintLevel);
true);
else {
Error(
"StandardHypoTestInvDemo",
"Invalid - calculator type = %d supported values are only :\n\t\t\t 0 "
"(Frequentist) , 1 (Hybrid) , 2 (Asymptotic) ",
return nullptr;
}
if (testStatType == 0)
testStat = &slrts;
if (testStatType == 1 || testStatType == 11)
testStat = &ropl;
if (testStatType == 2 || testStatType == 3 || testStatType == 4)
testStat = &profll;
if (testStatType == 5)
testStat = &maxll;
if (testStatType == 6)
testStat = &nevtts;
if (testStat == nullptr) {
Error(
"StandardHypoTestInvDemo",
"Invalid - test statistic type = %d supported values are only :\n\t\t\t 0 (SLR) "
", 1 (Tevatron) , 2 (PLR), 3 (PLR1), 4(MLE)",
testStatType);
return nullptr;
}
if (toymcs && (
type == 0 ||
type == 1)) {
if (useNumberCounting)
Warning(
"StandardHypoTestInvDemo",
"Pdf is extended: but number counting flag is set: ignore it ");
} else {
if (!useNumberCounting) {
int nEvents =
data->numEntries();
Info(
"StandardHypoTestInvDemo",
"Pdf is not extended: number of events to generate taken from observed data set is %d", nEvents);
} else {
Info(
"StandardHypoTestInvDemo",
"using a number counting pdf");
}
}
if (
data->isWeighted() && !mGenerateBinned) {
Info(
"StandardHypoTestInvDemo",
"Data set is weighted, nentries = %d and sum of weights = %8.1f but toy "
"generation is unbinned - it would be faster to set mGenerateBinned to true\n",
data->numEntries(),
data->sumEntries());
}
Warning(
"StandardHypoTestInvDemo",
"generate binned is activated but the number of observable is %d. Too much "
"memory could be needed for allocating all the bins",
}
if (mRandomSeed >= 0)
}
if (mReuseAltToys) {
}
assert(hhc);
hhc->
SetToys(ntoys, ntoys / mNToysRatio);
ToyMCSampler::SetAlwaysUseMultiGen(false);
if (nuisPriorName)
nuisPdf =
w->pdf(nuisPriorName);
if (!nuisPdf) {
Info(
"StandardHypoTestInvDemo",
"No nuisance pdf given for the HybridCalculator - try to deduce pdf from the model");
else
}
if (!nuisPdf) {
Info(
"StandardHypoTestInvDemo",
"No nuisance pdf given - try to use %s that is defined as a prior pdf in the B model",
} else {
Error(
"StandardHypoTestInvDemo",
"Cannot run Hybrid calculator because no prior on the nuisance "
"parameter is specified or can be derived");
return nullptr;
}
}
assert(nuisPdf);
Info(
"StandardHypoTestInvDemo",
"Using as nuisance Pdf ... ");
if (
np->getSize() == 0) {
"Prior nuisance does not depend on nuisance parameters. They will be smeared in their full range");
}
}
if (testStatType == 3)
if (testStatType != 2 && testStatType != 3)
"Only the PL test statistic can be used with AsymptoticCalculator - use by default a two-sided PL");
if (mEnableDetOutput)
}
calc.SetConfidenceLevel(optHTInv.confLevel);
calc.UseCLs(useCLs);
calc.SetVerbose(true);
if (mUseProof) {
}
if (npoints > 0) {
if (poimin > poimax) {
}
std::cout << "Doing a fixed scan in interval : " << poimin << " , " << poimax << std::endl;
calc.SetFixedScan(npoints, poimin, poimax);
} else {
std::cout <<
"Doing an automatic scan in interval : " << poi->
getMin() <<
" , " << poi->
getMax() << std::endl;
}
std::cout << "Time to perform limit scan \n";
if (mRebuild) {
std::cout << "\n***************************************************************\n";
std::cout << "Rebuild the upper limit distribution by re-generating new set of pseudo-experiment and re-compute "
"for each of them a new upper limit\n\n";
if (mRebuildParamValues != 0) {
allParams->assign(initialParameters);
}
if (mRebuildParamValues == 0 || mRebuildParamValues == 1) {
if (mRebuildParamValues == 0) {
std::cout << "rebuild using fitted parameter value for B-model snapshot" << std::endl;
constrainParams.
Print(
"v");
}
}
std::cout << "StandardHypoTestInvDemo: Initial parameters used for rebuilding: ";
calc.SetCloseProof(true);
std::cout << "Time to rebuild distributions " << std::endl;
if (limDist) {
std::cout << "Expected limits after rebuild distribution " << std::endl;
std::cout <<
"expected upper limit (median of limit distribution) " << limDist->
InverseCDF(0.5) << std::endl;
std::cout << "expected -1 sig limit (0.16% quantile of limit dist) "
std::cout << "expected +1 sig limit (0.84% quantile of limit dist) "
std::cout << "expected -2 sig limit (.025% quantile of limit dist) "
std::cout << "expected +2 sig limit (.975% quantile of limit dist) "
new TCanvas(
"limPlot",
"Upper Limit Distribution");
} else
std::cout << "ERROR : failed to re-build distributions " << std::endl;
}
}
void ReadResult(const char *fileName, const char *resultName = "", bool useCLs = true)
{
StandardHypoTestInvDemo(fileName, resultName, "", "", "", 0, 0, useCLs);
}
#ifdef USE_AS_MAIN
{
StandardHypoTestInvDemo();
}
#endif
void Info(const char *location, const char *msgfmt,...)
Use this function for informational messages.
void Error(const char *location, const char *msgfmt,...)
Use this function in case an error occurred.
void Warning(const char *location, const char *msgfmt,...)
Use this function in warning situations.
winID h TVirtualViewer3D TVirtualGLPainter char TVirtualGLPainter plot
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t WindowAttributes_t Float_t Float_t Float_t Int_t Int_t UInt_t UInt_t Rectangle_t Int_t Int_t Window_t TString Int_t GCValues_t GetPrimarySelectionOwner GetDisplay GetScreen GetColormap GetNativeEvent const char const char dpyName wid window const char font_name cursor keysym reg const char only_if_exist regb h Point_t winding char text const char depth char const char Int_t count const char ColorStruct_t color const char filename
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t WindowAttributes_t Float_t Float_t Float_t Int_t Int_t UInt_t UInt_t Rectangle_t Int_t Int_t Window_t TString Int_t GCValues_t GetPrimarySelectionOwner GetDisplay GetScreen GetColormap GetNativeEvent const char const char dpyName wid window const char font_name cursor keysym reg const char only_if_exist regb h Point_t np
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t WindowAttributes_t Float_t r
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void value
R__EXTERN TSystem * gSystem
static void SetDefaultMinimizer(const char *type, const char *algo=nullptr)
Set the default Minimizer type and corresponding algorithms.
static void SetDefaultStrategy(int strat)
Set the default strategy.
static const std::string & DefaultMinimizerType()
void Print(Option_t *options=nullptr) const override
Print the object to the defaultPrintStream().
RooFit::OwningPtr< RooArgSet > getParameters(const RooAbsData *data, bool stripDisconnected=true) const
Create a list of leaf nodes in the arg tree starting with ourself as top node that don't match any of...
RooFit::OwningPtr< RooArgSet > getObservables(const RooArgSet &set, bool valueOnly=true) const
Given a set of possible observables, return the observables that this PDF depends on.
Int_t getSize() const
Return the number of elements in the collection.
virtual bool add(const RooAbsArg &var, bool silent=false)
Add the specified argument to list.
RooAbsArg * first() const
void Print(Option_t *options=nullptr) const override
This method must be overridden when a class wants to print itself.
Abstract base class for binned and unbinned datasets.
static void setDefaultStorageType(StorageType s)
Abstract interface for all probability density functions.
bool canBeExtended() const
If true, PDF can provide extended likelihood term.
RooFit::OwningPtr< RooFitResult > fitTo(RooAbsData &data, CmdArgs_t const &... cmdArgs)
Fit PDF to given dataset.
virtual double getMax(const char *name=nullptr) const
Get maximum of currently defined range.
virtual double getMin(const char *name=nullptr) const
Get minimum of currently defined range.
double getVal(const RooArgSet *normalisationSet=nullptr) const
Evaluate object.
RooArgSet is a container object that can hold multiple RooAbsArg objects.
static RooMsgService & instance()
Return reference to singleton instance.
StreamConfig & getStream(Int_t id)
static TRandom * randomGenerator()
Return a pointer to a singleton random-number generator implementation.
Variable that can be changed from the outside.
void setVal(double value) override
Set value of variable to 'value'.
void setMax(const char *name, double value)
Set maximum of name range to given value.
Hypothesis Test Calculator based on the asymptotic formulae for the profile likelihood ratio.
Does a frequentist hypothesis test.
Same purpose as HybridCalculatorOriginal, but different implementation.
virtual void ForcePriorNuisanceNull(RooAbsPdf &priorNuisance)
Override the distribution used for marginalizing nuisance parameters that is inferred from ModelConfi...
virtual void ForcePriorNuisanceAlt(RooAbsPdf &priorNuisance)
void SetToys(int toysNull, int toysAlt)
set number of toys
Common base class for the Hypothesis Test Calculators.
TestStatSampler * GetTestStatSampler(void) const
Returns instance of TestStatSampler.
void UseSameAltToys()
Set this for re-using always the same toys for alternate hypothesis in case of calls at different nul...
Class to plot a HypoTestInverterResult, the output of the HypoTestInverter calculator.
HypoTestInverterResult class holds the array of hypothesis test results and compute a confidence inte...
A class for performing a hypothesis test inversion by scanning the hypothesis test results of a HypoT...
MaxLikelihoodEstimateTestStat: TestStatistic that returns maximum likelihood estimate of a specified ...
ModelConfig is a simple class that holds configuration information specifying how a model should be u...
virtual void SetSnapshot(const RooArgSet &set)
Set parameter values for a particular hypothesis if using a common PDF by saving a snapshot in the wo...
const RooArgSet * GetGlobalObservables() const
get RooArgSet for global observables (return nullptr if not existing)
ModelConfig * Clone(const char *name="") const override
clone
const RooArgSet * GetParametersOfInterest() const
get RooArgSet containing the parameter of interest (return nullptr if not existing)
const RooArgSet * GetNuisanceParameters() const
get RooArgSet containing the nuisance parameters (return nullptr if not existing)
void LoadSnapshot() const
load the snapshot from ws if it exists
const RooArgSet * GetObservables() const
get RooArgSet for observables (return nullptr if not existing)
const RooArgSet * GetSnapshot() const
get RooArgSet for parameters for a particular hypothesis (return nullptr if not existing)
RooAbsPdf * GetPdf() const
get model PDF (return nullptr if pdf has not been specified or does not exist)
RooAbsPdf * GetPriorPdf() const
get parameters prior pdf (return nullptr if not existing)
virtual void SetGlobalObservables(const RooArgSet &set)
Specify the global observables.
NumEventsTestStat is a simple implementation of the TestStatistic interface used for simple number co...
ProfileLikelihoodTestStat is an implementation of the TestStatistic interface that calculates the pro...
Holds configuration options for proof and proof-lite.
TestStatistic that returns the ratio of profiled likelihoods.
This class provides simple and straightforward utilities to plot SamplingDistribution objects.
void Draw(Option_t *options=nullptr) override
Draw this plot and all of the elements it contains.
void SetLineColor(Color_t color, const SamplingDistribution *samplDist=nullptr)
Sets line color for given sampling distribution and fill color for the associated shaded TH1F.
void SetLogYaxis(bool ly)
changes plot to log scale on y axis
double AddSamplingDistribution(const SamplingDistribution *samplingDist, Option_t *drawOptions="NORMALIZE HIST")
adds the sampling distribution and returns the scale factor
TH1F * GetTH1F(const SamplingDistribution *samplDist=nullptr)
Returns the TH1F associated with the give SamplingDistribution.
This class simply holds a sampling distribution of some test statistic.
double InverseCDF(double pvalue)
get the inverse of the Cumulative distribution function
TestStatistic class that returns -log(L[null] / L[alt]) where L is the likelihood.
TestStatistic is an interface class to provide a facility for construction test statistics distributi...
ToyMCSampler is an implementation of the TestStatSampler interface.
void SetProofConfig(ProofConfig *pc=nullptr)
calling with argument or nullptr deactivates proof
virtual void SetTestStatistic(TestStatistic *testStatistic, unsigned int i)
Set the TestStatistic (want the argument to be a function of the data & parameter points.
void SetGenerateBinned(bool binned=true)
control to use bin data generation (=> see RooFit::AllBinned() option)
virtual void SetNEventsPerToy(const Int_t nevents)
Forces the generation of exactly n events even for extended PDFs.
void SetUseMultiGen(bool flag)
Persistable container for RooFit projects.
TObject * Get(const char *namecycle) override
Return pointer to object identified by namecycle.
A ROOT file is an on-disk file, usually with extension .root, that stores objects in a file-system-li...
void ls(Option_t *option="") const override
List file contents.
static TFile * Open(const char *name, Option_t *option="", const char *ftitle="", Int_t compress=ROOT::RCompressionSetting::EDefaults::kUseCompiledDefault, Int_t netopt=0)
Create / open a file.
virtual void SetStats(Bool_t stats=kTRUE)
Set statistics option on/off.
const char * GetName() const override
Returns name of object.
virtual void SetName(const char *name)
Set the name of the TNamed.
Mother of all ROOT objects.
virtual Int_t Write(const char *name=nullptr, Int_t option=0, Int_t bufsize=0)
Write this object to the current directory.
virtual void Draw(Option_t *option="")
Default Draw method for all objects.
virtual void SetSeed(ULong_t seed=0)
Set the random generator seed.
void Start(Bool_t reset=kTRUE)
Start the stopwatch.
void Print(Option_t *option="") const override
Print the real and cpu time passed between the start and stop events.
TString & Replace(Ssiz_t pos, Ssiz_t n, const char *s)
static TString Format(const char *fmt,...)
Static method which formats a string using a printf style format descriptor and return a TString.
virtual Bool_t AccessPathName(const char *path, EAccessMode mode=kFileExists)
Returns FALSE if one can access a file using the specified access mode.
RooCmdArg InitialHesse(bool flag=true)
RooCmdArg Offset(std::string const &mode)
RooCmdArg Constrain(const RooArgSet ¶ms)
RooCmdArg Minimizer(const char *type, const char *alg=nullptr)
RooCmdArg Hesse(bool flag=true)
RooCmdArg Strategy(Int_t code)
RooCmdArg Save(bool flag=true)
RooCmdArg PrintLevel(Int_t code)
double normal_cdf(double x, double sigma=1, double x0=0)
Cumulative distribution function of the normal (Gaussian) distribution (lower tail).
The namespace RooFit contains mostly switches that change the behaviour of functions of PDFs (or othe...
Namespace for the RooStats classes.
bool SetAllConstant(const RooAbsCollection &coll, bool constant=true)
utility function to set all variable constant in a collection (from G.
void RemoveConstantParameters(RooArgSet *set)
RooAbsPdf * MakeNuisancePdf(RooAbsPdf &pdf, const RooArgSet &observables, const char *name)
extract constraint terms from pdf
void UseNLLOffset(bool on)
function to set a global flag in RooStats to use NLL offset when performing nll computations Note tha...
bool IsNLLOffset()
function returning if the flag to check if the flag to use NLLOffset is set
void PrintListContent(const RooArgList &l, std::ostream &os=std::cout)
useful function to print in one line the content of a set with their values
Double_t Sqrt(Double_t x)
Returns the square root of x.
Int_t CeilNint(Double_t x)
Returns the nearest integer of TMath::Ceil(x).
void removeTopic(RooFit::MsgTopic oldTopic)