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Reference Guide
TwoSidedFrequentistUpperLimitWithBands.C
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1 /// \file
2 /// \ingroup tutorial_roostats
3 /// \notebook -js
4 /// TwoSidedFrequentistUpperLimitWithBands
5 ///
6 ///
7 /// This is a standard demo that can be used with any ROOT file
8 /// prepared in the standard way. You specify:
9 /// - name for input ROOT file
10 /// - name of workspace inside ROOT file that holds model and data
11 /// - name of ModelConfig that specifies details for calculator tools
12 /// - name of dataset
13 ///
14 /// With default parameters the macro will attempt to run the
15 /// standard hist2workspace example and read the ROOT file
16 /// that it produces.
17 ///
18 /// You may want to control:
19 /// ~~~{.cpp}
20 /// double confidenceLevel=0.95;
21 /// double additionalToysFac = 1.;
22 /// int nPointsToScan = 12;
23 /// int nToyMC = 200;
24 /// ~~~
25 ///
26 /// This uses a modified version of the profile likelihood ratio as
27 /// a test statistic for upper limits (eg. test stat = 0 if muhat>mu).
28 ///
29 /// Based on the observed data, one defines a set of parameter points
30 /// to be tested based on the value of the parameter of interest
31 /// and the conditional MLE (eg. profiled) values of the nuisance parameters.
32 ///
33 /// At each parameter point, pseudo-experiments are generated using this
34 /// fixed reference model and then the test statistic is evaluated.
35 /// The auxiliary measurements (global observables) associated with the
36 /// constraint terms in nuisance parameters are also fluctuated in the
37 /// process of generating the pseudo-experiments in a frequentist manner
38 /// forming an 'unconditional ensemble'. One could form a 'conditional'
39 /// ensemble in which these auxiliary measurements are fixed. Note that the
40 /// nuisance parameters are not randomized, which is a Bayesian procedure.
41 /// Note, the nuisance parameters are floating in the fits. For each point,
42 /// the threshold that defines the 95% acceptance region is found. This
43 /// forms a "Confidence Belt".
44 ///
45 /// After constructing the confidence belt, one can find the confidence
46 /// interval for any particular dataset by finding the intersection
47 /// of the observed test statistic and the confidence belt. First
48 /// this is done on the observed data to get an observed 1-sided upper limt.
49 ///
50 /// Finally, there expected limit and bands (from background-only) are
51 /// formed by generating background-only data and finding the upper limit.
52 /// The background-only is defined as such that the nuisance parameters are
53 /// fixed to their best fit value based on the data with the signal rate fixed to 0.
54 /// The bands are done by hand for now, will later be part of the RooStats tools.
55 ///
56 /// On a technical note, this technique IS the generalization of Feldman-Cousins
57 /// with nuisance parameters.
58 ///
59 /// Building the confidence belt can be computationally expensive.
60 /// Once it is built, one could save it to a file and use it in a separate step.
61 ///
62 /// We can use PROOF to speed things along in parallel, however,
63 /// the test statistic has to be installed on the workers
64 /// so either turn off PROOF or include the modified test statistic
65 /// in your $ROOTSYS/roofit/roostats/inc directory,
66 /// add the additional line to the LinkDef.h file,
67 /// and recompile root.
68 ///
69 /// Note, if you have a boundary on the parameter of interest (eg. cross-section)
70 /// the threshold on the two-sided test statistic starts off at moderate values and plateaus.
71 ///
72 /// [#0] PROGRESS:Generation -- generated toys: 500 / 999
73 /// NeymanConstruction: Prog: 12/50 total MC = 39 this test stat = 0
74 /// SigXsecOverSM=0.69 alpha_syst1=0.136515 alpha_syst3=0.425415 beta_syst2=1.08496 [-1e+30, 0.011215] in interval = 1
75 ///
76 /// this tells you the values of the parameters being used to generate the pseudo-experiments
77 /// and the threshold in this case is 0.011215. One would expect for 95% that the threshold
78 /// would be ~1.35 once the cross-section is far enough away from 0 that it is essentially
79 /// unaffected by the boundary. As one reaches the last points in the scan, the
80 /// theshold starts to get artificially high. This is because the range of the parameter in
81 /// the fit is the same as the range in the scan. In the future, these should be independently
82 /// controlled, but they are not now. As a result the ~50% of pseudo-experiments that have an
83 /// upward fluctuation end up with muhat = muMax. Because of this, the upper range of the
84 /// parameter should be well above the expected upper limit... but not too high or one will
85 /// need a very large value of nPointsToScan to resolve the relevant region. This can be
86 /// improved, but this is the first version of this script.
87 ///
88 /// Important note: when the model includes external constraint terms, like a Gaussian
89 /// constraint to a nuisance parameter centered around some nominal value there is
90 /// a subtlety. The asymptotic results are all based on the assumption that all the
91 /// measurements fluctuate... including the nominal values from auxiliary measurements.
92 /// If these do not fluctuate, this corresponds to an "conditional ensemble". The
93 /// result is that the distribution of the test statistic can become very non-chi^2.
94 /// This results in thresholds that become very large.
95 ///
96 /// \macro_image
97 /// \macro_output
98 /// \macro_code
99 ///
100 /// \author Kyle Cranmer,Contributions from Aaron Armbruster, Haoshuang Ji, Haichen Wang and Daniel Whiteson
101 
102 #include "TFile.h"
103 #include "TROOT.h"
104 #include "TH1F.h"
105 #include "TCanvas.h"
106 #include "TSystem.h"
107 #include <iostream>
108 
109 #include "RooWorkspace.h"
110 #include "RooSimultaneous.h"
111 #include "RooAbsData.h"
112 
113 #include "RooStats/ModelConfig.h"
114 #include "RooStats/FeldmanCousins.h"
115 #include "RooStats/ToyMCSampler.h"
117 #include "RooStats/ConfidenceBelt.h"
118 
119 #include "RooStats/RooStatsUtils.h"
121 
122 using namespace RooFit;
123 using namespace RooStats;
124 using namespace std;
125 
126 bool useProof = false; // flag to control whether to use Proof
127 int nworkers = 0; // number of workers (default use all available cores)
128 
129 // -------------------------------------------------------
130 
131 void TwoSidedFrequentistUpperLimitWithBands(const char *infile = "", const char *workspaceName = "combined",
132  const char *modelConfigName = "ModelConfig",
133  const char *dataName = "obsData")
134 {
135 
136  double confidenceLevel = 0.95;
137  // degrade/improve number of pseudo-experiments used to define the confidence belt.
138  // value of 1 corresponds to default number of toys in the tail, which is 50/(1-confidenceLevel)
139  double additionalToysFac = 0.5;
140  int nPointsToScan = 20; // number of steps in the parameter of interest
141  int nToyMC = 200; // number of toys used to define the expected limit and band
142 
143  // -------------------------------------------------------
144  // First part is just to access a user-defined file
145  // or create the standard example file if it doesn't exist
146  const char *filename = "";
147  if (!strcmp(infile, "")) {
148  filename = "results/example_combined_GaussExample_model.root";
149  bool fileExist = !gSystem->AccessPathName(filename); // note opposite return code
150  // if file does not exists generate with histfactory
151  if (!fileExist) {
152 #ifdef _WIN32
153  cout << "HistFactory file cannot be generated on Windows - exit" << endl;
154  return;
155 #endif
156  // Normally this would be run on the command line
157  cout << "will run standard hist2workspace example" << endl;
158  gROOT->ProcessLine(".! prepareHistFactory .");
159  gROOT->ProcessLine(".! hist2workspace config/example.xml");
160  cout << "\n\n---------------------" << endl;
161  cout << "Done creating example input" << endl;
162  cout << "---------------------\n\n" << endl;
163  }
164 
165  } else
166  filename = infile;
167 
168  // Try to open the file
169  TFile *file = TFile::Open(filename);
170 
171  // if input file was specified byt not found, quit
172  if (!file) {
173  cout << "StandardRooStatsDemoMacro: Input file " << filename << " is not found" << endl;
174  return;
175  }
176 
177  // -------------------------------------------------------
178  // Now get the data and workspace
179 
180  // get the workspace out of the file
181  RooWorkspace *w = (RooWorkspace *)file->Get(workspaceName);
182  if (!w) {
183  cout << "workspace not found" << endl;
184  return;
185  }
186 
187  // get the modelConfig out of the file
188  ModelConfig *mc = (ModelConfig *)w->obj(modelConfigName);
189 
190  // get the modelConfig out of the file
191  RooAbsData *data = w->data(dataName);
192 
193  // make sure ingredients are found
194  if (!data || !mc) {
195  w->Print();
196  cout << "data or ModelConfig was not found" << endl;
197  return;
198  }
199 
200  cout << "Found data and ModelConfig:" << endl;
201  mc->Print();
202 
203  // -------------------------------------------------------
204  // Now get the POI for convenience
205  // you may want to adjust the range of your POI
206  RooRealVar *firstPOI = (RooRealVar *)mc->GetParametersOfInterest()->first();
207  /* firstPOI->setMin(0);*/
208  /* firstPOI->setMax(10);*/
209 
210  // -------------------------------------------------------
211  // create and use the FeldmanCousins tool
212  // to find and plot the 95% confidence interval
213  // on the parameter of interest as specified
214  // in the model config
215  // REMEMBER, we will change the test statistic
216  // so this is NOT a Feldman-Cousins interval
217  FeldmanCousins fc(*data, *mc);
218  fc.SetConfidenceLevel(confidenceLevel);
219  fc.AdditionalNToysFactor(additionalToysFac); // improve sampling that defines confidence belt
220  // fc.UseAdaptiveSampling(true); // speed it up a bit, but don't use for expected limits
221  fc.SetNBins(nPointsToScan); // set how many points per parameter of interest to scan
222  fc.CreateConfBelt(true); // save the information in the belt for plotting
223 
224  // -------------------------------------------------------
225  // Feldman-Cousins is a unified limit by definition
226  // but the tool takes care of a few things for us like which values
227  // of the nuisance parameters should be used to generate toys.
228  // so let's just change the test statistic and realize this is
229  // no longer "Feldman-Cousins" but is a fully frequentist Neyman-Construction.
230  // fc.GetTestStatSampler()->SetTestStatistic(&onesided);
231  // ((ToyMCSampler*) fc.GetTestStatSampler())->SetGenerateBinned(true);
232  ToyMCSampler *toymcsampler = (ToyMCSampler *)fc.GetTestStatSampler();
233  ProfileLikelihoodTestStat *testStat = dynamic_cast<ProfileLikelihoodTestStat *>(toymcsampler->GetTestStatistic());
234 
235  // Since this tool needs to throw toy MC the PDF needs to be
236  // extended or the tool needs to know how many entries in a dataset
237  // per pseudo experiment.
238  // In the 'number counting form' where the entries in the dataset
239  // are counts, and not values of discriminating variables, the
240  // datasets typically only have one entry and the PDF is not
241  // extended.
242  if (!mc->GetPdf()->canBeExtended()) {
243  if (data->numEntries() == 1)
244  fc.FluctuateNumDataEntries(false);
245  else
246  cout << "Not sure what to do about this model" << endl;
247  }
248 
249  // We can use PROOF to speed things along in parallel
250  // However, the test statistic has to be installed on the workers
251  // so either turn off PROOF or include the modified test statistic
252  // in your $ROOTSYS/roofit/roostats/inc directory,
253  // add the additional line to the LinkDef.h file,
254  // and recompile root.
255  if (useProof) {
256  ProofConfig pc(*w, nworkers, "", false);
257  toymcsampler->SetProofConfig(&pc); // enable proof
258  }
259 
260  if (mc->GetGlobalObservables()) {
261  cout << "will use global observables for unconditional ensemble" << endl;
262  mc->GetGlobalObservables()->Print();
263  toymcsampler->SetGlobalObservables(*mc->GetGlobalObservables());
264  }
265 
266  // Now get the interval
267  PointSetInterval *interval = fc.GetInterval();
268  ConfidenceBelt *belt = fc.GetConfidenceBelt();
269 
270  // print out the interval on the first Parameter of Interest
271  cout << "\n95% interval on " << firstPOI->GetName() << " is : [" << interval->LowerLimit(*firstPOI) << ", "
272  << interval->UpperLimit(*firstPOI) << "] " << endl;
273 
274  // get observed UL and value of test statistic evaluated there
275  RooArgSet tmpPOI(*firstPOI);
276  double observedUL = interval->UpperLimit(*firstPOI);
277  firstPOI->setVal(observedUL);
278  double obsTSatObsUL = fc.GetTestStatSampler()->EvaluateTestStatistic(*data, tmpPOI);
279 
280  // Ask the calculator which points were scanned
281  RooDataSet *parameterScan = (RooDataSet *)fc.GetPointsToScan();
282  RooArgSet *tmpPoint;
283 
284  // make a histogram of parameter vs. threshold
285  TH1F *histOfThresholds =
286  new TH1F("histOfThresholds", "", parameterScan->numEntries(), firstPOI->getMin(), firstPOI->getMax());
287  histOfThresholds->GetXaxis()->SetTitle(firstPOI->GetName());
288  histOfThresholds->GetYaxis()->SetTitle("Threshold");
289 
290  // loop through the points that were tested and ask confidence belt
291  // what the upper/lower thresholds were.
292  // For FeldmanCousins, the lower cut off is always 0
293  for (Int_t i = 0; i < parameterScan->numEntries(); ++i) {
294  tmpPoint = (RooArgSet *)parameterScan->get(i)->clone("temp");
295  // cout <<"get threshold"<<endl;
296  double arMax = belt->GetAcceptanceRegionMax(*tmpPoint);
297  double poiVal = tmpPoint->getRealValue(firstPOI->GetName());
298  histOfThresholds->Fill(poiVal, arMax);
299  }
300  TCanvas *c1 = new TCanvas();
301  c1->Divide(2);
302  c1->cd(1);
303  histOfThresholds->SetMinimum(0);
304  histOfThresholds->Draw();
305  c1->cd(2);
306 
307  // -------------------------------------------------------
308  // Now we generate the expected bands and power-constraint
309 
310  // First: find parameter point for mu=0, with conditional MLEs for nuisance parameters
311  RooAbsReal *nll = mc->GetPdf()->createNLL(*data);
312  RooAbsReal *profile = nll->createProfile(*mc->GetParametersOfInterest());
313  firstPOI->setVal(0.);
314  profile->getVal(); // this will do fit and set nuisance parameters to profiled values
315  RooArgSet *poiAndNuisance = new RooArgSet();
316  if (mc->GetNuisanceParameters())
317  poiAndNuisance->add(*mc->GetNuisanceParameters());
318  poiAndNuisance->add(*mc->GetParametersOfInterest());
319  w->saveSnapshot("paramsToGenerateData", *poiAndNuisance);
320  RooArgSet *paramsToGenerateData = (RooArgSet *)poiAndNuisance->snapshot();
321  cout << "\nWill use these parameter points to generate pseudo data for bkg only" << endl;
322  paramsToGenerateData->Print("v");
323 
324  RooArgSet unconditionalObs;
325  unconditionalObs.add(*mc->GetObservables());
326  unconditionalObs.add(*mc->GetGlobalObservables()); // comment this out for the original conditional ensemble
327 
328  double CLb = 0;
329  double CLbinclusive = 0;
330 
331  // Now we generate background only and find distribution of upper limits
332  TH1F *histOfUL = new TH1F("histOfUL", "", 100, 0, firstPOI->getMax());
333  histOfUL->GetXaxis()->SetTitle("Upper Limit (background only)");
334  histOfUL->GetYaxis()->SetTitle("Entries");
335  for (int imc = 0; imc < nToyMC; ++imc) {
336 
337  // set parameters back to values for generating pseudo data
338  // cout << "\n get current nuis, set vals, print again" << endl;
339  w->loadSnapshot("paramsToGenerateData");
340  // poiAndNuisance->Print("v");
341 
342  RooDataSet *toyData = 0;
343  // now generate a toy dataset for the main measurement
344  if (!mc->GetPdf()->canBeExtended()) {
345  if (data->numEntries() == 1)
346  toyData = mc->GetPdf()->generate(*mc->GetObservables(), 1);
347  else
348  cout << "Not sure what to do about this model" << endl;
349  } else {
350  // cout << "generating extended dataset"<<endl;
351  toyData = mc->GetPdf()->generate(*mc->GetObservables(), Extended());
352  }
353 
354  // generate global observables
355  // need to be careful for simpdf.
356  // In ROOT 5.28 there is a problem with generating global observables
357  // with a simultaneous PDF. In 5.29 there is a solution with
358  // RooSimultaneous::generateSimGlobal, but this may change to
359  // the standard generate interface in 5.30.
360 
361  RooSimultaneous *simPdf = dynamic_cast<RooSimultaneous *>(mc->GetPdf());
362  if (!simPdf) {
363  RooDataSet *one = mc->GetPdf()->generate(*mc->GetGlobalObservables(), 1);
364  const RooArgSet *values = one->get();
365  RooArgSet *allVars = mc->GetPdf()->getVariables();
366  *allVars = *values;
367  delete allVars;
368  delete one;
369  } else {
370  RooDataSet *one = simPdf->generateSimGlobal(*mc->GetGlobalObservables(), 1);
371  const RooArgSet *values = one->get();
372  RooArgSet *allVars = mc->GetPdf()->getVariables();
373  *allVars = *values;
374  delete allVars;
375  delete one;
376  }
377 
378  // get test stat at observed UL in observed data
379  firstPOI->setVal(observedUL);
380  double toyTSatObsUL = fc.GetTestStatSampler()->EvaluateTestStatistic(*toyData, tmpPOI);
381  // toyData->get()->Print("v");
382  // cout <<"obsTSatObsUL " <<obsTSatObsUL << "toyTS " << toyTSatObsUL << endl;
383  if (obsTSatObsUL < toyTSatObsUL) // not sure about <= part yet
384  CLb += (1.) / nToyMC;
385  if (obsTSatObsUL <= toyTSatObsUL) // not sure about <= part yet
386  CLbinclusive += (1.) / nToyMC;
387 
388  // loop over points in belt to find upper limit for this toy data
389  double thisUL = 0;
390  for (Int_t i = 0; i < parameterScan->numEntries(); ++i) {
391  tmpPoint = (RooArgSet *)parameterScan->get(i)->clone("temp");
392  double arMax = belt->GetAcceptanceRegionMax(*tmpPoint);
393  firstPOI->setVal(tmpPoint->getRealValue(firstPOI->GetName()));
394  // double thisTS = profile->getVal();
395  double thisTS = fc.GetTestStatSampler()->EvaluateTestStatistic(*toyData, tmpPOI);
396 
397  // cout << "poi = " << firstPOI->getVal()
398  // << " max is " << arMax << " this profile = " << thisTS << endl;
399  // cout << "thisTS = " << thisTS<<endl;
400  if (thisTS <= arMax) {
401  thisUL = firstPOI->getVal();
402  } else {
403  break;
404  }
405  }
406 
407  histOfUL->Fill(thisUL);
408 
409  // for few events, data is often the same, and UL is often the same
410  // cout << "thisUL = " << thisUL<<endl;
411 
412  delete toyData;
413  }
414  histOfUL->Draw();
415  c1->SaveAs("two-sided_upper_limit_output.pdf");
416 
417  // if you want to see a plot of the sampling distribution for a particular scan point:
418  /*
419  SamplingDistPlot sampPlot;
420  int indexInScan = 0;
421  tmpPoint = (RooArgSet*) parameterScan->get(indexInScan)->clone("temp");
422  firstPOI->setVal( tmpPoint->getRealValue(firstPOI->GetName()) );
423  toymcsampler->SetParametersForTestStat(tmpPOI);
424  SamplingDistribution* samp = toymcsampler->GetSamplingDistribution(*tmpPoint);
425  sampPlot.AddSamplingDistribution(samp);
426  sampPlot.Draw();
427  */
428 
429  // Now find bands and power constraint
430  Double_t *bins = histOfUL->GetIntegral();
431  TH1F *cumulative = (TH1F *)histOfUL->Clone("cumulative");
432  cumulative->SetContent(bins);
433  double band2sigDown = 0, band1sigDown = 0, bandMedian = 0, band1sigUp = 0, band2sigUp = 0;
434  for (int i = 1; i <= cumulative->GetNbinsX(); ++i) {
435  if (bins[i] < RooStats::SignificanceToPValue(2))
436  band2sigDown = cumulative->GetBinCenter(i);
437  if (bins[i] < RooStats::SignificanceToPValue(1))
438  band1sigDown = cumulative->GetBinCenter(i);
439  if (bins[i] < 0.5)
440  bandMedian = cumulative->GetBinCenter(i);
441  if (bins[i] < RooStats::SignificanceToPValue(-1))
442  band1sigUp = cumulative->GetBinCenter(i);
443  if (bins[i] < RooStats::SignificanceToPValue(-2))
444  band2sigUp = cumulative->GetBinCenter(i);
445  }
446  cout << "-2 sigma band " << band2sigDown << endl;
447  cout << "-1 sigma band " << band1sigDown << " [Power Constraint)]" << endl;
448  cout << "median of band " << bandMedian << endl;
449  cout << "+1 sigma band " << band1sigUp << endl;
450  cout << "+2 sigma band " << band2sigUp << endl;
451 
452  // print out the interval on the first Parameter of Interest
453  cout << "\nobserved 95% upper-limit " << interval->UpperLimit(*firstPOI) << endl;
454  cout << "CLb strict [P(toy>obs|0)] for observed 95% upper-limit " << CLb << endl;
455  cout << "CLb inclusive [P(toy>=obs|0)] for observed 95% upper-limit " << CLbinclusive << endl;
456 
457  delete profile;
458  delete nll;
459 }
virtual RooAbsReal * createNLL(RooAbsData &data, const RooLinkedList &cmdList)
Construct representation of -log(L) of PDFwith given dataset.
Definition: RooAbsPdf.cxx:924
virtual Double_t getMin(const char *name=0) const
Get miniminum of currently defined range.
RooArgSet * getVariables(Bool_t stripDisconnected=kTRUE) const
Return RooArgSet with all variables (tree leaf nodes of expresssion tree)
Definition: RooAbsArg.cxx:1916
virtual const RooArgSet * get(Int_t index) const override
Return RooArgSet with coordinates of event &#39;index&#39;.
virtual const char * GetName() const
Returns name of object.
Definition: TNamed.h:47
virtual Bool_t AccessPathName(const char *path, EAccessMode mode=kFileExists)
Returns FALSE if one can access a file using the specified access mode.
Definition: TSystem.cxx:1279
Bool_t saveSnapshot(const char *name, const char *paramNames)
Save snapshot of values and attributes (including "Constant") of given parameters.
RooDataSet * generate(const RooArgSet &whatVars, Int_t nEvents, const RooCmdArg &arg1, const RooCmdArg &arg2=RooCmdArg::none(), const RooCmdArg &arg3=RooCmdArg::none(), const RooCmdArg &arg4=RooCmdArg::none(), const RooCmdArg &arg5=RooCmdArg::none())
See RooAbsPdf::generate(const RooArgSet&,const RooCmdArg&,const RooCmdArg&,const RooCmdArg&,const RooCmdArg&,const RooCmdArg&,const RooCmdArg&)
Definition: RooAbsPdf.h:55
virtual Int_t Fill(Double_t x)
Increment bin with abscissa X by 1.
Definition: TH1.cxx:3258
Holds configuration options for proof and proof-lite.
Definition: ProofConfig.h:46
virtual Double_t GetBinCenter(Int_t bin) const
Return bin center for 1D histogram.
Definition: TH1.cxx:8563
ModelConfig is a simple class that holds configuration information specifying how a model should be u...
Definition: ModelConfig.h:30
const RooArgSet * GetObservables() const
get RooArgSet for observables (return NULL if not existing)
Definition: ModelConfig.h:249
virtual Double_t getMax(const char *name=0) const
Get maximum of currently defined range.
virtual Bool_t add(const RooAbsCollection &col, Bool_t silent=kFALSE)
Add a collection of arguments to this collection by calling add() for each element in the source coll...
Definition: RooArgSet.h:88
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.
Definition: TFile.cxx:3925
virtual void SetGlobalObservables(const RooArgSet &o)
Definition: ToyMCSampler.h:175
return c1
Definition: legend1.C:41
RooArgSet is a container object that can hold multiple RooAbsArg objects.
Definition: RooArgSet.h:28
A ROOT file is a suite of consecutive data records (TKey instances) with a well defined format...
Definition: TFile.h:48
virtual void SetMinimum(Double_t minimum=-1111)
Definition: TH1.h:395
#define gROOT
Definition: TROOT.h:415
1-D histogram with a float per channel (see TH1 documentation)}
Definition: TH1.h:571
int Int_t
Definition: RtypesCore.h:41
STL namespace.
virtual TestStatistic * GetTestStatistic(unsigned int i) const
Definition: ToyMCSampler.h:132
RooCmdArg Extended(Bool_t flag=kTRUE)
static struct mg_connection * fc(struct mg_context *ctx)
Definition: civetweb.c:3728
virtual void Print(Option_t *option="") const override
overload the print method
The namespace RooFit contains mostly switches that change the behaviour of functions of PDFs (or othe...
ConfidenceBelt is a concrete implementation of the ConfInterval interface.
virtual void Print(Option_t *options=0) const
This method must be overridden when a class wants to print itself.
virtual void SetContent(const Double_t *content)
Replace bin contents by the contents of array content.
Definition: TH1.cxx:7828
Double_t SignificanceToPValue(Double_t Z)
returns p-value corresponding to a 1-sided significance
Definition: RooStatsUtils.h:55
virtual TObject * clone(const char *newname) const
Definition: RooArgSet.h:84
RooRealVar represents a fundamental (non-derived) real-valued object.
Definition: RooRealVar.h:36
RooAbsData * data(const char *name) const
Retrieve dataset (binned or unbinned) with given name. A null pointer is returned if not found...
virtual void setVal(Double_t value)
Set value of variable to &#39;value&#39;.
Definition: RooRealVar.cxx:252
Double_t LowerLimit(RooRealVar &param)
return lower limit on a given parameter
virtual Double_t * GetIntegral()
Return a pointer to the array of bins integral.
Definition: TH1.cxx:2522
R__EXTERN TSystem * gSystem
Definition: TSystem.h:557
virtual void Draw(Option_t *option="")
Draw this histogram with options.
Definition: TH1.cxx:2981
static constexpr double pc
RooAbsArg * first() const
Double_t getVal(const RooArgSet *normalisationSet=nullptr) const
Evaluate object.
Definition: RooAbsReal.h:87
void SetProofConfig(ProofConfig *pc=NULL)
Definition: ToyMCSampler.h:233
TAxis * GetYaxis()
Definition: TH1.h:317
RooAbsData is the common abstract base class for binned and unbinned datasets.
Definition: RooAbsData.h:44
Bool_t loadSnapshot(const char *name)
Load the values and attributes of the parameters in the snapshot saved with the given name...
ProfileLikelihoodTestStat is an implementation of the TestStatistic interface that calculates the pro...
ToyMCSampler is an implementation of the TestStatSampler interface.
Definition: ToyMCSampler.h:71
RooDataSet is a container class to hold unbinned data.
Definition: RooDataSet.h:31
Bool_t canBeExtended() const
Definition: RooAbsPdf.h:236
TObject * obj(const char *name) const
Return any type of object (RooAbsArg, RooAbsData or generic object) with given name) ...
Double_t UpperLimit(RooRealVar &param)
return upper limit on a given parameter
The FeldmanCousins class (like the Feldman-Cousins technique) is essentially a specific configuration...
RooArgSet * snapshot(bool deepCopy=true) const
Use RooAbsCollection::snapshot(), but return as RooArgSet.
Definition: RooArgSet.h:134
The Canvas class.
Definition: TCanvas.h:31
Namespace for the RooStats classes.
Definition: Asimov.h:20
RooAbsPdf * GetPdf() const
get model PDF (return NULL if pdf has not been specified or does not exist)
Definition: ModelConfig.h:234
const RooArgSet * GetParametersOfInterest() const
get RooArgSet containing the parameter of interest (return NULL if not existing)
Definition: ModelConfig.h:237
PointSetInterval is a concrete implementation of the ConfInterval interface.
double Double_t
Definition: RtypesCore.h:55
RooAbsReal is the common abstract base class for objects that represent a real value and implements f...
Definition: RooAbsReal.h:59
virtual RooDataSet * generateSimGlobal(const RooArgSet &whatVars, Int_t nEvents)
Special generator interface for generation of &#39;global observables&#39; – for RooStats tools...
const RooArgSet * GetGlobalObservables() const
get RooArgSet for global observables (return NULL if not existing)
Definition: ModelConfig.h:255
Definition: file.py:1
const RooArgSet * GetNuisanceParameters() const
get RooArgSet containing the nuisance parameters (return NULL if not existing)
Definition: ModelConfig.h:240
Double_t getRealValue(const char *name, Double_t defVal=0, Bool_t verbose=kFALSE) const
Get value of a RooAbsReal stored in set with given name.
Definition: RooArgSet.cxx:472
virtual RooAbsReal * createProfile(const RooArgSet &paramsOfInterest)
Create a RooProfileLL object that eliminates all nuisance parameters in the present function...
Definition: RooAbsReal.cxx:517
TObject * Clone(const char *newname=0) const
Make a complete copy of the underlying object.
Definition: TH1.cxx:2664
virtual Int_t GetNbinsX() const
Definition: TH1.h:292
Double_t GetAcceptanceRegionMax(RooArgSet &, Double_t cl=-1., Double_t leftside=-1.)
virtual void SetTitle(const char *title="")
Set the title of the TNamed.
Definition: TNamed.cxx:164
void Print(Option_t *opts=0) const
Print contents of the workspace.
RooSimultaneous facilitates simultaneous fitting of multiple PDFs to subsets of a given dataset...
TAxis * GetXaxis()
Get the behaviour adopted by the object about the statoverflows. See EStatOverflows for more informat...
Definition: TH1.h:316
The RooWorkspace is a persistable container for RooFit projects.
Definition: RooWorkspace.h:43
virtual Int_t numEntries() const
Definition: RooAbsData.cxx:306