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OneSidedFrequentistUpperLimitWithBands.C
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1 /// \file
2 /// \ingroup tutorial_roostats
3 /// \notebook
4 /// OneSidedFrequentistUpperLimitWithBands
5 ///
6 /// This is a standard demo that can be used with any ROOT file
7 /// prepared in the standard way. You specify:
8 /// - name for input ROOT file
9 /// - name of workspace inside ROOT file that holds model and data
10 /// - name of ModelConfig that specifies details for calculator tools
11 /// - name of dataset
12 ///
13 /// With default parameters the macro will attempt to run the
14 /// standard hist2workspace example and read the ROOT file
15 /// that it produces.
16 ///
17 /// The first ~100 lines define a new test statistic, then the main macro starts.
18 /// You may want to control:
19 /// ~~~{.cpp}
20 /// double confidenceLevel=0.95;
21 /// int nPointsToScan = 12;
22 /// int nToyMC = 150;
23 /// ~~~
24 /// This uses a modified version of the profile likelihood ratio as
25 /// a test statistic for upper limits (eg. test stat = 0 if muhat>mu).
26 ///
27 /// Based on the observed data, one defines a set of parameter points
28 /// to be tested based on the value of the parameter of interest
29 /// and the conditional MLE (eg. profiled) values of the nuisance parameters.
30 ///
31 /// At each parameter point, pseudo-experiments are generated using this
32 /// fixed reference model and then the test statistic is evaluated.
33 /// Note, the nuisance parameters are floating in the fits. For each point,
34 /// the threshold that defines the 95% acceptance region is found. This
35 /// forms a "Confidence Belt".
36 ///
37 /// After constructing the confidence belt, one can find the confidence
38 /// interval for any particular dataset by finding the intersection
39 /// of the observed test statistic and the confidence belt. First
40 /// this is done on the observed data to get an observed 1-sided upper limt.
41 ///
42 /// Finally, there expected limit and bands (from background-only) are
43 /// formed by generating background-only data and finding the upper limit.
44 /// This is done by hand for now, will later be part of the RooStats tools.
45 ///
46 /// On a technical note, this technique is NOT the Feldman-Cousins technique,
47 /// because that is a 2-sided interval BY DEFINITION. However, like the
48 /// Feldman-Cousins technique this is a Neyman-Construction. For technical
49 /// reasons the easiest way to implement this right now is to use the
50 /// FeldmanCousins tool and then change the test statistic that it is using.
51 ///
52 /// Building the confidence belt can be computationally expensive. Once it is built,
53 /// one could save it to a file and use it in a separate step.
54 ///
55 /// We can use PROOF to speed things along in parallel, however,
56 /// the test statistic has to be installed on the workers
57 /// so either turn off PROOF or include the modified test statistic
58 /// in your `$ROOTSYS/roofit/roostats/inc` directory,
59 /// add the additional line to the LinkDef.h file,
60 /// and recompile root.
61 ///
62 /// Note, if you have a boundary on the parameter of interest (eg. cross-section)
63 /// the threshold on the one-sided test statistic starts off very small because we
64 /// are only including downward fluctuations. You can see the threshold in these printouts:
65 /// ~~~{.cpp}
66 /// [#0] PROGRESS:Generation -- generated toys: 500 / 999
67 /// NeymanConstruction: Prog: 12/50 total MC = 39 this test stat = 0
68 /// SigXsecOverSM=0.69 alpha_syst1=0.136515 alpha_syst3=0.425415 beta_syst2=1.08496 [-1e+30, 0.011215] in interval = 1
69 /// ~~~
70 /// this tells you the values of the parameters being used to generate the pseudo-experiments
71 /// and the threshold in this case is 0.011215. One would expect for 95% that the threshold
72 /// would be ~1.35 once the cross-section is far enough away from 0 that it is essentially
73 /// unaffected by the boundary. As one reaches the last points in the scan, the
74 /// theshold starts to get artificially high. This is because the range of the parameter in
75 /// the fit is the same as the range in the scan. In the future, these should be independently
76 /// controlled, but they are not now. As a result the ~50% of pseudo-experiments that have an
77 /// upward fluctuation end up with muhat = muMax. Because of this, the upper range of the
78 /// parameter should be well above the expected upper limit... but not too high or one will
79 /// need a very large value of nPointsToScan to resolve the relevant region. This can be
80 /// improved, but this is the first version of this script.
81 ///
82 /// Important note: when the model includes external constraint terms, like a Gaussian
83 /// constraint to a nuisance parameter centered around some nominal value there is
84 /// a subtlety. The asymptotic results are all based on the assumption that all the
85 /// measurements fluctuate... including the nominal values from auxiliary measurements.
86 /// If these do not fluctuate, this corresponds to an "conditional ensemble". The
87 /// result is that the distribution of the test statistic can become very non-chi^2.
88 /// This results in thresholds that become very large. This can be seen in the following
89 /// thought experiment. Say the model is
90 /// \f$ Pois(N | s + b)G(b0|b,sigma) \f$
91 /// where \f$ G(b0|b,sigma) \f$ is the external constraint and b0 is 100. If N is also 100
92 /// then the profiled value of b given s is going to be some trade off between 100-s and b0.
93 /// If sigma is \f$ \sqrt(N) \f$, then the profiled value of b is probably 100 - s/2 So for
94 /// s=60 we are going to have a profiled value of b~70. Now when we generate pseudo-experiments
95 /// for s=60, b=70 we will have N~130 and the average shat will be 30, not 60. In practice,
96 /// this is only an issue for values of s that are very excluded. For values of s near the 95%
97 /// limit this should not be a big effect. This can be avoided if the nominal values of the constraints also fluctuate,
98 /// but that requires that those parameters are RooRealVars in the model.
99 /// This version does not deal with this issue, but it will be addressed in a future version.
100 ///
101 /// \macro_image
102 /// \macro_output
103 /// \macro_code
104 ///
105 /// \authors Kyle Cranmer Haichen Wang Daniel Whiteson
106 
107 #include "TFile.h"
108 #include "TROOT.h"
109 #include "TH1F.h"
110 #include "TCanvas.h"
111 #include "TSystem.h"
112 
113 #include "RooWorkspace.h"
114 #include "RooSimultaneous.h"
115 #include "RooAbsData.h"
116 
117 #include "RooStats/ModelConfig.h"
118 #include "RooStats/FeldmanCousins.h"
119 #include "RooStats/ToyMCSampler.h"
121 #include "RooStats/ConfidenceBelt.h"
122 
123 #include "RooStats/RooStatsUtils.h"
125 
126 using namespace RooFit;
127 using namespace RooStats;
128 
129 bool useProof = false; // flag to control whether to use Proof
130 int nworkers = 0; // number of workers (default use all available cores)
131 
132 // -------------------------------------------------------
133 // The actual macro
134 
135 void OneSidedFrequentistUpperLimitWithBands(const char *infile = "", const char *workspaceName = "combined",
136  const char *modelConfigName = "ModelConfig",
137  const char *dataName = "obsData")
138 {
139 
140  double confidenceLevel = 0.95;
141  int nPointsToScan = 12;
142  int nToyMC = 150;
143 
144  // -------------------------------------------------------
145  // First part is just to access a user-defined file
146  // or create the standard example file if it doesn't exist
147  const char *filename = "";
148  if (!strcmp(infile, "")) {
149  filename = "results/example_combined_GaussExample_model.root";
150  bool fileExist = !gSystem->AccessPathName(filename); // note opposite return code
151  // if file does not exists generate with histfactory
152  if (!fileExist) {
153 #ifdef _WIN32
154  cout << "HistFactory file cannot be generated on Windows - exit" << endl;
155  return;
156 #endif
157  // Normally this would be run on the command line
158  cout << "will run standard hist2workspace example" << endl;
159  gROOT->ProcessLine(".! prepareHistFactory .");
160  gROOT->ProcessLine(".! hist2workspace config/example.xml");
161  cout << "\n\n---------------------" << endl;
162  cout << "Done creating example input" << endl;
163  cout << "---------------------\n\n" << endl;
164  }
165 
166  } else
167  filename = infile;
168 
169  // Try to open the file
170  TFile *file = TFile::Open(filename);
171 
172  // if input file was specified byt not found, quit
173  if (!file) {
174  cout << "StandardRooStatsDemoMacro: Input file " << filename << " is not found" << endl;
175  return;
176  }
177 
178  // -------------------------------------------------------
179  // Now get the data and workspace
180 
181  // get the workspace out of the file
182  RooWorkspace *w = (RooWorkspace *)file->Get(workspaceName);
183  if (!w) {
184  cout << "workspace not found" << endl;
185  return;
186  }
187 
188  // get the modelConfig out of the file
189  ModelConfig *mc = (ModelConfig *)w->obj(modelConfigName);
190 
191  // get the modelConfig out of the file
192  RooAbsData *data = w->data(dataName);
193 
194  // make sure ingredients are found
195  if (!data || !mc) {
196  w->Print();
197  cout << "data or ModelConfig was not found" << endl;
198  return;
199  }
200 
201  // -------------------------------------------------------
202  // Now get the POI for convenience
203  // you may want to adjust the range of your POI
204 
205  RooRealVar *firstPOI = (RooRealVar *)mc->GetParametersOfInterest()->first();
206  /* firstPOI->setMin(0);*/
207  /* firstPOI->setMax(10);*/
208 
209  // --------------------------------------------
210  // Create and use the FeldmanCousins tool
211  // to find and plot the 95% confidence interval
212  // on the parameter of interest as specified
213  // in the model config
214  // REMEMBER, we will change the test statistic
215  // so this is NOT a Feldman-Cousins interval
216  FeldmanCousins fc(*data, *mc);
217  fc.SetConfidenceLevel(confidenceLevel);
218  fc.AdditionalNToysFactor(
219  0.5); // degrade/improve sampling that defines confidence belt: in this case makes the example faster
220  /* fc.UseAdaptiveSampling(true); // speed it up a bit, 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  /* ProfileLikelihoodTestStatModified onesided(*mc->GetPdf());*/
231  /* fc.GetTestStatSampler()->SetTestStatistic(&onesided);*/
232  /* ((ToyMCSampler*) fc.GetTestStatSampler())->SetGenerateBinned(true); */
233  ToyMCSampler *toymcsampler = (ToyMCSampler *)fc.GetTestStatSampler();
234  ProfileLikelihoodTestStat *testStat = dynamic_cast<ProfileLikelihoodTestStat *>(toymcsampler->GetTestStatistic());
235  testStat->SetOneSided(true);
236 
237  // Since this tool needs to throw toy MC the PDF needs to be
238  // extended or the tool needs to know how many entries in a dataset
239  // per pseudo experiment.
240  // In the 'number counting form' where the entries in the dataset
241  // are counts, and not values of discriminating variables, the
242  // datasets typically only have one entry and the PDF is not
243  // extended.
244  if (!mc->GetPdf()->canBeExtended()) {
245  if (data->numEntries() == 1)
246  fc.FluctuateNumDataEntries(false);
247  else
248  cout << "Not sure what to do about this model" << endl;
249  }
250 
251  // We can use PROOF to speed things along in parallel
252  // However, the test statistic has to be installed on the workers
253  // so either turn off PROOF or include the modified test statistic
254  // in your `$ROOTSYS/roofit/roostats/inc` directory,
255  // add the additional line to the LinkDef.h file,
256  // and recompile root.
257  if (useProof) {
258  ProofConfig pc(*w, nworkers, "", false);
259  toymcsampler->SetProofConfig(&pc); // enable proof
260  }
261 
262  if (mc->GetGlobalObservables()) {
263  cout << "will use global observables for unconditional ensemble" << endl;
264  mc->GetGlobalObservables()->Print();
265  toymcsampler->SetGlobalObservables(*mc->GetGlobalObservables());
266  }
267 
268  // Now get the interval
269  PointSetInterval *interval = fc.GetInterval();
270  ConfidenceBelt *belt = fc.GetConfidenceBelt();
271 
272  // print out the interval on the first Parameter of Interest
273  cout << "\n95% interval on " << firstPOI->GetName() << " is : [" << interval->LowerLimit(*firstPOI) << ", "
274  << interval->UpperLimit(*firstPOI) << "] " << endl;
275 
276  // get observed UL and value of test statistic evaluated there
277  RooArgSet tmpPOI(*firstPOI);
278  double observedUL = interval->UpperLimit(*firstPOI);
279  firstPOI->setVal(observedUL);
280  double obsTSatObsUL = fc.GetTestStatSampler()->EvaluateTestStatistic(*data, tmpPOI);
281 
282  // Ask the calculator which points were scanned
283  RooDataSet *parameterScan = (RooDataSet *)fc.GetPointsToScan();
284  RooArgSet *tmpPoint;
285 
286  // make a histogram of parameter vs. threshold
287  TH1F *histOfThresholds =
288  new TH1F("histOfThresholds", "", parameterScan->numEntries(), firstPOI->getMin(), firstPOI->getMax());
289  histOfThresholds->GetXaxis()->SetTitle(firstPOI->GetName());
290  histOfThresholds->GetYaxis()->SetTitle("Threshold");
291 
292  // loop through the points that were tested and ask confidence belt
293  // what the upper/lower thresholds were.
294  // For FeldmanCousins, the lower cut off is always 0
295  for (Int_t i = 0; i < parameterScan->numEntries(); ++i) {
296  tmpPoint = (RooArgSet *)parameterScan->get(i)->clone("temp");
297  // cout <<"get threshold"<<endl;
298  double arMax = belt->GetAcceptanceRegionMax(*tmpPoint);
299  double poiVal = tmpPoint->getRealValue(firstPOI->GetName());
300  histOfThresholds->Fill(poiVal, arMax);
301  }
302  TCanvas *c1 = new TCanvas();
303  c1->Divide(2);
304  c1->cd(1);
305  histOfThresholds->SetMinimum(0);
306  histOfThresholds->Draw();
307  c1->cd(2);
308 
309  // -------------------------------------------------------
310  // Now we generate the expected bands and power-constraint
311 
312  // First: find parameter point for mu=0, with conditional MLEs for nuisance parameters
313  RooAbsReal *nll = mc->GetPdf()->createNLL(*data);
314  RooAbsReal *profile = nll->createProfile(*mc->GetParametersOfInterest());
315  firstPOI->setVal(0.);
316  profile->getVal(); // this will do fit and set nuisance parameters to profiled values
317  RooArgSet *poiAndNuisance = new RooArgSet();
318  if (mc->GetNuisanceParameters())
319  poiAndNuisance->add(*mc->GetNuisanceParameters());
320  poiAndNuisance->add(*mc->GetParametersOfInterest());
321  w->saveSnapshot("paramsToGenerateData", *poiAndNuisance);
322  RooArgSet *paramsToGenerateData = (RooArgSet *)poiAndNuisance->snapshot();
323  cout << "\nWill use these parameter points to generate pseudo data for bkg only" << endl;
324  paramsToGenerateData->Print("v");
325 
326  RooArgSet unconditionalObs;
327  unconditionalObs.add(*mc->GetObservables());
328  unconditionalObs.add(*mc->GetGlobalObservables()); // comment this out for the original conditional ensemble
329 
330  double CLb = 0;
331  double CLbinclusive = 0;
332 
333  // Now we generate background only and find distribution of upper limits
334  TH1F *histOfUL = new TH1F("histOfUL", "", 100, 0, firstPOI->getMax());
335  histOfUL->GetXaxis()->SetTitle("Upper Limit (background only)");
336  histOfUL->GetYaxis()->SetTitle("Entries");
337  for (int imc = 0; imc < nToyMC; ++imc) {
338 
339  // set parameters back to values for generating pseudo data
340  // cout << "\n get current nuis, set vals, print again" << endl;
341  w->loadSnapshot("paramsToGenerateData");
342  // poiAndNuisance->Print("v");
343 
344  RooDataSet *toyData = 0;
345  // now generate a toy dataset
346  if (!mc->GetPdf()->canBeExtended()) {
347  if (data->numEntries() == 1)
348  toyData = mc->GetPdf()->generate(*mc->GetObservables(), 1);
349  else
350  cout << "Not sure what to do about this model" << endl;
351  } else {
352  // cout << "generating extended dataset"<<endl;
353  toyData = mc->GetPdf()->generate(*mc->GetObservables(), Extended());
354  }
355 
356  // generate global observables
357  // need to be careful for simpdf
358  // RooDataSet* globalData = mc->GetPdf()->generate(*mc->GetGlobalObservables(),1);
359 
360  RooSimultaneous *simPdf = dynamic_cast<RooSimultaneous *>(mc->GetPdf());
361  if (!simPdf) {
362  RooDataSet *one = mc->GetPdf()->generate(*mc->GetGlobalObservables(), 1);
363  const RooArgSet *values = one->get();
364  RooArgSet *allVars = mc->GetPdf()->getVariables();
365  *allVars = *values;
366  delete allVars;
367  delete values;
368  delete one;
369  } else {
370 
371  // try fix for sim pdf
372  TIterator *iter = simPdf->indexCat().typeIterator();
373  RooCatType *tt = NULL;
374  while ((tt = (RooCatType *)iter->Next())) {
375 
376  // Get pdf associated with state from simpdf
377  RooAbsPdf *pdftmp = simPdf->getPdf(tt->GetName());
378 
379  // Generate only global variables defined by the pdf associated with this state
380  RooArgSet *globtmp = pdftmp->getObservables(*mc->GetGlobalObservables());
381  RooDataSet *tmp = pdftmp->generate(*globtmp, 1);
382 
383  // Transfer values to output placeholder
384  *globtmp = *tmp->get(0);
385 
386  // Cleanup
387  delete globtmp;
388  delete tmp;
389  }
390  }
391 
392  // globalData->Print("v");
393  // unconditionalObs = *globalData->get();
394  // mc->GetGlobalObservables()->Print("v");
395  // delete globalData;
396  // cout << "toy data = " << endl;
397  // toyData->get()->Print("v");
398 
399  // get test stat at observed UL in observed data
400  firstPOI->setVal(observedUL);
401  double toyTSatObsUL = fc.GetTestStatSampler()->EvaluateTestStatistic(*toyData, tmpPOI);
402  // toyData->get()->Print("v");
403  // cout <<"obsTSatObsUL " <<obsTSatObsUL << "toyTS " << toyTSatObsUL << endl;
404  if (obsTSatObsUL < toyTSatObsUL) // not sure about <= part yet
405  CLb += (1.) / nToyMC;
406  if (obsTSatObsUL <= toyTSatObsUL) // not sure about <= part yet
407  CLbinclusive += (1.) / nToyMC;
408 
409  // loop over points in belt to find upper limit for this toy data
410  double thisUL = 0;
411  for (Int_t i = 0; i < parameterScan->numEntries(); ++i) {
412  tmpPoint = (RooArgSet *)parameterScan->get(i)->clone("temp");
413  double arMax = belt->GetAcceptanceRegionMax(*tmpPoint);
414  firstPOI->setVal(tmpPoint->getRealValue(firstPOI->GetName()));
415  // double thisTS = profile->getVal();
416  double thisTS = fc.GetTestStatSampler()->EvaluateTestStatistic(*toyData, tmpPOI);
417 
418  // cout << "poi = " << firstPOI->getVal()
419  // << " max is " << arMax << " this profile = " << thisTS << endl;
420  // cout << "thisTS = " << thisTS<<endl;
421  if (thisTS <= arMax) {
422  thisUL = firstPOI->getVal();
423  } else {
424  break;
425  }
426  }
427 
428  /*
429  // loop over points in belt to find upper limit for this toy data
430  double thisUL = 0;
431  for(Int_t i=0; i<histOfThresholds->GetNbinsX(); ++i){
432  tmpPoint = (RooArgSet*) parameterScan->get(i)->clone("temp");
433  cout <<"---------------- "<<i<<endl;
434  tmpPoint->Print("v");
435  cout << "from hist " << histOfThresholds->GetBinCenter(i+1) <<endl;
436  double arMax = histOfThresholds->GetBinContent(i+1);
437  // cout << " threhold from Hist = aMax " << arMax<<endl;
438  // double arMax2 = belt->GetAcceptanceRegionMax(*tmpPoint);
439  // cout << "from scan arMax2 = "<< arMax2 << endl; // not the same due to TH1F not TH1D
440  // cout << "scan - hist" << arMax2-arMax << endl;
441  firstPOI->setVal( histOfThresholds->GetBinCenter(i+1));
442  // double thisTS = profile->getVal();
443  double thisTS = fc.GetTestStatSampler()->EvaluateTestStatistic(*toyData,tmpPOI);
444 
445  // cout << "poi = " << firstPOI->getVal()
446  // << " max is " << arMax << " this profile = " << thisTS << endl;
447  // cout << "thisTS = " << thisTS<<endl;
448 
449  // NOTE: need to add a small epsilon term for single precision vs. double precision
450  if(thisTS<=arMax + 1e-7){
451  thisUL = firstPOI->getVal();
452  } else{
453  break;
454  }
455  }
456  */
457 
458  histOfUL->Fill(thisUL);
459 
460  // for few events, data is often the same, and UL is often the same
461  // cout << "thisUL = " << thisUL<<endl;
462 
463  delete toyData;
464  }
465  histOfUL->Draw();
466  c1->SaveAs("one-sided_upper_limit_output.pdf");
467 
468  // if you want to see a plot of the sampling distribution for a particular scan point:
469  /*
470  SamplingDistPlot sampPlot;
471  int indexInScan = 0;
472  tmpPoint = (RooArgSet*) parameterScan->get(indexInScan)->clone("temp");
473  firstPOI->setVal( tmpPoint->getRealValue(firstPOI->GetName()) );
474  toymcsampler->SetParametersForTestStat(tmpPOI);
475  SamplingDistribution* samp = toymcsampler->GetSamplingDistribution(*tmpPoint);
476  sampPlot.AddSamplingDistribution(samp);
477  sampPlot.Draw();
478  */
479 
480  // Now find bands and power constraint
481  Double_t *bins = histOfUL->GetIntegral();
482  TH1F *cumulative = (TH1F *)histOfUL->Clone("cumulative");
483  cumulative->SetContent(bins);
484  double band2sigDown, band1sigDown, bandMedian, band1sigUp, band2sigUp;
485  for (int i = 1; i <= cumulative->GetNbinsX(); ++i) {
486  if (bins[i] < RooStats::SignificanceToPValue(2))
487  band2sigDown = cumulative->GetBinCenter(i);
488  if (bins[i] < RooStats::SignificanceToPValue(1))
489  band1sigDown = cumulative->GetBinCenter(i);
490  if (bins[i] < 0.5)
491  bandMedian = cumulative->GetBinCenter(i);
492  if (bins[i] < RooStats::SignificanceToPValue(-1))
493  band1sigUp = cumulative->GetBinCenter(i);
494  if (bins[i] < RooStats::SignificanceToPValue(-2))
495  band2sigUp = cumulative->GetBinCenter(i);
496  }
497  cout << "-2 sigma band " << band2sigDown << endl;
498  cout << "-1 sigma band " << band1sigDown << " [Power Constraint)]" << endl;
499  cout << "median of band " << bandMedian << endl;
500  cout << "+1 sigma band " << band1sigUp << endl;
501  cout << "+2 sigma band " << band2sigUp << endl;
502 
503  // print out the interval on the first Parameter of Interest
504  cout << "\nobserved 95% upper-limit " << interval->UpperLimit(*firstPOI) << endl;
505  cout << "CLb strict [P(toy>obs|0)] for observed 95% upper-limit " << CLb << endl;
506  cout << "CLb inclusive [P(toy>=obs|0)] for observed 95% upper-limit " << CLbinclusive << endl;
507 
508  delete profile;
509  delete nll;
510 }
virtual RooAbsReal * createNLL(RooAbsData &data, const RooLinkedList &cmdList)
Construct representation of -log(L) of PDFwith given dataset.
Definition: RooAbsPdf.cxx:918
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:1914
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:1287
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:3257
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:8575
ModelConfig is a simple class that holds configuration information specifying how a model should be u...
Definition: ModelConfig.h:30
auto * tt
Definition: textangle.C:16
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
RooArgSet * getObservables(const RooArgSet &set, Bool_t valueOnly=kTRUE) const
Return the observables of this pdf given a set of observables.
Definition: RooAbsArg.h:239
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
virtual TestStatistic * GetTestStatistic(unsigned int i) const
Definition: ToyMCSampler.h:132
Iterator abstract base class.
Definition: TIterator.h:30
static struct mg_connection * fc(struct mg_context *ctx)
Definition: civetweb.c:3728
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.
RooCatType is an auxilary class for RooAbsCategory and defines a a single category state...
Definition: RooCatType.h:22
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:7840
Double_t SignificanceToPValue(Double_t Z)
returns p-value corresponding to a 1-sided significance
Definition: RooStatsUtils.h:56
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:232
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:2521
R__EXTERN TSystem * gSystem
Definition: TSystem.h:557
virtual void Draw(Option_t *option="")
Draw this histogram with options.
Definition: TH1.cxx:2980
static constexpr double pc
const RooAbsCategoryLValue & indexCat() const
RooAbsArg * first() const
Double_t getVal(const RooArgSet *normalisationSet=nullptr) const
Evaluate object.
Definition: RooAbsReal.h:89
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:33
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:27
Namespace for the RooStats classes.
Definition: Asimov.h:19
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.
RooAbsReal is the common abstract base class for objects that represent a real value and implements f...
Definition: RooAbsReal.h:59
RooCmdArg Extended(Bool_t flag=kTRUE)
RooAbsPdf * getPdf(const char *catName) const
Return the p.d.f associated with the given index category name.
RooAbsPdf, the base class of all PDFs
Definition: RooAbsPdf.h:40
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
virtual TObject * Next()=0
TObject * Clone(const char *newname=0) const
Make a complete copy of the underlying object.
Definition: TH1.cxx:2663
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