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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/// \authors 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"
118
121
122using namespace RooFit;
123using namespace RooStats;
124using namespace std;
125
126bool useProof = false; // flag to control whether to use Proof
127int nworkers = 0; // number of workers (default use all available cores)
128
129// -------------------------------------------------------
130
131void 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;
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}
int Int_t
Definition RtypesCore.h:45
double Double_t
Definition RtypesCore.h:59
#define gROOT
Definition TROOT.h:406
R__EXTERN TSystem * gSystem
Definition TSystem.h:559
static struct mg_connection * fc(struct mg_context *ctx)
Definition civetweb.c:3728
RooArgSet * getVariables(Bool_t stripDisconnected=kTRUE) const
Return RooArgSet with all variables (tree leaf nodes of expresssion tree)
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.
RooAbsArg * first() const
virtual void Print(Option_t *options=0) const
This method must be overridden when a class wants to print itself.
RooAbsData is the common abstract base class for binned and unbinned datasets.
Definition RooAbsData.h:49
virtual Int_t numEntries() const
Return number of entries in dataset, i.e., count unweighted entries.
virtual RooAbsReal * createNLL(RooAbsData &data, const RooLinkedList &cmdList)
Construct representation of -log(L) of PDFwith given dataset.
Bool_t canBeExtended() const
If true, PDF can provide extended likelihood term.
Definition RooAbsPdf.h:238
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&,...
Definition RooAbsPdf.h:58
virtual Double_t getMax(const char *name=0) const
Get maximum of currently defined range.
virtual Double_t getMin(const char *name=0) const
Get miniminum of currently defined range.
RooAbsReal is the common abstract base class for objects that represent a real value and implements f...
Definition RooAbsReal.h:61
virtual RooAbsReal * createProfile(const RooArgSet &paramsOfInterest)
Create a RooProfileLL object that eliminates all nuisance parameters in the present function.
Double_t getVal(const RooArgSet *normalisationSet=nullptr) const
Evaluate object.
Definition RooAbsReal.h:91
RooArgSet is a container object that can hold multiple RooAbsArg objects.
Definition RooArgSet.h:29
TObject * clone(const char *newname) const override
Definition RooArgSet.h:83
RooArgSet * snapshot(bool deepCopy=true) const
Use RooAbsCollection::snapshot(), but return as RooArgSet.
Definition RooArgSet.h:118
Bool_t add(const RooAbsArg &var, Bool_t silent=kFALSE) override
Add element to non-owning set.
RooDataSet is a container class to hold unbinned data.
Definition RooDataSet.h:33
virtual const RooArgSet * get(Int_t index) const override
Return RooArgSet with coordinates of event 'index'.
RooRealVar represents a variable that can be changed from the outside.
Definition RooRealVar.h:39
virtual void setVal(Double_t value)
Set value of variable to 'value'.
RooSimultaneous facilitates simultaneous fitting of multiple PDFs to subsets of a given dataset.
virtual RooDataSet * generateSimGlobal(const RooArgSet &whatVars, Int_t nEvents)
Special generator interface for generation of 'global observables' – for RooStats tools.
ConfidenceBelt is a concrete implementation of the ConfInterval interface.
Double_t GetAcceptanceRegionMax(RooArgSet &, Double_t cl=-1., Double_t leftside=-1.)
The FeldmanCousins class (like the Feldman-Cousins technique) is essentially a specific configuration...
ModelConfig is a simple class that holds configuration information specifying how a model should be u...
Definition ModelConfig.h:30
const RooArgSet * GetGlobalObservables() const
get RooArgSet for global observables (return NULL if not existing)
const RooArgSet * GetParametersOfInterest() const
get RooArgSet containing the parameter of interest (return NULL if not existing)
const RooArgSet * GetNuisanceParameters() const
get RooArgSet containing the nuisance parameters (return NULL if not existing)
virtual void Print(Option_t *option="") const override
overload the print method
const RooArgSet * GetObservables() const
get RooArgSet for observables (return NULL if not existing)
RooAbsPdf * GetPdf() const
get model PDF (return NULL if pdf has not been specified or does not exist)
PointSetInterval is a concrete implementation of the ConfInterval interface.
Double_t UpperLimit(RooRealVar &param)
return upper limit on a given parameter
Double_t LowerLimit(RooRealVar &param)
return lower limit on a given parameter
ProfileLikelihoodTestStat is an implementation of the TestStatistic interface that calculates the pro...
Holds configuration options for proof and proof-lite.
Definition ProofConfig.h:46
ToyMCSampler is an implementation of the TestStatSampler interface.
void SetProofConfig(ProofConfig *pc=NULL)
virtual TestStatistic * GetTestStatistic(unsigned int i) const
virtual void SetGlobalObservables(const RooArgSet &o)
The RooWorkspace is a persistable container for RooFit projects.
RooAbsData * data(const char *name) const
Retrieve dataset (binned or unbinned) with given name. A null pointer is returned if not found.
void Print(Option_t *opts=0) const
Print contents of the workspace.
Bool_t saveSnapshot(const char *name, const char *paramNames)
Save snapshot of values and attributes (including "Constant") of given parameters.
Bool_t loadSnapshot(const char *name)
Load the values and attributes of the parameters in the snapshot saved with the given name.
TObject * obj(const char *name) const
Return any type of object (RooAbsArg, RooAbsData or generic object) with given name)
The Canvas class.
Definition TCanvas.h:23
A ROOT file is a suite of consecutive data records (TKey instances) with a well defined format.
Definition TFile.h:54
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:3997
1-D histogram with a float per channel (see TH1 documentation)}
Definition TH1.h:575
virtual Double_t GetBinCenter(Int_t bin) const
Return bin center for 1D histogram.
Definition TH1.cxx:8981
TAxis * GetXaxis()
Get the behaviour adopted by the object about the statoverflows. See EStatOverflows for more informat...
Definition TH1.h:320
TObject * Clone(const char *newname=0) const
Make a complete copy of the underlying object.
Definition TH1.cxx:2740
virtual Int_t GetNbinsX() const
Definition TH1.h:296
virtual Int_t Fill(Double_t x)
Increment bin with abscissa X by 1.
Definition TH1.cxx:3350
TAxis * GetYaxis()
Definition TH1.h:321
virtual void SetContent(const Double_t *content)
Replace bin contents by the contents of array content.
Definition TH1.cxx:8246
virtual void SetMinimum(Double_t minimum=-1111)
Definition TH1.h:399
virtual void Draw(Option_t *option="")
Draw this histogram with options.
Definition TH1.cxx:3073
virtual Double_t * GetIntegral()
Return a pointer to the array of bins integral.
Definition TH1.cxx:2578
virtual void SetTitle(const char *title="")
Set the title of the TNamed.
Definition TNamed.cxx:164
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:1294
RooCmdArg Extended(Bool_t flag=kTRUE)
return c1
Definition legend1.C:41
The namespace RooFit contains mostly switches that change the behaviour of functions of PDFs (or othe...
Namespace for the RooStats classes.
Definition Asimov.h:19
Double_t SignificanceToPValue(Double_t Z)
returns p-value corresponding to a 1-sided significance
Definition file.py:1