Data and categories: using weights in unbinned datasets

**Author:** Wouter Verkerke

*This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Wednesday, January 19, 2022 at 10:21 AM.*

In [ ]:

```
%%cpp -d
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooDataHist.h"
#include "RooGaussian.h"
#include "RooFormulaVar.h"
#include "RooGenericPdf.h"
#include "RooPolynomial.h"
#include "RooChi2Var.h"
#include "RooMinimizer.h"
#include "TCanvas.h"
#include "TAxis.h"
#include "RooPlot.h"
#include "RooFitResult.h"
```

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```
%%cpp -d
// This is a workaround to make sure the namespace is used inside functions
using namespace RooFit;
```

Declare observable

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```
RooRealVar x("x", "x", -10, 10);
x.setBins(40);
```

Construction a uniform pdf

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```
RooPolynomial p0("px", "px", x);
```

Sample 1000 events from pdf

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```
RooDataSet *data = p0.generate(x, 1000);
```

Construct formula to calculate (fake) weight for events

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```
RooFormulaVar wFunc("w", "event weight", "(x*x+10)", x);
```

Add column with variable w to previously generated dataset

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```
RooRealVar *w = (RooRealVar *)data->addColumn(wFunc);
```

Dataset d is now a dataset with two observable (x,w) with 1000 entries

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```
data->Print();
```

Instruct dataset wdata in interpret w as event weight rather than as observable

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```
RooDataSet wdata(data->GetName(), data->GetTitle(), data, *data->get(), 0, w->GetName());
```

Dataset d is now a dataset with one observable (x) with 1000 entries and a sum of weights of ~430k

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```
wdata.Print();
```

Construction quadratic polynomial pdf for fitting

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```
RooRealVar a0("a0", "a0", 1);
RooRealVar a1("a1", "a1", 0, -1, 1);
RooRealVar a2("a2", "a2", 1, 0, 10);
RooPolynomial p2("p2", "p2", x, RooArgList(a0, a1, a2), 0);
```

Fit quadratic polynomial to weighted data

Note: a plain maximum likelihood fit to weighted data does in general NOT result in correct error estimates, unless individual event weights represent Poisson statistics themselves.

Fit with 'wrong' errors

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```
RooFitResult *r_ml_wgt = p2.fitTo(wdata, Save());
```

A first order correction to estimated parameter errors in an (unbinned) ML fit can be obtained by calculating the covariance matrix as

```
V' = V C-1 V
```

where V is the covariance matrix calculated from a fit to -logL = - sum [ w_i log f(x_i) ] and C is the covariance matrix calculated from -logL' = -sum [ w_i^2 log f(x_i) ] (i.e. the weights are applied squared)

A fit in this mode can be performed as follows:

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```
RooFitResult *r_ml_wgt_corr = p2.fitTo(wdata, Save(), SumW2Error(kTRUE));
```

Construct plot frame

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```
RooPlot *frame = x.frame(Title("Unbinned ML fit, binned chi^2 fit to weighted data"));
```

Plot data using sum-of-weights-squared error rather than poisson errors

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```
wdata.plotOn(frame, DataError(RooAbsData::SumW2));
```

Overlay result of 2nd order polynomial fit to weighted data

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```
p2.plotOn(frame);
```

Construct a pdf with the same shape as p0 after weighting

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```
RooGenericPdf genPdf("genPdf", "x*x+10", x);
```

Sample a dataset with the same number of events as data

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```
RooDataSet *data2 = genPdf.generate(x, 1000);
```

Sample a dataset with the same number of weights as data

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```
RooDataSet *data3 = genPdf.generate(x, 43000);
```

Fit the 2nd order polynomial to both unweighted datasets and save the results for comparison

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```
RooFitResult *r_ml_unw10 = p2.fitTo(*data2, Save());
RooFitResult *r_ml_unw43 = p2.fitTo(*data3, Save());
```

Construct binned clone of unbinned weighted dataset

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```
RooDataHist *binnedData = wdata.binnedClone();
binnedData->Print("v");
```

Perform chi2 fit to binned weighted dataset using sum-of-weights errors

NB: Within the usual approximations of a chi2 fit, a chi2 fit to weighted data using sum-of-weights-squared errors does give correct error estimates

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```
RooChi2Var chi2("chi2", "chi2", p2, *binnedData, DataError(RooAbsData::SumW2));
RooMinimizer m(chi2);
m.migrad();
m.hesse();
```

Plot chi^2 fit result on frame as well

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```
RooFitResult *r_chi2_wgt = m.save();
p2.plotOn(frame, LineStyle(kDashed), LineColor(kRed));
```

Note that ml fit on 1kevt of weighted data is closer to result of ml fit on 43kevt of unweighted data than to 1Kevt of unweighted data, whereas the reference chi^2 fit with SumW2 error gives a result closer to that of an unbinned ML fit to 1Kevt of unweighted data.

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```
cout << "==> ML Fit results on 1K unweighted events" << endl;
r_ml_unw10->Print();
cout << "==> ML Fit results on 43K unweighted events" << endl;
r_ml_unw43->Print();
cout << "==> ML Fit results on 1K weighted events with a summed weight of 43K" << endl;
r_ml_wgt->Print();
cout << "==> Corrected ML Fit results on 1K weighted events with a summed weight of 43K" << endl;
r_ml_wgt_corr->Print();
cout << "==> Chi2 Fit results on 1K weighted events with a summed weight of 43K" << endl;
r_chi2_wgt->Print();
new TCanvas("rf403_weightedevts", "rf403_weightedevts", 600, 600);
gPad->SetLeftMargin(0.15);
frame->GetYaxis()->SetTitleOffset(1.8);
frame->Draw();
```

Draw all canvases

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```
%jsroot on
gROOT->GetListOfCanvases()->Draw()
```