Do Fit 1
 
Do Fit 2
****************************************
Minimizer is Minuit2 / Migrad
Chi2                      =      65.1586
NDf                       =           56
Edm                       =  1.93774e-09
NCalls                    =           69
Constant                  =      36.3132   +/-   1.52625       -1.51651     +1.53547      (Minos) 
Mean                      =     0.013082   +/-   0.0347499     -0.0347674   +0.0347613    (Minos) 
Sigma                     =      1.03413   +/-   0.0288039     -0.0286274   +0.0290102    (Minos)      (limited)
 
Do Fit 3
****************************************
Minimizer is Minuit2 / Migrad
Chi2                      =      65.1586
NDf                       =           56
Edm                       =  6.86315e-08
NCalls                    =           57
Constant                  =       36.327   +/-   2             -1.51685     +1.53726      (Minos) 
Mean                      =    0.0130817   +/-   2           
Sigma                     =      1.03373   +/-   6.72116        (limited)
 
Do Fit 4
****************************************
Minimizer is Minuit2 / Migrad
MinFCN                    =      43.3935
Chi2                      =      86.7869
NDf                       =           97
Edm                       =  9.97216e-08
NCalls                    =           62
Constant                  =       38.427   +/-   1.48837       -1.46667     +1.51031      (Minos) 
Mean                      =     0.027601   +/-   0.032831      -0.0328395   +0.0328395    (Minos) 
Sigma                     =      1.03819   +/-   0.0232194     -0.0227841   +0.0236699    (Minos)      (limited)
 
Do Fit 1
 
Do Fit 2
****************************************
Minimizer is Minuit2 / Fumili
Chi2                      =      65.1586
NDf                       =           56
Edm                       =  1.62112e-07
NCalls                    =           59
Constant                  =      36.3134   +/-   1.52626       -1.51677     +1.53521      (Minos) 
Mean                      =    0.0130884   +/-   0.0347495     -0.0347737   +0.0347549    (Minos) 
Sigma                     =      1.03413   +/-   0.0288038     -0.0286304   +0.0290072    (Minos)      (limited)
 
Do Fit 3
****************************************
Minimizer is Minuit2 / Fumili
Chi2                      =      65.1586
NDf                       =           56
Edm                       =  1.10318e-10
NCalls                    =           60
Constant                  =      36.3273   +/-   1.52734       -1.51756     +1.53661      (Minos) 
Mean                      =    0.0130817   +/-   0.0347499     -0.0347671   +0.0347615    (Minos) 
Sigma                     =      1.03373   +/-   0.0288151     -0.0286383   +0.0290218    (Minos)      (limited)
 
Do Fit 4
****************************************
Minimizer is Minuit2 / Fumili
MinFCN                    =      43.3935
Chi2                      =      86.7869
NDf                       =           97
Edm                       =  1.39887e-09
NCalls                    =           32
Constant                  =      38.4264   +/-   1.48835       -1.46601     +1.51097      (Minos) 
Mean                      =     0.027601   +/-   0.0328313     -0.0328395   +0.0328395    (Minos) 
Sigma                     =       1.0382   +/-   0.0232198     -0.0227933   +0.0236607    (Minos)      (limited)
   
 
#include <iostream>
#include <string>
 
 
 
 
 
   TH1D * h2 = 
new TH1D(
name.c_str(),
"Chi2 Fit with Minos Error",100, -5, 5. );
 
   TH1D * h3 = 
new TH1D(
name.c_str(),
"Chi2 Fit with Integral and Minos",100, -5, 5. );
 
   TH1D * 
h4 = 
new TH1D(
name.c_str(),
"Likelihood Fit with Minos Error",100, -5, 5. );
 
 
 
   for (
int i = 0; i < 
n; ++i) {
 
   }
 
 
   std::cout << "\nDo Fit 1\n";
   std::cout << "\nDo Fit 2\n";
   h2->Fit("gaus","E");
   h2->Draw();
   std::cout << "\nDo Fit 3\n";
   h3->Fit("gaus","IGE");
   h3->Draw();
   std::cout << "\nDo Fit 4\n";
 
}
 
 
 
}
ROOT::Detail::TRangeCast< T, true > TRangeDynCast
TRangeDynCast is an adapter class that allows the typed iteration through a TCollection.
 
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t WindowAttributes_t Float_t Float_t Float_t Int_t Int_t UInt_t UInt_t Rectangle_t Int_t Int_t Window_t TString Int_t GCValues_t GetPrimarySelectionOwner GetDisplay GetScreen GetColormap GetNativeEvent const char const char dpyName wid window const char font_name cursor keysym reg const char only_if_exist regb h Point_t winding char text const char depth char const char Int_t count const char cname
 
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t WindowAttributes_t Float_t Float_t Float_t Int_t Int_t UInt_t UInt_t Rectangle_t Int_t Int_t Window_t TString Int_t GCValues_t GetPrimarySelectionOwner GetDisplay GetScreen GetColormap GetNativeEvent const char const char dpyName wid window const char font_name cursor keysym reg const char only_if_exist regb h Point_t winding char text const char depth char const char Int_t count const char ColorStruct_t color const char Pixmap_t Pixmap_t PictureAttributes_t attr const char char ret_data h unsigned char height h Atom_t Int_t ULong_t ULong_t unsigned char prop_list Atom_t Atom_t Atom_t Time_t type
 
R__EXTERN TRandom * gRandom
 
R__EXTERN TStyle * gStyle
 
1-D histogram with a double per channel (see TH1 documentation)
 
virtual TFitResultPtr Fit(const char *formula, Option_t *option="", Option_t *goption="", Double_t xmin=0, Double_t xmax=0)
Fit histogram with function fname.
 
virtual Int_t Fill(Double_t x)
Increment bin with abscissa X by 1.
 
void Draw(Option_t *option="") override
Draw this histogram with options.
 
Random number generator class based on M.
 
virtual Double_t Gaus(Double_t mean=0, Double_t sigma=1)
Samples a random number from the standard Normal (Gaussian) Distribution with the given mean and sigm...
 
void SetOptStat(Int_t stat=1)
The type of information printed in the histogram statistics box can be selected via the parameter mod...
 
void SetOptFit(Int_t fit=1)
The type of information about fit parameters printed in the histogram statistics box can be selected ...
 
static void SetDefaultFitter(const char *name="")
static: set name of default fitter