#include "TROOT.h"
#include "TFumili.h"
#include "TMath.h"
#include "TF1.h"
#include "TF2.h"
#include "TF3.h"
#include "TH1.h"
#include "TGraphAsymmErrors.h"
#include "Riostream.h"

Include dependency graph for TFumili.cxx:

Functions
void	H1FitChisquareFumili (Int_t &npar, Double_t gin, Double_t &f, Double_t u, Int_t flag)
	Minimization function for H1s using a Chisquare method More...

void	H1FitLikelihoodFumili (Int_t &npar, Double_t gin, Double_t &f, Double_t u, Int_t flag)
	----Minimization function for H1s using a Likelihood method----- Basically, it forms the likelihood by determining the Poisson probability that given a number of entries in a particular bin, the fit would predict it's value. More...

void	GraphFitChisquareFumili (Int_t &npar, Double_t gin, Double_t &f, Double_t u, Int_t flag)
	-----Minimization function for Graphs using a Chisquare method----* - ========================================================= More...

	ClassImp (TFumili)

Variables
TFumili *	gFumili =0

static const Double_t	gMAXDOUBLE =1e300

static const Double_t	gMINDOUBLE =-1e300

Function Documentation

ClassImp ( TFumili )

void GraphFitChisquareFumili	(	Int_t &	npar,
		Double_t *	gin,
		Double_t &	f,
		Double_t *	u,
		Int_t	flag
	)

*-*-*-*-*-*Minimization function for Graphs using a Chisquare method*-*-*-*-* *-* =========================================================

In case of a TGraphErrors object, ex, the error along x, is projected along the y-direction by calculating the function at the points x-exlow and x+exhigh.

The chisquare is computed as the sum of the quantity below at each point:

                (y - f(x))**2
    -----------------------------------
    ey**2 + (0.5*(exl + exh)*f'(x))**2

where x and y are the point coordinates and f'(x) is the derivative of function f(x). This method to approximate the uncertainty in y because of the errors in x, is called "effective variance" method. The improvement, compared to the previously used method (f(x+ exhigh) - f(x-exlow))/2 is of (error of x)**2 order. NOTE: 1) By using the "effective variance" method a simple linear regression becomes a non-linear case , which takes several iterations instead of 0 as in the linear case .

2) The effective variance technique assumes that there is no correlation between the x and y coordinate .

In case the function lies below (above) the data point, ey is ey_low (ey_high).

Definition at line 2148 of file TFumili.cxx.

Referenced by TFumili::SetFitMethod().

void H1FitChisquareFumili	(	Int_t &	npar,
		Double_t *	gin,
		Double_t &	f,
		Double_t *	u,
		Int_t	flag
	)

Minimization function for H1s using a Chisquare method

Definition at line 2091 of file TFumili.cxx.

Referenced by TFumili::Chisquare(), and TFumili::SetFitMethod().

void H1FitLikelihoodFumili	(	Int_t &	npar,
		Double_t *	gin,
		Double_t &	f,
		Double_t *	u,
		Int_t	flag
	)

----Minimization function for H1s using a Likelihood method-----

Basically, it forms the likelihood by determining the Poisson probability that given a number of entries in a particular bin, the fit would predict it's value.

This is then done for each bin, and the sum of the logs is taken as the likelihood. PDF: P=exp(-f(x_i))/[F_i]!*(f(x_i))^[F_i] where F_i - experimental value, f(x_i) - expected theoretical value [F_i] - integer part of F_i. drawback is that if F_i>Int_t - GetSumLog will fail for big F_i is faster to use Euler's Gamma-function

Definition at line 2110 of file TFumili.cxx.

Referenced by TFumili::SetFitMethod().