class TFitter: public TVirtualFitter

default constructor*-*-
*-*                  ===================

default destructor*-*-*-
*-*                  ==================

 return a chisquare equivalent

 reset the fitter environment

 Execute a fitter command;
   command : command string
   args    : list of nargs command arguments

 Fix parameter ipar.

Computes point-by-point confidence intervals for the fitted function
Parameters:
n - number of points
ndim - dimensions of points
x - points, at which to compute the intervals, for ndim > 1
    should be in order: (x0,y0, x1, y1, ... xn, yn)
ci - computed intervals are returned in this array
cl - confidence level, default=0.95
NOTE, that the intervals are approximate for nonlinear(in parameters) models

Computes confidence intervals at level cl. Default is 0.95
The TObject parameter can be a TGraphErrors, a TGraph2DErrors or a TH1,2,3.
For Graphs, confidence intervals are computed for each point,
the value of the graph at that point is set to the function value at that
point, and the graph y-errors (or z-errors) are set to the value of
the confidence interval at that point.
For Histograms, confidence intervals are computed for each bin center
The bin content of this bin is then set to the function value at the bin
center, and the bin error is set to the confidence interval value.
NOTE: confidence intervals are approximate for nonlinear models!

Allowed combinations:
Fitted object               Passed object
TGraph                      TGraphErrors, TH1
TGraphErrors, AsymmErrors   TGraphErrors, TH1
TH1                         TGraphErrors, TH1
TGraph2D                    TGraph2DErrors, TH2
TGraph2DErrors              TGraph2DErrors, TH2
TH2                         TGraph2DErrors, TH2
TH3                         TH3

 return a pointer to the covariance matrix

 return element i,j from the covariance matrix

 return current errors for a parameter
   ipar     : parameter number
   eplus    : upper error
   eminus   : lower error
   eparab   : parabolic error
   globcc   : global correlation coefficient

 return the total number of parameters (free + fixed)

 return the number of free parameters

 return error of parameter ipar

 return current value of parameter ipar

 return current values for a parameter
   ipar     : parameter number
   parname  : parameter name
   value    : initial parameter value
   verr     : initial error for this parameter
   vlow     : lower value for the parameter
   vhigh    : upper value for the parameter
  WARNING! parname must be suitably dimensionned in the calling function.

 return name of parameter ipar

 return global fit parameters
   amin     : chisquare
   edm      : estimated distance to minimum
   errdef
   nvpar    : number of variable parameters
   nparx    : total number of parameters

 return Sum(log(i) i=0,n
 used by log likelihood fits

return kTRUE if parameter ipar is fixed, kFALSE othersise)

 Print fit results

 Release parameter ipar.

 Specify the address of the fitting algorithm (from the interpreter)

 Specify the address of the fitting algorithm

 ret fit method (chisquare or loglikelihood)

 set initial values for a parameter
   ipar     : parameter number
   parname  : parameter name
   value    : initial parameter value
   verr     : initial error for this parameter
   vlow     : lower value for the parameter
   vhigh    : upper value for the parameter

  Minimization function for H1s using a Chisquare method
  Default method (function evaluated at center of bin)
  for each point the cache contains the following info
    -1D : bc,e, xc  (bin content, error, x of center of bin)
    -2D : bc,e, xc,yc
    -3D : bc,e, xc,yc,zc

  Minimization function for H1s using a Chisquare method
  The "I"ntegral method is used
  for each point the cache contains the following info
    -1D : bc,e, xc,xw  (bin content, error, x of center of bin, x bin width of bin)
    -2D : bc,e, xc,xw,yc,yw
    -3D : bc,e, xc,xw,yc,yw,zc,zw

  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.
  Default method (function evaluated at center of bin)
  for each point the cache contains the following info
    -1D : bc,e, xc  (bin content, error, x of center of bin)
    -2D : bc,e, xc,yc
    -3D : bc,e, xc,yc,zc

  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.
  The "I"ntegral method is used
  for each point the cache contains the following info
    -1D : bc,e, xc,xw  (bin content, error, x of center of bin, x bin width of bin)
    -2D : bc,e, xc,xw,yc,yw
    -3D : bc,e, xc,xw,yc,yw,zc,zw