Renormalize fitted parameters error when using the option "W" in TH1::Fit

hist/hist/src/HFitImpl.cxx

@@ -244,6 +244,8 @@ TFitResultPtr HFit::Fit(FitObject * h1, TF1 *f1 , Foption_t & fitOption , const
 
    // error normalization in case of zero error in the data
    if (fitdata->GetErrorType() == ROOT::Fit::BinData::kNoError) fitConfig.SetNormErrors(true);
+   // error normalization also in case of W1 option (weights = 1)
+   if (fitdata->Opt().fErrors1)  fitConfig.SetNormErrors(true);
    // normalize errors also in case you are fitting a Ndim histo with a N-1 function
    if (int(fitdata->NDim())  == hdim -1 ) fitConfig.SetNormErrors(true);
 

hist/hist/src/TH1.cxx

@@ -3826,6 +3826,9 @@ TFitResultPtr TH1::Fit(const char *fname ,Option_t *option ,Option_t *goption, D
 ///        -  "L"  Use Loglikelihood method (default is chisquare method)
 ///        - "WL" Use Loglikelihood method and bin contents are not integer,
 ///          i.e. histogram is weighted (must have Sumw2() set)
+///        -"MULTI" Use Loglikelihood method based on multi-nomial distribution.
+///              In this case function must be normalized and one fits only the function shape (a not extended binned
+///              likelihood fit)
 ///        - "P"  Use Pearson chi2 (using expected errors instead of observed errors)
 ///        - "U"  Use a User specified fitting algorithm (via SetFCN)
 ///        - "Q"  Quiet mode (minimum printing)
@@ -3895,12 +3898,13 @@ TFitResultPtr TH1::Fit(const char *fname ,Option_t *option ,Option_t *goption, D
 /// When the lower limit and upper limit are equal, the parameter is fixed.
 /// However to fix a parameter to 0, one must call the FixParameter function.
 ///
-/// Note that option "I" gives better results but is slower.
 ///
 /// #### Changing the fitting objective function
 ///
 /// By default a chi square function is used for fitting. When option "L" (or "LL") is used
 /// a Poisson likelihood function (see note below) is used.
+/// Using option "MULTI" a multinomial likelihood fit is used. In this case the function normalization is not fitted
+/// but only the function shape. Therefore the provided function must be normalized.
 /// The functions are defined in the header Fit/Chi2Func.h or Fit/PoissonLikelihoodFCN and they
 /// are implemented using the routines FitUtil::EvaluateChi2 or FitUtil::EvaluatePoissonLogL in
 /// the file math/mathcore/src/FitUtil.cxx.
@@ -3927,9 +3931,9 @@ TFitResultPtr TH1::Fit(const char *fname ,Option_t *option ,Option_t *goption, D
 /// \f]
 ///
 /// where y(i) is the bin content for each bin i, x(i) is the bin center and e(i) is the bin error (sqrt(y(i) for
-/// an un-weighted histogram. Bins with zero errors are excluded from the fit. See also later the note on the treatment of empty bins.
-/// When using option "I" the residual is computed not using the function value at the bin center, f (x(i) | p), but the integral
-/// of the function in the bin,   Integral{ f(x|p)dx } divided by the bin volume
+/// an un-weighted histogram. Bins with zero errors are excluded from the fit. See also later the note on the treatment
+/// of empty bins. When using option "I" the residual is computed not using the function value at the bin center, f
+/// (x(i) | p), but the integral of the function in the bin,   Integral{ f(x|p)dx } divided by the bin volume
 ///
 /// #### Likelihood Fits
 ///
@@ -3967,14 +3971,18 @@ TFitResultPtr TH1::Fit(const char *fname ,Option_t *option ,Option_t *goption, D
 /// Note that if the histogram is having bins with zero content and non zero-errors they are considered as
 /// any other bins in the fit. Instead bins with zero error and non-zero content are excluded in the chi2 fit.
 /// A likelihood fit should also not be performed on such an histogram, since we are assuming a wrong pdf for each bin.
-/// In general, one should not fit an histogram with non-empty bins and zero errors, apart if all the bins have zero errors.
-/// In this case one could use the option "w", which gives a weight=1 for each bin (unweighted least-square fit).
+/// In general, one should not fit an histogram with non-empty bins and zero errors, apart if all the bins have zero
+/// errors. In this case one could use the option "w", which gives a weight=1 for each bin (unweighted least-square
+/// fit).
+/// Note that in case of histogram with no errors (chi2 fit with option W or W1) the resulting fitted parameter errors
+/// are corrected by the obtained chi2 value using this  expression:  errorp *= sqrt(chisquare/(ndf-1))
 ///
 /// #### Fitting a histogram of dimension N with a function of dimension N-1
 ///
 /// It is possible to fit a TH2 with a TF1 or a TH3 with a TF2.
 /// In this case the option "Integral" is not allowed and each cell has
-/// equal weight.
+/// equal weight. Also in this case th eobtained parameter error are corrected as in the case when the
+/// option "W" is used (see above)
 ///
 /// #### Associated functions
 ///