TGraphQQ

class TGraphQQ : public TGraph

    protected:

      void MakeFunctionQuantiles()
      void MakeQuantiles()
      void Quartiles()

    public:

                      TGraphQQ()
                      TGraphQQ(Int_t n, Double_t* x)
                      TGraphQQ(Int_t n, Double_t* x, TF1* f)
                      TGraphQQ(Int_t nx, Double_t* x, Int_t ny, Double_t* y)
                      TGraphQQ(const TGraphQQ&)
              virtual ~TGraphQQ()
       static TClass* Class()
      virtual TClass* IsA() const
            TGraphQQ& operator=(const TGraphQQ&)
         virtual void Paint(Option_t* opt = "")
                 void SetFunction(TF1* f)
         virtual void ShowMembers(TMemberInspector& insp, char* parent)
         virtual void Streamer(TBuffer& b)
                 void StreamerNVirtual(TBuffer& b)

Data Members

    protected:

          Int_t fNy0  size of the fY0 dataset
       Double_t fXq1  x1 coordinate of the interquartile line
       Double_t fXq2  x2 coordinate of the interquartile line
       Double_t fYq1  y1 coordinate of the interquartile line
       Double_t fYq2  y2 coordinate of the interquartile line
      Double_t* fY0   second dataset, if specified
           TF1* fF    theoretical density function, if specified

Class Description

 This class allows to draw quantile-quantile plots

 Plots can be drawn for 2 datasets or for a dataset and a theoretical
 distribution function

 2 datasets:
   Quantile-quantile plots are used to determine whether 2 samples come from
   the same distribution.
   A qq-plot draws the quantiles of one dataset against the quantile of the
   the other. The quantiles of the dataset with fewer entries are on Y axis,
   with more entries - on X axis.
   A straight line, going through 0.25 and 0.75 quantiles is also plotted
   for reference. It represents a robust linear fit, not sensitive to the
   extremes of the datasets.
   If the datasets come from the same distribution, points of the plot should
   fall approximately on the 45 degrees line. If they have the same
   distribution function, but location or scale different parameters,
   they should still fall on the straight line, but not the 45 degrees one.
   The greater their departure from the straight line, the more evidence there
   is, that the datasets come from different distributions.
   The advantage of qq-plot is that it not only shows that the underlying
   distributions are different, but, unlike the analytical methods, it also
   gives information on the nature of this difference: heavier tails,
   different location/scale, different shape, etc.

   Some examples of qqplots of 2 datasets:

 1 dataset:
   Quantile-quantile plots are used to determine if the dataset comes from the
   specified theoretical distribution, such as normal.
   A qq-plot draws quantiles of the dataset against quantiles of the specified
   theoretical distribution.
   (NOTE, that density, not CDF should be specified)
   A straight line, going through 0.25 and 0.75 quantiles can also be plotted
   for reference. It represents a robust linear fit, not sensitive to the
   extremes of the dataset.
   As in the 2 datasets case, departures from straight line indicate departures
   from the specified distribution.

   " The correlation coefficient associated with the linear fit to the data
     in the probability plot (qq plot in our case) is a measure of the
     goodness of the fit.
     Estimates of the location and scale parameters  of the distribution
     are given by the intercept and slope. Probability plots can be generated
     for several competing distributions to see which provides the best fit,
     and the probability plot generating the highest correlation coefficient
     is the best choice since it generates the straightest probability plot."
   From "Engineering statistic handbook",
   http://www.itl.nist.gov/div898/handbook/eda/section3/probplot.htm

   Example of a qq-plot of a dataset from N(3, 2) distribution and
           TMath::Gaus(0, 1) theoretical function. Fitting parameters
           are estimates of the distribution mean and sigma.

 References:
 http://www.itl.nist.gov/div898/handbook/eda/section3/qqplot.htm
 http://www.itl.nist.gov/div898/handbook/eda/section3/probplot.htm

Creates a quantile-quantile plot of dataset x.
Theoretical distribution function can be defined later by SetFunction method

Creates a quantile-quantile plot of dataset x against function f

Creates a quantile-quantile plot of dataset x against dataset y
Parameters nx and ny are respective array sizes

Destroys a TGraphQQ

Computes quantiles of theoretical distribution function

When sample sizes are not equal, computes quantiles of the bigger sample
by linear interpolation

 compute quartiles
 a quartile is a 25 per cent or 75 per cent quantile

 paint this graphQQ. No options for the time being

Sets the theoretical distribution function (density!)
and computes its quantiles

Inline Functions

           TGraphQQ TGraphQQ(Int_t nx, Double_t* x, Int_t ny, Double_t* y)
            TClass* Class()
            TClass* IsA() const
               void ShowMembers(TMemberInspector& insp, char* parent)
               void Streamer(TBuffer& b)
               void StreamerNVirtual(TBuffer& b)
           TGraphQQ TGraphQQ(const TGraphQQ&)
          TGraphQQ& operator=(const TGraphQQ&)