class RooAbsData: public TNamed, public RooPrintable

 Destructor

 Create a reduced copy of this dataset. The caller takes ownership of the returned dataset

 The following optional named arguments are accepted

   SelectVars(const RooArgSet& vars) -- Only retain the listed observables in the output dataset
   Cut(const char* expression)       -- Only retain event surviving the given cut expression
   Cut(const RooFormulaVar& expr)    -- Only retain event surviving the given cut formula
   CutRange(const char* name)        -- Only retain events inside range with given name. Multiple CutRange
                                        arguments may be given to select multiple ranges
   EventRange(int lo, int hi)        -- Only retain events with given sequential event numbers
   Name(const char* name)            -- Give specified name to output dataset
   Title(const char* name)           -- Give specified title to output dataset

 Create a subset of the data set by applying the given cut on the data points.
 The cut expression can refer to any variable in the data set. For cuts involving
 other variables, such as intermediate formula objects, use the equivalent
 reduce method specifying the as a RooFormulVar reference.

 Create a subset of the data set by applying the given cut on the data points.
 The 'cutVar' formula variable is used to select the subset of data points to be
 retained in the reduced data collection.

 Create a subset of the data set by applying the given cut on the data points
 and reducing the dimensions to the specified set.

 The cut expression can refer to any variable in the data set. For cuts involving
 other variables, such as intermediate formula objects, use the equivalent
 reduce method specifying the as a RooFormulVar reference.

 Create a subset of the data set by applying the given cut on the data points
 and reducing the dimensions to the specified set.

 The 'cutVar' formula variable is used to select the subset of data points to be
 retained in the reduced data collection.

 Return error on current weight (dummy implementation returning zero)

 Return asymmetric error on weight. (Dummy implementation returning zero)

 Plot dataset on specified frame. By default an unbinned dataset will use the default binning of
 the target frame. A binned dataset will by default retain its intrinsic binning.

 The following optional named arguments can be used to modify the default behavior

 Data representation options

 Asymmetry(const RooCategory& c) -- Show the asymmetry of the daya in given two-state category [F(+)-F(-)] / [F(+)+F(-)].
                                    Category must have two states with indices -1 and +1 or three states with indeces -1,0 and +1.
 ErrorType(RooAbsData::EType)    -- Select the type of error drawn: Poisson (default) draws asymmetric Poisson
                                    confidence intervals. SumW2 draws symmetric sum-of-weights error
 Binning(double xlo, double xhi, -- Use specified binning to draw dataset
                      int nbins)
 Binning(const RooAbsBinning&)   -- Use specified binning to draw dataset
 Binning(const char* name)       -- Use binning with specified name to draw dataset
 RefreshNorm(Bool_t flag)        -- Force refreshing for PDF normalization information in frame.
                                    If set, any subsequent PDF will normalize to this dataset, even if it is
                                    not the first one added to the frame. By default only the 1st dataset
                                    added to a frame will update the normalization information

 Histogram drawing options

 DrawOption(const char* opt)     -- Select ROOT draw option for resulting TGraph object
 LineStyle(Int_t style)          -- Select line style by ROOT line style code, default is solid
 LineColor(Int_t color)          -- Select line color by ROOT color code, default is black
 LineWidth(Int_t width)          -- Select line with in pixels, default is 3
 MarkerStyle(Int_t style)        -- Select the ROOT marker style, default is 21
 MarkerColor(Int_t color)        -- Select the ROOT marker color, default is black
 MarkerSize(Double_t size)       -- Select the ROOT marker size
 XErrorSize(Double_t frac)       -- Select size of X error bar as fraction of the bin width, default is 1


 Misc. other options

 Name(const chat* name)          -- Give curve specified name in frame. Useful if curve is to be referenced later
 Invisble(Bool_t flag)           -- Add curve to frame, but do not display. Useful in combination AddTo()
 AddTo(const char* name,         -- Add constructed histogram to already existing histogram with given name and relative weight factors
 double_t wgtSelf, double_t wgtOther)

 Create and fill a ROOT histogram TH1,TH2 or TH3 with the values of this dataset.

 This function accepts the following arguments

 name -- Name of the ROOT histogram
 xvar -- Observable to be mapped on x axis of ROOT histogram

 Binning(const char* name)                    -- Apply binning with given name to x axis of histogram
 Binning(RooAbsBinning& binning)              -- Apply specified binning to x axis of histogram
 Binning(int nbins, double lo, double hi)     -- Apply specified binning to x axis of histogram

 YVar(const RooAbsRealLValue& var,...)    -- Observable to be mapped on y axis of ROOT histogram
 ZVar(const RooAbsRealLValue& var,...)    -- Observable to be mapped on z axis of ROOT histogram

 The YVar() and ZVar() arguments can be supplied with optional Binning() arguments to control the binning of the Y and Z axes, e.g.
 createHistogram("histo",x,Binning(-1,1,20), YVar(y,Binning(-1,1,30)), ZVar(z,Binning("zbinning")))

 The caller takes ownership of the returned histogram

 Print name of dataset

 Print title of dataset

 Print class name of dataset

 Define default print options, for a given print style

 Change name of observable

 Add one ore more rows of data

 Load a given row of data

 Do events in dataset have weights?

 Plot the distribution of a real valued arg

 Split a dataset by a category

 Fill an existing histogram

 Printing interface (human readable)

 Control propagation of dirty flags from observables in dataset

 Column structure definition