PotFit               package:fExtremes               R Documentation

_M_o_d_e_l_l_i_n_g _P_e_a_k_s _O_v_e_r _a _T_h_r_e_s_h_o_l_d

_D_e_s_c_r_i_p_t_i_o_n:

     a collection and description of functions to model  point
     processes over a threshold, POT, based on R's  'evir' package.  

     The functions are:

       'potSim'         generates data from a point process,
       'potFit'         fits empirical or simulated data to a point process,
       'print'          print method for a fitted POT object of class ...,
       'plot'           plot method for a fitted GEV object,
       'summary'        summary method for a fitted GEV object,
       'gevrlevelPlot'  k-block return level with confidence intervals.

_U_s_a_g_e:

     potSim(x, threshold, nextremes = NA, run = NA)
     potFit(x, threshold = NA, nextremes = NA, run = NA, ...) 

     ## S3 method for class 'potFit':
     print(x, ...)
     ## S3 method for class 'potFit':
     plot(x, which = "all", ...)
     ## S3 method for class 'potFit':
     summary(object, doplot = TRUE, which  = "all", ...)

_A_r_g_u_m_e_n_t_s:

  doplot: a logical. Should the results be plotted? 

nextremes: the number of upper extremes to be used (either this or
          'threshold' must be given but not both). 

  object: [summary] - 
           a fitted object of class '"potFit"'. 

     run: if the data are to be declustered the run length parameter
          for  the runs method, see 'deCluster', should be entered 
          here. 

threshold: a threshold value, either 'threshold' or 'nextremes' must be
          given, but not both). 

   which: if 'which' is set to 'ask' the function will  interactively
          ask which plot should be displayed. By default this value is
          set to 'FALSE' and then those plots will be displayed for
          which the elements in the logical vector 'which' ar set to
          'TRUE'; by default all four elements are set to '"all"'. 

       x: numeric vector of data, which may have a 'times' attribute 
          containing (in an object of class '"POSIXct"', or an object 
          that can be converted to that class; see 'as.POSIXct')  the
          times/dates of each observation. If no 'times' attribute
          exists, the data are assumed to be equally spaced. Note, the
          argument name is different for  'potFit' and 'pot'. 
           [print][plot] - 
           a fitted object of class '"potFit"'. 

     ...: control parameters and plot parameters optionally passed to
          the  optimization and/or plot function. Parameters for the
          optimization function are passed to components of the
          'control' argument  of 'optim'. 

_D_e_t_a_i_l_s:

     *Parameter Estimation:* 

      'potFit' uses 'optim' for point process likelihood  maximization.       

     *Methods:* 

      The plot method 'plot.pot' provides seven different plots for 
     assessing fitted POT model. The user selects the plot type from a 
     menu. Plot 1 displays the exceedance process of the chosen
     threshold.  Plots 2-4  assess the Poisson nature of the exceedance
     process  by looking at the scaled gaps between exceedances, which
     should be iid unit exponentially distributed. Plots 5-6 assess the
     GPD nature of the excesses by looking at suitably defined
     residuals, which should again be iid unit exponentially
     distributed. Option 8 allows the user to call GPD plotting
     functions. If plot 1 or 2  from the GPD plots is selected as the
     final plot (i.e. option 8 is  selected, followed by option 1 or
     2), a list object containing  details of the plot is returned
     invisibly. This object should be  used as the first argument of
     'gpdqPlot' or 'gpdsfallPlot'  to add quantile estimates or
     expected shortfall estimates to the plot.

_V_a_l_u_e:

     Both, 'potFit' and 'pot' return an object of class '"pot"'
     describing the fit including parameter estimates and standard
     errors.

_E_x_a_m_p_l_e_s:

     ## SOURCE("fExtremes.53C-PotFit")

     ## Use Danish Fire Insurance Loss Data:
        data(danish)
        
     ## Fit:
        xmpExtremes("\nStart: POT Parameter Estimate >")
        fit = potFit(danish, threshold = 10)  
        print(fit)
        
     ## Summary with Diagnostic Plots:
        xmpExtremes("\nNext: Diagnostic Analysis >")
        par(mfrow = c(3, 3), cex = 0.5)
        summary(fit)

