gpd                   package:evir                   R Documentation

_F_i_t _G_e_n_e_r_a_l_i_z_e_d _P_a_r_e_t_o _M_o_d_e_l

_D_e_s_c_r_i_p_t_i_o_n:

     Returns an object of class '"gpd"' representing the fit of a
     generalized Pareto model to excesses over a high threshold.

_U_s_a_g_e:

     gpd(data, threshold = NA, nextremes = NA, method = c("ml", "pwm"),
         information = c("observed", "expected"), ...)

_A_r_g_u_m_e_n_t_s:

    data: data vector

threshold: a threshold value (either this or 'nextremes' must be given
          but not both)

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

  method: whether parameters should be estimated by the maximum
          likelihood method '"ml"' or the probability-weighted moments
          method '"pwm"'

information: whether standard errors should be calculated with
          '"observed"' or '"expected"' information. This only applies
          to the maximum likelihood method; for the
          probability-weighted moments method '"expected"' information
          is used if possible

     ...: arguments passed to 'optim'

_D_e_t_a_i_l_s:

     The function uses the general purpose optimization function
     'optim' when 'method = "ml"' is chosen.

_V_a_l_u_e:

     An object of class '"gpd"' describing the fit and including
     parameter estimates and standard errors.

_R_e_f_e_r_e_n_c_e_s:

     Parameter and  quantile  estimation  for  the  generalized Pareto 
      distribution,   JRM   Hosking   and   JR  Wallis, _Technometrics_
     *29*(3), pages 339-349, 1987.

_S_e_e _A_l_s_o:

     'plot.gpd', 'shape',  'quant', 'optim'

_E_x_a_m_p_l_e_s:

     data(danish)
     out <- gpd(danish, 10) 
     # Fits GPD to excess losses over 10 for the Danish
     # fire insurance data 

