quant                  package:evir                  R Documentation

_P_l_o_t _o_f _G_P_D _T_a_i_l _E_s_t_i_m_a_t_e _o_f _a _H_i_g_h _Q_u_a_n_t_i_l_e

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

     Creates a plot showing how the estimate of a high quantile in the
     tail of a dataset based on the GPD approximation varies with
     threshold or number of extremes.

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

     quant(data, p = 0.99, models = 30, start = 15, end = 500, reverse =
         TRUE, ci = 0.95, auto.scale = TRUE, labels = TRUE, ...)

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

    data: numeric vector of data

       p: desired probability for quantile estimate (e.g. 0.99 gives
          99th percentile)

  models: number of consecutive gpd models to be fitted

   start: lowest number of exceedances to be considered

     end: maximum number of exceedances to be considered

 reverse: should plot be by increasing threshold ('TRUE') or number of
          extremes ('FALSE')

      ci: probability for asymptotic confidence band; for no confidence
          band set to zero

auto.scale: whether or not plot should be automatically scaled; if not,
          xlim and ylim graphical parameters may be entered

  labels: whether or not axes should be labelled

     ...: other graphics parameters

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

     For every model 'gpd' is called. Evaluation may be slow. 
     Confidence intervals by the Wald method (which is fastest).

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

     A table of results is returned invisibly.

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

     'gpd', 'plot.gpd', 'gpd.q', 'shape'

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

     ## Not run: data(danish)
     ## Not run: quant(danish, 0.999) 
     # Estimates of the 99.9th percentile of the Danish losses using 
     # the GPD model with various thresholds 

