mrlplot                 package:evd                 R Documentation

_E_m_p_i_r_i_c_a_l _M_e_a_n _R_e_s_i_d_u_a_l _L_i_f_e _P_l_o_t

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

     The empirical mean residual life plot.

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

     mrlplot(data, tlim, nt = max(100, length(data)), lty = c(2,1,2),
         col = 1, conf = 0.95, main = "Mean Residual Life Plot", xlab =
         "Threshold", ylab = "Mean Excess", ...)

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

    data: A numeric vector.

    tlim: A numeric vector of length two, giving the limits for the
          thresholds at which the mean residual life plot is evaluated.
          If 'tlim' is not given, sensible defaults are used.

      nt: The number of thresholds at which the mean residual life plot
          is evaluated.

lty, col: Arguments passed to 'matplot'. The first and last elements of
          'lty' correspond to the lower and upper confidence limits
          respectively. Use zero to supress.

    conf: The (pointwise) confidence coefficient for the plotted
          confidence intervals.

    main: Plot title.

xlab, ylab: x and y axis labels.

     ...: Other arguments to be passed to 'matplot'.

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

     The empirical mean residual life plot is the locus of points

                 {u,1/n_u sum_{i=1}^{n_u} (x(i) - u)}

     where x(1), ..., x(n_u) are the n_u observations that exceed the
     threshold u. If the exceedances of a threshold u0 are generalized
     Pareto, the empirical mean residual life plot should be
     approximately linear for u > u0.

     The confidence intervals within the plot are symmetric intervals
     based on the approximate normality of sample means.

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

     A list with components 'x' and 'y' is invisibly returned. The
     components contain those objects that were passed to the formal
     arguments 'x' and 'y' of 'matplot' in order to create the mean
     residual life plot.

_A_u_t_h_o_r(_s):

     Stuart Coles and Alec Stephenson

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

     'fpot', 'matplot', 'tcplot'

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

     data(portpirie)
     mrlplot(portpirie)

