pairs-methods              package:ghyp              R Documentation

_P_a_i_r_s _p_l_o_t _f_o_r _m_u_l_t_i_v_a_r_i_a_t_e _g_e_n_e_r_a_l_i_z_e_d _h_y_p_e_r_b_o_l_i_c _d_i_s_t_r_i_b_u_t_i_o_n_s

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

     This function is intended to be used as a graphical diagnostic
     tool for fitted multivariate generalized hyperbolic distributions.
     An array of graphics  is created and qq-plots are drawn into the
     diagonal part of the  graphics array. The upper part of the
     graphics matrix shows scatter plots whereas the lower part shows
     2-dimensional histogramms.

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

     ## S4 method for signature 'ghyp':
     pairs(x, data = ghyp.data(x), main = "'ghyp' pairwise plot", 
           nbins = 30, qq = T, gaussian = T,
           hist.col = c("white", topo.colors(40)), 
           spline.points = 150, root.tol = .Machine$double.eps^0.5, 
           rel.tol = root.tol, abs.tol = root.tol^1.5, ...)

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

       x: Usually a fitted multivariate generalized hyperbolic
          distribution of class 'mle.ghyp'. Alternatively an object of
          class 'ghyp' and a data matrix.

    data: A matirx.

    main: The title of the plot.

   nbins: The number of bins passed to 'hist2d'.

      qq: If 'TRUE' qq-plots are drawn.

gaussian: If 'TRUE' qq-plots with the normal distribution are plotted.

hist.col: A vector of colors passed to 'hist2d'.

spline.points: The number of support points when computing the
          quantiles used by the  qq-plot. Passed to 'qqghyp'.

root.tol: The tolerance of the quantiles. Passed to 'uniroot' via
          'qqghyp'.

 rel.tol: The tolerance of the quantiles. Passed to 'integrate' via
          'qqghyp'.

 abs.tol: The tolerance of the quantiles. Passed to 'integrate' via
          'qqghyp'.

     ...: Arguments passed to 'plot' and 'axis'.

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

     David Lthi

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

     'pairs', 'fit.ghypmv',  'qqghyp', 'hist2d'

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

       data(smi.stocks)
       fitted.smi.stocks <- fit.NIGmv(data = smi.stocks[1:200, ])
       pairs(fitted.smi.stocks)

