hist-methods              package:ghyp              R Documentation

_H_i_s_t_o_g_r_a_m _f_o_r _u_n_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:

     The function 'hist' computes a histogram of the given data values
     and the univariate generalized hyperbolic density.

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

     ## S4 method for signature 'ghyp':
     hist(x, data = ghyp.data(x), gaussian = TRUE, 
          log.hist = F, ylim = NULL, ghyp.col = 1, ghyp.lwd = 1, 
          ghyp.lty = "solid", col = 1, nclass = 30, plot.legend = TRUE, 
          location = if (log.hist) "bottom" else "topright", legend.cex = 1, ...)

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

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

    data: A vector.

gaussian: If 'TRUE' the probability density of the normal distribution
          is plotted as a reference.

log.hist: If 'TRUE' the logarithm of the histogramm is plotted.

    ylim: The y limits of the plot.

ghyp.col: The color of the density of the generalized hyperbolic
          distribution.

ghyp.lwd: The line width of the density of the generalized hyperbolic
          distribution.

ghyp.lty: The line type of the density of the generalized hyperbolic
          distribution.

     col: The color of the histogramm.

  nclass: A single number giving the number of cells for the
          histogramm.

plot.legend: If 'TRUE' a legend is drawn.

location: The location of the legend. See 'legend' for possible values.

legend.cex: The character expansion of the legend.

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

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

     No value returned

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

     David Lthi

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

     'qqghyp', 'fit.ghypuv',  'hist', 'legend', 'plot',  'lines'.

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

       data(smi.stocks)
       univariate.fit <- fit.ghypuv(data = smi.stocks[,"SMI"],
                                    opt.pars = c(mu = FALSE, sigma = FALSE), 
                                    symmetric = TRUE)
       hist(univariate.fit)

