Gld                  package:Davies                  R Documentation

_T_h_e _G_e_n_e_r_a_l_i_z_e_d _L_a_m_b_d_a _D_i_s_t_r_i_b_u_t_i_o_n

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

     Density, distribution function, quantile function and random
     generation for the Generalized Lambda Distribution

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

     dgld(x, params)
     dgld.p(x, params)
     pgld(x, params)
     qgld(p, params)
     rgld(n, params)

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

       x: vector of quantiles.

       p: vector of probabilities.

       n: number of observations.  If 'length(n)> 1', the length is
          taken to be the number required

  params: vector of parameters:  params[1]==lambda1 etseq

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

     The Generalized Lambda distribution has quantile function

         f(x)=lambda1 +(p^lambda3 - (1-p)^lambda_4)/lambda_2

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

     'dgld' gives the density, 'dgld.p' gives the density in terms of
     the quantile, 'pgld' gives the distribution function, 'qgld' gives
     the quantile function, and 'rgld' generates random deviates.

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

     Wichura, M. J. (1988) Algorithm AS 241: The Percentage Points of
     the Normal Distribution. _Applied Statistics_, *37*, 477-484.

     A. \"{O}zt\"{u}rk and R. F. Dale, "Least squares estimation of the
     parameters of the generalized lambda distribution", Technometrics
     1985,  27(1):84

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

     'Davies', 'expected.gld'

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

     params <- c(4.114,0.1333,0.0193,0.1588)  #taken straight from some paper

     gld.rv <- rgld(100,params)

     hist(gld.rv)
     fit.davies.q(gld.rv)  #remember the Davies distn has 3 DF and the GLD 4...

