quagam                package:lmomco                R Documentation

_Q_u_a_n_t_i_l_e _F_u_n_c_t_i_o_n _o_f _t_h_e _G_a_m_m_a _D_i_s_t_r_i_b_u_t_i_o_n

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

     This function computes the quantiles of the Gamma distribution
     given parameters (alpha and beta) of the distribution computed by
     'pargam'.  The quantile function has no explicit form. See the
     'qgamma' function and 'cdfgam'. The parameters have the following
     interpretations: alpha is a shape parameter and beta is a scale
     parameter in the R syntax.

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

     quagam(f, para)

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

       f: Nonexceedance probability (0 <= F <= 1).

    para: The parameters from 'pargam' or similar.

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

     Quantile value for nonexceedance probability F.

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

     W.H. Asquith

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

     Hosking, J.R.M., 1990, L-moments-Analysis and estimation of
     distributions using linear combinations of order statistics:
     Journal of the Royal Statistical Society, Series B, vol. 52, p.
     105-124.

     Hosking, J.R.M., 1996, FORTRAN routines for use with the method of
     L-moments: Version 3, IBM Research Report RC20525, T.J. Watson
     Research Center, Yorktown Heights, New York.

     Hosking, J.R.M. and Wallis, J.R., 1997, Regional frequency
     analysis-An approach based on L-moments: Cambridge University
     Press.

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

     'cdfgam', 'pargam'

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

       lmr <- lmom.ub(c(123,34,4,654,37,78))
       g <- pargam(lmr)
       quagam(0.5,g)
      
       # generate 50 random samples from this fitted parent
       Qsim <- rlmomco(5000,g)
       # compute the apparent gamma parameter for this parent
       gsim <- pargam(lmoms(Qsim))

