data2normpoly            package:Lmoments            R Documentation

_E_s_t_i_m_a_t_i_o_n _o_f _n_o_r_m_a_l-_p_o_l_y_n_o_m_i_a_l _q_u_a_n_t_i_l_e _m_i_x_t_u_r_e

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

     Estimates the parameters of normal-polynomial quantile mixture
     from data or from L-moments

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

     data2normpoly4(data)
     lmom2normpoly4(lmom)
     data2normpoly6(data)
     lmom2normpoly6(lmom)

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

    data: matrix or data frame

    lmom: vector or matrix of L-moments

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

     vector or matrix containing the four or six parameters of
     normal-polynomial quantile mixture

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

     Juha Karvanen juha.karvanen@ktl.fi

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

     Karvanen, J. 2005. Estimation of quantile mixtures via L-moments
     and trimmed L-moments,  _Computational Statistics & Data
     Analysis_, in press, <URL:
     http://www.bsp.brain.riken.jp/publications/2005/karvanen_quantile_
     mixtures.pdf>.

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

     'dnormpoly' for L-moments,  'dnormpoly' for the normal-polynomial
     quantile mixture and 'data2cauchypoly4' for the estimation of
     Cauchy-polynomial quantile mixture.

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

     #Generates a sample 500 observations from the normal-polynomial quantile mixture, 
     #calculates L-moments and their covariance matrix,
     #estimates parameters via L-moments and 
     #plots the true pdf and the estimated pdf together with the histogram of the data.
     true_params<-lmom2normpoly4(c(0,1,0.2,0.05));
     x<-rnormpoly(500,true_params);
     lmoments<-Lmoments(x);
     lmomcov<-Lmomcov(x);
     estim_params<-lmom2normpoly4(lmoments);
     hist(x,30,freq=FALSE);
     plotpoints<-seq(min(x)-1,max(x)+1,by=0.01);
     lines(plotpoints,dnormpoly(plotpoints,estim_params),col='red');
     lines(plotpoints,dnormpoly(plotpoints,true_params),col='blue');

