rlnorm             package:compositions             R Documentation

_T_h_e _m_u_l_t_i_v_a_r_i_a_t_e _l_o_g_n_o_r_m_a_l _d_i_s_t_r_i_b_u_t_i_o_n

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

     Generates random amounts with a multivariate lognormal
     distribution .

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

     rlnorm.rplus(n,meanlog,varlog)
     dlnorm.rplus(x,meanlog,varlog)
               

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

       n: number of datasets to be simulated

 meanlog: The mean-vector of the logs

  varlog: The variance/covariance matrix of the logs

       x: vectors in the sample space

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

     'rlnorm.rplus' gives a generated random dataset of class '"rplus"'
     following a lognormal distribution with logs having mean 'meanlog'
     and variance 'varlog'. 
      'dlnorm.rplus' gives the density of the distribution with respect
     to the Lesbesgue measure on R+ as a subset of R.

_N_o_t_e:

     The main difference between 'rlnorm.rplus' and 'rnorm.aplus' is
     that rlnorm.rplus needs a loged mean. The additional difference
     for the calculation of the density by 'dlnorm.rplus' and
     'dnorm.aplus' is the reference measure.

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

     Aitchison, J. (1986) _The Statistical Analysis of Compositional
     Data_ Monographs on Statistics and Applied Probability. Chapman &
     Hall Ltd., London (UK). 416p.

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

     'rnorm.acomp'

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

     MyVar <- matrix(c(
     0.2,0.1,0.0,
     0.1,0.2,0.0,
     0.0,0.0,0.2),byrow=TRUE,nrow=3)
     MyMean <- c(1,1,2)

     plot(rlnorm.rplus(100,log(MyMean),MyVar))
     plot(rnorm.aplus(100,MyMean,MyVar))
     x <- rnorm.aplus(5,MyMean,MyVar)
     dnorm.aplus(x,MyMean,MyVar)
     dlnorm.rplus(x,log(MyMean),MyVar)

