variation            package:compositions            R Documentation

_V_a_r_i_a_t_i_o_n _m_a_t_r_i_c_e_s _o_f _a_m_o_u_n_t_s _a_n_d _c_o_m_p_o_s_i_t_i_o_n_s

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

     Compute the variation  matrix in the various approaches of
     compositional and amount  data analysis. Pay attention that this
     is not computing the variance or covariance matrix!

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

         variation(x,...)
               ## S3 method for class 'acomp':
               variation(x, ...)
               ## S3 method for class 'rcomp':
               variation(x, ...)
               ## S3 method for class 'aplus':
               variation(x, ...)
               ## S3 method for class 'rplus':
               variation(x, ...)
               ## S3 method for class 'rmult':
               variation(x, ...)
               

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

       x: a dataset, eventually of amounts or compositions

     ...: currently unused

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

     The variation matrix was defined in the 'acomp' context of
     analysis of compositions as the matrix of variances of all 
     possible log-ratios among components (Aitchison, 1986). The 
     generalization to rcomp objects is simply to reproduce the 
     variance of all possible differences between components. The 
     amount ('aplus', 'rplus') and rmult objects  should not be treated
     with variation  matrices, because this was intended to skip the
     existence of a closure (which does not exist in the case of
     amounts).

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

     The variation matrix of x.

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

     K.Gerald v.d. Boogaart <URL: http://www.stat.boogaart.de>

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

     'cdt', 'clrvar2ilr', 'clo', 'mean.acomp', 'acomp', 'rcomp',
     'aplus', 'rplus'

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

     data(SimulatedAmounts)
     mean.col(sa.lognormals)
     variation(acomp(sa.lognormals))
     variation(rcomp(sa.lognormals))
     variation(aplus(sa.lognormals))
     variation(rplus(sa.lognormals))
     variation(rmult(sa.lognormals))

