summary.acomp          package:compositions          R Documentation

_S_u_m_m_a_r_i_z_i_n_g _a _c_o_m_p_o_s_i_t_i_o_n_a_l _d_a_t_a_s_e_t _i_n _t_e_r_m_s _o_f _r_a_t_i_o_s

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

     Summaries in terms of compositions are quite different from
     classical ones. Instead of analysing each variable individually,
     we must analyse each pairwise ratio in a log geometry.

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

               ## S3 method for class 'acomp':
               summary( object, ... )
               

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

  object: a data.matrix of compositions, not necessarily closed

     ...: not used, only here for generics

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

     It is quite difficult to summarize a composition in a consistent
     and interpretable way. We tried to provide such a summary here.

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

     The result is an object of type '"summary.acomp"' 

    mean: The 'mean.acomp' composition

mean.ratio: A matrix containing the geometric mean of the pairwise
          ratios

variation: The variation matrix of the dataset ('{variation.acomp}')

   expsd: A matrix containing the  one-sigma factor for each ratio,
          computed as 'exp(sqrt(variation.acomp(W)))'. To obtain
          two-sigma-factor it needs to be squared. To obtain the
          reverse bound we compute 1/expsd

     min: A matrix containing the minimum of each of the pairwise
          ratios

      q1: A matrix containing the 1-Quartile of each of the pairwise
          ratios

  median: A matrix containing the median of each of the pairwise ratios

      q1: A matrix containing the 3-Quartile of each of the pairwise
          ratios

     max: A matrix containing the maximum of each of the pairwise
          ratios

_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:

     'acomp'

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

     data(SimulatedAmounts)
     summary(acomp(sa.lognormals))

