mean.acomp           package:compositions           R Documentation

_M_e_a_n _a_m_o_u_n_t_s _a_n_d _m_e_a_n _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 mean in the several approaches of compositional and
     amount data analysis.

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

               mean.acomp(x,..., na.action=get(getOption("na.action")))
               mean.rcomp(x,..., na.action=get(getOption("na.action")))
               mean.aplus(x,..., na.action=get(getOption("na.action")))
               mean.rplus(x,..., na.action=get(getOption("na.action")))
               mean.rmult(x,..., na.action=get(getOption("na.action")))
               

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

       x: a classed dataset of amounts or compositions

     ...: further arguments to 'mean' e.g. 'trim'

na.action: The na.action to be used: one of
          'na.omit','na.fail','na.pass'

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

     The different compositional approaches 'acomp', 'rcomp', 'aplus',
     'rplus' correpond to different geometries. The mean is calculated
     in the respective canonical geometry by applying a canonical
     transform (see 'cdt'), taking ordinary 'mean.col' and
     backtransforming.

     The Aitchison geometries imply that 'mean.acomp' and 'mean.aplus'
     are geometric means, the first one closed. The real geometry
     implies that 'mean.rcomp' and 'mean.rplus' are arithmetic means,
     the first one resulting in a closed composition.

     In all cases the mean is again an object of the same class.

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

     The mean is given as a composition or amount vector of the same
     class as the original dataset.

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

     'clo', 'mean.col', 'geometricmean', 'acomp', 'rcomp', 'aplus',
     'rplus'

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

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

