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:

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

_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.

_M_i_s_s_i_n_g _P_o_l_i_c_y:

     For the additive scales (rcomp,rplus) the SZ and BDT are  treated
     as zeros and MAR and MNAR as missing information.  This is not
     strictly correct for MNAR.  
      For relative scales (acomp,aplus), all four types of missings 
     are treated as missing information. This corresponds to the  idea
     that BDT are truncated values (and have the correspoding  effect
     in taking means). For SZ and MAR, only the components in  the
     observed subcomposition are fully relevant. Finally, for MNAR  the
     problem is again that nothing could be done without knowing the
     MNAR mechanism, so the analysis is limited to taking them as  MAR,
     and being _careful_ with the interpretation. Missing and Below
     Detecion Limit Policy is explained in more detail  in
     compositions.missing.

_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:

     '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))

