missingsummary         package:compositions         R Documentation

_C_l_a_s_s_i_f_y _a_n_d _s_u_m_m_a_r_i_z_e _m_i_s_s_i_n_g _v_a_l_u_e_s _i_n _a _d_a_t_a_s_e_t

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

     Routines classifies codes of missing valuesas numbers in objects
     of the compositions package.

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

        missingSummary(x,..., vlabs = colnames(x), 
                      mc=attr(x,"missingClassifier"), values=eval(formals(missingType)$values))
        missingType(x,..., mc=attr(x,"missingClassifier"),
                      values=c("NMV", "BDT", "MAR", "MNAR", "SZ", "Err"))

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

       x: a dataset which might contain missings

     ...: additional arguments for mc

      mc: optionally in missingSummary, an alternate routine to be used
           instead of 'missingType' 

   vlabs: labels for the variables

  values: the names of the different types of missings. '"Err"' is a
          value that can not be classified e.g. 'Inf'.

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

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

     'missingType' returns a character vector/matrix with the same
     dimension and dimnames as 'x' giving the type of every value.
      'missingSummary' returns a table giving the number of missings of
     each type for each variable.

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

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

     Boogaart, K.G., R. Tolosana-Delgado, M. Bren (2006) Concepts for
     the handling of zeros and missings in compositional data,
     _Proceedings of IAMG 2006, Liege_

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

     compositions.missing

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

     data(SimulatedAmounts)
     x <- acomp(sa.lognormals)
     xnew <- simulateMissings(x,detectionlimit=0.05,MAR=0.05,MNAR=0.05,SZ=0.05)
     xnew
     missingSummary(xnew)

