sumprojector          package:compositions          R Documentation

_C_o_m_p_u_t_e _t_h_e _g_l_o_b_a_l _p_r_o_j_e_c_t_o_r _t_o _t_h_e _o_b_s_e_r_v_e_d _s_u_b_s_p_a_c_e.

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

     Routines to compute the global projector to the observed subspace,
      down-weighting the subspaces with more missing values.

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

     sumMissingProjector(x,...)
     ## S3 method for class 'acomp':
     sumMissingProjector(x,has=is.NMV(x),...)
     ## S3 method for class 'aplus':
     sumMissingProjector(x,has=is.NMV(x),...)
     ## S3 method for class 'rcomp':
     sumMissingProjector(x,has=!(is.MAR(x)|is.MNAR(x)),...)
     ## S3 method for class 'rplus':
     sumMissingProjector(x,has=!(is.MAR(x)|is.MNAR(x)),...)
     ## S3 method for class 'rmult':
     sumMissingProjector(x,has=is.finite(x),...)
               

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

       x: a dataset of some type containing missings

     has: the values to be regarded as non missing

     ...: further generic arguments that might be useful for other
          functions.

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

     The function 'missingProjector' generates a list of N square 
     matrices of dimension DxD (with N and D respectively  equal to the
     number of rows and columns in 'x'). Each of these  matrices gives
     the projection of a data row onto its observed sub-space. Then,
     the function 'sumMissingProjector' takes all these matrices and 
     sums them in a efficient way, generating a "summary" of observed
     sub-spaces.

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

     The matrix of rotation/re-weighting of the original data set, 
     down-weighting the subspaces with more missing values. This matrix
     is useful to obtain estimates of the mean (and variance, in the
     future) still unbiased  in the presence of lost values (only of
     type MAR, stricly-speaking, but anyway useful for any type of
     missing value, when used with care). This matrix is  the Fisher
     Information in the presence of missing values.

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

     No missing policy is given by the routine itself. Its treatment of
     missing values depends on the "has" argument.

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

     K.Gerald v.d. Boogaart <URL: http://www.stat.boogaart.de>, Raimon
     Tolosana-Delgado

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

     Boogaart, K.G. v.d., R. Tolosana-Delgado, M. Bren (2006) Concepts
     for handling of zeros and missing values in compositional data, in
     E. Pirard (ed.) (2006)Proccedings of the IAMG'2006 Annual
     Conference on "Quantitative Geology from multiple sources",
     September 2006, Liege, Belgium, S07-01, 4pages, <URL:
     http://www.math-inf.uni-greifswald.de/~boogaart/Publications/iamg0
     6_s07_01.pdf>

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

     'missingProjector', 'clr','rcomp', 'aplus', 'princomp.acomp', 
     'plot.acomp', 'boxplot.acomp', 'barplot.acomp', 'mean.acomp',
     'var.acomp', 'variation.acomp', 'cov.acomp', 'msd'

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

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

