rplus              package:compositions              R Documentation

_A_m_o_u_n_t_s _i._e. _p_o_s_i_t_i_v_e _n_u_m_b_e_r_s _a_n_a_l_y_s_e_d _a_s _o_b_j_e_c_t_s _o_f _t_h_e _r_e_a_l _v_e_c_t_o_r _s_p_a_c_e

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

     A class to analyse positive amounts in a classical
     (non-logarithmic) framework.

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

               rplus(X,parts=1:NCOL(oneOrDataset(X)),total=NA)
               

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

       X: vector or dataset of positive numbers considered as amounts

   parts: vector containing the indices  xor names of the columns to be
          used

   total: a numeric vectors giving the total amount of each dataset. 

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

     Many multivariate datasets essentially describe amounts of D
     different parts in a whole. When the whole is large in relation to
     the considered parts, such that they do not exclude each other,
     and when the total amount of each componenten is actually
     determined by the phenomenon under investigation and not by
     sampling artifacts (such as dilution or sample preparation) then
     the parts can be treated as amounts rather than as a composition
     (cf. 'rcomp', 'aplus'). 
      In principle, amounts are just real-scaled numbers with the
     single restriction that they are nonnegative. Thus they can be
     analysed by any multivariate analysis method. This class provides
     a simple access interface to do so. It tries to keep in mind the
     positivity property of amounts and the special point zero. However
     there are strong arguments why an analyis based on log-scale might
     be much more adapted to the problem. This log-approach is provided
     by the class 'aplus'. 

     The classes rcomp, acomp, aplus, and rplus are designed in a
     fashion as similar as possible in order to allow direct comparison
     between results obtained   by the different approaches. In
     particular, the aplus logistic transform 'ilt' is mirrored by the
     simple identity transform 'iit'. In terms of computer science,
     this identity mapping is actually mapping an object of type
     "rplus" to a class-less datamatrix.

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

     a vector of class '"rplus"' representing a vector of amounts or a
     matrix of class '"rplus"' representing multiple vectors of
     amounts, by rows.

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

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

     'iit','rcomp', 'aplus', 'princomp.rplus',  'plot.rplus',
     'boxplot.rplus', 'barplot.rplus', 'mean.rplus', 'var.rplus',
     'variation.rplus', 'cov.rplus', 'msd'

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

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

