flou                 package:multiv                 R Documentation

_F_u_z_z_y _C_o_d_i_n_g (_3-_W_a_y)

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

     Simple fuzzy, or piecewise linear, coding of a vector: each value
     in the  vector is replaced by a 1 (if it is above or equal to the
     67th quantile), by a 0 (if it is below or equal to the 33rd
     quantile), and by a linearly interpolated value between 0 and 1
     (if it lies between the 33rd and 67th quantiles).

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

     flou(a)

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

       a: real-valued vector, with no missing values. 

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

     matrix of `length(a)' rows, and two columns.  The first column
     contains the fuzzily coded values of `a', and the second column
     contains their  complements.  Hence each row of this returned
     matrix necessarily sums to 1.

_B_a_c_k_g_r_o_u_n_d:

     This form of coding is suitable for a subsequent correspondence
     analysis. When all variable have been fuzzily (or logically)
     coded, the row masses  (proportional to the row sums) are
     identical.

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

     J.-P. Benzecri Correspondence Analysis Handbook Marcel Dekker,
     Basel, 1992.

     F.J. Gallego, Codage flou en analyse des correspondances, Les
     Cahiers de l'Analyse des Donnees vol. VII, 413-430, 1982

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

     `logique', `ca', `supplr', `supplc'.

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

     data(USArrests)
     USArrests <- as.matrix(USArrests)
     # Fuzzy coding of input variables
     a.fuzz <- flou(USArrests[,1])
     b.fuzz <- flou(USArrests[,2])
     c.fuzz <- flou(USArrests[,3])
     d.fuzz <- flou(USArrests[,4])
     newdata <- cbind(a.fuzz, b.fuzz, c.fuzz, d.fuzz)
     ca.newdata <- ca(newdata)

