

corresp(MASS)                                R Documentation

_S_i_m_p_l_e _C_o_r_r_e_s_p_o_n_d_e_n_c_e _A_n_a_l_y_s_i_s

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

     Find the principal canonical correlation and corre-
     sponding row- and column-scores from a correspondence
     analysis of a two-way contingency table.

_U_s_a_g_e_:

     corresp(tabl, nf=1, ...)

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

    tabl: The function is generic, accepting various forms
          of the principal argument for specifying a two-way
          frequency table.  Currently accepted forms are
          matrices, data frames (coerced to frequency
          tables), objects of class `crosstabs' and formulae
          of the form `~ F1 + F2', where `F1' and `F2' are
          factors.

      nf: The number of factors to be computed. Note that
          although 1 is the most usual, one school of
          thought takes the first two singular vectors for a
          sort of biplot.

     ...: If the principal argument is a formula, a data
          frame may be specified as well from which vari-
          ables in the formula are preferentially satisfied.

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

     See the reference.  The `plot' method produces a graph-
     ical representation of the table if `nf=1', with the
     areas of circles representing the numbers of points.
     If `nf' is two or more the `biplot' method is called,
     which plots the second and third columns of the matri-
     ces `A = Dr^(-1/2}) U L' and `B = Dc^(-1/2) U V' where
     the singular value decomposition is `U L V'.  Thus the
     x-axis is the canonical correlation times the row and
     column scores. Although this is called a biplot, it
     does not have any useful inner product relationship
     between the row and column scores.  Think of this as an
     equally-scaled plot with two unrelated sets of labels.

_V_a_l_u_e_:

     An list object of class `correspondence' for which
     `print', `plot' and `biplot' methods are supplied.  The
     main components are the canonical correlation(s) and
     the row and column scores.

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

     Venables  Ripley (1999), chapter 11.

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

     `svd', `princomp'

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

     data(quine)
     ct <- corresp(~ Age + Eth, data=quine)
     ct
     plot(ct)

     data(caith)
     library(mva)
     corresp(caith)
     biplot(corresp(caith, nf=2))

