kernelpls              package:pls.pcr              R Documentation

_K_e_r_n_e_l _P_L_S (_D_a_y_a_l _a_n_d _M_a_c_G_r_e_g_o_r)

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

     This function should not be called directly, but through the
     generic 'pls' function with the argument 'method="kernel"'
     (default). Kernel PLS is particularly efficient  when the number
     of objects is (much) larger than the number of variables. The
     results are equal to the NIPALS algorithm. Several different forms
     of kernel PLS have been described in literature, e.g. by De Jong
     and Ter Braak, and two algorithms by Dayal and MacGregor. This
     function implements the fastest of the latter, not calculating the
     crossproduct matrix of X. In the Dyal & MacGregor paper, this is
     'algorithm 1'. ('kernelpls').

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

     kernelpls(X, Y, ncomp, newX)

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

       X: a matrix of observations. 'NA's and 'Inf's are not allowed.

       Y: a vector or matrix of responses. 'NA's and 'Inf's are not
          allowed.

   ncomp: the number of latent variables to be used in the modelling.
          The default number of latent variables is the smallest of the
          number of objects or the number of variables in 'X'.

    newX: optional new measurements: if present, predictions will be
          made for them.

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

     A list containing the following components is returned: 

       B: an array of regression coefficients for all items in 'ncomp'.
          The dimensions of 'B' are 'c(nvar, npred, length(ncomp))'
          with 'nvar' the number of 'X' variables and 'npred' the
          number of variables to be predicted in 'Y'.

XvarExpl: Fraction of X-variance explained.

YvarExpl: Fraction of Y-variance explained (one column, even for
          multiple Y). 

   Ypred: predictions for 'newX' (if asked).

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

     S. de Jong and C.J.F. ter Braak, J. Chemometrics, 8 (1994) 169-174
     B.S. Dayal and J. MacGregor, J. Chemometrics, 11 (1997) 73-85

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

     'pls' 'simpls' 'mvr'

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

     data(NIR)
     attach(NIR)
     NIR.kernelpls <- mvr(Xtrain, Ytrain, 1:6, validation="CV", method="kernelPLS")

