simpls                package:pls.pcr                R Documentation

_D_e _J_o_n_g'_s _S_I_M_P_L_S

_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="simpls"'. It is
     much faster than the NIPALS algorithm, especially when the number
     of X variables increases, but gives slightly different results in
     the case of multivariate Y. SIMPLS truly maximises the covariance
     criterion. According to De Jong, the standard PLS2 algorithms lie
     closer to ordinary least-squares regression where a precise fit is
     sought; SIMPLS lies closer to PCR with stable predictions.

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

     simpls(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, Chemometrics and Intelligent Laboratory Systems, 18
     (1993) 251-263.

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

     'kernelpls' 'mvr'

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

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

