%pls_transform  Partial Least Squares transformation%%  T = pls_transform(X,R)%  T = pls_transform(X,R,Options)%% INPUT%  X       [N -by- d_X]    the input  data matrix, N samples, d_X variables%  R       [d_X -by- nLV]  the transformation matrix: T_new = X_new*R%          (X_new here after preprocessing, preprocessing and un-preprocessing %          could be done automatically (than Options contains info about%          preprocessing) or manually); normally, R as a field of XRes%          output parameter of pls_train routine%%  Options structure returned by pls_train (if not supplied then will be%  no preprocessing performed)  %% OUTPUT%  T [N -by- nLV]   scores -- transformed data%% DESCRIPTION% Applys PLS (Partial Least Squares) regression model%% SEE ALSO% pls_train, pls_apply% Copyright: S.Verzakov, serguei@ph.tn.tudelft.nl% Faculty of Applied Sciences, Delft University of Technology% P.O. Box 5046, 2600 GA Delft, The Netherlands% $Id: pls_transform.m,v 1.1 2007/08/28 11:00:39 davidt Exp $function T = pls_transform(X,R,Options)if nargin < 3  Options  = [];endDefaultOptions.X_centering = [];DefaultOptions.Y_centering = [];DefaultOptions.X_scaling = [];DefaultOptions.Y_scaling = [];Options = pls_updstruct(DefaultOptions, Options);[N, d_X]    = size(X);[d_XR, nLv] = size(R);if d_X ~= d_XR  error('size(X,2) must be equal to size(R,1)');endT = pls_prepro(X, Options.X_centering, Options.X_scaling)*R;return;