%PLS_TRANSFORM Partial Least Squares transformation
%
% T = PLS_TRANSFORM(X,R)
% T = PLS_TRANSFORM(X,R,OPTIONS)
%
% INPUT
% X input data matrix size [N -by- d_X], N samples, d_X variables
% R transformation matrix size [d_X -by- nLV]: 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 (<a href="http://37steps.com/prtools">PRTools Guide</a>)
% 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 = [];
end
DefaultOptions.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)');
end
T = pls_prepro(X, Options.X_centering, Options.X_scaling)*R;
return;