brede_mat_pcr_est - Principal component regression estimation
B = brede_mat_pcr_est(X, Y, 'PropertyName', 'PropertyValue') )
Input: X Independent variable as 'mat' structure or matrix
Y Dependent variable as 'mat' structure or matrix
Property: Components [ An integer ] Number of principal
components used.
Info [ {0} | An integer ] Amount of debug information
Output: B Estimated parameters in 'mat' structure
Principal component regression estimation. The model is:
Y = X*B + U
where U is Gaussian distributed and B is estimated based on a
principal component analysis of X.
Presently only the highest variance principal components are
used in the regression. If the number of components are not given
then it is set by a rule of thumb as the square root of half the
minimum of the dimension of either X or Y.
See also BREDE, BREDE_MAT, BREDE_MAT_GLM_EST, BREDE_MAT_NLS_EST,
BREDE_MAT_NMF.
$Id: brede_mat_pcr_est.m,v 1.1 2008/04/16 22:21:22 fn Exp $