brede_mat_nls_est - Non-negative least squares estimation
B = brede_mat_nls_est(X, Y, 'PropertyName', 'PropertyValue') )
Input: X Independent variable as 'mat' structure or matrix
Y Dependent variable as 'mat' structure or matrix
Property: Binit Starting guess for B regression parameters
Info [ {0} | An integer ] Amount of debug information
Output: B Estimated parameters in 'mat' structure
Non-negative least squares estimation. The model is:
Y = X*B + U
where U is Gaussian distributed and B is estimated to be
non-negative.
Presently an estimation with iterations similar to non-negative
matrix factorizations is implemented.
Example:
% A small Rasmus Bro example. First b should be 0.65 other two
% parameters zero
X = [ 73 71 52 ; 87 74 46 ; 72 2 7 ; 80 89 71 ];
y = [ 49 67 68 20 ]';
Bols = brede_mat_glm_est(X,y); Bols.matrix
Bnls = brede_mat_nls_est(X,y); Bnls.matrix
See also BREDE, BREDE_MAT, BREDE_MAT_GLM_EST, BREDE_MAT_NMF.
$Id: brede_mat_nls_est.m,v 1.3 2008/06/05 18:17:16 fn Exp $