general linear regression
[p,y_var,r,p_var]=LinearRegression(F,y)
[p,y_var,r,p_var]=LinearRegression(F,y,weight)
determine the parameters p_j (j=1,2,...,m) such that the function
f(x) = sum_(i=1,...,m) p_j*f_j(x) fits as good as possible to the
given values y_i = f(x_i)
parameters
F n*m matrix with the values of the basis functions at the support points
in column j give the values of f_j at the points x_i (i=1,2,...,n)
y n column vector of given values
weight n column vector of given weights
return values
p m vector with the estimated values of the parameters
y_var estimated variance of the error
r weighted norm of residual
p_var estimated variance of the parameters p_j