general linear regression determine the parameters p_j (j=1,2,...,m) such that the function f(x) = sum_(i=1,...,m) p_j*f_j(x) is the best fit to the given values y_i = f(x_i) parameters:
return values:
- F is an 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 is a column vector of length n with the given values
- w is n column vector of of length n vector with the weights of data points
Caution: do NOT request y_var for large data sets, as a n by n matrix is generated
- p is the vector of length m with the estimated values of the parameters
- e_var is the estimated variance of the difference between fitted and measured values
- r is the weighted norm of the residual
- p_var is the estimated variance of the parameters p_j
- y_var is the estimated variance of the dependend variables
See also regress, leasqr, nonlin_curvefit, polyfit, wpolyfit, expfit.