27.02.2013 16:40, David Pine kirjoitti: [clip] > 2. I am sorry but I don't understand your response. The matrix Vbase > in the code is already the covariance matrix, _before_ it is scaled by > fac. Scaling it by fac and returning Vbase*fac as the covariance > matrix is wrong, at least according to the references I know, including > "Numerical Recipes", by Press et al, "Data Reduction and Error Analysis > for the Physical Sciences" by Bevington, both standard works.
The covariance matrix is (A^T A)^-1 only if the data is weighed by its standard errors prior to lstsq. Polyfit estimates the standard errors from the fit itself, which results in the `fac` multiplication. This is apparently what some people expect. The way the weight parameters work is however confusing, as they are w[i]=sigma_estimate/sigma[i], rather than being absolute errors. Anyway, as Josef noted, it's the same problem that curve_fit in Scipy had and probably the same fix needs to be done here. -- Pauli Virtanen _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion