Evelien wrote:
Thanks Robert for your reply. I must say I am kind of disappointed if that is the only solution. I thought that such a standard problem as least squares fitting, would always give you an estimation of the error bars, without having to look up how you can convert a covariance matrix into error bars...
If you want an all-singing, all-dancing statistics-oriented nonlinear least-squares interface, you can use scipy.odr. The numerical algorithm in leastsq() outputs a covariance matrix naturally. Interpreting that to give you statistical error bars requires some care and judgment. scipy.optimize is not a statistical package.
-- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco -- http://mail.python.org/mailman/listinfo/python-list
