[SciPy-user] polynomal regression
robert.kern at gmail.com
Mon Oct 16 13:27:51 CDT 2006
Travis Oliphant wrote:
> Christian Meesters wrote:
>>> It's linalg.lstsq.
>>> Are you looking for Total Least Squares ?
>>> AFAIK, it's not implemented yet.
>> Thanks, Nils.
>> Too bad. Well, back to Origin for me in this case ...
> Total Least Squares is pretty easy to implement using the SVD. Try
> your hand.
Total Least Squares can also be implemented as Orthogonal Distance Regression
(ODR). However, this is neither here nor there for Christian's original
question, which is how to find estimates of the uncertainties in the estimated
parameters. This has nothing to do with Total Least Squares.
A. M. Archibald was closest, but got the name of the function wrong. In short,
the only functions currently implemented that give uncertainties on estimated
parameters are the non-linear least squares implementations. They aren't too bad
for linear problems, either. The function A. M. was thinking of was
scipy.optimize.leastsq() with full_output=True. An alternative is the
scipy.sandbox.odr package, which can do ordinary least squares or ODR (of which
Total Least Squares is the linear special case) and provide uncertainty
estimates for both algorithms.
"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
More information about the SciPy-user