[SciPy-User] How to fit data with errorbars
Tue Feb 16 22:44:59 CST 2010
On Tue, Feb 16, 2010 at 11:36 PM, <firstname.lastname@example.org> wrote:
> On Tue, Feb 16, 2010 at 11:18 PM, Nathaniel Smith <email@example.com> wrote:
>> On Tue, Feb 16, 2010 at 7:48 PM, <firstname.lastname@example.org> wrote:
>>> I didn't realize that it is a problem linear in parameters if the
>>> objective is to fit a polynomial.
>> I dunno, I'm just going off a quick glance at the documentation for
>> "polyfit", which the OP wanted to use in the first place :-).
>>> Essentially the same calculations are done in statsmodels.WLS plus
>>> you get additional results and test statistics.
>>> something like
>>> wls_results = scikits.statsmodels.WLS(Y, np.vander(X,2), weights=1/stddevs)
>>> example in statsmodels\examples\tut_ols_wls.py
>> Yeah, using real statistics code is always a better idea when
>> available. (Actually, I would use R for this. Don't tell anyone!)
> But it's more fun trying to figure out how to do it in python than how
> to do it in R or rpy.
> (But maybe not so much if I have to figure out both for doing the
> validation tests. I've seen that your incremental_ls also uses R only
> for validation and not for the heavy duty stuff.)
But this lets you be on both sides and try to convince people that R
isn't fun and you know by experience ;)
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