[SciPy-user] polynomal regression
Nils Wagner
nwagner at iam.uni-stuttgart.de
Mon Oct 16 13:35:31 EDT 2006
On Mon, 16 Oct 2006 19:16:22 +0200
Christian Meesters <meesters at uni-mainz.de> wrote:
> On Monday 16 October 2006 15:27, A. M. Archibald wrote:
>>
>> numpy's least-squares fitting procedure will do just
>>what you're
>> asking for. I think it's called numpy.lstsqr (but it may
>>have a
>> different number of ss and ts). What you really want is
>>probably the
>> full covariance matrix, and I think it can give that to
>>you.
> Thanks, but I'm not sure what you mean: In my numpy
>there is no lstsqr in the
> namespace, if I do 'from numpy import *' (fresh download
>from svn - I needed
> an upgrade anyway).
>
> Perhaps my English prevents me from being understood
>here ... Another attempt:
> Currently what I'm using is scipy.linalg.lstsq for
>linear regressions (mostly)
> and scipy.polyfit in other cases. For calculating
>'errors' / 'deviations' /
> 'uncertainties' of the calculated coefficients the
>function needs the input
> with errors in x & y, right? Is there any such function
>in scipy?
>
> Christian
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It's linalg.lstsq.
Are you looking for Total Least Squares ?
AFAIK, it's not implemented yet.
Nils
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