[SciPy-user] polynomal regression
meesters at uni-mainz.de
Mon Oct 16 12:16:22 CDT 2006
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?
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