I believe I'm bound to python.
In terms of forcing the regression through the origin, the purpose is
partly for visualization but it also should fit the data. It would
not make sense to model the data with an initial value other than 0.
On Jun 16, 2008, at 4:33 PM, Simon Palmer wrote:
At the risk of uttering a heresy, are you bound to Python for this?
I bet you could find a C library that will work well, plus it is not
a hard algorithm to code yourself. I am pretty sure I have used a
numerical recipes algorithm for regression in my distant past.
Also I can't help thinking the idea of forcing your regression fit
through the origin is a of a bit strange thing to do. Do you want
it to pass through the origin for visualisation purposes? What if
the origin is not a statistically valid place for the regression fit
to pass through?
On Mon, Jun 16, 2008 at 9:25 PM, Charles R Harris <[EMAIL PROTECTED]
> wrote:
On Mon, Jun 16, 2008 at 1:47 PM, Chandler Latour
<[EMAIL PROTECTED]> wrote:
Yes, exactly what I meant.
Polyfit just fits polynomials, there is no way of fixing the
constant to zero. Your best bet is to use linalg.lstsq directly to
fit the function you want.
Chuck
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