However, after a quick spruce-up to current scipy and numpy notation
(which I could post here if it's useful) it seems, from a quick test,
to perform as advertised.
Here begin my questions. I have looked through the scipy
documentation, and can't see any other routines that do the same task,
apart, perhaps from the odr module or using routines from the lapack
or blas libraries. These latter options, however, I don't know
anything about, and there aren't readily applicable examples floating
around to base my effort on.
(1) Have I missed some multilinear regression routine directly
implemented in scipy? If yes, how can we improve the documentation so
the next person can find it more easily.
(2) If there isn't an equivalent routine, would it be useful to
include this one? It could perhaps go in scipy.linalg.
Thanks for your thoughts,
AJC McMorland, PhD candidate
Physiology, University of Auckland
On Apr 7, 2008, at 11:54 PM, Angus McMorland wrote:
> (1) Have I missed some multilinear regression routine directly
> implemented in scipy? If yes, how can we improve the documentation so
> the next person can find it more easily.
scipy.stats.models in the current SVN branch has a bunch of modelling
tools for least-squares estimation, robust estimation, and some other
statistical methods. It's more object oriented than the matlab
equivalent, but you can do simple multivariate regressions without
too much work:
>>> import scipy.stats.models.regression as R
>>> from numpy.random import standard_normal as W
>>> X = W((40,10))
>>> Y = W((40,))
>>> model = R.OLSModel(design=X)
>>> result = model.fit(Y)
array([-0.2296546 , -0.15835343, -0.07127199, 0.02934717, 0.15778939,
0.14087653, 0.09279021, -0.03412604, -0.28726236,