Multi-variate regression using NumPy in Python?

Question

Is it possible to perform multi-variate regression in Python using NumPy?

The documentation here suggests that it is, but I cannot find any more details on the topic.

Answer 1

You might want to look into the scipy.optimize.leastsq function. It's rather complicated but I seem to remember that being the thing I would look to when I wanted to do a multivariate regression. (It's been a while so I could be misremembering)

Answer 2

The webpage that you linked to mentions numpy.linalg.lstsq to find the vector x which minimizes |b - Ax|. Here is a little example of how it can be used:

First we setup some "random" data:

import numpy as np
c1,c2 = 5.0,2.0
x = np.arange(1,11)/10.0
y = c1*np.exp(-x)+c2*x
b = y + 0.01*max(y)*np.random.randn(len(y))
A = np.column_stack((np.exp(-x),x))
c,resid,rank,sigma = np.linalg.lstsq(A,b)
print(c)
# [ 4.96579654  2.03913202]

Answer 3

Yes, download this ( http://www.scipy.org/Cookbook/OLS?action=AttachFile&do=get&target=ols.0.2.py ) from http://www.scipy.org/Cookbook/OLS

Or you can install R and a python-R link. R can do anything.