Multiple linear regression python

Question

I use multiple linear regression, I have one dependant variable (var) and several independant variables (varM1, varM2,...) I use this code in python:

z=array([varM1, varM2, varM3],int32)
n=max(shape(var))
X = vstack([np.ones(n), z]).T
a = np.linalg.lstsq(X, var)[0]

How can I calculate the R-square change for every variable with python ? I would like to see how the regression changes if I add or remove predictor variables.

it is the dependant variable. in my case, it is a concentration — user2050187
– user2050187, Commented Jul 22, 2013 at 6:32

Saullo G. P. Castro · Accepted Answer · 2013-08-04 15:14:09Z

2

If the broadcasting is correct along the way the following should give you the correlation coefficient R:

R = np.sqrt( ((var - X.dot(a))**2).sum() )

One full example of multi-variate regression:

import numpy as np

x1 = np.array([1,2,3,4,5,6])
x2 = np.array([1,1.5,2,2.5,3.5,6])
x3 = np.array([6,5,4,3,2,1])
y = np.random.random(6)

nvar = 3
one = np.ones(x1.shape)
A = np.vstack((x1,one,x2,one,x3,one)).T.reshape(nvar,x1.shape[0],2)

for i,Ai in enumerate(A):
    a = np.linalg.lstsq(Ai,y)[0]
    R = np.sqrt( ((y - Ai.dot(a))**2).sum() )
    print R

edited Aug 4, 2013 at 15:14

answered Aug 4, 2013 at 14:24

Saullo G. P. Castro

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3 Comments

rjurney Over a year ago

There is no such variable shape, so this implementation does not run.

Saullo G. P. Castro Over a year ago

@rjurney it does run. shape actually is not a variable but an attribute of the ndarray object... check if you have such an object...

ejectamenta Over a year ago

shape tells you the size of the array

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