Capturing high multi-collinearity in statsmodels

Question

Say I fit a model in statsmodels

mod = smf.ols('dependent ~ first_category + second_category + other', data=df).fit() 

When I do mod.summary() I may see the following:

Warnings: [1] The condition number is large, 1.59e+05. This might indicate that there are strong multicollinearity or other numerical problems. 

Sometimes the warning is different (e.g. based on eigenvalues of the design matrix). How can I capture high-multi-collinearity conditions in a variable? Is this warning stored somewhere in the model object?

Also, where can I find a description of the fields in summary()?

Answer 1

You can detect high-multi-collinearity by inspecting the eigen values of correlation matrix. A very low eigen value shows that the data are collinear, and the corresponding eigen vector shows which variables are collinear.

If there is no collinearity in the data, you would expect that none of the eigen values are close to zero:

>>> xs = np.random.randn(100, 5)      # independent variables >>> corr = np.corrcoef(xs, rowvar=0)  # correlation matrix >>> w, v = np.linalg.eig(corr)        # eigen values & eigen vectors >>> w array([ 1.256 ,  1.1937,  0.7273,  0.9516,  0.8714]) 

However, if say x[4] - 2 * x[0] - 3 * x[2] = 0, then

>>> noise = np.random.randn(100)                      # white noise >>> xs[:,4] = 2 * xs[:,0] + 3 * xs[:,2] + .5 * noise  # collinearity >>> corr = np.corrcoef(xs, rowvar=0) >>> w, v = np.linalg.eig(corr) >>> w array([ 0.0083,  1.9569,  1.1687,  0.8681,  0.9981]) 

one of the eigen values (here the very first one), is close to zero. The corresponding eigen vector is:

>>> v[:,0] array([-0.4077,  0.0059, -0.5886,  0.0018,  0.6981]) 

Ignoring almost zero coefficients, above basically says x[0], x[2] and x[4] are colinear (as expected). If one standardizes xs values and multiplies by this eigen vector, the result will hover around zero with small variance:

>>> std_xs = (xs - xs.mean(axis=0)) / xs.std(axis=0)  # standardized values >>> ys = std_xs.dot(v[:,0]) >>> ys.mean(), ys.var() (0, 0.0083) 

Note that ys.var() is basically the eigen value which was close to zero.

So, in order to capture high multi-linearity, look at the eigen values of correlation matrix.