How to extract the regression coefficient from statsmodels.api?

Question

 result = sm.OLS(gold_lookback, silver_lookback ).fit()

After I get the result, how can I get the coefficient and the constant?

In other words, if y = ax + c how to get the values a and c?

Solution 1

You can use the params property of a fitted model to get the coefficients.

For example, the following code:

import statsmodels.api as sm
import numpy as np
np.random.seed(1)
X = sm.add_constant(np.arange(100))
y = np.dot(X, [1,2]) + np.random.normal(size=100)
result = sm.OLS(y, X).fit()
print(result.params)

will print you a numpy array [ 0.89516052 2.00334187] - estimates of intercept and slope respectively.

If you want more information, you can use the object result.summary() that contains 3 detailed tables with model description.

Solution 2

Cribbing from this answer Converting statsmodels summary object to Pandas Dataframe, it seems that the result.summary() is a set of tables, which you can export as html and then use Pandas to convert to a dataframe, which will allow you to directly index the values you want.

So, for your case (putting the answer from the above link into one line):

df = pd.read_html(result.summary().tables[1].as_html(),header=0,index_col=0)[0]

And then

a=df['coef'].values[1]
c=df['coef'].values[0]

Solution 3

Adding up details on @IdiotTom answer.

You may use:

head = pd.read_html(res.summary2().as_html())[0]
body = pd.read_html(res.summary2().as_html())[1]

Not as nice, but the info is there.