I have a dataset with about 100+ features. I also have a small set of covariates.
I build an OLS linear model using statsmodels for y = x + C1 + C2 + C3 + C4 + ... + Cn for each covariate, and a feature x, and a dependent variable y.
I'm trying to perform hypothesis testing on the regression coefficients to test if the coefficients are equal to 0. I figured a t-test would be the appropriate approach to this, but I'm not quite sure how to go about implementing this in Python, using statsmodels.
I know, particularly, that I'd want to use http://www.statsmodels.org/devel/generated/statsmodels.regression.linear_model.RegressionResults.t_test.html#statsmodels.regression.linear_model.RegressionResults.t_test
But I am not certain I understand the r_matrix parameter. What could I provide to this? I did look at the examples but it is unclear to me.
Furthermore, I am not interested in doing the t-tests on the covariates themselves, but just the regression co-eff of x.
Any help appreciated!
Are you sure you don't want
statsmodels.regression.linear_model.OLS? This will perform a OLS regression, making available the parameter estimates and the corresponding p-values (and many other things).
from statsmodels.regression import linear_model from statsmodels.api import add_constant Y = [1,2,3,5,6,7,9] X = add_constant(range(len(Y))) model = linear_model.OLS(Y, X) results = model.fit() print(results.params) # [ 0.75 1.32142857] print(results.pvalues) # [ 2.00489220e-02 4.16826428e-06]
These p-values are from the t-tests of each fit parameter being equal to 0.
It seems like
RegressionResults.t_test would be useful for less conventional hypotheses.