Generalized Linear Models (Formula)

This notebook illustrates how you can use R-style formulas to fit Generalized Linear Models.

To begin, we load the Star98 dataset and we construct a formula and pre-process the data:

from __future__ import print_function
import statsmodels.api as sm
import statsmodels.formula.api as smf
star98 = sm.datasets.star98.load_pandas().data
formula = 'SUCCESS ~ LOWINC + PERASIAN + PERBLACK + PERHISP + PCTCHRT + \
           PCTYRRND + PERMINTE*AVYRSEXP*AVSALK + PERSPENK*PTRATIO*PCTAF'
dta = star98[['NABOVE', 'NBELOW', 'LOWINC', 'PERASIAN', 'PERBLACK', 'PERHISP',
              'PCTCHRT', 'PCTYRRND', 'PERMINTE', 'AVYRSEXP', 'AVSALK',
              'PERSPENK', 'PTRATIO', 'PCTAF']].copy()
endog = dta['NABOVE'] / (dta['NABOVE'] + dta.pop('NBELOW'))
del dta['NABOVE']
dta['SUCCESS'] = endog

Then, we fit the GLM model:

mod1 = smf.glm(formula=formula, data=dta, family=sm.families.Binomial()).fit()
mod1.summary()

Generalized Linear Model Regression Results

Dep. Variable:	SUCCESS	No. Observations:	303
Model:	GLM	Df Residuals:	282
Model Family:	Binomial	Df Model:	20
Link Function:	logit	Scale:	1.0000
Method:	IRLS	Log-Likelihood:	-127.33
Date:	Mon, 14 May 2018	Deviance:	8.5477
Time:	21:44:55	Pearson chi2:	8.48
No. Iterations:	4	Covariance Type:	nonrobust

	coef	std err	z	P>|z|	[0.025	0.975]
Intercept	0.4037	25.036	0.016	0.987	-48.665	49.472
LOWINC	-0.0204	0.010	-1.982	0.048	-0.041	-0.000
PERASIAN	0.0159	0.017	0.910	0.363	-0.018	0.050
PERBLACK	-0.0198	0.020	-1.004	0.316	-0.058	0.019
PERHISP	-0.0096	0.010	-0.951	0.341	-0.029	0.010
PCTCHRT	-0.0022	0.022	-0.103	0.918	-0.045	0.040
PCTYRRND	-0.0022	0.006	-0.348	0.728	-0.014	0.010
PERMINTE	0.1068	0.787	0.136	0.892	-1.436	1.650
AVYRSEXP	-0.0411	1.176	-0.035	0.972	-2.346	2.264
PERMINTE:AVYRSEXP	-0.0031	0.054	-0.057	0.954	-0.108	0.102
AVSALK	0.0131	0.295	0.044	0.965	-0.566	0.592
PERMINTE:AVSALK	-0.0019	0.013	-0.145	0.885	-0.028	0.024
AVYRSEXP:AVSALK	0.0008	0.020	0.038	0.970	-0.039	0.041
PERMINTE:AVYRSEXP:AVSALK	5.978e-05	0.001	0.068	0.946	-0.002	0.002
PERSPENK	-0.3097	4.233	-0.073	0.942	-8.606	7.987
PTRATIO	0.0096	0.919	0.010	0.992	-1.792	1.811
PERSPENK:PTRATIO	0.0066	0.206	0.032	0.974	-0.397	0.410
PCTAF	-0.0143	0.474	-0.030	0.976	-0.944	0.916
PERSPENK:PCTAF	0.0105	0.098	0.107	0.915	-0.182	0.203
PTRATIO:PCTAF	-0.0001	0.022	-0.005	0.996	-0.044	0.044
PERSPENK:PTRATIO:PCTAF	-0.0002	0.005	-0.051	0.959	-0.010	0.009

Finally, we define a function to operate customized data transformation using the formula framework:

def double_it(x):
    return 2 * x
formula = 'SUCCESS ~ double_it(LOWINC) + PERASIAN + PERBLACK + PERHISP + PCTCHRT + \
           PCTYRRND + PERMINTE*AVYRSEXP*AVSALK + PERSPENK*PTRATIO*PCTAF'
mod2 = smf.glm(formula=formula, data=dta, family=sm.families.Binomial()).fit()
mod2.summary()

Generalized Linear Model Regression Results

Dep. Variable:	SUCCESS	No. Observations:	303
Model:	GLM	Df Residuals:	282
Model Family:	Binomial	Df Model:	20
Link Function:	logit	Scale:	1.0000
Method:	IRLS	Log-Likelihood:	-127.33
Date:	Mon, 14 May 2018	Deviance:	8.5477
Time:	21:44:55	Pearson chi2:	8.48
No. Iterations:	4	Covariance Type:	nonrobust

	coef	std err	z	P>|z|	[0.025	0.975]
Intercept	0.4037	25.036	0.016	0.987	-48.665	49.472
double_it(LOWINC)	-0.0102	0.005	-1.982	0.048	-0.020	-0.000
PERASIAN	0.0159	0.017	0.910	0.363	-0.018	0.050
PERBLACK	-0.0198	0.020	-1.004	0.316	-0.058	0.019
PERHISP	-0.0096	0.010	-0.951	0.341	-0.029	0.010
PCTCHRT	-0.0022	0.022	-0.103	0.918	-0.045	0.040
PCTYRRND	-0.0022	0.006	-0.348	0.728	-0.014	0.010
PERMINTE	0.1068	0.787	0.136	0.892	-1.436	1.650
AVYRSEXP	-0.0411	1.176	-0.035	0.972	-2.346	2.264
PERMINTE:AVYRSEXP	-0.0031	0.054	-0.057	0.954	-0.108	0.102
AVSALK	0.0131	0.295	0.044	0.965	-0.566	0.592
PERMINTE:AVSALK	-0.0019	0.013	-0.145	0.885	-0.028	0.024
AVYRSEXP:AVSALK	0.0008	0.020	0.038	0.970	-0.039	0.041
PERMINTE:AVYRSEXP:AVSALK	5.978e-05	0.001	0.068	0.946	-0.002	0.002
PERSPENK	-0.3097	4.233	-0.073	0.942	-8.606	7.987
PTRATIO	0.0096	0.919	0.010	0.992	-1.792	1.811
PERSPENK:PTRATIO	0.0066	0.206	0.032	0.974	-0.397	0.410
PCTAF	-0.0143	0.474	-0.030	0.976	-0.944	0.916
PERSPENK:PCTAF	0.0105	0.098	0.107	0.915	-0.182	0.203
PTRATIO:PCTAF	-0.0001	0.022	-0.005	0.996	-0.044	0.044
PERSPENK:PTRATIO:PCTAF	-0.0002	0.005	-0.051	0.959	-0.010	0.009

As expected, the coefficient for double_it(LOWINC) in the second model is half the size of the LOWINC coefficient from the first model:

print(mod1.params[1])
print(mod2.params[1] * 2)

-0.020395987154757125
-0.020395987154757402