I performed a poisson regression in python and then did poisson.fit().summary to get the following output:
poisson.fit().summary()
<class 'statsmodels.iolib.summary.Summary'>
"""
Generalized Linear Model Regression Results
==============================================================================
Dep. Variable: Y No. Observations: 28
Model: GLM Df Residuals: 26
Model Family: Poisson Df Model: 1
Link Function: log Scale: 1.0000
Method: IRLS Log-Likelihood: -1.5464e+07
Date: Wed, 13 Feb 2019 Deviance: 3.0928e+07
Time: 19:54:52 Pearson chi2: 4.43e+07
No. Iterations: 6 Covariance Type: nonrobust
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
Intercept 12.8383 0.000 2.95e+04 0.000 12.837 12.839
x 0.0094 1.11e-05 848.646 0.000 0.009 0.009
==============================================================================
However, my question is, how can I extract the intercept and X value separately?
I tried poisson.params (as suggested in previous posts), but it doesn't seem to work for me. I get such errors
*** AttributeError: 'GLM' object has no attribute 'params'
I expect for each of the coefficients to be stored in separate variables:
Intercept = 12.8383
X = 0.0094
is this possible?
Doe without code, its hard to say why you are getting the behaviour you are seeing?
Here's a sample complete code that works.
import numpy as np
import pandas as pd
import statsmodels.api as sm
import statsmodels.formula.api as smf
df = pd.DataFrame(np.random.randint(100, size=(50,2)))
df.rename(columns={0:'X1', 1:'X2'}, inplace=True)
# GLM Model
model = smf.glm("X2 ~ X1", data=df, family= sm.families.Poisson()).fit()
print(model.summary())
print(model.params)
# Poisson Model
poisson = smf.poisson("X2 ~ X1", data=df).fit()
print (poisson.summary())
print (poisson.params)