np.random.seed(98765678)
nobs = 1000
rvs = np.random.randn(nobs,6)
data_exog = rvs
data_exog = sm.add_constant(data_exog, prepend=False)
xbeta = 1 + 0.1*rvs.sum(1)
data_endog = np.random.poisson(np.exp(xbeta))
#print(data_endog
modp = MyPoisson(data_endog, data_exog)
resp = modp.fit()
print(resp.params)
print(resp.bse)
from statsmodels.discretemod import Poisson
resdp = Poisson(data_endog, data_exog).fit()
print('\ncompare with discretemod')
print('compare params')
print(resdp.params - resp.params)
print('compare bse')
print(resdp.bse - resp.bse)
gmlp = sm.GLM(data_endog, data_exog, family=sm.families.Poisson())
resgp = gmlp.fit()
''' this creates a warning, bug bse is double defined ???
c:\josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\decorators.py:105: CacheWriteWarning: The attribute 'bse' cannot be overwritten
warnings.warn(errmsg, CacheWriteWarning)
'''
print('\ncompare with GLM')
print('compare params')
print(resgp.params - resp.params)
np.random.seed(98765678)
nobs = 1000
rvs = np.random.randn(nobs,6)
data_exog = rvs
data_exog = sm.add_constant(data_exog, prepend=False)
xbeta = 1 + 0.1*rvs.sum(1)
data_endog = np.random.poisson(np.exp(xbeta))
#print(data_endog
modp = MyPoisson(data_endog, data_exog)
resp = modp.fit()
print(resp.params)
print(resp.bse)
from statsmodels.discretemod import Poisson
resdp = Poisson(data_endog, data_exog).fit()
print('\ncompare with discretemod')
print('compare params')
print(resdp.params - resp.params)
print('compare bse')
print(resdp.bse - resp.bse)
gmlp = sm.GLM(data_endog, data_exog, family=sm.families.Poisson())
resgp = gmlp.fit()
''' this creates a warning, bug bse is double defined ???
c:\josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\decorators.py:105: CacheWriteWarning: The attribute 'bse' cannot be overwritten
warnings.warn(errmsg, CacheWriteWarning)
'''
print('\ncompare with GLM')
print('compare params')
print(resgp.params - resp.params)