def __init__(self):
#from results.results_discrete import Anes
data = sm.datasets.anes96.load()
exog = data.exog
exog[:,0] = np.log(exog[:,0] + .1)
exog = np.column_stack((exog[:,0],exog[:,2],
exog[:,5:8]))
exog = sm.add_constant(exog)
self.mod = sm.MNLogit(data.endog, exog)
def loglikeflat(self, params):
#reshapes flattened params
return self.loglike(params.reshape(6,6))
self.mod.loglike = loglikeflat #need instance method
self.params = [np.ones((6,6))]
"""Example: scikits.statsmodels.discretemod
"""
import numpy as np
import scikits.statsmodels.api as sm
anes_data = sm.datasets.anes96.load()
anes_exog = anes_data.exog
anes_exog[:, 0] = np.log(anes_exog[:, 0] + .1)
anes_exog = np.column_stack((anes_exog[:, 0], anes_exog[:, 2], anes_exog[:,
5:8]))
anes_exog = sm.add_constant(anes_exog, prepend=False)
mlogit_mod = sm.MNLogit(anes_data.endog, anes_exog)
mlogit_res = mlogit_mod.fit()
# The default method for the fit is Newton-Raphson
# However, you can use other solvers
mlogit_res = mlogit_mod.fit(method='bfgs', maxiter=100)
# The below needs a lot of iterations to get it right?
#TODO: Add a technical note on algorithms
#mlogit_res = mlogit_mod.fit(method='ncg') # this takes forever
from scikits.statsmodels.iolib.summary import (summary_params_2d,
summary_params_2dflat)
exog_names = [anes_data.exog_name[i] for i in [0, 2] + range(5, 8)] + ['const']
endog_names = [
anes_data.endog_name + '_%d' % i
for i in np.unique(mlogit_res.model.endog)[1:]
]
print '\n\nMultinomial'
print 'cs', numdiff.approx_fprime_cs(test_params, score)
#print 'fd', numdiff.approx_hess(test_params, loglike, epsilon) #TODO: bug
'''
Traceback (most recent call last):
File "C:\Josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\sandbox\regression\test_numdiff.py", line 74, in <module>
print 'fd', numdiff.approx_hess(test_params, loglike, epsilon)
File "C:\Josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\sandbox\regression\numdiff.py", line 118, in approx_hess
xh = x + h
TypeError: can only concatenate list (not "float") to list
'''
hesscs = numdiff.approx_hess_cs(test_params, loglike)
print 'cs', hesscs
print maxabs(hess(test_params), hesscs)
data = sm.datasets.anes96.load()
exog = data.exog
exog[:,0] = np.log(exog[:,0] + .1)
exog = np.column_stack((exog[:,0],exog[:,2],
exog[:,5:8]))
exog = sm.add_constant(exog)
res1 = sm.MNLogit(data.endog, exog).fit(method="newton", disp=0)
datap = sm.datasets.randhie.load()
nobs = len(datap.endog)
exogp = sm.add_constant(datap.exog.view(float).reshape(nobs,-1))
modp = sm.Poisson(datap.endog, exogp)
resp = modp.fit(method='newton', disp=0)