def setupClass(cls):
from results.results_discrete import Anes
data = sm.datasets.anes96.load()
cls.data = data
exog = data.exog
exog = sm.add_constant(exog, prepend=False)
cls.res1 = MNLogit(data.endog, exog).fit(method="newton", disp=0)
res2 = Anes()
res2.mnlogit_basezero()
cls.res2 = res2
def setupClass(cls):
from results.results_discrete import Anes
data = sm.datasets.anes96.load()
cls.data = data
exog = data.exog
exog = sm.add_constant(exog, prepend=False)
cls.res1 = MNLogit(data.endog, exog).fit(method="newton", disp=0)
res2 = Anes()
res2.mnlogit_basezero()
cls.res2 = res2
def setupClass(cls):
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)
cls.res1 = MNLogit(data.endog, exog).fit(method="newton", disp=0)
res2 = Anes()
res2.mnlogit_basezero()
cls.res2 = res2
def setupClass(cls):
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)
cls.res1 = MNLogit(data.endog, exog).fit(method="newton", disp=0)
res2 = Anes()
res2.mnlogit_basezero()
cls.res2 = res2
def setupClass(cls):
from results.results_discrete import Anes
data = sm.datasets.anes96.load()
cls.data = data
exog = data.exog
exog = sm.add_constant(exog, prepend=False)
mymodel = MNLogit(data.endog, exog)
cls.res1 = mymodel.fit(method="lbfgs", disp=0, maxiter=50000,
#m=12, pgtol=1e-7, factr=1e3, # 5 failures
#m=20, pgtol=1e-8, factr=1e2, # 3 failures
#m=30, pgtol=1e-9, factr=1e1, # 1 failure
m=40, pgtol=1e-10, factr=5e0,
loglike_and_score=mymodel.loglike_and_score)
res2 = Anes()
res2.mnlogit_basezero()
cls.res2 = res2
