def setup_class(cls):
data = datasets.randhie.load()
exog = add_constant(np.asarray(data.exog)[:, :4], prepend=False)
mod = TruncatedLFPoisson(data.endog, exog, truncation=0)
cls.res1 = mod.fit(maxiter=500)
res2 = RandHIE()
res2.zero_truncated_poisson()
cls.res2 = res2
def setup_class(cls):
cls.expected_params = [1, 0.5]
np.random.seed(123)
nobs = 200
exog = np.ones((nobs, 2))
exog[:nobs//2, 1] = 2
mu_true = exog.dot(cls.expected_params)
cls.endog = truncatedpoisson.rvs(mu_true, 0, size=mu_true.shape)
model = TruncatedLFPoisson(cls.endog, exog, truncation=0)
cls.res = model.fit(method='bfgs', maxiter=5000)
def setup_class(cls):
endog = DATA["docvis"]
exog_names = ['aget', 'totchr', 'const']
exog = DATA[exog_names]
cls.res1 = TruncatedLFPoisson(endog, exog).fit(method="bfgs",
maxiter=300)
cls.res2 = results_ts.results_trunc_poisson