def setup_class(cls):
model = "I(food/income) ~ income + persons"
cls.income_fit = BetaModel.from_formula(model, income).fit()
model = cls.model = "methylation ~ gender + CpG"
Z = cls.Z = patsy.dmatrix("~ age", methylation)
mod = BetaModel.from_formula(model, methylation, exog_precision=Z,
link_precision=links.identity())
cls.meth_fit = mod.fit()
mod = BetaModel.from_formula(model, methylation, exog_precision=Z,
link_precision=links.Log())
cls.meth_log_fit = mod.fit()
def setup_class(cls):
formula = "methylation ~ gender + CpG"
mod = BetaModel.from_formula(formula, methylation,
exog_precision_formula="~ age",
link_precision=links.Log())
cls.res1 = mod.fit(cov_type="eim")
cls.res2 = resultsb.results_meth
def test_precision_formula(self):
m = BetaModel.from_formula(self.model, methylation,
exog_precision_formula='~ age',
link_precision=links.identity())
rslt = m.fit()
assert_close(rslt.params, self.meth_fit.params, 1e-10)
assert isinstance(rslt.params, pd.Series)
def setup_class(cls):
formula = "I(food/income) ~ income + persons"
exog_prec = patsy.dmatrix("~ persons", income)
mod_income = BetaModel.from_formula(formula,
income,
exog_precision=exog_prec,
link_precision=links.Log())
res_income = mod_income.fit(method="newton")
mod_restricted = BetaModel.from_formula(formula,
income,
link_precision=links.Log())
res_restricted = mod_restricted.fit(method="newton")
cls.res1 = res_income
cls.resr = res_restricted
def test_scores(self):
model, Z = self.model, self.Z
for link in (links.identity(), links.log()):
mod2 = BetaModel.from_formula(model, methylation, exog_precision=Z,
link_precision=link)
rslt_m = mod2.fit()
# evaluate away from optimum to get larger score
analytical = rslt_m.model.score(rslt_m.params * 1.01)
numerical = rslt_m.model._score_check(rslt_m.params * 1.01)
assert_allclose(analytical, numerical, rtol=1e-6, atol=1e-6)
assert_allclose(link.inverse(analytical[3:]),
link.inverse(numerical[3:]), rtol=5e-7, atol=5e-6)