def setup_class(cls):
cls.idx = [6, 2, 3, 4, 5, 0] # 2 is dropped baseline for categorical
# example without offset
formula = 'deaths ~ smokes + C(agecat)'
mod = cls.model_cls.from_formula(formula,
data=data,
offset=np.log(data['pyears'].values))
constr = 'C(agecat)[T.5] - C(agecat)[T.4] = 0.5'
lc = patsy.DesignInfo(mod.exog_names).linear_constraint(constr)
cls.res1 = fit_constrained(mod,
lc.coefs,
lc.constants,
fit_kwds={
'method': 'newton',
'disp': 0
})
cls.constraints = lc
# TODO: bfgs fails
# test method of Poisson, not monkey patched
cls.res1m = mod.fit_constrained(constr,
method='bfgs',
disp=0,
start_params=cls.res1[0])
def setup_class(cls):
mod1 = cls.model_cls(spector_data.endog,
spector_data.exog,
family=families.Binomial())
# not used to match Stata for HC
# nobs, k_params = mod1.exog.shape
# k_params -= 1 # one constraint
cov_type = 'HC0'
cov_kwds = {'scaling_factor': 32 / 31.}
# looks like nobs / (nobs - 1) and not (nobs - 1.) / (nobs - k_params)}
constr = 'x1 - x3 = 0'
cls.res1m = mod1.fit_constrained(constr,
cov_type=cov_type,
cov_kwds=cov_kwds,
atol=1e-10)
R, q = cls.res1m.constraints.coefs, cls.res1m.constraints.constants
cls.res1 = fit_constrained(mod1,
R,
q,
fit_kwds={
'atol': 1e-10,
'cov_type': cov_type,
'cov_kwds': cov_kwds
})
cls.constraints_rq = (R, q)
def setup_class(cls):
# example without offset
formula = 'deaths ~ logpyears + smokes + C(agecat)'
mod = cls.model_cls.from_formula(formula, data=data)
# get start_params, example fails to converge on one py TravisCI
k_vars = len(mod.exog_names)
start_params = np.zeros(k_vars)
start_params[0] = np.log(mod.endog.mean())
# if we need it, this is desired params
# p = np.array([-9.43762015, 1.52762442, 2.74155711, 3.58730007,
# 4.08730007, 1.15987869, 0.12111539])
constr = 'C(agecat)[T.5] - C(agecat)[T.4] = 0.5'
lc = patsy.DesignInfo(mod.exog_names).linear_constraint(constr)
cls.res1 = fit_constrained(mod,
lc.coefs,
lc.constants,
start_params=start_params,
fit_kwds={
'method': 'bfgs',
'disp': 0
})
# TODO: Newton fails
# test method of Poisson, not monkey patched
cls.res1m = mod.fit_constrained(constr,
start_params=start_params,
method='bfgs',
disp=0)
def setup_class(cls):
# example without offset
formula = 'deaths ~ logpyears + smokes + C(agecat)'
mod = cls.model_cls.from_formula(formula, data=data)
# get start_params, example fails to converge on one py TravisCI
k_vars = len(mod.exog_names)
start_params = np.zeros(k_vars)
start_params[0] = np.log(mod.endog.mean())
# if we need it, this is desired params
# p = np.array([-3.93478643, 1.37276214, 2.33077032, 2.71338891,
# 2.71338891, 0.57966535, 0.97254074])
constr = 'C(agecat)[T.4] = C(agecat)[T.5]'
lc = patsy.DesignInfo(mod.exog_names).linear_constraint(constr)
cls.res1 = fit_constrained(mod,
lc.coefs,
lc.constants,
start_params=start_params,
fit_kwds={
'method': 'bfgs',
'disp': 0
})
# TODO: Newton fails
# test method of Poisson, not monkey patched
cls.res1m = mod.fit_constrained(constr,
start_params=start_params,
method='bfgs',
disp=0)
def setup_class(cls):
mod1 = cls.model_cls(spector_data.endog,
spector_data.exog,
family=families.Binomial())
constr = 'x1 - x3 = 0'
cls.res1m = mod1.fit_constrained(constr, atol=1e-10)
R, q = cls.res1m.constraints.coefs, cls.res1m.constraints.constants
cls.res1 = fit_constrained(mod1, R, q, fit_kwds={'atol': 1e-10})
cls.constraints_rq = (R, q)
def setup_class(cls):
cls.res2 = reslogit.results_constraint1
mod1 = cls.model_cls(spector_data.endog,
spector_data.exog,
family=families.Binomial())
constr = 'x1 = 2.8'
cls.res1m = mod1.fit_constrained(constr)
R, q = cls.res1m.constraints.coefs, cls.res1m.constraints.constants
cls.res1 = fit_constrained(mod1, R, q)
def setup_class(cls):
# example without offset
formula = 'deaths ~ logpyears + smokes + C(agecat)'
mod = cls.model_cls.from_formula(formula,
data=data,
family=families.Poisson())
constr = 'C(agecat)[T.4] = C(agecat)[T.5]'
lc = patsy.DesignInfo(mod.exog_names).linear_constraint(constr)
cls.res1 = fit_constrained(mod,
lc.coefs,
lc.constants,
fit_kwds={'atol': 1e-10})
cls.constraints = lc
cls.res1m = mod.fit_constrained(constr, atol=1e-10)