def setup_class(cls):
cls.idx = slice(None) # params sequence same as Stata
# res1ul = Logit(data.endog, data.exog).fit(method="newton", disp=0)
cls.res2 = reslogit.results_constraint2_robust
mod1 = Logit(spector_data.endog, spector_data.exog)
# 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,
tol=1e-10,
)
R, q = cls.res1m.constraints.coefs, cls.res1m.constraints.constants
cls.res1 = fit_constrained(mod1,
R,
q,
fit_kwds={
'tol': 1e-10,
'cov_type': cov_type,
'cov_kwds': cov_kwds
})
cls.constraints_rq = (R, q)
def setup_class(cls):
cls.idx = slice(None)
# params sequence same as Stata, but Stata reports param = nan
# and we have param = value = 0
# res1ul = Logit(data.endog, data.exog).fit(method="newton", disp=0)
cls.res2 = reslogit.results_constraint1
mod1 = Logit(spector_data.endog, spector_data.exog)
constr = 'x1 = 2.8'
# newton doesn't work, raises hessian singular
cls.res1m = mod1.fit_constrained(constr, method='bfgs')
R, q = cls.res1m.constraints.coefs, cls.res1m.constraints.constants
cls.res1 = fit_constrained(mod1, R, q, fit_kwds={'method': 'bfgs'})
