def ci_cons(X, v, alpha_level, L, theta_l, theta_u, K, test="lr", corrected=True, verbose=False):
arr = array_from_data(X, [v])
fit_model = StationaryLogistic()
fit_model.beta["x_0"] = None
fit_model.confidence_cons(arr, "x_0", alpha_level, K, L, theta_l, theta_u, test, verbose)
method = "conservative-%s" % test
return fit_model.conf["x_0"][method]
def ci_brazzale(X, v, alpha_level):
arr = array_from_data(X, [v])
arr.offset_extremes()
alpha_zero(arr)
fit_model = NonstationaryLogistic()
fit_model.beta['x_0'] = None
fit_model.fit_brazzale(arr, 'x_0', alpha_level = alpha_level)
return safe_ci(fit_model, 'x_0', 'brazzale')
def ci_umle_boot(X, v, alpha_level):
arr = array_from_data(X, [v])
arr.offset_extremes()
alpha_zero(arr)
fit_model = NonstationaryLogistic()
fit_model.beta['x_0'] = None
fit_model.confidence_boot(arr, alpha_level = alpha_level)
return fit_model.conf['x_0']['pivotal']
def ci_umle_wald(X, v, alpha_level):
arr = array_from_data(X, [v])
arr.offset_extremes()
alpha_zero(arr)
fit_model = NonstationaryLogistic()
fit_model.beta['x_0'] = None
fit_model.confidence_wald(arr, strict = False, alpha_level = alpha_level)
return safe_ci(fit_model, 'x_0', 'wald_inverse')
def ci_umle_wald(X, v, alpha_level):
arr = array_from_data(X, [v])
arr.offset_extremes()
alpha_zero(arr)
fit_model = NonstationaryLogistic()
fit_model.beta["x_0"] = None
fit_model.confidence_wald(arr, alpha_level=alpha_level)
return safe_ci(fit_model, "x_0", "wald")
def ci_cons(X, v, alpha_level, L, theta_l, theta_u,
K, test = 'lr', corrected = True, verbose = False):
arr = array_from_data(X, [v])
fit_model = StationaryLogistic()
fit_model.beta['x_0'] = None
fit_model.confidence_cons(arr, 'x_0', alpha_level, K,
L, theta_l, theta_u, test, verbose)
method = 'conservative-%s' % test
return fit_model.conf['x_0'][method]
def ci_cons(X, v, alpha_level, L, theta_l, theta_u,
K, test = 'lr', corrected = True, verbose = False):
arr = array_from_data(X, [v])
fit_model = StationaryLogistic()
fit_model.beta['x_0'] = None
fit_model.confidence_cons(arr, 'x_0', alpha_level, K,
L, theta_l, theta_u, test, corrected,
verbose)
method = 'conservative-%s' % test
return fit_model.conf['x_0'][method]
def ci_cmle_boot(X, v, alpha_level):
arr = array_from_data(X, [v])
A = arr.as_dense()
r = A.sum(1)
c = A.sum(0)
s_model = StationaryLogistic()
s_model.beta['x_0'] = None
fit_model = FixedMargins(s_model)
arr.new_row_covariate('r', np.int)[:] = r
arr.new_col_covariate('c', np.int)[:] = c
fit_model.fit = fit_model.base_model.fit_conditional
fit_model.confidence_boot(arr, alpha_level = alpha_level)
return fit_model.conf['x_0']['pivotal']
def ci_cmle_wald(X, v, alpha_level):
arr = array_from_data(X, [v])
A = arr.as_dense()
r = A.sum(1)
c = A.sum(0)
s_model = StationaryLogistic()
s_model.beta["x_0"] = None
fit_model = FixedMargins(s_model)
arr.new_row_covariate("r", np.int)[:] = r
arr.new_col_covariate("c", np.int)[:] = c
fit_model.fit = fit_model.base_model.fit_conditional
fit_model.confidence_wald(arr, alpha_level=alpha_level)
return safe_ci(fit_model, "x_0", "wald")
def ci_umle(X, v, theta_grid, alpha_level):
arr = array_from_data(X, [v])
arr.offset_extremes()
alpha_zero(arr)
fit_model = NonstationaryLogistic()
umle = np.empty_like(theta_grid)
for l, theta_l in enumerate(theta_grid):
fit_model.beta['x_0'] = theta_l
fit_model.fit(arr, fix_beta = True)
umle[l] = -fit_model.nll(arr)
crit = -0.5 * chi2.ppf(1 - alpha_level, 1)
ci = invert_test(theta_grid, umle - umle.max(), crit)
if params['plot']:
plot_statistics(ax_umle, theta_grid, umle - umle.max(), crit)
umle_coverage_data['cis'].append(ci)
umle_coverage_data['theta_grid'] = theta_grid
umle_coverage_data['crit'] = crit
return ci