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")
Example #6
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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