results_meth.table_mean = np.array([
1.44224319715775, 0.0698572427112336, 0.607345321898288, 0.973547608125426,
0.0340120810079364, 0.0435912797271355, 0.0483424930413969,
0.0531104241011462, 42.4038504677562, 1.60255085761448, 12.5633843785881,
18.3306314080896, 0, 0.109033850726723, 3.35661710796797e-36,
4.71401008973566e-75
]).reshape(4, 4, order='F')
results_meth.table_precision = np.array([
8.22828526376512, -0.0347054296138766, 1.79098056245575,
0.0327648100640521, 4.59429065633335, -1.05922877459173,
4.34223794561173e-06, 0.289495603466561
]).reshape(2, 4, order='F')
results_meth.aic = -196.296056810686
results_meth.bic = -186.79494317995
results_meth.table_mean_oim = np.array([
1.44224320770907, 0.069857238768632, 0.607345313356895, 0.973547591731571,
0.0340453325782864, 0.0435867955242771, 0.0490089283252544,
0.053386889034385, 42.362435567127, 1.60271563734762, 12.3925442590004,
18.2357056075048, 0, 0.108997449531221, 2.86797597854623e-35,
2.68762966306205e-74
]).reshape(4, 4, order='F')
results_meth.table_precision_oim = np.array([
8.22828540005571, -0.0347054322904486, 1.83887205150239,
0.0336205378385678, 4.4746372611042, -1.0322688012039,
7.65411434417314e-06, 0.301946212204644
]).reshape(2, 4, order='F')
# flake8: noqa
import numpy as np
from statsmodels.tools.testing import Holder
hurdle_poisson = Holder()
# r library pscl, docvis data
# > mod = hurdle( docvis ~ aget + totchr, data=dt, zero.dist = "poisson")
hurdle_poisson.method = 'BFGS'
hurdle_poisson.n = 3629
hurdle_poisson.df_null = 3627
hurdle_poisson.df_residual = 3623
hurdle_poisson.loglik = -13612.9091771797
hurdle_poisson.aic = 27237.81835436
hurdle_poisson.bic = 27274.9986288
hurdle_poisson.vcov = np.array([
0.000239404800324688, -4.59559682721834e-05, -4.59865258972631e-05, 0, 0,
0, -4.59559682721834e-05, 2.54346275490526e-05, -1.20164687288645e-06, 0,
0, 0, -4.59865258972631e-05, -1.20164687288644e-06, 2.01936456643824e-05,
0, 0, 0, 0, 0, 0, 0.00241829560973498, -0.000548499729173446,
-0.000636055275016966, 0, 0, 0, -0.000548499729173446,
0.000351548196602719, -6.30088654100178e-05, 0, 0, 0,
-0.000636055275016966, -6.30088654100178e-05, 0.000562508220544602
]).reshape(6, 6, order='F')
hurdle_poisson.count = np.array([
1.54175599063303, 0.0122763123129474, 0.209943725275436,
0.0154727114729348, 0.00504327547820388, 0.00449373404468738,
99.6435559035596, 2.43419427830254, 46.719214619218, 0, 0.0149249819228085,
0
