File: C:/Users/fred/anaconda3/Lib/site-packages/statsmodels/discrete/tests/results/results_predict.py
"""This file has been manually edited based on the generated results
edits
- rearrange Bunch
- use DataFrame
note seond `_cons` in params_table rownames is lnalpha
"""
# flake8: noqa
import numpy as np
import pandas as pd
from statsmodels.tools.testing import ParamsTableTestBunch
est = dict(
rank = 9,
N = 3629,
ic = 4,
k = 9,
k_eq = 2,
k_dv = 1,
converged = 1,
rc = 0,
k_autoCns = 0,
ll = -10404.95565541838,
k_eq_model = 1,
ll_0 = -10786.68925314471,
rank0 = 2,
df_m = 7,
chi2 = 763.467195452653,
p = 1.4153888670e-160,
ll_c = -14287.94887436967,
chi2_c = 7765.986437902575,
r2_p = .0353893200005773,
k_aux = 1,
alpha = .6166738507905131,
cmdline = "nbreg docvis private medicaid aget aget2 educyr actlim totchr",
cmd = "nbreg",
predict = "nbreg_p",
dispers = "mean",
diparm_opt2 = "noprob",
chi2_ct = "LR",
chi2type = "LR",
opt = "moptimize",
vce = "oim",
title = "Negative binomial regression",
diparm1 = "lnalpha, exp label(",
user = "nbreg_lf",
crittype = "log likelihood",
ml_method = "e2",
singularHmethod = "m-marquardt",
technique = "nr",
which = "max",
depvar = "docvis",
properties = "b V",
)
params_table = np.array([
.18528179233626, .03348067897193, 5.5339914848088, 3.130241768e-08,
.11966086737334, .25090271729919, np.nan, 1.9599639845401,
0, .08475784499449, .04718372808048, 1.7963363312438,
.07244104305261, -.00772056269958, .17723625268856, np.nan,
1.9599639845401, 0, .22409326577213, .04170620298531,
5.3731399583668, 7.737722210e-08, .14235060998901, .30583592155526,
np.nan, 1.9599639845401, 0, -.04817183015548,
.00992361535076, -4.8542621265318, 1.208358166e-06, -.06762175883941,
-.02872190147156, np.nan, 1.9599639845401, 0,
.02692548760568, .00419162167105, 6.4236445267994, 1.330497007e-10,
.01871006009359, .03514091511776, np.nan, 1.9599639845401,
0, .17048038202011, .03448967943245, 4.9429390132204,
7.695356233e-07, .10288185249418, .23807891154605, np.nan,
1.9599639845401, 0, .27516170294682, .01205852749453,
22.818847746673, 2.98049648e-115, .25152742335095, .29879598254269,
np.nan, 1.9599639845401, 0, .67840343342789,
.0664120899438, 10.21505924602, 1.697754022e-24, .54823812900001,
.80856873785576, np.nan, 1.9599639845401, 0,
-.48341499971517, .03134835693943, -15.420744399751, 1.187278967e-53,
-.54485665029097, -.42197334913938, np.nan, 1.9599639845401,
0]).reshape(9,9)
params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
params_table_rownames = 'private medicaid aget aget2 educyr actlim totchr _cons _cons'.split()
# results for
# margins , predict(n) predict(pr(0)) predict(pr(1)) predict(pr(0, 1)) predict(pr(2, .)) atmeans
table = np.array([
6.1604164491362, .09102737953925, 67.676521946673, 0,
5.9820060636322, 6.3388268346402, np.nan, 1.9599639845401,
0, .07860475517176, .00344783069748, 22.798322211469,
4.76427218e-115, .07184713117991, .08536237916362, np.nan,
1.9599639845401, 0, .10090462231979, .00218578691875,
46.16397941361, 0, .09662055868115, .10518868595842,
np.nan, 1.9599639845401, 0, .17950937749155,
.00553924697666, 32.406819599838, 2.20005624e-230, .16865265291582,
.19036610206727, np.nan, 1.9599639845401, 0,
.82049062250845, .00553924699078, 148.12313368113, 0,
.80963389790505, .83134734711186, np.nan, 1.9599639845401,
0]).reshape(5,9)
table_colnames = 'b se z pvalue ll ul df crit eform'.split()
table_rownames = '1bn._predict 2._predict 3._predict 4._predict 5._predict'.split()
dframe_atmeans = pd.DataFrame(table, index=table_rownames, columns=table_colnames)
# result for
# margins, predict(n) predict(pr(0)) predict(pr(1)) predict(pr(0, 1)) predict(pr(2, .))
table = np.array([
6.8071952338104, .10838829819462, 62.803783685096, 0,
6.5947580730033, 7.0196323946174, np.nan, 1.9599639845401,
0, .08826646029161, .00350687276409, 25.169564517851,
8.63155623e-140, .08139311597563, .09513980460758, np.nan,
1.9599639845401, 0, .10719978561286, .00205026104517,
52.285920305334, 0, .10318134780543, .1112182234203,
np.nan, 1.9599639845401, 0, .19546624590447,
.0054522133947, 35.850806223874, 1.78661674e-281, .18478010401484,
.2061523877941, np.nan, 1.9599639845401, 0,
.80453375409553, .00545221340471, 147.56094348787, 0,
.79384761218628, .81521989600478, np.nan, 1.9599639845401,
0]).reshape(5,9)
table_colnames = 'b se z pvalue ll ul df crit eform'.split()
table_rownames = '1bn._predict 2._predict 3._predict 4._predict 5._predict'.split()
dframe_mean = pd.DataFrame(table, index=table_rownames, columns=table_colnames)
results_nb_docvis = ParamsTableTestBunch(
params_table=params_table,
params_table_colnames=params_table_colnames,
params_table_rownames=params_table_rownames,
results_margins_atmeans=dframe_atmeans,
results_margins_mean=dframe_mean,
**est,
)
# ############################# ZINBP
est = dict(
rank = 11,
N = 3629,
ic = 8,
k = 11,
k_eq = 3,
k_dv = 1,
converged = 1,
rc = 0,
k_autoCns = 0,
ll = -10404.95308201019,
k_eq_model = 1,
ll_0 = -10775.51516555833,
chi2 = 741.1241670962918,
p = 9.2654212845e-153,
N_zero = 392,
df_m = 7,
df_c = 2,
k_aux = 1,
cmdline = "zinb docvis private medicaid aget aget2 educyr actlim totchr, inflate(aget)",
cmd = "zinb",
predict = "zip_p",
inflate = "logit",
chi2type = "LR",
opt = "moptimize",
vce = "oim",
title = "Zero-inflated negative binomial regression",
diparm1 = "lnalpha, exp label(",
user = "zinb_llf",
crittype = "log likelihood",
ml_method = "e2",
singularHmethod = "m-marquardt",
technique = "nr",
which = "max",
depvar = "docvis",
properties = "b V",
)
params_table = np.array([
.18517571292817, .03350948180038, 5.5260691296648, 3.274851365e-08,
.11949833545881, .25085309039752, np.nan, 1.9599639845401,
0, .08473133853831, .04717665613525, 1.7960437529823,
.07248755882811, -.00773320839781, .17719588547443, np.nan,
1.9599639845401, 0, .22335574980273, .04293022169984,
5.2027625518539, 1.963476761e-07, .13921406142272, .30749743818274,
np.nan, 1.9599639845401, 0, -.04804896097964,
.01006690700638, -4.7729616404713, 1.815363776e-06, -.06777973614785,
-.02831818581142, np.nan, 1.9599639845401, 0,
.0269244937276, .00419096037609, 6.4244209707123, 1.323724094e-10,
.01871036232982, .03513862512538, np.nan, 1.9599639845401,
0, .17042579343453, .03449014549225, 4.9412894901473,
7.760757819e-07, .10282635044816, .23802523642089, np.nan,
1.9599639845401, 0, .27500074932161, .01226558007071,
22.420525383741, 2.48238139e-111, .25096065413353, .29904084450969,
np.nan, 1.9599639845401, 0, .67986743798706,
.06944204778986, 9.7904289925953, 1.237696321e-22, .54376352530622,
.81597135066789, np.nan, 1.9599639845401, 0,
-1.2833474076485, 3.692336844421, -.34757051204241, .72816275506989,
-8.520194641504, 5.9534998262071, np.nan, 1.9599639845401,
0, -6.5587800419911, 13.305282477745, -.49294556902205,
.62205104781253, -32.636654502503, 19.519094418521, np.nan,
1.9599639845401, 0, -.4845756474516, .03531398529193,
-13.721919048382, 7.505227546e-43, -.55378978677435, -.41536150812884,
np.nan, 1.9599639845401, 0]).reshape(11,9)
params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
params_table_rownames = 'private medicaid aget aget2 educyr actlim totchr _cons aget _cons _cons'.split()
# results for
# margins , predict(n) predict(pr(0)) predict(pr(1)) predict(pr(0, 1)) predict(pr(2, .)) atmeans
table = np.array([
6.1616899436815, .09285785618544, 66.356151184199, 0,
5.9796918898764, 6.3436879974865, np.nan, 1.9599639845401,
0, .07857785668717, .00351221423708, 22.372740209725,
7.25412664e-111, .0716940432765, .08546167009785, np.nan,
1.9599639845401, 0, .10079961393875, .00263347068017,
38.276338027191, 0, .09563810625128, .10596112162622,
np.nan, 1.9599639845401, 0, .17937747062593,
.00586287199331, 30.595494977635, 1.40505722e-205, .16788645267307,
.19086848857879, np.nan, 1.9599639845401, 0,
.82062252937407, .00586287199668, 139.96937505016, 0,
.80913151141461, .83211354733353, np.nan, 1.9599639845401,
0]).reshape(5,9)
table_colnames = 'b se z pvalue ll ul df crit eform'.split()
table_rownames = '1bn._predict 2._predict 3._predict 4._predict 5._predict'.split()
dframe_atmeans = pd.DataFrame(table, index=table_rownames, columns=table_colnames)
# result for
# margins, predict(n) predict(pr(0)) predict(pr(1)) predict(pr(0, 1)) predict(pr(2, .))
table = np.array([
6.8063733751586, .10879833124057, 62.559538345387, 0,
6.593132564349, 7.0196141859682, np.nan, 1.9599639845401,
0, .08842743693234, .00405939469823, 21.7834045482,
3.33356305e-105, .08047116952478, .0963837043399, np.nan,
1.9599639845401, 0, .10706809868425, .00273617889716,
39.130518401155, 0, .10170528659055, .11243091077794,
np.nan, 1.9599639845401, 0, .19549553561658,
.00545764150876, 35.820516115406, 5.29431574e-281, .18479875481889,
.20619231641428, np.nan, 1.9599639845401, 0,
.80450446438342, .0054576415014, 147.40881462742, 0,
.79380768360013, .8152012451667, np.nan, 1.9599639845401,
0]).reshape(5,9)
table_colnames = 'b se z pvalue ll ul df crit eform'.split()
table_rownames = '1bn._predict 2._predict 3._predict 4._predict 5._predict'.split()
dframe_mean = pd.DataFrame(table, index=table_rownames, columns=table_colnames)
results_zinb_docvis = ParamsTableTestBunch(
params_table=params_table,
params_table_colnames=params_table_colnames,
params_table_rownames=params_table_rownames,
results_margins_atmeans=dframe_atmeans,
results_margins_mean=dframe_mean,
**est,
)