"""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, )