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alkaline-ml / statsmodels python
Repository URL to install this package:
|
Version:
0.11.1 ▾
|
import numpy as np
from statsmodels.tools.testing import ParamsTableTestBunch
est = dict(
rank=8,
N=3629,
Q=4.59536484786e-20,
J=1.66765790329e-16,
J_df=0,
k_1=8,
converged=1,
has_xtinst=0,
type=1,
n_eq=1,
k=8,
n_moments=8,
k_aux=8,
k_eq_model=0,
k_eq=8,
cmdline="gmm ( docvis - exp({xb:private medicaid aget aget2 educyr actlim totchr}+{b0})), instruments(incomet ssiratio aget aget2 educyr actlim totchr) onestep vce(robust)", # noqa:E501
cmd="gmm",
estat_cmd="gmm_estat",
predict="gmm_p",
marginsnotok="_ALL",
eqnames="1",
technique="gn",
winit="Unadjusted",
estimator="onestep",
wmatrix="robust",
vce="robust",
vcetype="Robust",
params="xb_private xb_medicaid xb_aget xb_aget2 xb_educyr xb_actlim xb_totchr b0", # noqa:E501
inst_1="incomet ssiratio aget aget2 educyr actlim totchr _cons",
params_1="xb_private xb_medicaid xb_aget xb_aget2 xb_educyr xb_actlim xb_totchr b0", # noqa:E501
sexp_1="docvis - exp( ({xb_private} *private + {xb_medicaid} *medicaid + {xb_aget} *aget + {xb_aget2} *aget2 + {xb_educyr} *educyr + {xb_actlim} *actlim + {xb_totchr} *totchr) + {b0} )", # noqa:E501
properties="b V",
)
params_table = np.array([
.62093805844748, .35860052573857, 1.731559252928, .08335206643438,
-.08190605683724, 1.3237821737322, np.nan, 1.9599639845401,
0, .68895699568302, .43817618784254, 1.5723286997298,
.11587434043505, -.1698525513714, 1.5477665427374, np.nan,
1.9599639845401, 0, .25750627258076, .05009451793791,
5.1404082358855, 2.741421857e-07, .15932282159956, .35568972356197,
np.nan, 1.9599639845401, 0, -.05352997420414,
.01103202674353, -4.8522339048464, 1.220785200e-06, -.07515234929795,
-.03190759911034, np.nan, 1.9599639845401, 0,
.03106248018916, .01032090201131, 3.0096671933432, .00261534090329,
.01083388395902, .05129107641931, np.nan, 1.9599639845401,
0, .14175365608301, .0494498280382, 2.8666157539212,
.00414886404159, .04483377408643, .23867353807958, np.nan,
1.9599639845401, 0, .23128095221422, .01565221628818,
14.776243054406, 2.084750820e-49, .20060317201116, .26195873241727,
np.nan, 1.9599639845401, 0, .34763567088735,
.31615794015526, 1.0995633091379, .27152243570261, -.27202250524333,
.96729384701803, np.nan, 1.9599639845401, 0
]).reshape(8, 9)
params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
params_table_rownames = ['_cons'] * 8
cov = np.array([
.12859433705998, .13265896898444, .00910916927048, -.00144786113189,
-.00037337560793, -.00152379041042, -.00336772308907, -.09899309651531,
.13265896898444, .19199837159222, .00979636564963, -.00135323134276,
.00180599814286, -.00930935415071, -.00460031335865, -.13429156867927,
.00910916927048, .00979636564963, .00250946072743, -.00052373946978,
5.155389870e-07, -.00016461502154, -.00025816911604, -.00869892550441,
-.00144786113189, -.00135323134276, -.00052373946978, .00012170561407,
8.334416260e-06, -.00002526568199, .00003797456789, .00131001446811,
-.00037337560793, .00180599814286, 5.155389870e-07, 8.334416260e-06,
.00010652101833, -.00026856403693, -.00003344387872, -.00122933496346,
-.00152379041042, -.00930935415071, -.00016461502154, -.00002526568199,
-.00026856403693, .00244528549301, .00003610001892, .00527355381855,
-.00336772308907, -.00460031335865, -.00025816911604, .00003797456789,
-.00003344387872, .00003610001892, .00024499187473, .00300075896709,
-.09899309651531, -.13429156867927, -.00869892550441, .00131001446811,
-.00122933496346, .00527355381855, .00300075896709, .09995584312322
]).reshape(8, 8)
cov_colnames = ['_cons'] * 8
cov_rownames = ['_cons'] * 8
results_addonestep = ParamsTableTestBunch(
params_table=params_table,
params_table_colnames=params_table_colnames,
params_table_rownames=params_table_rownames,
cov=cov,
cov_colnames=cov_colnames,
cov_rownames=cov_rownames,
**est
)
est = dict(
rank=8,
N=3629,
Q=6.09567389485e-33,
J=2.21212005644e-29,
J_df=0,
k_1=8,
converged=1,
has_xtinst=0,
type=1,
n_eq=1,
k=8,
n_moments=8,
k_aux=8,
k_eq_model=0,
k_eq=8,
cmdline="gmm ( docvis - exp({xb:private medicaid aget aget2 educyr actlim totchr}+{b0})), instruments(incomet ssiratio aget aget2 educyr actlim totchr) twostep vce(robust)", # noqa:E501
cmd="gmm",
estat_cmd="gmm_estat",
predict="gmm_p",
marginsnotok="_ALL",
eqnames="1",
technique="gn",
winit="Unadjusted",
estimator="twostep",
wmatrix="robust",
vce="robust",
vcetype="Robust",
params="xb_private xb_medicaid xb_aget xb_aget2 xb_educyr xb_actlim xb_totchr b0", # noqa:E501
inst_1="incomet ssiratio aget aget2 educyr actlim totchr _cons",
params_1="xb_private xb_medicaid xb_aget xb_aget2 xb_educyr xb_actlim xb_totchr b0", # noqa:E501
sexp_1="docvis - exp( ({xb_private} *private + {xb_medicaid} *medicaid + {xb_aget} *aget + {xb_aget2} *aget2 + {xb_educyr} *educyr + {xb_actlim} *actlim + {xb_totchr} *totchr) + {b0} )", # noqa:E501
properties="b V",
)
params_table = np.array([
.6209380584426, .35860052570457, 1.7315592530786, .08335206640755,
-.08190605677548, 1.3237821736607, np.nan, 1.9599639845401,
0, .68895699501744, .43817618789764, 1.5723286980131,
.11587434083298, -.16985255214498, 1.5477665421799, np.nan,
1.9599639845401, 0, .25750627271754, .05009451794125,
5.1404082382732, 2.741421823e-07, .15932282172979, .35568972370529,
np.nan, 1.9599639845401, 0, -.05352997423123,
.01103202674378, -4.8522339071944, 1.220785186e-06, -.07515234932551,
-.03190759913694, np.nan, 1.9599639845401, 0,
.03106248018903, .01032090201422, 3.0096671924822, .0026153409107,
.01083388395319, .05129107642488, np.nan, 1.9599639845401,
0, .14175365616691, .04944982804302, 2.8666157553386,
.00414886402301, .04483377416089, .23867353817294, np.nan,
1.9599639845401, 0, .23128095224221, .01565221628892,
14.776243055497, 2.084750786e-49, .20060317203771, .26195873244672,
np.nan, 1.9599639845401, 0, .34763567064032,
.31615794015859, 1.099563308345, .27152243604826, -.27202250549689,
.96729384677754, np.nan, 1.9599639845401, 0
]).reshape(8, 9)
params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
params_table_rownames = ['_cons'] * 8
cov = np.array([
.12859433703559, .1326589689683, .00910916927021, -.00144786113188,
-.00037337560766, -.00152379040753, -.00336772308885, -.09899309649807,
.1326589689683, .1919983716405, .00979636565235, -.00135323134324,
.00180599814488, -.00930935415256, -.00460031335946, -.13429156869395,
.00910916927021, .00979636565235, .00250946072777, -.00052373946983,
5.155391569e-07, -.00016461502162, -.00025816911611, -.00869892550672,
-.00144786113188, -.00135323134324, -.00052373946983, .00012170561408,
8.334416227e-06, -.00002526568198, .0000379745679, .00131001446858,
-.00037337560766, .00180599814488, 5.155391569e-07, 8.334416227e-06,
.00010652101839, -.00026856403706, -.00003344387875, -.00122933496459,
-.00152379040753, -.00930935415256, -.00016461502162, -.00002526568198,
-.00026856403706, .00244528549348, .00003610001887, .00527355381795,
-.00336772308885, -.00460031335946, -.00025816911611, .0000379745679,
-.00003344387875, .00003610001887, .00024499187475, .00300075896724,
-.09899309649807, -.13429156869395, -.00869892550672, .00131001446858,
-.00122933496459, .00527355381795, .00300075896724, .09995584312533
]).reshape(8, 8)
cov_colnames = ['_cons'] * 8
cov_rownames = ['_cons'] * 8
results_addtwostep = ParamsTableTestBunch(
params_table=params_table,
params_table_colnames=params_table_colnames,
params_table_rownames=params_table_rownames,
cov=cov,
cov_colnames=cov_colnames,
cov_rownames=cov_rownames,
**est
)
est = dict(
rank=8,
N=3629,
Q=.0002538911897719,
J=.9213711276820714,
J_df=1,
k_1=8,
converged=1,
has_xtinst=0,
type=1,
n_eq=1,
k=8,
n_moments=9,
k_aux=8,
k_eq_model=0,
k_eq=8,
cmdline="gmm ( (docvis / exp({xb:private medicaid aget aget2 educyr actlim totchr}+{b0})) - 1), instruments(income ssiratio medicaid aget aget2 educyr actlim totchr) onestep vce(robust)", # noqa:E501
cmd="gmm",
estat_cmd="gmm_estat",
predict="gmm_p",
marginsnotok="_ALL",
eqnames="1",
technique="gn",
winit="Unadjusted",
estimator="onestep",
wmatrix="robust",
vce="robust",
vcetype="Robust",
params="xb_private xb_medicaid xb_aget xb_aget2 xb_educyr xb_actlim xb_totchr b0", # noqa:E501
inst_1="income ssiratio medicaid aget aget2 educyr actlim totchr _cons",
params_1="xb_private xb_medicaid xb_aget xb_aget2 xb_educyr xb_actlim xb_totchr b0", # noqa:E501
sexp_1="(docvis / exp( ({xb_private} *private + {xb_medicaid} *medicaid + {xb_aget} *aget + {xb_aget2} *aget2 + {xb_educyr} *educyr + {xb_actlim} *actlim + {xb_totchr} *totchr) + {b0} )) - 1", # noqa:E501
properties="b V",
)
params_table = np.array([
.67045580921478, .25039046077656, 2.6776411814389, .00741425985435,
.17969952402034, 1.1612120944092, np.nan, 1.9599639845401,
0, .28551241628798, .10358919281318, 2.7561988710819,
.00584774303307, .08248132918657, .4885435033894, np.nan,
1.9599639845401, 0, .2672004738793, .05203985579809,
5.1345352476769, 2.828420839e-07, .16520423075439, .36919671700421,
np.nan, 1.9599639845401, 0, -.0560702624564,
.01191485946838, -4.7059105149509, 2.527353692e-06, -.07942295789528,
-.03271756701753, np.nan, 1.9599639845401, 0,
.01448379701656, .00782559934942, 1.8508227127214, .06419506241955,
-.00085409586574, .02982168989887, np.nan, 1.9599639845401,
0, .18130374188096, .0382173439987, 4.7440173206998,
2.095209222e-06, .10639912405874, .25620835970318, np.nan,
1.9599639845401, 0, .28146161235562, .01380395117777,
20.389931022715, 2.054354003e-92, .25440636520284, .30851685950839,
np.nan, 1.9599639845401, 0, .51399857133918,
.10262653035745, 5.0084375799215, 5.487366567e-07, .31285426798028,
.71514287469808, np.nan, 1.9599639845401, 0
]).reshape(8, 9)
params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
params_table_rownames = ['_cons'] * 8
cov = np.array([
.0626953828479, .02323594786658, .00535172023578, -.00103050587759,
-.00154311442856, .00154515839603, -.00043159973572, -.01570852578318,
.02323594786658, .01073072086769, .00207768328305, -.00039713375955,
-.00049396171685, .00027652302157, -.00020408147523, -.00701276303887,
.00535172023578, .00207768328305, .00270814659149, -.00059652725999,
-.00012298559534, .00021079055266, -.00004341699196, -.0031278522429,
-.00103050587759, -.00039713375955, -.00059652725999, .00014196387615,
.00002481291175, -.00006035908648, .00001093157006, .00059187926133,
-.00154311442856, -.00049396171685, -.00012298559534, .00002481291175,
.00006124000518, -.00001857594061, .00001436652009, .00008106194688,
.00154515839603, .00027652302157, .00021079055266, -.00006035908648,
-.00001857594061, .00146056538231, -.00016708887634, -.00074321753343,
-.00043159973572, -.00020408147523, -.00004341699196, .00001093157006,
.00001436652009, -.00016708887634, .00019054906812, -.00028024031412,
-.01570852578318, -.00701276303887, -.0031278522429, .00059187926133,
.00008106194688, -.00074321753343, -.00028024031412, .01053220473321
]).reshape(8, 8)
cov_colnames = ['_cons'] * 8
cov_rownames = ['_cons'] * 8
results_multonestep = ParamsTableTestBunch(
params_table=params_table,
params_table_colnames=params_table_colnames,
params_table_rownames=params_table_rownames,
cov=cov,
cov_colnames=cov_colnames,
cov_rownames=cov_rownames,
**est
)
est = dict(
rank=8,
N=3629,
Q=.0002589826272982,
J=.9398479544653281,
J_df=1,
k_1=8,
converged=1,
has_xtinst=0,
type=1,
n_eq=1,
k=8,
n_moments=9,
k_aux=8,
k_eq_model=0,
k_eq=8,
cmdline="gmm ( (docvis / exp({xb:private medicaid aget aget2 educyr actlim totchr}+{b0})) - 1), instruments(income ssiratio medicaid aget aget2 educyr actlim totchr) twostep vce(robust)", # noqa:E501
cmd="gmm",
estat_cmd="gmm_estat",
predict="gmm_p",
marginsnotok="_ALL",
eqnames="1",
technique="gn",
winit="Unadjusted",
estimator="twostep",
wmatrix="robust",
vce="robust",
vcetype="Robust",
params="xb_private xb_medicaid xb_aget xb_aget2 xb_educyr xb_actlim xb_totchr b0", # noqa:E501
inst_1="income ssiratio medicaid aget aget2 educyr actlim totchr _cons",
params_1="xb_private xb_medicaid xb_aget xb_aget2 xb_educyr xb_actlim xb_totchr b0", # noqa:E501
sexp_1="(docvis / exp( ({xb_private} *private + {xb_medicaid} *medicaid + {xb_aget} *aget + {xb_aget2} *aget2 + {xb_educyr} *educyr + {xb_actlim} *actlim + {xb_totchr} *totchr) + {b0} )) - 1", # noqa:E501
properties="b V",
)
params_table = np.array([
.67815288158883, .25053953449054, 2.7067699433856, .00679413212727,
.18710441728393, 1.1692013458937, np.nan, 1.9599639845401,
0, .28872837589732, .1032733938985, 2.7957672833051,
.00517766683505, .08631624329503, .49114050849961, np.nan,
1.9599639845401, 0, .27067071818542, .05199695467114,
5.2055109745809, 1.934635127e-07, .16875855972422, .37258287664662,
np.nan, 1.9599639845401, 0, -.05690856524563,
.01189861686254, -4.7827882772482, 1.728801925e-06, -.08022942576205,
-.03358770472921, np.nan, 1.9599639845401, 0,
.01438118999252, .00783219080428, 1.8361644081315, .06633334485657,
-.00096962190392, .02973200188896, np.nan, 1.9599639845401,
0, .18038262255626, .03826653224544, 4.7138481584715,
2.430818311e-06, .10538159754195, .25538364757056, np.nan,
1.9599639845401, 0, .28251027986119, .01378475918788,
20.494393555287, 2.415775858e-93, .25549264831739, .30952791140498,
np.nan, 1.9599639845401, 0, .5077134442587,
.10235830367214, 4.9601588346456, 7.043556343e-07, .30709485554269,
.7083320329747, np.nan, 1.9599639845401, 0
]).reshape(8, 9)
params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
params_table_rownames = ['_cons'] * 8
cov = np.array([
.06277005834274, .02315710174743, .00533574120292, -.00102544979294,
-.00154463417995, .0015508406274, -.00043796451278, -.01559999387335,
.02315710174743, .01066539388732, .00206217803508, -.00039331197813,
-.00049172930967, .00027603135609, -.00020644763374, -.00694810289238,
.00533574120292, .00206217803508, .00270368329507, -.0005950942106,
-.00012276584915, .00021462173623, -.00004681980342, -.00310767551047,
-.00102544979294, -.00039331197813, -.0005950942106, .00014157708324,
.00002474211336, -.00006134660609, .00001178280314, .00058658157366,
-.00154463417995, -.00049172930967, -.00012276584915, .00002474211336,
.00006134321279, -.00001855941375, .00001443470174, .0000776612477,
.0015508406274, .00027603135609, .00021462173623, -.00006134660609,
-.00001855941375, .00146432749009, -.00016643326394, -.00074847803836,
-.00043796451278, -.00020644763374, -.00004681980342, .00001178280314,
.00001443470174, -.00016643326394, .00019001958587, -.00027573517109,
-.01559999387335, -.00694810289238, -.00310767551047, .00058658157366,
.0000776612477, -.00074847803836, -.00027573517109, .01047722233064
]).reshape(8, 8)
cov_colnames = ['_cons'] * 8
cov_rownames = ['_cons'] * 8
results_multtwostep = ParamsTableTestBunch(
params_table=params_table,
params_table_colnames=params_table_colnames,
params_table_rownames=params_table_rownames,
cov=cov,
cov_colnames=cov_colnames,
cov_rownames=cov_rownames,
**est
)
est = dict(
rank=8,
N=3629,
Q=.0002590497181628,
J=.940091427212973,
J_df=1,
k_1=8,
converged=1,
has_xtinst=0,
type=1,
n_eq=1,
k=8,
n_moments=9,
k_aux=8,
k_eq_model=0,
k_eq=8,
cmdline="gmm ( (docvis / exp({xb:private medicaid aget aget2 educyr actlim totchr}+{b0})) - 1), instruments(income ssiratio medicaid aget aget2 educyr actlim totchr) twostep wmatrix(robust) vce(unadjusted) center", # noqa:E501
cmd="gmm",
estat_cmd="gmm_estat",
predict="gmm_p",
marginsnotok="_ALL",
eqnames="1",
technique="gn",
winit="Unadjusted",
estimator="twostep",
wmatrix="robust",
vce="unadjusted",
params="xb_private xb_medicaid xb_aget xb_aget2 xb_educyr xb_actlim xb_totchr b0", # noqa:E501
inst_1="income ssiratio medicaid aget aget2 educyr actlim totchr _cons",
params_1="xb_private xb_medicaid xb_aget xb_aget2 xb_educyr xb_actlim xb_totchr b0", # noqa:E501
sexp_1="(docvis / exp( ({xb_private} *private + {xb_medicaid} *medicaid + {xb_aget} *aget + {xb_aget2} *aget2 + {xb_educyr} *educyr + {xb_actlim} *actlim + {xb_totchr} *totchr) + {b0} )) - 1", # noqa:E501
properties="b V",
)
params_table = np.array([
.67815486150911, .25018082946574, 2.7106587781218, .00671496899138,
.1878094461339, 1.1685002768843, np.nan, 1.9599639845401,
0, .28872920226215, .10311429027815, 2.8000891193967,
.00510884999633, .08662890702558, .49082949749873, np.nan,
1.9599639845401, 0, .27067161407481, .0518802415232,
5.2172388972735, 1.816099638e-07, .16898820918009, .37235501896953,
np.nan, 1.9599639845401, 0, -.05690878166227,
.0118728670827, -4.7931793783164, 1.641587211e-06, -.08017917353758,
-.03363838978695, np.nan, 1.9599639845401, 0,
.01438116368432, .00781887593806, 1.8392878718448, .0658728559523,
-.00094355155385, .0297058789225, np.nan, 1.9599639845401,
0, .18038238197017, .03819661477822, 4.7224703816696,
2.329970297e-06, .10551839267351, .25524637126682, np.nan,
1.9599639845401, 0, .28251055147828, .01376659609161,
20.521452768591, 1.385109204e-93, .25552851894901, .30949258400755,
np.nan, 1.9599639845401, 0, .50771182444237,
.10208891085993, 4.9732318639284, 6.584582712e-07, .30762123593598,
.70780241294876, np.nan, 1.9599639845401, 0
]).reshape(8, 9)
params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
params_table_rownames = ['xb_private', 'xb_medicaid', 'xb_aget', 'xb_aget2',
'xb_educyr', 'xb_actlim', 'xb_totchr', 'b0']
cov = np.array([
.06259044743217, .02308524749042, .00531802921719, -.0010223122446,
-.00154027662468, .00154945994717, -.00043816683551, -.01554486097815,
.02308524749042, .01063255685957, .00205438168765, -.00039193802388,
-.00049039628782, .0002760841411, -.0002064504141, -.00691934867666,
.00531802921719, .00205438168765, .00269155946051, -.00059250696972,
-.00012247118567, .00021403084056, -.00004749600121, -.00308951213731,
-.0010223122446, -.00039193802388, -.00059250696972, .00014096497276,
.00002468288871, -.00006115240604, .00001190303672, .00058327928125,
-.00154027662468, -.00049039628782, -.00012247118567, .00002468288871,
.00006113482093, -.00001854325518, .00001439868646, .00007784185009,
.00154945994717, .0002760841411, .00021403084056, -.00006115240604,
-.00001854325518, .00145898138052, -.00016596475072, -.00074697007542,
-.00043816683551, -.0002064504141, -.00004749600121, .00001190303672,
.00001439868646, -.00016596475072, .00018951916795, -.00027350320218,
-.01554486097815, -.00691934867666, -.00308951213731, .00058327928125,
.00007784185009, -.00074697007542, -.00027350320218, .01042214572057
]).reshape(8, 8)
cov_colnames = ['xb_private', 'xb_medicaid', 'xb_aget', 'xb_aget2',
'xb_educyr', 'xb_actlim', 'xb_totchr', 'b0']
cov_rownames = ['xb_private', 'xb_medicaid', 'xb_aget', 'xb_aget2',
'xb_educyr', 'xb_actlim', 'xb_totchr', 'b0']
results_multtwostepdefault = ParamsTableTestBunch(
params_table=params_table,
params_table_colnames=params_table_colnames,
params_table_rownames=params_table_rownames,
cov=cov,
cov_colnames=cov_colnames,
cov_rownames=cov_rownames,
**est
)
est = dict(
rank=8,
N=3629,
Q=.0002590497181628,
J=.940091427212973,
J_df=1,
k_1=8,
converged=1,
has_xtinst=0,
type=1,
n_eq=1,
k=8,
n_moments=9,
k_aux=8,
k_eq_model=0,
k_eq=8,
cmdline="gmm ( (docvis / exp({xb:private medicaid aget aget2 educyr actlim totchr}+{b0})) - 1), instruments(income ssiratio medicaid aget aget2 educyr actlim totchr) twostep wmatrix(robust) center", # noqa:E501
cmd="gmm",
estat_cmd="gmm_estat",
predict="gmm_p",
marginsnotok="_ALL",
eqnames="1",
technique="gn",
winit="Unadjusted",
estimator="twostep",
wmatrix="robust",
vce="robust",
vcetype="Robust",
params="xb_private xb_medicaid xb_aget xb_aget2 xb_educyr xb_actlim xb_totchr b0", # noqa:E501
inst_1="income ssiratio medicaid aget aget2 educyr actlim totchr _cons",
params_1="xb_private xb_medicaid xb_aget xb_aget2 xb_educyr xb_actlim xb_totchr b0", # noqa:E501
sexp_1="(docvis / exp( ({xb_private} *private + {xb_medicaid} *medicaid + {xb_aget} *aget + {xb_aget2} *aget2 + {xb_educyr} *educyr + {xb_actlim} *actlim + {xb_totchr} *totchr) + {b0} )) - 1", # noqa:E501
properties="b V",
)
params_table = np.array([
.67815486150911, .25053960844836, 2.7067770469869, .00679398676131,
.18710625224955, 1.1692034707687, np.nan, 1.9599639845401,
0, .28872920226215, .10327332768441, 2.7957770775479,
.00517750993835, .08631719943712, .49114120508719, np.nan,
1.9599639845401, 0, .27067161407481, .05199697557915,
5.2055261110869, 1.934477426e-07, .16875941463467, .37258381351495,
np.nan, 1.9599639845401, 0, -.05690878166227,
.01189862079945, -4.7828048831437, 1.728659059e-06, -.08022964989488,
-.03358791342965, np.nan, 1.9599639845401, 0,
.01438116368432, .00783219272776, 1.8361605982125, .06633390816397,
-.00096965198207, .02973197935072, np.nan, 1.9599639845401,
0, .18038238197017, .03826654814775, 4.71383991244,
2.430916736e-06, .10538132578791, .25538343815243, np.nan,
1.9599639845401, 0, .28251055147828, .01378476509846,
20.494404471929, 2.415234157e-93, .25549290834996, .3095281946066,
np.nan, 1.9599639845401, 0, .50771182444237,
.10235828870929, 4.960143734762, 7.044103886e-07, .307093265053,
.70833038383174, np.nan, 1.9599639845401, 0
]).reshape(8, 9)
params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
params_table_rownames = ['_cons'] * 8
cov = np.array([
.06277009540146, .02315708886727, .00533574465012, -.0010254503134,
-.00154463481696, .00155084007911, -.00043796389511, -.01559997980204,
.02315708886727, .01066538021101, .00206217721135, -.00039331175814,
-.00049172883672, .00027603038575, -.00020644729789, -.00694809209467,
.00533574465012, .00206217721135, .00270368546938, -.00059509464294,
-.000122765895, .00021462183651, -.00004681968717, -.003107676362,
-.0010254503134, -.00039331175814, -.00059509464294, .00014157717693,
.00002474211983, -.00006134664668, .00001178278294, .00058658166731,
-.00154463481696, -.00049172883672, -.000122765895, .00002474211983,
.00006134324292, -.00001855938213, .00001443468876, .00007766055925,
.00155084007911, .00027603038575, .00021462183651, -.00006134664668,
-.00001855938213, .00146432870714, -.00016643336248, -.00074847778305,
-.00043796389511, -.00020644729789, -.00004681968717, .00001178278294,
.00001443468876, -.00016643336248, .00019001974882, -.00027573582025,
-.01559997980204, -.00694809209467, -.003107676362, .00058658166731,
.00007766055925, -.00074847778305, -.00027573582025, .0104772192675
]).reshape(8, 8)
cov_colnames = ['_cons'] * 8
cov_rownames = ['_cons'] * 8
results_multtwostepcenter = ParamsTableTestBunch(
params_table=params_table,
params_table_colnames=params_table_colnames,
params_table_rownames=params_table_rownames,
cov=cov,
cov_colnames=cov_colnames,
cov_rownames=cov_rownames,
**est
)