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# flake8: noqa
# file is mostly autogenerated
import numpy as np
class Bunch(dict):
def __init__(self, **kw):
dict.__init__(self, kw)
self.__dict__ = self
table = np.array([
-239.63921146434, 23.82402100183, -10.058722305774, 8.408247034e-24,
-286.33343459485, -192.94498833383, np.nan, 1.9599639845401,
0, 3403.2422719355, 9.5252071796205, 357.28800515928,
0, 3384.5732089181, 3421.9113349528, np.nan,
1.9599639845401, 0, 64.408589362646, 27.526987262221,
2.3398343141983, .01929229642928, 10.456685725799, 118.36049299949,
np.nan, 1.9599639845401, 0, 160.95125844367,
26.616203359744, 6.0471155960247, 1.474619593e-09, 108.78445845338,
213.11805843396, np.nan, 1.9599639845401, 0,
2.5468279240787, 2.0843242435815, 1.221896224602, .22174687335763,
-1.5383725254448, 6.6320283736022, np.nan, 1.9599639845401,
0, -71.328597501646, 19.64700882358, -3.6305067169329,
.00028286534285, -109.8360271998, -32.821167803489, np.nan,
1.9599639845401, 0, 3202.7457127058, 54.010820719402,
59.298223393878, 0, 3096.8864493203, 3308.6049760912,
np.nan, 1.9599639845401, 0, 25.111330718591,
40.375410330977, .62194614278197, .5339772824573, -54.023019391151,
104.24568082833, np.nan, 1.9599639845401, 0,
133.66170465247, 40.86442742188, 3.2708571509534, .00107222056235,
53.568898656735, 213.75451064821, np.nan, 1.9599639845401,
0, -7.3708810422984, 4.2181700723084, -1.7474120094605,
.08056589577917, -15.638342464688, .89658038009082, np.nan,
1.9599639845401, 0, 41.439913440842, 39.70711783121,
1.0436394204434, .29665224735146, -36.384607438218, 119.2644343199,
np.nan, 1.9599639845401, 0, 3227.169253308,
104.40590209261, 30.909835446329, 8.81095776e-210, 3022.537445433,
3431.8010611829, np.nan, 1.9599639845401, 0
]).reshape(12,9)
table_colnames = 'b se z pvalue ll ul df crit eform'.split()
table_rownames = 'r1vs0.mbsmoke 0.mbsmoke prenatal1 mmarried mage fbaby _cons prenatal1 mmarried mage fbaby _cons'.split()
table_t = np.array([
-223.30165083857, 22.742195167601, -9.8188257198974, 9.342544938e-23,
-267.87553429645, -178.72776738069, np.nan, 1.9599639845401,
0, 3360.9613730608, 12.757489072993, 263.45006872676,
0, 3335.9571539446, 3385.965592177, np.nan,
1.9599639845401, 0, 64.408589362646, 27.526987262221,
2.3398343141983, .01929229642928, 10.456685725799, 118.36049299949,
np.nan, 1.9599639845401, 0, 160.95125844367,
26.616203359744, 6.0471155960247, 1.474619593e-09, 108.78445845338,
213.11805843396, np.nan, 1.9599639845401, 0,
2.5468279240787, 2.0843242435815, 1.221896224602, .22174687335761,
-1.5383725254448, 6.6320283736022, np.nan, 1.9599639845401,
0, -71.328597501645, 19.64700882358, -3.6305067169329,
.00028286534285, -109.8360271998, -32.821167803489, np.nan,
1.9599639845401, 0, 3202.7457127058, 54.010820719402,
59.298223393877, 0, 3096.8864493203, 3308.6049760912,
np.nan, 1.9599639845401, 0, 25.111330718591,
40.375410330977, .62194614278197, .5339772824573, -54.023019391151,
104.24568082833, np.nan, 1.9599639845401, 0,
133.66170465247, 40.86442742188, 3.2708571509534, .00107222056235,
53.568898656735, 213.7545106482, np.nan, 1.9599639845401,
0, -7.3708810422983, 4.2181700723084, -1.7474120094604,
.08056589577917, -15.638342464688, .89658038009089, np.nan,
1.9599639845401, 0, 41.439913440842, 39.70711783121,
1.0436394204434, .29665224735146, -36.384607438218, 119.2644343199,
np.nan, 1.9599639845401, 0, 3227.169253308,
104.40590209261, 30.909835446328, 8.81095776e-210, 3022.537445433,
3431.8010611829, np.nan, 1.9599639845401, 0
]).reshape(12,9)
table_t_colnames = 'b se z pvalue ll ul df crit eform'.split()
table_t_rownames = 'r1vs0.mbsmoke 0.mbsmoke prenatal1 mmarried mage fbaby _cons prenatal1 mmarried mage fbaby _cons'.split()
results_ra = Bunch(
table=table,
table_colnames=table_colnames,
table_rownames=table_rownames,
table_t=table_t,
table_t_colnames=table_t_colnames,
table_t_rownames=table_t_rownames,
)
table = np.array([
-230.68863779526, 25.815243801554, -8.9361401956379, 4.030006076e-19,
-281.28558589843, -180.09168969209, np.nan, 1.9599639845401,
0, 3403.4627086846, 9.5713688979534, 355.58787305882,
0, 3384.7031703618, 3422.2222470073, np.nan,
1.9599639845401, 0, -.64848213804682, .05541728623223,
-11.70180249046, 1.247746040e-31, -.75709802318294, -.53986625291071,
np.nan, 1.9599639845401, 0, .17443269674281,
.03637184725595, 4.7958162673267, 1.620137296e-06, .10314518606995,
.24572020741566, np.nan, 1.9599639845401, 0,
-.00325591262232, .00066777909855, -4.8757330521042, 1.084051264e-06,
-.00456473560511, -.00194708963953, np.nan, 1.9599639845401,
0, -.2175961616731, .04956043775733, -4.3905213819646,
.00001130791946, -.31473283473551, -.12045948861068, np.nan,
1.9599639845401, 0, -.08636308703575, .01001479395511,
-8.6235510608446, 6.490921364e-18, -.10599172250036, -.06673445157114,
np.nan, 1.9599639845401, 0, -1.5582552629264,
.46396908940251, -3.3585324939062, .00078357508242, -2.4676179680951,
-.64889255775762, np.nan, 1.9599639845401, 0
]).reshape(8,9)
table_colnames = 'b se z pvalue ll ul df crit eform'.split()
table_rownames = 'r1vs0.mbsmoke 0.mbsmoke mmarried mage c.mage#c.mage fbaby medu _cons'.split()
table_t = np.array([
-225.17726054799, 23.664582726945, -9.5153700002322, 1.810673837e-21,
-271.55899040197, -178.795530694, np.nan, 1.9599639845401,
0, 3362.8369827702, 14.20149077868, 236.7946460817,
0, 3335.0025723172, 3390.6713932232, np.nan,
1.9599639845401, 0, -.64848213804682, .05541728623191,
-11.701802490528, 1.247746039e-31, -.75709802318231, -.53986625291133,
np.nan, 1.9599639845401, 0, .17443269674281,
.03637184724264, 4.7958162690822, 1.620137282e-06, .10314518609605,
.24572020738957, np.nan, 1.9599639845401, 0,
-.00325591262232, .00066777909835, -4.8757330536115, 1.084051255e-06,
-.0045647356047, -.00194708963993, np.nan, 1.9599639845401,
0, -.2175961616731, .04956043776041, -4.3905213816925,
.00001130791948, -.31473283474153, -.12045948860466, np.nan,
1.9599639845401, 0, -.08636308703575, .01001479395498,
-8.62355106096, 6.490921358e-18, -.1059917225001, -.06673445157141,
np.nan, 1.9599639845401, 0, -1.5582552629264,
.46396908922586, -3.3585324951848, .00078357507879, -2.4676179677489,
-.64889255810383, np.nan, 1.9599639845401, 0
]).reshape(8,9)
table_t_colnames = 'b se z pvalue ll ul df crit eform'.split()
table_t_rownames = 'r1vs0.mbsmoke 0.mbsmoke mmarried mage c.mage#c.mage fbaby medu _cons'.split()
results_ipw = Bunch(
table=table,
table_colnames=table_colnames,
table_rownames=table_rownames,
table_t=table_t,
table_t_colnames=table_t_colnames,
table_t_rownames=table_t_rownames,
)
table = np.array([
-230.98920111258, 26.210564625435, -8.8128281253592, 1.220275657e-18,
-282.36096379289, -179.61743843227, np.nan, 1.9599639845401,
0, 3403.3552531738, 9.5684720516903, 355.68429680187,
0, 3384.6013925654, 3422.1091137822, np.nan,
1.9599639845401, 0, 64.408589362646, 27.526987262221,
2.3398343141983, .01929229642928, 10.456685725799, 118.36049299949,
np.nan, 1.9599639845401, 0, 160.95125844367,
26.616203359739, 6.0471155960257, 1.474619593e-09, 108.78445845339,
213.11805843395, np.nan, 1.9599639845401, 0,
2.5468279240787, 2.0843242435815, 1.2218962246021, .2217468733576,
-1.5383725254446, 6.6320283736021, np.nan, 1.9599639845401,
0, -71.328597501645, 19.64700882358, -3.6305067169329,
.00028286534285, -109.8360271998, -32.821167803489, np.nan,
1.9599639845401, 0, 3202.7457127058, 54.010820719402,
59.298223393878, 0, 3096.8864493203, 3308.6049760912,
np.nan, 1.9599639845401, 0, 25.111330718591,
40.375410330977, .62194614278197, .5339772824573, -54.023019391151,
104.24568082833, np.nan, 1.9599639845401, 0,
133.66170465247, 40.86442742188, 3.2708571509534, .00107222056235,
53.568898656735, 213.75451064821, np.nan, 1.9599639845401,
0, -7.3708810422983, 4.2181700723084, -1.7474120094604,
.08056589577917, -15.638342464688, .89658038009083, np.nan,
1.9599639845401, 0, 41.439913440842, 39.70711783121,
1.0436394204434, .29665224735146, -36.384607438218, 119.2644343199,
np.nan, 1.9599639845401, 0, 3227.169253308,
104.40590209261, 30.909835446329, 8.81095776e-210, 3022.537445433,
3431.8010611829, np.nan, 1.9599639845401, 0,
-.64848213804682, .05541728623246, -11.701802490411, 1.247746040e-31,
-.75709802318339, -.53986625291025, np.nan, 1.9599639845401,
0, .17443269674281, .03637184725678, 4.7958162672167,
1.620137297e-06, .10314518606832, .2457202074173, np.nan,
1.9599639845401, 0, -.00325591262232, .00066777909857,
-4.8757330520018, 1.084051264e-06, -.00456473560514, -.0019470896395,
np.nan, 1.9599639845401, 0, -.2175961616731,
.04956043775735, -4.3905213819631, .00001130791946, -.31473283473554,
-.12045948861065, np.nan, 1.9599639845401, 0,
-.08636308703575, .01001479395507, -8.6235510608833, 6.490921362e-18,
-.10599172250028, -.06673445157123, np.nan, 1.9599639845401,
0, -1.5582552629264, .46396908941501, -3.3585324938157,
.00078357508267, -2.4676179681196, -.64889255773313, np.nan,
1.9599639845401, 0]).reshape(18,9)
table_colnames = 'b se z pvalue ll ul df crit eform'.split()
table_rownames = 'r1vs0.mbsmoke 0.mbsmoke prenatal1 mmarried mage fbaby _cons prenatal1 mmarried mage fbaby _cons mmarried mage c.mage#c.mage fbaby medu _cons'.split()
results_aipw = Bunch(
table=table,
table_colnames=table_colnames,
table_rownames=table_rownames,
)
table = np.array([
-227.19561818675, 27.347935624441, -8.3075966430061, 9.765983730e-17,
-280.79658706217, -173.59464931133, np.nan, 1.9599639845401,
0, 3403.2506509758, 9.596621896179, 354.6300654328,
0, 3384.441617686, 3422.0596842655, np.nan,
1.9599639845401, 0, 82.181272088395, 58.620922322422,
1.4019102537553, .1609420481386, -32.713624404072, 197.07616858086,
np.nan, 1.9599639845401, 0, 118.76459672483,
35.33781015982, 3.3608363446321, .00077706853832, 49.503761519072,
188.02543193059, np.nan, 1.9599639845401, 0,
5.5147235203491, 4.0179588195471, 1.3725186762792, .16990202893509,
-2.3603310573283, 13.389778098026, np.nan, 1.9599639845401,
0, -111.48446442703, 55.345523286143, -2.0143357187291,
.04397429826544, -219.95969677339, -3.0092320806668, np.nan,
1.9599639845401, 0, 3154.0113942013, 88.314554758981,
35.713381591617, 2.45059114e-279, 2980.918047563, 3327.1047408395,
np.nan, 1.9599639845401, 0, 95.066642475617,
77.968463953496, 1.2192960801731, .22273183586194, -57.748738803144,
247.88202375438, np.nan, 1.9599639845401, 0,
96.67302454497, 48.157019046638, 2.0074544990284, .04470129085208,
2.2870016107499, 191.05904747919, np.nan, 1.9599639845401,
0, -3.4220909568961, 11.838775715374, -.28905784172025,
.77253711451199, -26.625664980076, 19.781483066284, np.nan,
1.9599639845401, 0, -9.8880616310089, 130.98498723825,
-.07549003774779, .93982482364584, -266.61391913341, 246.83779587139,
np.nan, 1.9599639845401, 0, 3142.5011834805,
343.51518833541, 9.1480705662777, 5.795943393e-20, 2469.2237862006,
3815.7785807604, np.nan, 1.9599639845401, 0,
-.64848213804682, .05541728623247, -11.70180249041, 1.247746040e-31,
-.7570980231834, -.53986625291024, np.nan, 1.9599639845401,
0, .17443269674281, .03637184725647, 4.7958162672578,
1.620137296e-06, .10314518606893, .24572020741669, np.nan,
1.9599639845401, 0, -.00325591262232, .00066777909856,
-4.8757330520397, 1.084051264e-06, -.00456473560513, -.00194708963951,
np.nan, 1.9599639845401, 0, -.2175961616731,
.04956043775737, -4.3905213819612, .00001130791946, -.31473283473559,
-.12045948861061, np.nan, 1.9599639845401, 0,
-.08636308703575, .01001479395509, -8.6235510608616, 6.490921363e-18,
-.10599172250032, -.06673445157118, np.nan, 1.9599639845401,
0, -1.5582552629264, .46396908941001, -3.3585324938518,
.00078357508257, -2.4676179681098, -.64889255774291, np.nan,
1.9599639845401, 0]).reshape(18,9)
table_colnames = 'b se z pvalue ll ul df crit eform'.split()
table_rownames = 'r1vs0.mbsmoke 0.mbsmoke prenatal1 mmarried mage fbaby _cons prenatal1 mmarried mage fbaby _cons mmarried mage c.mage#c.mage fbaby medu _cons'.split()
results_aipw_wls = Bunch(
table=table,
table_colnames=table_colnames,
table_rownames=table_rownames,
)
table = np.array([
-229.96707793513, 26.626675723594, -8.6367175655861, 5.785117089e-18,
-282.1544033814, -177.77975248886, np.nan, 1.9599639845401,
0, 3403.3356393074, 9.57125961016, 355.57865713879,
0, 3384.5763151848, 3422.09496343, np.nan,
1.9599639845401, 0, 67.985490604489, 28.784283742461,
2.3618962074155, .01818173182612, 11.569331148484, 124.40165006049,
np.nan, 1.9599639845401, 0, 155.58930106521,
26.469032273847, 5.8781635631969, 4.148429157e-09, 103.71095110284,
207.46765102758, np.nan, 1.9599639845401, 0,
2.893051083978, 2.1347878922477, 1.3551936913657, .17535585413018,
-1.2910562994597, 7.0771584674157, np.nan, 1.9599639845401,
0, -71.921496436385, 20.393170550443, -3.5267442234392,
.00042070296711, -111.89137624584, -31.951616626934, np.nan,
1.9599639845401, 0, 3194.8075652619, 55.049108896772,
58.035590934864, 0, 3086.9132944432, 3302.7018360806,
np.nan, 1.9599639845401, 0, 34.769226459004,
43.185336354577, .80511649078124, .42075246042071, -49.872477456214,
119.41093037422, np.nan, 1.9599639845401, 0,
124.09407253083, 40.297750215194, 3.07942929489, .00207397592636,
45.111933451056, 203.0762116106, np.nan, 1.9599639845401,
0, -5.0688328301824, 5.954425242531, -.85127155413371,
.39461852267686, -16.739291854179, 6.6016261938146, np.nan,
1.9599639845401, 0, 39.896915302387, 56.820722936052,
.70215430640135, .48258293810814, -71.469655227805, 151.26348583258,
np.nan, 1.9599639845401, 0, 3175.5506552136,
153.83122218596, 20.643082789623, 1.126438084e-94, 2874.0470000313,
3477.0543103958, np.nan, 1.9599639845401, 0,
-.64848213804682, .0554172862325, -11.701802490402, 1.247746040e-31,
-.75709802318348, -.53986625291016, np.nan, 1.9599639845401,
0, .17443269674281, .03637184725653, 4.7958162672504,
1.620137296e-06, .10314518606882, .2457202074168, np.nan,
1.9599639845401, 0, -.00325591262232, .00066777909856,
-4.8757330520424, 1.084051264e-06, -.00456473560512, -.00194708963951,
np.nan, 1.9599639845401, 0, -.2175961616731,
.04956043775739, -4.3905213819596, .00001130791946, -.31473283473562,
-.12045948861057, np.nan, 1.9599639845401, 0,
-.08636308703575, .01001479395511, -8.6235510608441, 6.490921364e-18,
-.10599172250036, -.06673445157114, np.nan, 1.9599639845401,
0, -1.5582552629264, .46396908941052, -3.3585324938482,
.00078357508258, -2.4676179681108, -.64889255774192, np.nan,
1.9599639845401, 0]).reshape(18,9)
table_colnames = 'b se z pvalue ll ul df crit eform'.split()
table_rownames = 'r1vs0.mbsmoke 0.mbsmoke prenatal1 mmarried mage fbaby _cons prenatal1 mmarried mage fbaby _cons mmarried mage c.mage#c.mage fbaby medu _cons'.split()
table_t = np.array([
-223.54526198669, 23.79401566757, -9.3950203744454, 5.720675031e-21,
-270.18067574271, -176.90984823067, np.nan, 1.9599639845401,
0, 3361.2049842089, 14.465011372588, 232.36794618624,
0, 3332.8540828827, 3389.5558855351, np.nan,
1.9599639845401, 0, 78.699052591474, 39.923632779925,
1.9712397673151, .0486964572387, .45017021081746, 156.94793497213,
np.nan, 1.9599639845401, 0, 138.08012522442,
29.391214670734, 4.6980067605684, 2.627127381e-06, 80.4744030079,
195.68584744095, np.nan, 1.9599639845401, 0,
4.4536627925561, 3.0377853203718, 1.4660887201901, .14262411828721,
-1.5002870281371, 10.407612613249, np.nan, 1.9599639845401,
0, -74.283881352771, 32.351302431096, -2.2961635473869,
.0216665319432, -137.69126897068, -10.876493734861, np.nan,
1.9599639845401, 0, 3157.0675716992, 72.926678364308,
43.290982703585, 0, 3014.133908593, 3300.0012348054,
np.nan, 1.9599639845401, 0, 25.111330718591,
40.375410330977, .62194614278197, .5339772824573, -54.023019391152,
104.24568082833, np.nan, 1.9599639845401, 0,
133.66170465247, 40.86442742188, 3.2708571509534, .00107222056235,
53.568898656735, 213.75451064821, np.nan, 1.9599639845401,
0, -7.3708810422984, 4.2181700723084, -1.7474120094604,
.08056589577917, -15.638342464688, .89658038009091, np.nan,
1.9599639845401, 0, 41.439913440842, 39.70711783121,
1.0436394204434, .29665224735146, -36.384607438218, 119.2644343199,
np.nan, 1.9599639845401, 0, 3227.169253308,
104.40590209261, 30.909835446329, 8.81095776e-210, 3022.537445433,
3431.8010611829, np.nan, 1.9599639845401, 0,
-.64848213804682, .05541728623085, -11.701802490751, 1.247746035e-31,
-.75709802318024, -.5398662529134, np.nan, 1.9599639845401,
0, .17443269674281, .03637184726815, 4.7958162657175,
1.620137309e-06, .10314518604603, .24572020743958, np.nan,
1.9599639845401, 0, -.00325591262232, .00066777909873,
-4.875733050792, 1.084051271e-06, -.00456473560546, -.00194708963917,
np.nan, 1.9599639845401, 0, -.2175961616731,
.04956043776294, -4.3905213814679, .00001130791949, -.3147328347465,
-.1204594885997, np.nan, 1.9599639845401, 0,
-.08636308703575, .01001479395705, -8.6235510591753, 6.490921459e-18,
-.10599172250416, -.06673445156734, np.nan, 1.9599639845401,
0, -1.5582552629264, .46396908950784, -3.3585324931437,
.00078357508458, -2.4676179683016, -.64889255755117, np.nan,
1.9599639845401, 0]).reshape(18,9)
table_t_colnames = 'b se z pvalue ll ul df crit eform'.split()
table_t_rownames = 'r1vs0.mbsmoke 0.mbsmoke prenatal1 mmarried mage fbaby _cons prenatal1 mmarried mage fbaby _cons mmarried mage c.mage#c.mage fbaby medu _cons'.split()
results_ipwra = Bunch(
table=table,
table_colnames=table_colnames,
table_rownames=table_rownames,
table_t=table_t,
table_t_colnames=table_t_colnames,
table_t_rownames=table_t_rownames,
)