Learn more »
Push, build, and install
RubyGems
npm packages
Python packages
Maven artifacts
PHP packages
Go Modules
Bower components
Debian packages
RPM packages
NuGet packages
import numpy as np
from statsmodels.tools.testing import MarginTableTestBunch
est = dict(
rank=7,
N=17,
ic=6,
k=7,
k_eq=1,
k_dv=1,
converged=1,
rc=0,
k_autoCns=0,
ll=-28.46285727296058,
k_eq_model=1,
ll_0=-101.6359341820935,
df_m=6,
chi2=146.3461538182658,
p=4.58013206701e-29,
r2_p=.719952814897477,
properties="b V",
depvar="sexecutions",
which="max",
technique="nr",
singularHmethod="m-marquardt",
ml_method="e2",
crittype="log likelihood",
user="poiss_lf",
title="Poisson regression",
vce="oim",
opt="moptimize",
chi2type="LR",
gof="poiss_g",
estat_cmd="poisson_estat",
predict="poisso_p",
cmd="poisson",
cmdline="poisson sexecutions sincome sperpoverty sperblack LN_VC100k96 south sdegree", # noqa:E501
)
margins_table = np.array([
47.514189267677, 12.722695157081, 3.7346009380122, .000188013074,
22.578164973516, 72.450213561838, np.nan, 1.9599639845401,
0, 2.3754103372885, 7.6314378245266, .31126642081184,
.75559809249357, -12.58193294904, 17.332753623617, np.nan,
1.9599639845401, 0, -11.583732327397, 3.8511214886273,
-3.007885459237, .00263072269737, -19.131791745195, -4.0356729095995,
np.nan, 1.9599639845401, 0, -1.807106397978,
14.19277372084, -.12732580914219, .89868253380624, -29.624431731551,
26.010218935595, np.nan, 1.9599639845401, 0,
10.852916363139, 2.6197368291491, 4.1427506161617, .00003431650408,
5.7183265290336, 15.987506197244, np.nan, 1.9599639845401,
0, -26.588397789444, 7.6315578612519, -3.4840065780596,
.00049396734722, -41.545976343431, -11.630819235457, np.nan,
1.9599639845401, 0]).reshape(6, 9)
margins_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
margins_table_rownames = ['sincome', 'sperpoverty', 'sperblack',
'LN_VC100k96', 'south', 'sdegree']
margins_cov = np.array([
10.87507957467, 3.4816608831283, .87483487811437, 3.1229403520191,
-.87306122632875, -2.2870394487277, -12.321063650937, 3.4816608831283,
5.1715652306254, .27473956091394, 1.7908952063684, -.92880259796684,
1.8964947971413, -9.0063087868006, .87483487811437, .27473956091394,
1.1098392181639, -.99390727840297, -.34477731736542, -.98869834020742,
.41772084541889, 3.1229403520191, 1.7908952063684, -.99390727840297,
17.912620004361, -.30763138390107, 2.8490197200257, -21.269786576194,
-.87306122632875, -.92880259796684, -.34477731736542, -.30763138390107,
.42666000427673, .05265352402592, 1.461997775289, -2.2870394487277,
1.8964947971413, -.98869834020742, 2.8490197200257, .05265352402592,
4.0773252373088, -4.46154120848, -12.321063650937, -9.0063087868006,
.41772084541889, -21.269786576194, 1.461997775289, -4.46154120848,
37.559994394326]).reshape(7, 7)
margins_cov_colnames = ['sincome', 'sperpoverty', 'sperblack', 'LN_VC100k96',
'south', 'sdegree', '_cons']
margins_cov_rownames = ['sincome', 'sperpoverty', 'sperblack', 'LN_VC100k96',
'south', 'sdegree', '_cons']
results_poisson_margins_cont = MarginTableTestBunch(
margins_table=margins_table,
margins_table_colnames=margins_table_colnames,
margins_table_rownames=margins_table_rownames,
margins_cov=margins_cov,
margins_cov_colnames=margins_cov_colnames,
margins_cov_rownames=margins_cov_rownames,
**est
)
est = dict(
alpha=1.1399915663048,
rank=8,
N=17,
ic=6,
k=8,
k_eq=2,
k_dv=1,
converged=1,
rc=0,
k_autoCns=0,
ll=-27.58269157281191,
k_eq_model=1,
ll_0=-32.87628220135203,
rank0=2,
df_m=6,
chi2=10.58718125708024,
p=.1020042170100994,
ll_c=-28.46285727296058,
chi2_c=1.760331400297339,
r2_p=.1610154881905236,
k_aux=1,
properties="b V",
depvar="sexecutions",
which="max",
technique="nr",
singularHmethod="m-marquardt",
ml_method="e2",
crittype="log likelihood",
user="nbreg_lf",
diparm1="lnalpha, exp label(",
title="Negative binomial regression",
vce="oim",
opt="moptimize",
chi2type="LR",
chi2_ct="LR",
diparm_opt2="noprob",
dispers="mean",
predict="nbreg_p",
cmd="nbreg",
cmdline="nbreg sexecutions sincome sperpoverty sperblack LN_VC100k96 south sdegree", # noqa:E501
)
margins_table = np.array([
38.76996449636, 35.863089953808, 1.0810547709719, .27967275079666,
-31.520400187424, 109.06032918014, np.nan, 1.9599639845401,
0, 2.5208248279391, 11.710699937092, .21525825454332,
.82956597472339, -20.431725282518, 25.473374938396, np.nan,
1.9599639845401, 0, -8.225606184332, 9.557721280021,
-.86062419517573, .38944505570119, -26.958395667445, 10.507183298781,
np.nan, 1.9599639845401, 0, -4.4150939806524,
28.010544627225, -.15762256819387, .87475421903252, -59.314752637366,
50.484564676062, np.nan, 1.9599639845401, 0,
7.0049476220304, 6.3399264323903, 1.1048941492826, .26920545789466,
-5.4210798500881, 19.430975094149, np.nan, 1.9599639845401,
0, -25.128303596214, 23.247820190364, -1.0808885904335,
.279746674501, -70.693193888391, 20.436586695964, np.nan,
1.9599639845401, 0]).reshape(6, 9)
margins_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
margins_table_rownames = ['sincome', 'sperpoverty', 'sperblack',
'LN_VC100k96', 'south', 'sdegree']
margins_cov = np.array([
44.468037032422, 13.291812805254, .84306554343753, -.38095027773819,
-2.1265212254924, -18.06714825989, -30.427077474507, .36347806905257,
13.291812805254, 15.093124820143, 3.3717840254072, -7.6860995498613,
-3.3867901970823, -1.4200645173727, -12.979849717094, .51706617429388,
.84306554343753, 3.3717840254072, 5.6928040093481, -12.140553562993,
-2.5831646721297, -1.8071496111137, 7.961664784177, .27439267406128,
-.38095027773819, -7.6860995498613, -12.140553562993, 91.950706114029,
6.6107070350689, 9.5470604840407, -82.665769963947, -1.1433180909155,
-2.1265212254924, -3.3867901970823, -2.5831646721297, 6.6107070350689,
2.0499053083335, 1.7094543055869, -3.029543334606, -.34297224102579,
-18.06714825989, -1.4200645173727, -1.8071496111137, 9.5470604840407,
1.7094543055869, 18.442703265156, -6.5839965105886, -.61952491151176,
-30.427077474507, -12.979849717094, 7.961664784177, -82.665769963947,
-3.029543334606, -6.5839965105886, 111.12618806587, .88600743091011,
.36347806905257, .51706617429388, .27439267406128, -1.1433180909155,
-.34297224102579, -.61952491151176, .88600743091011, .71851239110057
]).reshape(8, 8)
margins_cov_colnames = ['sincome', 'sperpoverty', 'sperblack', 'LN_VC100k96',
'south', 'sdegree', '_cons', '_cons']
margins_cov_rownames = ['sincome', 'sperpoverty', 'sperblack', 'LN_VC100k96',
'south', 'sdegree', '_cons', '_cons']
results_negbin_margins_cont = MarginTableTestBunch(
margins_table=margins_table,
margins_table_colnames=margins_table_colnames,
margins_table_rownames=margins_table_rownames,
margins_cov=margins_cov,
margins_cov_colnames=margins_cov_colnames,
margins_cov_rownames=margins_cov_rownames,
**est
)
est = dict(
rank=7,
N=17,
ic=6,
k=8,
k_eq=1,
k_dv=1,
converged=1,
rc=0,
k_autoCns=1,
ll=-28.46285727296058,
k_eq_model=1,
ll_0=-101.6359341820935,
df_m=6,
chi2=146.3461538182658,
p=4.58013206701e-29,
r2_p=.719952814897477,
properties="b V",
depvar="sexecutions",
which="max",
technique="nr",
singularHmethod="m-marquardt",
ml_method="e2",
crittype="log likelihood",
user="poiss_lf",
title="Poisson regression",
vce="oim",
opt="moptimize",
chi2type="LR",
gof="poiss_g",
estat_cmd="poisson_estat",
predict="poisso_p",
cmd="poisson",
cmdline="poisson sexecutions sincome sperpoverty sperblack LN_VC100k96 i.south sdegree", # noqa:E501
)
margins_table = np.array([
47.514189267677, 12.72269515678, 3.7346009381004, .00018801307393,
22.578164974105, 72.450213561249, np.nan, 1.9599639845401,
0, 2.3754103372885, 7.6314378245485, .31126642081095,
.75559809249425, -12.581932949083, 17.33275362366, np.nan,
1.9599639845401, 0, -11.583732327397, 3.8511214887188,
-3.0078854591656, .00263072269799, -19.131791745374, -4.0356729094203,
np.nan, 1.9599639845401, 0, -1.807106397978,
14.192773720841, -.12732580914219, .89868253380624, -29.624431731552,
26.010218935596, np.nan, 1.9599639845401, 0,
0, np.nan, np.nan, np.nan,
np.nan, np.nan, np.nan, 1.9599639845401,
0, 12.894515685772, 5.7673506886042, 2.2357779822979,
.02536631788468, 1.5907160498956, 24.198315321648, np.nan,
1.9599639845401, 0, -26.588397789444, 7.6315578608763,
-3.4840065782311, .00049396734691, -41.545976342695, -11.630819236193,
np.nan, 1.9599639845401, 0]).reshape(7, 9)
margins_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
margins_table_rownames = ['sincome', 'sperpoverty', 'sperblack', 'LN_VC100k96',
'0b.south', '1.south', 'sdegree']
margins_cov = np.array([
10.875079574674, 3.4816608831298, .87483487811447, 3.1229403520208,
0, -.873061226329, -2.2870394487282, -12.321063650942,
3.4816608831298, 5.1715652306252, .27473956091396, 1.7908952063684,
0, -.92880259796679, 1.8964947971405, -9.0063087868012,
.87483487811447, .27473956091396, 1.109839218164, -.9939072784041,
0, -.34477731736544, -.98869834020768, .41772084541996,
3.1229403520208, 1.7908952063684, -.9939072784041, 17.912620004373,
0, -.30763138390086, 2.8490197200274, -21.269786576207,
0, 0, 0, 0,
0, 0, 0, 0,
-.873061226329, -.92880259796679, -.34477731736544, -.30763138390086,
0, .42666000427672, .05265352402609, 1.4619977752889,
-2.2870394487282, 1.8964947971405, -.98869834020768, 2.8490197200274,
0, .05265352402609, 4.0773252373089, -4.4615412084808,
-12.321063650942, -9.0063087868012, .41772084541996, -21.269786576207,
0, 1.4619977752889, -4.4615412084808, 37.559994394343
]).reshape(8, 8)
margins_cov_colnames = ['sincome', 'sperpoverty', 'sperblack', 'LN_VC100k96',
'0b.south', '1.south', 'sdegree', '_cons']
margins_cov_rownames = ['sincome', 'sperpoverty', 'sperblack', 'LN_VC100k96',
'0b.south', '1.south', 'sdegree', '_cons']
results_poisson_margins_dummy = MarginTableTestBunch(
margins_table=margins_table,
margins_table_colnames=margins_table_colnames,
margins_table_rownames=margins_table_rownames,
margins_cov=margins_cov,
margins_cov_colnames=margins_cov_colnames,
margins_cov_rownames=margins_cov_rownames,
**est
)
est = dict(
alpha=1.139991566304804,
rank=8,
N=17,
ic=6,
k=9,
k_eq=2,
k_dv=1,
converged=1,
rc=0,
k_autoCns=1,
ll=-27.58269157281191,
k_eq_model=1,
ll_0=-32.87628220135203,
rank0=2,
df_m=6,
chi2=10.58718125708025,
p=.1020042170100991,
ll_c=-28.46285727296058,
chi2_c=1.760331400297339,
r2_p=.1610154881905237,
k_aux=1,
properties="b V",
depvar="sexecutions",
which="max",
technique="nr",
singularHmethod="m-marquardt",
ml_method="e2",
crittype="log likelihood",
user="nbreg_lf",
diparm1="lnalpha, exp label(",
title="Negative binomial regression",
vce="oim",
opt="moptimize",
chi2type="LR",
chi2_ct="LR",
diparm_opt2="noprob",
dispers="mean",
predict="nbreg_p",
cmd="nbreg",
cmdline="nbreg sexecutions sincome sperpoverty sperblack LN_VC100k96 i.south sdegree", # noqa:E501
)
margins_table = np.array([
38.769964496355, 35.863089979665, 1.0810547701924, .27967275114341,
-31.520400238107, 109.06032923082, np.nan, 1.9599639845401,
0, 2.5208248279388, 11.710699937639, .21525825453324,
.82956597473124, -20.43172528359, 25.473374939467, np.nan,
1.9599639845401, 0, -8.2256061843309, 9.5577212853699,
-.86062419469397, .38944505596662, -26.958395677928, 10.507183309266,
np.nan, 1.9599639845401, 0, -4.4150939806521,
28.010544626815, -.15762256819618, .87475421903071, -59.314752636561,
50.484564675257, np.nan, 1.9599639845401, 0,
0, np.nan, np.nan, np.nan,
np.nan, np.nan, np.nan, 1.9599639845401,
0, 8.0380552593041, 8.8634487485248, .90687671214231,
.36447199739385, -9.3339850666211, 25.410095585229, np.nan,