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import numpy as np
from statsmodels.tools.testing import ParamsTableTestBunch
est = dict(
rank=7,
N=10,
ic=3,
k=8,
k_eq=1,
k_dv=1,
converged=1,
rc=0,
k_autoCns=1,
ll=-33.45804471711131,
k_eq_model=1,
ll_0=-349.6684656479622,
df_m=6,
chi2=632.4208418617018,
p=2.3617193197e-133,
r2_p=.9043149497192691,
cmdline="poisson deaths lnpyears smokes i.agecat",
cmd="poisson",
predict="poisso_p",
estat_cmd="poisson_estat",
gof="poiss_g",
chi2type="LR",
opt="moptimize",
vce="oim",
title="Poisson regression",
user="poiss_lf",
crittype="log likelihood",
ml_method="e2",
singularHmethod="m-marquardt",
technique="nr",
which="max",
depvar="deaths",
properties="b V",
)
params_table = np.array([
.66308184237808, .63593388706566, 1.0426899019923, .29709193621918,
-.58332567281917, 1.9094893575753, np.nan, 1.9599639845401,
0, .84966723812924, .94279599903649, .90122066597395,
.36747100512904, -.99817896475073, 2.6975134410092, np.nan,
1.9599639845401, 0, 0, np.nan,
np.nan, np.nan, np.nan, np.nan,
np.nan, 1.9599639845401, 0, 1.3944392032504,
.25613243411925, 5.4442117338454, 5.203529593e-08, .8924288571041,
1.8964495493967, np.nan, 1.9599639845401, 0,
2.389284381366, .48305517266329, 4.9461935542328, 7.567871319e-07,
1.4425136404002, 3.3360551223318, np.nan, 1.9599639845401,
0, 2.8385093615484, .98099727008295, 2.8934936397003,
.00380982006764, .91579004325369, 4.7612286798431, np.nan,
1.9599639845401, 0, 2.9103531988515, 1.500316321385,
1.9398263935201, .05240079188831, -.03021275648066, 5.8509191541838,
np.nan, 1.9599639845401, 0, -4.724924181641,
6.0276019460727, -.78388125558284, .43310978942119, -16.538806909087,
7.088958545805, np.nan, 1.9599639845401, 0
]).reshape(8, 9)
params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
params_table_rownames = ['lnpyears', 'smokes', '1b.agecat', '2.agecat',
'3.agecat', '4.agecat', '5.agecat', '_cons']
cov = np.array([
.40441190871844, -.59566294916097, 0, .1055698685775,
.28413388045122, .61269322798077, .94624135329227, -3.8311942353131,
-.59566294916097, .88886429579921, 0, -.15587944298625,
-.4190789999425, -.90299843943229, -1.3940094688194, 5.6335527795822,
0, 0, 0, 0,
0, 0, 0, 0,
.1055698685775, -.15587944298625, 0, .06560382380785,
.10360281461667, .18937107288073, .27643306166968, -1.029211453947,
.28413388045122, -.4190789999425, 0, .10360281461667,
.23334229983676, .45990880867889, .69424104947043, -2.7206801001387,
.61269322798077, -.90299843943229, 0, .18937107288073,
.45990880867889, .96235564391021, 1.4630024143274, -5.8333014154113,
.94624135329227, -1.3940094688194, 0, .27643306166968,
.69424104947043, 1.4630024143274, 2.2509490642142, -8.993394678922,
-3.8311942353131, 5.6335527795822, 0, -1.029211453947,
-2.7206801001387, -5.8333014154113, -8.993394678922, 36.331985220299
]).reshape(8, 8)
cov_colnames = ['lnpyears', 'smokes', '1b.agecat', '2.agecat',
'3.agecat', '4.agecat', '5.agecat', '_cons']
cov_rownames = ['lnpyears', 'smokes', '1b.agecat', '2.agecat',
'3.agecat', '4.agecat', '5.agecat', '_cons']
results_noexposure_noconstraint = 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=6,
N=10,
ic=3,
k=7,
k_eq=1,
k_dv=1,
converged=1,
rc=0,
k_autoCns=1,
ll=-33.6001534405213,
k_eq_model=1,
ll_0=-495.0676356770329,
df_m=5,
chi2=922.9349644730232,
p=2.8920463572e-197,
r2_p=.9321301757191799,
cmdline="poisson deaths smokes i.agecat, exposure(pyears)",
cmd="poisson",
predict="poisso_p",
estat_cmd="poisson_estat",
offset="ln(pyears)",
gof="poiss_g",
chi2type="LR",
opt="moptimize",
vce="oim",
title="Poisson regression",
user="poiss_lf",
crittype="log likelihood",
ml_method="e2",
singularHmethod="m-marquardt",
technique="nr",
which="max",
depvar="deaths",
properties="b V",
)
params_table = np.array([
.35453563725291, .10737411818853, 3.3018723993653, .00096041750265,
.14408623273163, .56498504177418, np.nan, 1.9599639845401,
0, 0, np.nan, np.nan,
np.nan, np.nan, np.nan, np.nan,
1.9599639845401, 0, 1.4840070063099, .19510337263434,
7.606260139291, 2.821411159e-14, 1.1016114226842, 1.8664025899355,
np.nan, 1.9599639845401, 0, 2.6275051184579,
.18372726944827, 14.301116684248, 2.153264398e-46, 2.2674062873614,
2.9876039495544, np.nan, 1.9599639845401, 0,
3.350492785161, .18479918093323, 18.130452571495, 1.832448146e-73,
2.9882930461593, 3.7126925241626, np.nan, 1.9599639845401,
0, 3.7000964518246, .19221951212105, 19.24932807807,
1.430055953e-82, 3.3233531309415, 4.0768397727077, np.nan,
1.9599639845401, 0, -7.919325711822, .19176181876223,
-41.297719029467, 0, -8.2951719702059, -7.5434794534381,
np.nan, 1.9599639845401, 0]).reshape(7, 9)
params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
params_table_rownames = ['smokes', '1b.agecat', '2.agecat', '3.agecat',
'4.agecat', '5.agecat', '_cons']
cov = np.array([
.01152920125677, 0, -.00061561668833, -.00090117889461,
-.00087280941113, -.00045274641397, -.00921219275997, 0,
0, 0, 0, 0,
0, 0, -.00061561668833, 0,
.0380653260133, .02945988432334, .02945836949789, .0294359396881,
-.0289198676971, -.00090117889461, 0, .02945988432334,
.03375570953892, .0294799877675, .02944715358419, -.02869169455392,
-.00087280941113, 0, .02945836949789, .0294799877675,
.03415073727359, .02944603952766, -.02871436265941, -.00045274641397,
0, .0294359396881, .02944715358419, .02944603952766,
.03694834084006, -.02905000614546, -.00921219275997, 0,
-.0289198676971, -.02869169455392, -.02871436265941, -.02905000614546,
.036772595135]).reshape(7, 7)
cov_colnames = ['smokes', '1b.agecat', '2.agecat', '3.agecat',
'4.agecat', '5.agecat', '_cons']
cov_rownames = ['smokes', '1b.agecat', '2.agecat', '3.agecat',
'4.agecat', '5.agecat', '_cons']
results_exposure_noconstraint = 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=6,
N=10,
ic=4,
k=8,
k_eq=1,
k_dv=1,
converged=1,
rc=0,
k_autoCns=1,
ll=-33.46699798755848,
k_eq_model=1,
df_m=5,
chi2=452.5895246742914,
p=1.35732711092e-95,
r2_p=np.nan,
cmdline="poisson deaths lnpyears smokes i.agecat, constraints(1)",
cmd="poisson",
predict="poisso_p",
estat_cmd="poisson_estat",
gof="poiss_g",
chi2type="Wald",
opt="moptimize",
vce="oim",
title="Poisson regression",
user="poiss_lf",
crittype="log likelihood",
ml_method="e2",
singularHmethod="m-marquardt",
technique="nr",
which="max",
depvar="deaths",
properties="b V",
)
params_table = np.array([
.57966535352347, .13107152221057, 4.4225117992619, 9.756001957e-06,
.32276989059191, .83656081645503, np.nan, 1.9599639845401,
0, .97254074124891, .22289894431919, 4.3631464663029,
.00001282050472, .5356668381913, 1.4094146443065, np.nan,
1.9599639845401, 0, 0, np.nan,
np.nan, np.nan, np.nan, np.nan,
np.nan, 1.9599639845401, 0, 1.3727621378494,
.19798042377276, 6.9338276567436, 4.096036246e-12, .98472763761078,
1.760796638088, np.nan, 1.9599639845401, 0,
2.3307703209845, .20530981936838, 11.352454199, 7.210981748e-30,
1.92837046935, 2.7331701726189, np.nan, 1.9599639845401,
0, 2.71338890728, .29962471107816, 9.0559583604312,
1.353737255e-19, 2.1261352646886, 3.3006425498714, np.nan,
1.9599639845401, 0, 2.71338890728, .29962471107816,
9.0559583604312, 1.353737255e-19, 2.1261352646886, 3.3006425498714,
np.nan, 1.9599639845401, 0, -3.9347864312059,
1.2543868840549, -3.1368204508696, .00170790683415, -6.3933395466329,
-1.476233315779, np.nan, 1.9599639845401, 0
]).reshape(8, 9)
params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
params_table_rownames = ['lnpyears', 'smokes', '1b.agecat', '2.agecat',
'3.agecat', '4.agecat', '5.agecat', '_cons']
cov = np.array([
.0171797439346, -.02561346650005, 0, .00445310785396,
.01204526460873, .03142116278001, .03142116278001, -.16245493266167,
-.02561346650005, .04968393937861, 0, -.0069699735991,
-.01845598801461, -.04723465558226, -.04723465558226, .2326939064726,
0, 0, 0, 0,
0, 0, 0, 0,
.00445310785396, -.0069699735991, 0, .03919624819724,
.03254829669461, .03756752462584, .03756752462584, -.07124751761252,
.01204526460873, -.01845598801461, 0, .03254829669461,
.04215212192908, .05145895528528, .05145895528528, -.14290240509701,
.03142116278001, -.04723465558226, 0, .03756752462584,
.05145895528528, .08977496748867, .08977496748867, -.32621483141938,
.03142116278001, -.04723465558226, 0, .03756752462584,
.05145895528528, .08977496748867, .08977496748867, -.32621483141938,
-.16245493266167, .2326939064726, 0, -.07124751761252,
-.14290240509701, -.32621483141938, -.32621483141938, 1.5734864548889
]).reshape(8, 8)
cov_colnames = ['lnpyears', 'smokes', '1b.agecat', '2.agecat',
'3.agecat', '4.agecat', '5.agecat', '_cons']
cov_rownames = ['lnpyears', 'smokes', '1b.agecat', '2.agecat',
'3.agecat', '4.agecat', '5.agecat', '_cons']
results_noexposure_constraint = 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=5,
N=10,
ic=3,
k=7,
k_eq=1,
k_dv=1,
converged=1,
rc=0,
k_autoCns=1,
ll=-38.45090497564205,
k_eq_model=1,
df_m=4,
chi2=641.6446542589836,
p=1.5005477751e-137,
r2_p=np.nan,
cmdline=("poisson deaths smokes i.agecat, "
"exposure(pyears) constraints(1)"),
cmd="poisson",
predict="poisso_p",
estat_cmd="poisson_estat",
offset="ln(pyears)",
gof="poiss_g",
chi2type="Wald",
opt="moptimize",
vce="oim",
title="Poisson regression",
user="poiss_lf",
crittype="log likelihood",
ml_method="e2",
singularHmethod="m-marquardt",
technique="nr",
which="max",
depvar="deaths",
properties="b V",
)
params_table = np.array([
.34304077058284, .1073083520206, 3.196776058186, .00138972774083,
.13272026538212, .55336127578356, np.nan, 1.9599639845401,
0, 0, np.nan, np.nan,
np.nan, np.nan, np.nan, np.nan,
1.9599639845401, 0, 1.4846230896448, .19510453584194,
7.6093724999174, 2.754298692e-14, 1.1022252261742, 1.8670209531154,
np.nan, 1.9599639845401, 0, 2.6284071093765,
.18373002757074, 14.305811326156, 2.012766793e-46, 2.2683028724593,
2.9885113462937, np.nan, 1.9599639845401, 0,
3.4712405808805, .17983994458502, 19.301833020969, 5.183735658e-83,
3.1187607665121, 3.8237203952488, np.nan, 1.9599639845401,
0, 3.4712405808805, .17983994458502, 19.301833020969,
5.183735658e-83, 3.1187607665121, 3.8237203952488, np.nan,
1.9599639845401, 0, -7.9101515866812, .19164951521841,
-41.274049546467, 0, -8.2857777341639, -7.5345254391986,
np.nan, 1.9599639845401, 0]).reshape(7, 9)