some new features

.venv/lib/python3.12/site-packages/statsmodels/discrete/tests/results/mn_logit_summary.txt

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+==============================================================================
+       y=1       coef    std err          z      P>|z|      [0.025      0.975]
+------------------------------------------------------------------------------
+const         -0.2794      0.612     -0.457      0.648      -1.479       0.920
+x1            -0.0119      0.034     -0.350      0.727      -0.079       0.055
+x2             0.2946      0.093      3.152      0.002       0.111       0.478
+x3            -0.0257      0.007     -3.873      0.000      -0.039      -0.013
+x4             0.0897      0.069      1.297      0.195      -0.046       0.225
+------------------------------------------------------------------------------
+       y=2       coef    std err          z      P>|z|      [0.025      0.975]
+------------------------------------------------------------------------------
+const         -1.7392      0.720     -2.414      0.016      -3.151      -0.327
+x1            -0.0941      0.039     -2.415      0.016      -0.170      -0.018
+x2             0.3838      0.108      3.559      0.000       0.172       0.595
+x3            -0.0230      0.008     -2.914      0.004      -0.038      -0.008
+x4             0.2451      0.080      3.053      0.002       0.088       0.402
+------------------------------------------------------------------------------
+       y=3       coef    std err          z      P>|z|      [0.025      0.975]
+------------------------------------------------------------------------------
+const         -3.0091      1.069     -2.815      0.005      -5.104      -0.914
+x1            -0.1115      0.057     -1.965      0.049      -0.223      -0.000
+x2             0.5641      0.159      3.557      0.000       0.253       0.875
+x3            -0.0156      0.011     -1.391      0.164      -0.038       0.006
+x4             0.0687      0.119      0.577      0.564      -0.165       0.302
+------------------------------------------------------------------------------
+       y=4       coef    std err          z      P>|z|      [0.025      0.975]
+------------------------------------------------------------------------------
+const         -6.5607      0.870     -7.540      0.000      -8.266      -4.855
+x1            -0.0949      0.043     -2.183      0.029      -0.180      -0.010
+x2             1.2668      0.128      9.897      0.000       1.016       1.518
+x3            -0.0109      0.008     -1.315      0.189      -0.027       0.005
+x4             0.3067      0.088      3.475      0.001       0.134       0.480
+------------------------------------------------------------------------------
+       y=5       coef    std err          z      P>|z|      [0.025      0.975]
+------------------------------------------------------------------------------
+const         -6.0760      0.774     -7.851      0.000      -7.593      -4.559
+x1            -0.0967      0.039     -2.479      0.013      -0.173      -0.020
+x2             1.3377      0.116     11.493      0.000       1.110       1.566
+x3            -0.0198      0.008     -2.631      0.009      -0.035      -0.005
+x4             0.3193      0.080      4.001      0.000       0.163       0.476
+------------------------------------------------------------------------------
+       y=6       coef    std err          z      P>|z|      [0.025      0.975]
+------------------------------------------------------------------------------
+const        -10.5973      0.957    -11.076      0.000     -12.473      -8.722
+x1            -0.1447      0.042     -3.475      0.001      -0.226      -0.063
+x2             2.0395      0.141     14.479      0.000       1.763       2.316
+x3            -0.0129      0.008     -1.612      0.107      -0.029       0.003
+x4             0.4576      0.085      5.352      0.000       0.290       0.625
+==============================================================================

.venv/lib/python3.12/site-packages/statsmodels/discrete/tests/results/mnlogit_resid.csv

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.venv/lib/python3.12/site-packages/statsmodels/discrete/tests/results/results_count_margins.py

@@ -0,0 +1,394 @@
+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,
+    1.9599639845401,                0,  -25.12830359621,  23.247820207656,
+    -1.0808885896294,  .27974667485873, -70.693193922279,  20.436586729858,
+    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([
+    44.468037032424,  13.291812805256,  .84306554343906, -.38095027774827,
+    0, -2.1265212254934, -18.067148259892, -30.427077474499,
+    .36347806905277,  13.291812805256,  15.093124820144,  3.3717840254072,
+    -7.6860995498609,                0, -3.3867901970823, -1.4200645173736,
+    -12.979849717095,  .51706617429393,  .84306554343906,  3.3717840254072,
+    5.6928040093478,  -12.14055356299,                0, -2.5831646721296,
+    -1.8071496111144,  7.9616647841741,  .27439267406129, -.38095027774827,
+    -7.6860995498609,  -12.14055356299,  91.950706114005,                0,
+    6.6107070350678,  9.5470604840447, -82.665769963921, -1.1433180909154,
+    0,                0,                0,                0,
+    0,                0,                0,                0,
+    0, -2.1265212254934, -3.3867901970823, -2.5831646721296,
+    6.6107070350678,                0,  2.0499053083335,  1.7094543055874,
+    -3.0295433346046, -.34297224102581, -18.067148259892, -1.4200645173736,
+    -1.8071496111144,  9.5470604840447,                0,  1.7094543055874,
+    18.442703265157, -6.5839965105912, -.61952491151187, -30.427077474499,
+    -12.979849717095,  7.9616647841741, -82.665769963921,                0,
+    -3.0295433346046, -6.5839965105912,  111.12618806584,  .88600743090998,
+    .36347806905277,  .51706617429393,  .27439267406129, -1.1433180909154,
+    0, -.34297224102581, -.61952491151187,  .88600743090998,
+    .71851239110059]).reshape(9, 9)
+
+margins_cov_colnames = ['sincome', 'sperpoverty', 'sperblack', 'LN_VC100k96',
+                        '0b.south', '1.south', 'sdegree', '_cons', '_cons']
+
+margins_cov_rownames = ['sincome', 'sperpoverty', 'sperblack', 'LN_VC100k96',
+                        '0b.south', '1.south', 'sdegree', '_cons', '_cons']
+
+
+results_negbin_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
+                )

.venv/lib/python3.12/site-packages/statsmodels/discrete/tests/results/results_count_robust_cluster.py

@@ -0,0 +1,689 @@
+import numpy as np
+
+from statsmodels.tools.testing import ParamsTableTestBunch
+
+est = dict(
+           rank=3,
+           N=34,
+           ic=1,
+           k=3,
+           k_eq=1,
+           k_dv=1,
+           converged=1,
+           rc=0,
+           k_autoCns=0,
+           N_clust=5,
+           ll=-354.2436413025559,
+           k_eq_model=1,
+           ll_0=-356.2029100704882,
+           df_m=2,
+           chi2=5.204189583786304,
+           p=.0741181533729996,
+           r2_p=.0055004288638308,
+           cmdline="poisson accident yr_con op_75_79, vce(cluster ship)",
+           cmd="poisson",
+           predict="poisso_p",
+           estat_cmd="poisson_estat",
+           gof="poiss_g",
+           chi2type="Wald",
+           opt="moptimize",
+           vcetype="Robust",
+           clustvar="ship",
+           vce="cluster",
+           title="Poisson regression",
+           user="poiss_lf",
+           crittype="log pseudolikelihood",
+           ml_method="e2",
+           singularHmethod="m-marquardt",
+           technique="nr",
+           which="max",
+           depvar="accident",
+           properties="b V",
+          )
+
+params_table = np.array([
+    -.02172061893549,  .19933709357097, -.10896426022065,  .91323083771076,
+    -.41241414311748,  .36897290524649, np.nan,  1.9599639845401,
+    0,  .22148585072024,  .11093628220713,  1.9965140918162,
+    .04587799343723,  .00405473301549,  .43891696842499, np.nan,
+    1.9599639845401,                0,  2.2697077143215,  1.1048569901548,
+    2.054299999499,  .03994666479943,  .10422780555076,  4.4351876230922,
+    np.nan,  1.9599639845401,                0]).reshape(3, 9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'yr_con op_75_79 _cons'.split()
+
+cov = np.array([
+    .03973527687332,  .00976206273414, -.21171095768584,  .00976206273414,
+    .01230685870994, -.06297293767114, -.21171095768584, -.06297293767114,
+    1.2207089686939]).reshape(3, 3)
+
+cov_colnames = 'yr_con op_75_79 _cons'.split()
+
+cov_rownames = 'yr_con op_75_79 _cons'.split()
+
+
+results_poisson_clu = 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=3,
+           N=34,
+           ic=1,
+           k=3,
+           k_eq=1,
+           k_dv=1,
+           converged=1,
+           rc=0,
+           k_autoCns=0,
+           ll=-354.2436413025559,
+           k_eq_model=1,
+           ll_0=-356.2029100704882,
+           df_m=2,
+           chi2=.1635672212515404,
+           p=.9214713337295277,
+           r2_p=.0055004288638308,
+           cmdline="poisson accident yr_con op_75_79, vce(robust)",
+           cmd="poisson",
+           predict="poisso_p",
+           estat_cmd="poisson_estat",
+           gof="poiss_g",
+           chi2type="Wald",
+           opt="moptimize",
+           vcetype="Robust",
+           vce="robust",
+           title="Poisson regression",
+           user="poiss_lf",
+           crittype="log pseudolikelihood",
+           ml_method="e2",
+           singularHmethod="m-marquardt",
+           technique="nr",
+           which="max",
+           depvar="accident",
+           properties="b V",
+          )
+
+params_table = np.array([
+    -.02172061893549,  .19233713248134, -.11292993014545,  .91008610728406,
+    -.39869447148862,  .35525323361764, np.nan,  1.9599639845401,
+    0,  .22148585072024,  .55301404772037,    .400506735106,
+    .68878332380143,  -.8624017657564,  1.3053734671969, np.nan,
+    1.9599639845401,                0,  2.2697077143215,  .66532523368388,
+    3.4114258702533,  .00064624070669,  .96569421829539,  3.5737212103476,
+    np.nan,  1.9599639845401,                0]).reshape(3, 9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'yr_con op_75_79 _cons'.split()
+
+cov = np.array([
+    .03699357253114, -.01521223175214, -.09585501859714, -.01521223175214,
+    .30582453697607,  -.1649339692102, -.09585501859714,  -.1649339692102,
+    .44265766657651]).reshape(3, 3)
+
+cov_colnames = 'yr_con op_75_79 _cons'.split()
+
+cov_rownames = 'yr_con op_75_79 _cons'.split()
+
+
+results_poisson_hc1 = 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=3,
+           N=34,
+           ic=4,
+           k=3,
+           k_eq=1,
+           k_dv=1,
+           converged=1,
+           rc=0,
+           k_autoCns=0,
+           ll=-91.28727940081573,
+           k_eq_model=1,
+           ll_0=-122.0974139280415,
+           df_m=2,
+           chi2=61.62026905445154,
+           p=4.16225408420e-14,
+           r2_p=.2523405986746273,
+           cmdline="poisson accident yr_con op_75_79, exposure(service)",
+           cmd="poisson",
+           predict="poisso_p",
+           estat_cmd="poisson_estat",
+           offset="ln(service)",
+           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="accident",
+           properties="b V",
+          )
+
+params_table = np.array([
+    .30633819450439,  .05790831365493,   5.290055523458,  1.222792336e-07,
+    .19283998533528,   .4198364036735, np.nan,  1.9599639845401,
+    0,  .35592229608495,  .12151759298719,  2.9289775030556,
+    .00340079035234,  .11775219034206,  .59409240182785, np.nan,
+    1.9599639845401,                0,  -6.974712802772,  .13252425018256,
+    -52.629709605328,                0,  -7.234455560208,  -6.714970045336,
+    np.nan,  1.9599639845401,                0]).reshape(3, 9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'yr_con op_75_79 _cons'.split()
+
+cov = np.array([
+    .00335337279036, -.00315267340017, -.00589654294427, -.00315267340017,
+    .0147665254054, -.00165060980569, -.00589654294427, -.00165060980569,
+    .01756267688645]).reshape(3, 3)
+
+cov_colnames = 'yr_con op_75_79 _cons'.split()
+
+cov_rownames = 'yr_con op_75_79 _cons'.split()
+
+
+results_poisson_exposure_nonrobust = 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=3,
+           N=34,
+           ic=4,
+           k=3,
+           k_eq=1,
+           k_dv=1,
+           converged=1,
+           rc=0,
+           k_autoCns=0,
+           ll=-91.28727940081573,
+           k_eq_model=1,
+           ll_0=-122.0974139280415,
+           df_m=2,
+           chi2=15.1822804640621,
+           p=.0005049050167458,
+           r2_p=.2523405986746273,
+           cmdline="poisson accident yr_con op_75_79, exposure(service) vce(robust)",  # noqa:E501
+           cmd="poisson",
+           predict="poisso_p",
+           estat_cmd="poisson_estat",
+           offset="ln(service)",
+           gof="poiss_g",
+           chi2type="Wald",
+           opt="moptimize",
+           vcetype="Robust",
+           vce="robust",
+           title="Poisson regression",
+           user="poiss_lf",
+           crittype="log pseudolikelihood",
+           ml_method="e2",
+           singularHmethod="m-marquardt",
+           technique="nr",
+           which="max",
+           depvar="accident",
+           properties="b V",
+          )
+
+params_table = np.array([
+    .30633819450439,  .09144457613957,  3.3499875819514,  .00080815183366,
+    .12711011868929,  .48556627031949, np.nan,  1.9599639845401,
+    0,  .35592229608495,  .16103531267836,  2.2102127177276,
+    .02709040275274,  .04029888299621,  .67154570917369, np.nan,
+    1.9599639845401,                0,  -6.974712802772,   .2558675415017,
+    -27.259076168227,  1.29723387e-163, -7.4762039689282, -6.4732216366159,
+    np.nan,  1.9599639845401,                0]).reshape(3, 9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'yr_con op_75_79 _cons'.split()
+
+cov = np.array([
+    .00836211050535,  .00098797681063, -.01860743122756,  .00098797681063,
+    .02593237192942, -.02395236210603, -.01860743122756, -.02395236210603,
+    .06546819879413]).reshape(3, 3)
+
+cov_colnames = 'yr_con op_75_79 _cons'.split()
+
+cov_rownames = 'yr_con op_75_79 _cons'.split()
+
+
+results_poisson_exposure_hc1 = 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=3,
+           N=34,
+           ic=4,
+           k=3,
+           k_eq=1,
+           k_dv=1,
+           converged=1,
+           rc=0,
+           k_autoCns=0,
+           N_clust=5,
+           ll=-91.28727940081573,
+           k_eq_model=1,
+           ll_0=-122.0974139280415,
+           df_m=2,
+           chi2=340.7343047354823,
+           p=1.02443835269e-74,
+           r2_p=.2523405986746273,
+           cmdline="poisson accident yr_con op_75_79, exposure(service) vce(cluster ship)",  # noqa:E501
+           cmd="poisson",
+           predict="poisso_p",
+           estat_cmd="poisson_estat",
+           offset="ln(service)",
+           gof="poiss_g",
+           chi2type="Wald",
+           opt="moptimize",
+           vcetype="Robust",
+           clustvar="ship",
+           vce="cluster",
+           title="Poisson regression",
+           user="poiss_lf",
+           crittype="log pseudolikelihood",
+           ml_method="e2",
+           singularHmethod="m-marquardt",
+           technique="nr",
+           which="max",
+           depvar="accident",
+           properties="b V",
+          )
+
+params_table = np.array([
+    .30633819450439,  .03817694295902,  8.0241677504982,  1.022165435e-15,
+    .23151276126487,  .38116362774391, np.nan,  1.9599639845401,
+    0,  .35592229608495,  .09213163536669,  3.8631930787765,
+    .00011191448109,  .17534760892947,  .53649698324044, np.nan,
+    1.9599639845401,                0,  -6.974712802772,   .0968656626603,
+    -72.003975518463,                0, -7.1645660129248, -6.7848595926192,
+    np.nan,  1.9599639845401,                0]).reshape(3, 9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'yr_con op_75_79 _cons'.split()
+
+cov = np.array([
+    .0014574789737, -.00277745275086,  .00108765624666, -.00277745275086,
+    .00848823823534, -.00469929607507,  .00108765624666, -.00469929607507,
+    .00938295660262]).reshape(3, 3)
+
+cov_colnames = 'yr_con op_75_79 _cons'.split()
+
+cov_rownames = 'yr_con op_75_79 _cons'.split()
+
+
+results_poisson_exposure_clu = 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=4,
+           N=34,
+           ic=2,
+           k=4,
+           k_eq=2,
+           k_dv=1,
+           converged=1,
+           rc=0,
+           k_autoCns=0,
+           N_clust=5,
+           ll=-109.0877965183258,
+           k_eq_model=1,
+           ll_0=-109.1684720604314,
+           rank0=2,
+           df_m=2,
+           chi2=5.472439553195301,
+           p=.0648148991694882,
+           k_aux=1,
+           alpha=2.330298308905143,
+           cmdline="nbreg accident yr_con op_75_79, vce(cluster ship)",
+           cmd="nbreg",
+           predict="nbreg_p",
+           dispers="mean",
+           diparm_opt2="noprob",
+           chi2type="Wald",
+           opt="moptimize",
+           vcetype="Robust",
+           clustvar="ship",
+           vce="cluster",
+           title="Negative binomial regression",
+           diparm1="lnalpha, exp label(",
+           user="nbreg_lf",
+           crittype="log pseudolikelihood",
+           ml_method="e2",
+           singularHmethod="m-marquardt",
+           technique="nr",
+           which="max",
+           depvar="accident",
+           properties="b V",
+          )
+
+params_table = np.array([
+    -.03536709401845,  .27216090050938, -.12994921001605,  .89660661037787,
+    -.56879265701682,  .49805846897992, np.nan,  1.9599639845401,
+    0,  .23211570238882,  .09972456245386,  2.3275680201277,
+    .01993505322091,  .03665915160525,  .42757225317239, np.nan,
+    1.9599639845401,                0,  2.2952623989519,  1.2335785495143,
+    1.8606536242509,  .06279310688494, -.12250713019722,  4.7130319281011,
+    np.nan,  1.9599639845401,                0,  .84599628895555,
+    .22483100011931, np.nan, np.nan,  .40533562611357,
+    1.2866569517975, np.nan,  1.9599639845401,                0,
+    2.3302983089051,  .52392329936749, np.nan, np.nan,
+    1.4998057895818,  3.6206622525444, np.nan,  1.9599639845401,
+    0]).reshape(5, 9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'yr_con op_75_79 _cons _cons alpha'.split()
+
+cov = np.array([
+    .07407155576607, -.00421355148283, -.32663130963457,  .02015715724983,
+    -.00421355148283,  .00994498835661,  .00992613461881, -.00714955450361,
+    -.32663130963457,  .00992613461881,  1.5217160378218, -.09288283512096,
+    .02015715724983, -.00714955450361, -.09288283512096,  .05054897861465
+    ]).reshape(4, 4)
+
+cov_colnames = 'yr_con op_75_79 _cons _cons'.split()
+
+cov_rownames = 'yr_con op_75_79 _cons _cons'.split()
+
+
+results_negbin_clu = 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=4,
+           N=34,
+           ic=2,
+           k=4,
+           k_eq=2,
+           k_dv=1,
+           converged=1,
+           rc=0,
+           k_autoCns=0,
+           ll=-109.0877965183258,
+           k_eq_model=1,
+           ll_0=-109.1684720604314,
+           rank0=2,
+           df_m=2,
+           chi2=.1711221347493475,
+           p=.9179970816706797,
+           r2_p=.0007390003778831,
+           k_aux=1,
+           alpha=2.330298308905143,
+           cmdline="nbreg accident yr_con op_75_79, vce(robust)",
+           cmd="nbreg",
+           predict="nbreg_p",
+           dispers="mean",
+           diparm_opt2="noprob",
+           chi2type="Wald",
+           opt="moptimize",
+           vcetype="Robust",
+           vce="robust",
+           title="Negative binomial regression",
+           diparm1="lnalpha, exp label(",
+           user="nbreg_lf",
+           crittype="log pseudolikelihood",
+           ml_method="e2",
+           singularHmethod="m-marquardt",
+           technique="nr",
+           which="max",
+           depvar="accident",
+           properties="b V",
+          )
+
+params_table = np.array([
+    -.03536709401845,  .26106873337039, -.13547043172065,  .89223994079058,
+    -.5470524089139,    .476318220877, np.nan,  1.9599639845401,
+    0,  .23211570238882,  .56245325203342,  .41268443475019,
+    .67983783029986, -.87027241458412,  1.3345038193618, np.nan,
+    1.9599639845401,                0,  2.2952623989519,  .76040210713867,
+    3.0184850586341,  .00254041928465,  .80490165519179,  3.7856231427121,
+    np.nan,  1.9599639845401,                0,  .84599628895555,
+    .24005700345444, np.nan, np.nan,  .37549320794823,
+    1.3164993699629, np.nan,  1.9599639845401,                0,
+    2.3302983089051,  .55940442919073, np.nan, np.nan,
+    1.4557092049439,  3.7303399539165, np.nan,  1.9599639845401,
+    0]).reshape(5, 9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'yr_con op_75_79 _cons _cons alpha'.split()
+
+cov = np.array([
+    .06815688354362, -.03840590969835, -.16217402790798,  .02098165591138,
+    -.03840590969835,  .31635366072297, -.11049674936104, -.02643483668568,
+    -.16217402790798, -.11049674936104,  .57821136454093, -.03915049342584,
+    .02098165591138, -.02643483668568, -.03915049342584,  .05762736490753
+    ]).reshape(4, 4)
+
+cov_colnames = 'yr_con op_75_79 _cons _cons'.split()
+
+cov_rownames = 'yr_con op_75_79 _cons _cons'.split()
+
+
+results_negbin_hc1 = 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=4,
+           N=34,
+           ic=4,
+           k=4,
+           k_eq=2,
+           k_dv=1,
+           converged=1,
+           rc=0,
+           k_autoCns=0,
+           ll=-82.49115612464289,
+           k_eq_model=1,
+           ll_0=-84.68893065247886,
+           rank0=2,
+           df_m=2,
+           chi2=4.39554905567195,
+           p=.1110500222994781,
+           ll_c=-91.28727940081573,
+           chi2_c=17.5922465523457,
+           r2_p=.0259511427397111,
+           k_aux=1,
+           alpha=.2457422083490335,
+           cmdline="nbreg accident yr_con op_75_79, exposure(service)",
+           cmd="nbreg",
+           predict="nbreg_p",
+           offset="ln(service)",
+           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="accident",
+           properties="b V",
+          )
+
+params_table = np.array([
+    .28503762550355,  .14983643534827,  1.9023251910727,  .05712865433138,
+    -.00863639135093,  .57871164235802, np.nan,  1.9599639845401,
+    0,  .17127003537767,  .27580549562862,  .62098122804736,
+    .53461197443513, -.36929880279264,  .71183887354798, np.nan,
+    1.9599639845401,                0, -6.5908639033905,  .40391814231008,
+    -16.31732574748,  7.432080344e-60, -7.3825289150206, -5.7991988917604,
+    np.nan,  1.9599639845401,                0, -1.4034722260565,
+    .51305874839271, np.nan, np.nan, -2.4090488948595,
+    -.39789555725363, np.nan,  1.9599639845401,                0,
+    .24574220834903,  .12608018984282, np.nan, np.nan,
+    .089900758997,  .67173218155228, np.nan,  1.9599639845401,
+    0]).reshape(5, 9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'yr_con op_75_79 _cons _cons alpha'.split()
+
+cov = np.array([
+    .02245095735788, -.01097939549632, -.05127649084781,  .00045725833006,
+    -.01097939549632,  .07606867141895,  -.0197375670989, -.00926008351523,
+    -.05127649084781,  -.0197375670989,  .16314986568722,  .02198323898312,
+    .00045725833006, -.00926008351523,  .02198323898312,  .26322927930229
+    ]).reshape(4, 4)
+
+cov_colnames = 'yr_con op_75_79 _cons _cons'.split()
+
+cov_rownames = 'yr_con op_75_79 _cons _cons'.split()
+
+
+results_negbin_exposure_nonrobust = 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=4,
+           N=34,
+           ic=4,
+           k=4,
+           k_eq=2,
+           k_dv=1,
+           converged=1,
+           rc=0,
+           k_autoCns=0,
+           N_clust=5,
+           ll=-82.49115612464289,
+           k_eq_model=1,
+           ll_0=-84.68893065247886,
+           rank0=2,
+           df_m=2,
+           chi2=5.473741859983782,
+           p=.0647727084656973,
+           k_aux=1,
+           alpha=.2457422083490335,
+           cmdline="nbreg accident yr_con op_75_79, exposure(service) vce(cluster ship)",  # noqa:E501
+           cmd="nbreg",
+           predict="nbreg_p",
+           offset="ln(service)",
+           dispers="mean",
+           diparm_opt2="noprob",
+           chi2type="Wald",
+           opt="moptimize",
+           vcetype="Robust",
+           clustvar="ship",
+           vce="cluster",
+           title="Negative binomial regression",
+           diparm1="lnalpha, exp label(",
+           user="nbreg_lf",
+           crittype="log pseudolikelihood",
+           ml_method="e2",
+           singularHmethod="m-marquardt",
+           technique="nr",
+           which="max",
+           depvar="accident",
+           properties="b V",
+          )
+
+params_table = np.array([
+    .28503762550355,  .14270989695062,  1.9973220610073,  .04579020833966,
+    .00533136724292,  .56474388376418, np.nan,  1.9599639845401,
+    0,  .17127003537767,  .17997186802799,  .95164892854829,
+    .34127505843023, -.18146834418759,  .52400841494293, np.nan,
+    1.9599639845401,                0, -6.5908639033905,  .62542746996715,
+    -10.538174640357,  5.760612980e-26, -7.8166792194681, -5.3650485873129,
+    np.nan,  1.9599639845401,                0, -1.4034722260565,
+    .86579403765571, np.nan, np.nan, -3.1003973578913,
+    .29345290577817, np.nan,  1.9599639845401,                0,
+    .24574220834903,  .21276213878894, np.nan, np.nan,
+    .0450313052935,  1.3410500222158, np.nan,  1.9599639845401,
+    0]).reshape(5, 9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'yr_con op_75_79 _cons _cons alpha'.split()
+
+cov = np.array([
+    .02036611468766, -.00330004038514, -.08114367170947, -.07133030733881,
+    -.00330004038514,  .03238987328148, -.03020509748676, -.09492663454187,
+    -.08114367170947, -.03020509748676,  .39115952018952,  .43276143586693,
+    -.07133030733881, -.09492663454187,  .43276143586693,  .74959931564018
+    ]).reshape(4, 4)
+
+cov_colnames = 'yr_con op_75_79 _cons _cons'.split()
+
+cov_rownames = 'yr_con op_75_79 _cons _cons'.split()
+
+
+results_negbin_exposure_clu = 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
+                )

.venv/lib/python3.12/site-packages/statsmodels/discrete/tests/results/results_glm_logit_constrained.py

@@ -0,0 +1,697 @@
+import numpy as np
+
+from statsmodels.tools.testing import ParamsTableTestBunch
+
+est = dict(
+           k_eq_model=0,
+           phi=1,
+           vf=1,
+           df=28,
+           df_m=3,
+           power=0,
+           canonical=1,
+           rank=4,
+           aic=1.055602138883215,
+           rc=0,
+           p=.0388431588742135,
+           chi2=8.376256383189103,
+           ll=-12.88963422213144,
+           k_autoCns=0,
+           converged=1,
+           k_dv=1,
+           k_eq=1,
+           k=4,
+           ic=3,
+           N=32,
+           nbml=0,
+           bic=-71.26133683412948,
+           dispers_ps=.9734684585933382,
+           deviance_ps=27.25711684061347,
+           dispers_p=.9734684585933382,
+           deviance_p=27.25711684061347,
+           dispers_s=.920688158723674,
+           deviance_s=25.77926844426287,
+           dispers=.920688158723674,
+           deviance=25.77926844426287,
+           cmdline="glm grade  gpa tuce psi, family(binomial)",
+           cmd="glm",
+           predict="glim_p",
+           marginsnotok="stdp Anscombe Cooksd Deviance Hat Likelihood Pearson Response Score Working ADJusted STAndardized STUdentized MODified",  # noqa:E501
+           marginsok="default",
+           hac_lag="30",
+           vcetype="OIM",
+           vce="oim",
+           linkt="Logit",
+           linkf="ln(u/(1-u))",
+           varfunct="Bernoulli",
+           varfuncf="u*(1-u)",
+           opt1="ML",
+           oim="oim",
+           a="1",
+           m="1",
+           varfunc="glim_v2",
+           link="glim_l02",
+           chi2type="Wald",
+           opt="moptimize",
+           title="Generalized linear models",
+           user="glim_lf",
+           crittype="log likelihood",
+           ml_method="e2",
+           singularHmethod="m-marquardt",
+           technique="nr",
+           which="max",
+           depvar="grade",
+           properties="b V",
+          )
+
+params_table = np.array([
+    2.8261124216999,  1.2629410221647,  2.2377231969675,  .02523911156938,
+    .35079350365878,   5.301431339741, np.nan,  1.9599639845401,
+    0,  .09515765001172,    .141554201358,  .67223472774972,
+    .50143427587633, -.18228348651028,  .37259878653373, np.nan,
+    1.9599639845401,                0,  2.3786875040587,  1.0645642078703,
+    2.2344237073472,  .02545520725424,  .29217999740245,  4.4651950107149,
+    np.nan,  1.9599639845401,                0, -13.021345912635,
+    4.931323890811, -2.6405375515688,  .00827746189686, -22.686563134726,
+    -3.3561286905433, np.nan,  1.9599639845401,                0
+    ]).reshape(4, 9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'gpa tuce psi _cons'.split()
+
+cov = np.array([
+    1.5950200254665, -.03692058012179,  .42761557297075, -4.5734780841711,
+    -.03692058012179,   .0200375919221,  .01491263753083, -.34625566662867,
+    .42761557297075,  .01491263753083,  1.1332969526786, -2.3591604492672,
+    -4.5734780841711, -.34625566662867, -2.3591604492672,  24.317955316083
+    ]).reshape(4, 4)
+
+cov_colnames = 'gpa tuce psi _cons'.split()
+
+cov_rownames = 'gpa tuce psi _cons'.split()
+
+infocrit = np.array([
+    32, np.nan, -12.889634222131,                4,
+    33.779268444263,  39.642212055462])
+
+infocrit_colnames = 'N ll0 ll df AIC BIC'.split()
+
+infocrit_rownames = '.'.split()
+
+
+results_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,
+                infocrit=infocrit,
+                infocrit_colnames=infocrit_colnames,
+                infocrit_rownames=infocrit_rownames,
+                **est
+                )
+
+est = dict(
+           k_eq_model=0,
+           phi=1,
+           vf=1,
+           df=28,
+           df_m=3,
+           power=0,
+           canonical=1,
+           rank=4,
+           aic=1.055602138883215,
+           rc=0,
+           p=.0248623136764981,
+           chi2=9.360530997638559,
+           ll=-12.88963422213144,
+           k_autoCns=0,
+           converged=1,
+           k_dv=1,
+           k_eq=1,
+           k=4,
+           ic=3,
+           N=32,
+           nbml=0,
+           bic=-71.26133683412948,
+           dispers_ps=.9734684585933382,
+           deviance_ps=27.25711684061347,
+           dispers_p=.9734684585933382,
+           deviance_p=27.25711684061347,
+           dispers_s=.920688158723674,
+           deviance_s=25.77926844426287,
+           dispers=.920688158723674,
+           deviance=25.77926844426287,
+           cmdline="glm grade  gpa tuce psi, family(binomial) vce(robust)",
+           cmd="glm",
+           predict="glim_p",
+           marginsnotok="stdp Anscombe Cooksd Deviance Hat Likelihood Pearson Response Score Working ADJusted STAndardized STUdentized MODified",  # noqa:E501
+           marginsok="default",
+           hac_lag="30",
+           vcetype="Robust",
+           vce="robust",
+           linkt="Logit",
+           linkf="ln(u/(1-u))",
+           varfunct="Bernoulli",
+           varfuncf="u*(1-u)",
+           opt1="ML",
+           oim="oim",
+           a="1",
+           m="1",
+           varfunc="glim_v2",
+           link="glim_l02",
+           chi2type="Wald",
+           opt="moptimize",
+           title="Generalized linear models",
+           user="glim_lf",
+           crittype="log pseudolikelihood",
+           ml_method="e2",
+           singularHmethod="m-marquardt",
+           technique="nr",
+           which="max",
+           depvar="grade",
+           properties="b V",
+          )
+
+params_table = np.array([
+    2.8261124216999,   1.287827879216,  2.1944799202672,  .02820092594159,
+    .30201616014984,  5.3502086832499, np.nan,  1.9599639845401,
+    0,  .09515765001172,   .1198091371814,  .79424367999287,
+    .42705358424294, -.13966394388263,  .32997924390608, np.nan,
+    1.9599639845401,                0,  2.3786875040587,  .97985082470462,
+    2.4276016757712,  .01519902587997,  .45821517741577,  4.2991598307016,
+    np.nan,  1.9599639845401,                0, -13.021345912635,
+    5.2807513766642, -2.4658130981467,  .01367026437574, -23.371428422207,
+    -2.6712634030626, np.nan,  1.9599639845401,                0
+    ]).reshape(4, 9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'gpa tuce psi _cons'.split()
+
+cov = np.array([
+    1.6585006464861,  .00630184631279,  .20368998146717, -5.7738061195745,
+    .00630184631279,  .01435422935215,  .01997066738212, -.34768562593344,
+    .20368998146717,  .01997066738212,  .96010763867432, -1.5315997267117,
+    -5.7738061195745, -.34768562593344, -1.5315997267117,  27.886335102141
+    ]).reshape(4, 4)
+
+cov_colnames = 'gpa tuce psi _cons'.split()
+
+cov_rownames = 'gpa tuce psi _cons'.split()
+
+infocrit = np.array([
+    32, np.nan, -12.889634222131,                4,
+    33.779268444263,  39.642212055462])
+
+infocrit_colnames = 'N ll0 ll df AIC BIC'.split()
+
+infocrit_rownames = '.'.split()
+
+
+results_noconstraint_robust = 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,
+                infocrit=infocrit,
+                infocrit_colnames=infocrit_colnames,
+                infocrit_rownames=infocrit_rownames,
+                **est
+                )
+
+est = dict(
+           k_eq_model=0,
+           phi=1,
+           vf=1,
+           df=29,
+           df_m=2,
+           power=0,
+           canonical=1,
+           rank=3,
+           aic=.993115540206396,
+           rc=0,
+           p=.0600760311411508,
+           chi2=5.624288666552698,
+           ll=-12.88984864330234,
+           k_autoCns=0,
+           converged=1,
+           k_dv=1,
+           k_eq=1,
+           k=4,
+           ic=3,
+           N=32,
+           nbml=0,
+           bic=-74.7266438945874,
+           dispers_ps=.9340711710496038,
+           deviance_ps=27.08806396043851,
+           dispers_p=.9340711710496038,
+           deviance_p=27.08806396043851,
+           dispers_s=.8889550788484368,
+           deviance_s=25.77969728660467,
+           dispers=.8889550788484368,
+           deviance=25.77969728660467,
+           cmdline="glm grade  gpa tuce psi, family(binomial) constraints(1)",
+           cmd="glm",
+           predict="glim_p",
+           marginsnotok="stdp Anscombe Cooksd Deviance Hat Likelihood Pearson Response Score Working ADJusted STAndardized STUdentized MODified",  # noqa:E501
+           marginsok="default",
+           hac_lag="30",
+           vcetype="OIM",
+           vce="oim",
+           linkt="Logit",
+           linkf="ln(u/(1-u))",
+           varfunct="Bernoulli",
+           varfuncf="u*(1-u)",
+           opt1="ML",
+           oim="oim",
+           a="1",
+           m="1",
+           varfunc="glim_v2",
+           link="glim_l02",
+           chi2type="Wald",
+           opt="moptimize",
+           title="Generalized linear models",
+           user="glim_lf",
+           crittype="log likelihood",
+           ml_method="e2",
+           singularHmethod="m-marquardt",
+           technique="nr",
+           which="max",
+           depvar="grade",
+           properties="b V",
+          )
+
+params_table = np.array([
+    2.8, np.nan, np.nan, np.nan,
+    np.nan, np.nan, np.nan,  1.9599639845401,
+    0,  .09576464077943,  .13824841412912,  .69269974185736,
+    .48849800113543, -.17519727183342,  .36672655339228, np.nan,
+    1.9599639845401,                0,  2.3717067235827,  1.0071435928909,
+    2.3548843882081,  .01852846934254,  .39774155425619,  4.3456718929091,
+    np.nan,  1.9599639845401,                0, -12.946549758905,
+    3.3404275889275, -3.8757163309928,  .00010631147941, -19.493667526167,
+    -6.3994319916434, np.nan,  1.9599639845401,                0
+    ]).reshape(4, 9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'gpa tuce psi _cons'.split()
+
+cov = np.array([
+    0,                0,                0,                0,
+    0,  .01911262400922,  .02461998233256, -.45036648979107,
+    0,  .02461998233256,  1.0143382167012,  -1.126241119498,
+    0, -.45036648979107,  -1.126241119498,  11.158456476868
+    ]).reshape(4, 4)
+
+cov_colnames = 'gpa tuce psi _cons'.split()
+
+cov_rownames = 'gpa tuce psi _cons'.split()
+
+infocrit = np.array([
+    32, np.nan, -12.889848643302,                3,
+    31.779697286605,  36.176904995004])
+
+infocrit_colnames = 'N ll0 ll df AIC BIC'.split()
+
+infocrit_rownames = '.'.split()
+
+
+results_constraint1 = 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,
+                infocrit=infocrit,
+                infocrit_colnames=infocrit_colnames,
+                infocrit_rownames=infocrit_rownames,
+                **est
+                )
+
+est = dict(
+           k_eq_model=0,
+           phi=1,
+           vf=1,
+           df=29,
+           df_m=2,
+           power=0,
+           canonical=1,
+           rank=3,
+           aic=.9965088127779717,
+           rc=0,
+           p=.0151376593316312,
+           chi2=8.381139289068923,
+           ll=-12.94414100444755,
+           k_autoCns=0,
+           converged=1,
+           k_dv=1,
+           k_eq=1,
+           k=4,
+           ic=3,
+           N=32,
+           nbml=0,
+           bic=-74.61805917229698,
+           dispers_ps=.9101961406899989,
+           deviance_ps=26.39568808000997,
+           dispers_p=.9101961406899989,
+           deviance_p=26.39568808000997,
+           dispers_s=.892699379617072,
+           deviance_s=25.88828200889509,
+           dispers=.892699379617072,
+           deviance=25.88828200889509,
+           cmdline="glm grade  gpa tuce psi, family(binomial) constraints(2)",
+           cmd="glm",
+           predict="glim_p",
+           marginsnotok="stdp Anscombe Cooksd Deviance Hat Likelihood Pearson Response Score Working ADJusted STAndardized STUdentized MODified",  # noqa:E501
+           marginsok="default",
+           hac_lag="30",
+           vcetype="OIM",
+           vce="oim",
+           linkt="Logit",
+           linkf="ln(u/(1-u))",
+           varfunct="Bernoulli",
+           varfuncf="u*(1-u)",
+           opt1="ML",
+           oim="oim",
+           a="1",
+           m="1",
+           varfunc="glim_v2",
+           link="glim_l02",
+           chi2type="Wald",
+           opt="moptimize",
+           title="Generalized linear models",
+           user="glim_lf",
+           crittype="log likelihood",
+           ml_method="e2",
+           singularHmethod="m-marquardt",
+           technique="nr",
+           which="max",
+           depvar="grade",
+           properties="b V",
+          )
+
+params_table = np.array([
+    2.5537914524884,  .92662050289421,  2.7560273537138,  .00585081038138,
+    .73764863947939,  4.3699342654975, np.nan,  1.9599639845401,
+    0,  .10791139824293,  .13554656123081,  .79612051580696,
+    .42596199070477, -.15775497999771,  .37357777648357, np.nan,
+    1.9599639845401,                0,  2.5537914524884,  .92662050289421,
+    2.7560273537138,  .00585081038138,  .73764863947939,  4.3699342654975,
+    np.nan,  1.9599639845401,                0, -12.527922070831,
+    4.6393777844052, -2.7003453163357,  .00692675392223,  -21.62093543894,
+    -3.4349087027211, np.nan,  1.9599639845401,                0
+    ]).reshape(4, 9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'gpa tuce psi _cons'.split()
+
+cov = np.array([
+    .85862555638391, -.00408642741742,  .85862555638391, -3.1725052764862,
+    -.00408642741742,   .0183728702615, -.00408642741742, -.40376368789892,
+    .85862555638391, -.00408642741742,  .85862555638391, -3.1725052764862,
+    -3.1725052764862, -.40376368789892, -3.1725052764862,  21.523826226433
+    ]).reshape(4, 4)
+
+cov_colnames = 'gpa tuce psi _cons'.split()
+
+cov_rownames = 'gpa tuce psi _cons'.split()
+
+infocrit = np.array([
+    32, np.nan, -12.944141004448,                3,
+    31.888282008895,  36.285489717294])
+
+infocrit_colnames = 'N ll0 ll df AIC BIC'.split()
+
+infocrit_rownames = '.'.split()
+
+predict_mu = np.array([
+    .02720933393726,  .05877785527304,  .17341537851768,  .02240274574181,
+    .48834788561471,  .03262255746648,  .02545734725406,  .05057489993471,
+    .10986224061161,  .64848146294279,  .02525325609066,  .17259131542841,
+    .28314297612096,  .18171413480391,  .33018645131295,  .02988039105483,
+    .05693576903037,  .03731338966779,  .61672273095571,  .68861137241716,
+    .08792248035539,  .90822178043053,  .25295501355621,  .85758484919326,
+    .83972248507748,  .54158048311843,  .62661357692624,  .36224489285202,
+    .83387563062407,  .93837010344092,  .55200183830167,  .13940358008872
+    ])
+
+predict_mu_colnames = 'predict_mu'.split()
+
+predict_mu_rownames = ['r'+str(n) for n in range(1, 33)]
+
+predict_linpred_std = np.array([
+    1.2186852972383,  .98250143329647,  .71300625338041,  1.7281112031272,
+    .58278126610648,  1.2588643933597,   1.323097466817,  1.0187451680624,
+    .92583226681839,  .97445803529749,  1.2426520057509,  .66674211884633,
+    .53877733839827,  .77006015103931,  .70670367147137,  1.2036701125873,
+    1.1407798755705,  1.1376397495763,  .57331962577752,  .65764380198652,
+    .85122884445037,  1.1282943138296,  1.2981327331615,  .91561885084703,
+    .8524827403359,  .75030433039358,  1.0902299962647,  .53350768600347,
+    .96511132361274,  1.2127047415358,  .61923877005984,  .80300912367498
+    ])
+
+predict_linpred_std_colnames = 'predict_linpred_std'.split()
+
+predict_linpred_std_rownames = ['r'+str(n) for n in range(1, 33)]
+
+predict_hat = np.array([
+    .03931157544567,  .05340381182541,  .07287215399916,  .06540404284993,
+    .0848623883214,   .0500117280211,  .04343078449564,  .04983412818394,
+    .08382437063813,  .21645722203914,  .03801090644315,  .06348261316195,
+    .05891921860299,  .08817451110282,  .11045563375857,  .04199779738721,
+    .06987634275981,  .04648995770552,   .0776956378885,  .09273814423054,
+    .05810645039404,  .10611489289649,  .31844046474321,  .10239122636412,
+    .09780916971071,  .13976583081559,  .27809589396914,  .06575633167064,
+    .12902962938834,  .08505028097419,  .09482722348113,  .07735963673184
+    ])
+
+predict_hat_colnames = 'predict_hat'.split()
+
+predict_hat_rownames = ['r'+str(n) for n in range(1, 33)]
+
+
+results_constraint2 = 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,
+                infocrit=infocrit,
+                infocrit_colnames=infocrit_colnames,
+                infocrit_rownames=infocrit_rownames,
+                predict_mu=predict_mu,
+                predict_mu_colnames=predict_mu_colnames,
+                predict_mu_rownames=predict_mu_rownames,
+                predict_linpred_std=predict_linpred_std,
+                predict_linpred_std_colnames=predict_linpred_std_colnames,
+                predict_linpred_std_rownames=predict_linpred_std_rownames,
+                predict_hat=predict_hat,
+                predict_hat_colnames=predict_hat_colnames,
+                predict_hat_rownames=predict_hat_rownames,
+                **est
+                )
+
+est = dict(
+           k_eq_model=0,
+           phi=1,
+           vf=1,
+           df=29,
+           df_m=2,
+           power=0,
+           canonical=1,
+           rank=3,
+           aic=.9965088127779717,
+           rc=0,
+           p=.0085760854232441,
+           chi2=9.517555427941099,
+           ll=-12.94414100444755,
+           k_autoCns=0,
+           converged=1,
+           k_dv=1,
+           k_eq=1,
+           k=4,
+           ic=3,
+           N=32,
+           nbml=0,
+           bic=-74.61805917229698,
+           dispers_ps=.9101961406899989,
+           deviance_ps=26.39568808000997,
+           dispers_p=.9101961406899989,
+           deviance_p=26.39568808000997,
+           dispers_s=.892699379617072,
+           deviance_s=25.88828200889509,
+           dispers=.892699379617072,
+           deviance=25.88828200889509,
+           cmdline="glm grade  gpa tuce psi, family(binomial) constraints(2) vce(robust)",  # noqa:E501
+           cmd="glm",
+           predict="glim_p",
+           marginsnotok="stdp Anscombe Cooksd Deviance Hat Likelihood Pearson Response Score Working ADJusted STAndardized STUdentized MODified",  # noqa:E501
+           marginsok="default",
+           hac_lag="30",
+           vcetype="Robust",
+           vce="robust",
+           linkt="Logit",
+           linkf="ln(u/(1-u))",
+           varfunct="Bernoulli",
+           varfuncf="u*(1-u)",
+           opt1="ML",
+           oim="oim",
+           a="1",
+           m="1",
+           varfunc="glim_v2",
+           link="glim_l02",
+           chi2type="Wald",
+           opt="moptimize",
+           title="Generalized linear models",
+           user="glim_lf",
+           crittype="log pseudolikelihood",
+           ml_method="e2",
+           singularHmethod="m-marquardt",
+           technique="nr",
+           which="max",
+           depvar="grade",
+           properties="b V",
+          )
+
+params_table = np.array([
+    2.5537914524884,  .83609404798719,  3.0544308485827,  .00225487991353,
+    .91507723074524,  4.1925056742316, np.nan,  1.9599639845401,
+    0,  .10791139824293,  .12275592600281,   .8790728216287,
+    .37936179287834, -.13268579561143,   .3485085920973, np.nan,
+    1.9599639845401,                0,  2.5537914524884,  .83609404798719,
+    3.0544308485827,  .00225487991353,  .91507723074524,  4.1925056742316,
+    np.nan,  1.9599639845401,                0, -12.527922070831,
+    4.510414281113, -2.7775546302454,  .00547696322683, -21.368171617167,
+    -3.6876725244938, np.nan,  1.9599639845401,                0
+    ]).reshape(4, 9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'gpa tuce psi _cons'.split()
+
+cov = np.array([
+    .6990532570796,  .01512804251258,   .6990532570796, -2.9662622048441,
+    .01512804251258,  .01506901736881,  .01512804251258,  -.3968065659911,
+    .6990532570796,  .01512804251258,   .6990532570796, -2.9662622048441,
+    -2.9662622048441,  -.3968065659911, -2.9662622048441,  20.343836987269
+    ]).reshape(4, 4)
+
+cov_colnames = 'gpa tuce psi _cons'.split()
+
+cov_rownames = 'gpa tuce psi _cons'.split()
+
+infocrit = np.array([
+    32, np.nan, -12.944141004448,                3,
+    31.888282008895,  36.285489717294])
+
+infocrit_colnames = 'N ll0 ll df AIC BIC'.split()
+
+infocrit_rownames = '.'.split()
+
+
+results_constraint2_robust = 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,
+                infocrit=infocrit,
+                infocrit_colnames=infocrit_colnames,
+                infocrit_rownames=infocrit_rownames,
+                **est
+                )
+
+est = dict(
+           N_cds=0,
+           N_cdf=0,
+           p=.0151376589433054,
+           chi2=8.381139340374848,
+           df_m=2,
+           k_eq_model=1,
+           ll=-12.94414100444751,
+           k_autoCns=0,
+           rc=0,
+           converged=1,
+           k_dv=1,
+           k_eq=1,
+           k=4,
+           ic=5,
+           N=32,
+           rank=3,
+           cmdline="logit grade  gpa tuce psi, constraints(2)",
+           cmd="logit",
+           estat_cmd="logit_estat",
+           predict="logit_p",
+           marginsnotok="stdp DBeta DEviance DX2 DDeviance Hat Number Residuals RStandard SCore",  # noqa:E501
+           title="Logistic regression",
+           chi2type="Wald",
+           opt="moptimize",
+           vce="oim",
+           user="mopt__logit_d2()",
+           crittype="log likelihood",
+           ml_method="d2",
+           singularHmethod="m-marquardt",
+           technique="nr",
+           which="max",
+           depvar="grade",
+           properties="b V",
+          )
+
+params_table = np.array([
+    2.5537916456996,  .92662056628814,  2.7560273736742,  .00585081002433,
+    .73764870844071,  4.3699345829585, np.nan,  1.9599639845401,
+    0,  .10791141442743,  .13554656655573,  .79612060393329,
+    .42596193948753, -.15775497424986,  .37357780310472, np.nan,
+    1.9599639845401,                0,  2.5537916456996,  .92662056628814,
+    2.7560273736742,  .00585081002433,  .73764870844071,  4.3699345829585,
+    np.nan,  1.9599639845401,                0, -12.527923225554,
+    4.6393781670436, -2.7003453425175,  .00692675337706,  -21.62093734362,
+    -3.4349091074867, np.nan,  1.9599639845401,                0
+    ]).reshape(4, 9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'gpa tuce psi _cons'.split()
+
+cov = np.array([
+    .85862567386816, -.00408642236043,  .85862567386816,  -3.172505858545,
+    -.00408642236043,  .01837287170505, -.00408642236043, -.40376374127778,
+    .85862567386816, -.00408642236043,  .85862567386816,  -3.172505858545,
+    -3.172505858545, -.40376374127778,  -3.172505858545,  21.523829776841
+    ]).reshape(4, 4)
+
+cov_colnames = 'gpa tuce psi _cons'.split()
+
+cov_rownames = 'gpa tuce psi _cons'.split()
+
+infocrit = np.array([
+    32, np.nan, -12.944141004448,                3,
+    31.888282008895,  36.285489717294])
+
+infocrit_colnames = 'N ll0 ll df AIC BIC'.split()
+
+infocrit_rownames = '.'.split()
+
+
+results_logit_constraint2 = 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,
+                infocrit=infocrit,
+                infocrit_colnames=infocrit_colnames,
+                infocrit_rownames=infocrit_rownames,
+                **est
+                )

.venv/lib/python3.12/site-packages/statsmodels/discrete/tests/results/results_poisson_constrained.py

@@ -0,0 +1,665 @@
+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)
+
+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([
+    .01151508241338,                0, -.00061947268694, -.00090708285562,
+    -.00074959767622, -.00074959767622, -.00917958318314,                0,
+    0,                0,                0,                0,
+    0,                0, -.00061947268694,                0,
+    .0380657799061,  .02946056271023,   .0294520905375,   .0294520905375,
+    -.02891793401778, -.00090708285562,                0,  .02946056271023,
+    .03375672303114,  .02947081310555,  .02947081310555, -.02868865719866,
+    -.00074959767622,                0,   .0294520905375,  .02947081310555,
+    .03234240566834,  .03234240566834, -.02881420109427, -.00074959767622,
+    0,   .0294520905375,  .02947081310555,  .03234240566834,
+    .03234240566834, -.02881420109427, -.00917958318314,                0,
+    -.02891793401778, -.02868865719866, -.02881420109427, -.02881420109427,
+    .03672953668345]).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_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=6,
+           N=10,
+           ic=3,
+           k=8,
+           k_eq=1,
+           k_dv=1,
+           converged=1,
+           rc=0,
+           k_autoCns=1,
+           ll=-33.78306559091298,
+           k_eq_model=1,
+           df_m=5,
+           chi2=526.719430888018,
+           p=1.3614066522e-111,
+           r2_p=np.nan,
+           cmdline="poisson deaths lnpyears smokes i.agecat, constraints(2)",
+           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([
+    1.1598786864273,  .13082965708054,  8.8655639119598,  7.611783820e-19,
+    .90345727043975,  1.4163001024149, np.nan,  1.9599639845401,
+    0,  .12111539473831,  .22317899375276,  .54268277090847,
+    .58734823873758, -.31630739512299,  .55853818459962, np.nan,
+    1.9599639845401,                0,                0, np.nan,
+    np.nan, np.nan, np.nan, np.nan,
+    np.nan,  1.9599639845401,                0,  1.5276244194375,
+    .19848759770871,  7.6963217705896,  1.400389019e-14,  1.1385958765506,
+    1.9166529623245, np.nan,  1.9599639845401,                0,
+    2.7415571106656,  .20647039325801,  13.278209371354,  3.097119459e-40,
+    2.3368825760061,  3.1462316453252, np.nan,  1.9599639845401,
+    0,   3.587300073596,  .30160673316211,  11.893965482753,
+    1.272196529e-32,  2.9961617391034,  4.1784384080885, np.nan,
+    1.9599639845401,                0,   4.087300073596,  .30160673316211,
+    13.551753406643,  7.735990122e-42,  3.4961617391034,  4.6784384080885,
+    np.nan,  1.9599639845401,                0, -9.4376201542802,
+    1.2537557101599, -7.5274792990385,  5.172920628e-14, -11.894936191605,
+    -6.9803041169553, 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([
+    .01711639917181, -.02559852137367,                0,  .00475026273828,
+    .012305588195,  .03167368550108,  .03167368550108, -.16210959536359,
+    -.02559852137367,   .0498088632525,                0, -.00783669874902,
+    -.01946551099054,  -.0482099128044,  -.0482099128044,  .23336630265161,
+    0,                0,                0,                0,
+    0,                0,                0,                0,
+    .00475026273828, -.00783669874902,                0,  .03939732644417,
+    .0328943776068,   .0382554606876,   .0382554606876, -.07382466315002,
+    .012305588195, -.01946551099054,                0,   .0328943776068,
+    .04263002329212,  .05226051095238,  .05226051095238, -.14512177326509,
+    .03167368550108,  -.0482099128044,                0,   .0382554606876,
+    .05226051095238,  .09096662148872,  .09096662148872, -.32873181469848,
+    .03167368550108,  -.0482099128044,                0,   .0382554606876,
+    .05226051095238,  .09096662148872,  .09096662148872, -.32873181469848,
+    -.16210959536359,  .23336630265161,                0, -.07382466315002,
+    -.14512177326509, -.32873181469848, -.32873181469848,  1.5719033807586
+    ]).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_constraint2 = 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=-34.5367006700131,
+           k_eq_model=1,
+           df_m=4,
+           chi2=554.4168921897579,
+           p=1.1331093797e-118,
+           r2_p=np.nan,
+           cmdline=("poisson deaths smokes i.agecat, "
+                    "exposure(pyears) constraints(2)"),
+           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([
+    .35978347114582,  .10730668667519,  3.3528522992687,  .00079983377167,
+    .14946622996212,  .57010071232952, np.nan,  1.9599639845401,
+    0,                0, np.nan, np.nan,
+    np.nan, np.nan, np.nan, np.nan,
+    1.9599639845401,                0,  1.4837272702102,  .19510269288329,
+    7.6048528509946,  2.852282383e-14,  1.1013330188722,  1.8661215215483,
+    np.nan,  1.9599639845401,                0,  2.6270956495127,
+    .18372567328363,  14.299012231442,  2.219372691e-46,  2.2669999468414,
+    2.987191352184, np.nan,  1.9599639845401,                0,
+    3.2898291023835,  .17982035319735,  18.295087535352,  9.055555257e-75,
+    2.9373876864294,  3.6422705183376, np.nan,  1.9599639845401,
+    0,  3.7898291023835,  .17982035319735,  21.075640409983,
+    1.330935038e-98,  3.4373876864294,  4.1422705183376, np.nan,
+    1.9599639845401,                0, -7.9235211042587,  .19177810950798,
+    -41.316087245761,                0, -8.2993992919175, -7.5476429165999,
+    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([
+    .01151472500521,                0, -.00061274288972, -.00089685568608,
+    -.00069335681347, -.00069335681347, -.00921031399899,                0,
+    0,                0,                0,                0,
+    0,                0, -.00061274288972,                0,
+    .03806506077031,  .02945948985187,  .02944866089267,  .02944866089267,
+    -.02892164840477, -.00089685568608,                0,  .02945948985187,
+    .03375512302352,  .02946576868665,  .02946576868665,  -.0286943943397,
+    -.00069335681347,                0,  .02944866089267,  .02946576868665,
+    .03233535942402,  .03233535942402, -.02885716752919, -.00069335681347,
+    0,  .02944866089267,  .02946576868665,  .03233535942402,
+    .03233535942402, -.02885716752919, -.00921031399899,                0,
+    -.02892164840477,  -.0286943943397, -.02885716752919, -.02885716752919,
+    .03677884328645]).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_constraint2 = 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=-34.5367006700131,
+           k_eq_model=1,
+           df_m=4,
+           chi2=582.5215805315736,
+           p=9.3932644024e-125,
+           r2_p=np.nan,
+           cmdline=("poisson deaths smokes i.agecat,"
+                    "exposure(pyears) constraints(2) vce(robust)"),
+           cmd="poisson",
+           predict="poisso_p",
+           estat_cmd="poisson_estat",
+           offset="ln(pyears)",
+           gof="poiss_g",
+           chi2type="Wald",
+           opt="moptimize",
+           vcetype="Robust",
+           vce="robust",
+           title="Poisson regression",
+           user="poiss_lf",
+           crittype="log pseudolikelihood",
+           ml_method="e2",
+           singularHmethod="m-marquardt",
+           technique="nr",
+           which="max",
+           depvar="deaths",
+           properties="b V",
+          )
+
+params_table = np.array([
+    .35978347114582,   .1172393358046,  3.0687948603312,  .00214924117257,
+    .1299985953974,  .58956834689424, np.nan,  1.9599639845401,
+    0,                0, np.nan, np.nan,
+    np.nan, np.nan, np.nan, np.nan,
+    1.9599639845401,                0,  1.4837272702102,  .21969092615175,
+    6.7537030145039,  1.441186055e-11,  1.0531409672225,  1.9143135731979,
+    np.nan,  1.9599639845401,                0,  2.6270956495127,
+    .20894895542061,  12.572906355164,  2.975796525e-36,   2.217563222281,
+    3.0366280767443, np.nan,  1.9599639845401,                0,
+    3.2898291023835,   .2211846822073,  14.873675109654,  4.885611722e-50,
+    2.8563150913252,  3.7233431134417, np.nan,  1.9599639845401,
+    0,  3.7898291023835,   .2211846822073,  17.134229479922,
+    8.243780087e-66,  3.3563150913252,  4.2233431134417, np.nan,
+    1.9599639845401,                0, -7.9235211042587,   .2479876721169,
+    -31.951270144281,  5.18748229e-224, -8.4095680102177, -7.4374741982996,
+    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([
+    .0137450618599,                0,  .00249770233028,  .00412347653263,
+    .00486142402447,  .00486142402447, -.01620342093134,                0,
+    0,                0,                0,                0,
+    0,                0,  .00249770233028,                0,
+    .04826410303341,  .04389964215014,  .04391744129373,  .04391744129373,
+    -.04609122424924,  .00412347653263,                0,  .04389964215014,
+    .04365966597136,  .04367917402468,  .04367917402468, -.04726310745444,
+    .00486142402447,                0,  .04391744129373,  .04367917402468,
+    .04892266364314,  .04892266364314, -.04794543190806,  .00486142402447,
+    0,  .04391744129373,  .04367917402468,  .04892266364314,
+    .04892266364314, -.04794543190806, -.01620342093134,                0,
+    -.04609122424924, -.04726310745444, -.04794543190806, -.04794543190806,
+    .06149788552196]).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_constraint2_robust = 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
+                )

.venv/lib/python3.12/site-packages/statsmodels/discrete/tests/results/results_predict.py

@@ -0,0 +1,253 @@
+"""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,
+    )

.venv/lib/python3.12/site-packages/statsmodels/discrete/tests/results/results_truncated.py

@@ -0,0 +1,60 @@
+"""
+Created on Thu Jan  6 13:55:50 2022
+
+Author: Josef Perktod
+License: BSD-3
+"""
+# flake8: noqa
+
+import numpy as np
+
+from statsmodels.tools.testing import Holder
+
+hurdle_poisson = Holder()
+# r library pscl, docvis data
+# > mod = hurdle( docvis ~ aget + totchr, data=dt, zero.dist = "poisson")
+hurdle_poisson.method = 'BFGS'
+hurdle_poisson.n = 3629
+hurdle_poisson.df_null = 3627
+hurdle_poisson.df_residual = 3623
+hurdle_poisson.loglik = -13612.9091771797
+hurdle_poisson.aic = 27237.81835436
+hurdle_poisson.bic = 27274.9986288
+hurdle_poisson.vcov = np.array([
+    0.000239404800324688, -4.59559682721834e-05, -4.59865258972631e-05, 0, 0,
+    0, -4.59559682721834e-05, 2.54346275490526e-05, -1.20164687288645e-06, 0,
+    0, 0, -4.59865258972631e-05, -1.20164687288644e-06, 2.01936456643824e-05,
+    0, 0, 0, 0, 0, 0, 0.00241829560973498, -0.000548499729173446,
+    -0.000636055275016966, 0, 0, 0, -0.000548499729173446,
+    0.000351548196602719, -6.30088654100178e-05, 0, 0, 0,
+    -0.000636055275016966, -6.30088654100178e-05, 0.000562508220544602
+    ]).reshape(6, 6, order='F')
+
+hurdle_poisson.count = np.array([
+    1.54175599063303, 0.0122763123129474, 0.209943725275436,
+    0.0154727114729348, 0.00504327547820388, 0.00449373404468738,
+    99.6435559035596, 2.43419427830254, 46.719214619218, 0,
+    0.0149249819228085, 0
+    ]).reshape(3, 4, order='F')
+
+hurdle_poisson.zero = np.array([
+    0.216740121452838, 0.0189277243223132, 0.386748883124962,
+    0.0491761691242311, 0.0187496185721929, 0.0237172557549267,
+    4.40742183282514, 1.00949916657955, 16.3066455546664,
+    1.04608334100715e-05, 0.312735301122465, 8.85224303880408e-60
+    ]).reshape(3, 4, order='F')
+
+hurdle_poisson.params_table = np.concatenate((hurdle_poisson.zero,
+                                              hurdle_poisson.count), axis=0)
+
+# > dfm = data.frame(t(as.matrix(dtm)))  #at means
+# > predict(mod, dfm)
+hurdle_poisson.predict_mean = 6.530525236464
+# > predict(mod, dfm, type="prob", at=c(0, 1, 2, 3))
+hurdle_poisson.predict_prob = np.array([
+    0.07266312350019, 0.005744258908698, 0.02020842274659, 0.04739576870239,
+    ])
+# > predict(mod, dfm, type="count")
+hurdle_poisson.predict_mean_main = 7.036041748046
+# > predict(mod, dfm, type="zero")
+hurdle_poisson.predict_zero = 0.9281532813926

.venv/lib/python3.12/site-packages/statsmodels/discrete/tests/results/results_truncated_st.py

@@ -0,0 +1,746 @@
+# flake8: noqa
+# file is autogenerated
+
+import numpy as np
+
+est = dict(
+           r2_p = .0777591927880298,
+           p = 0,
+           chi2 = 2117.638300165287,
+           df_m = 2,
+           ll_0 = -13616.64276748558,
+           k_eq_model = 1,
+           ll = -12557.82361740294,
+           k_autoCns = 0,
+           rc = 0,
+           converged = 1,
+           k_dv = 1,
+           k_eq = 1,
+           k = 3,
+           ic = 2,
+           N = 3237,
+           rank = 3,
+           cmdline = "tpoisson docvis aget totchr if docvis > 0",
+           title = "Truncated Poisson regression",
+           title2 = "Truncation points: 0",
+           llopt = "0",
+           cmd = "tpoisson",
+           predict = "tpoisson_p",
+           chi2type = "LR",
+           opt = "moptimize",
+           vce = "oim",
+           user = "tpoiss_d2",
+           crittype = "log likelihood",
+           ml_method = "e2",
+           singularHmethod = "m-marquardt",
+           technique = "nr",
+           which = "max",
+           depvar = "docvis",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     .01227632422998,  .00504328169164,  2.4341936422712,  .01492500815025,
+     .00239167375047,  .02216097470949, np.nan,  1.9599639845401,
+                   0,  .20994371600381,  .00449374445996,   46.71910427362,
+                   0,  .20113613870656,  .21875129330105, np.nan,
+     1.9599639845401,                0,  1.5417559978312,  .01547272444235,
+     99.643472846381,                0,  1.5114300151815,   1.572081980481,
+    np.nan,  1.9599639845401,                0]).reshape(3,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'aget totchr _cons'.split()
+
+cov = np.array([
+     .00002543469022, -1.201653007e-06, -.00004595604713, -1.201653007e-06,
+     .00002019373927, -.00004598669366, -.00004595604713, -.00004598669366,
+     .00023940520167]).reshape(3,3)
+
+cov_colnames = 'aget totchr _cons'.split()
+
+cov_rownames = 'aget totchr _cons'.split()
+
+icr = np.array([
+                3237, -13616.642767486, -12557.823617403,                3,
+     25121.647234806,  25139.894441568])
+
+icr_colnames = 'N ll0 ll df AIC BIC'.split()
+
+icr_rownames = '.'.split()
+
+class Bunch(dict):
+    def __init__(self, **kw):
+        dict.__init__(self, kw)
+        self.__dict__ = self
+
+        if hasattr(self, 'params_table'):
+            for i,att in enumerate(['params', 'bse', 'tvalues', 'pvalues']):
+                self[att] = self.params_table[:,i]
+
+
+results_trunc_poisson = Bunch(
+                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,
+                icr=icr,
+                icr_colnames=icr_colnames,
+                icr_rownames=icr_rownames,
+                **est
+                )
+
+table = np.array([
+     7.5616311400077,  .04812900313217,  157.11173404616,                0,
+     7.4673000272568,  7.6559622527585, np.nan,  1.9599639845401,
+                   0])
+
+table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+table_rownames = '_cons'.split()
+
+
+results_trunc_poisson.margins_cm = Bunch(
+                table=table,
+                table_colnames=table_colnames,
+                table_rownames=table_rownames,
+                )
+
+table = np.array([
+     7.5503819076573,  .04846533862271,  155.78931504916,                0,
+     7.4553915894583,  7.6453722258564, np.nan,  1.9599639845401,
+                   0])
+
+table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+table_rownames = '_cons'.split()
+
+
+results_trunc_poisson.margins_means = Bunch(
+                table=table,
+                table_colnames=table_colnames,
+                table_rownames=table_rownames,
+                )
+
+table = np.array([
+     7.2430661225168,  .04840412009716,  149.63738846977,                0,
+      7.148195790423,  7.3379364546105, np.nan,  1.9599639845401,
+                   0])
+
+table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+table_rownames = '_cons'.split()
+
+
+results_trunc_poisson.margins_atmeans = Bunch(
+                table=table,
+                table_colnames=table_colnames,
+                table_rownames=table_rownames,
+                )
+
+table = np.array([
+     .01124923235036,  .00049084317554,  22.918180207056,  3.06106238e-116,
+     .01028719740425,  .01221126729646, np.nan,  1.9599639845401,
+                   0,  .03157461406623,  .00105301458741,  29.984973089368,
+     1.54089108e-197,  .02951074339971,  .03363848473275, np.nan,
+     1.9599639845401,                0,  .06090587962511,  .00146586027013,
+     41.549580724723,                0,  .05803284628928,  .06377891296095,
+    np.nan,  1.9599639845401,                0,   .8962702739583,
+     .00300215903947,  298.54190340135,                0,  .89038615036507,
+     .90215439755153, np.nan,  1.9599639845401,                0
+    ]).reshape(4,9)
+
+table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+table_rownames = '1bn._predict 2._predict 3._predict 4._predict'.split()
+
+
+results_trunc_poisson.margins_cpr = Bunch(
+                table=table,
+                table_colnames=table_colnames,
+                table_rownames=table_rownames,
+                )
+
+table = np.array([
+     .00204639655178,  .00011129186954,  18.387655453284,  1.649609265e-75,
+     .00182826849572,  .00226452460784, np.nan,  1.9599639845401,
+                   0,   .0111917391283,  .00048491494502,  23.079798309369,
+     7.38815718e-118,  .01024132330049,  .01214215495611, np.nan,
+     1.9599639845401,                0,  .03142959767164,  .00103996422108,
+     30.221806707029,  1.22479233e-200,  .02939130525312,  .03346789009016,
+    np.nan,  1.9599639845401,                0,  .06065903309159,
+     .00144697985213,  41.921131799076,                0,  .05782300469507,
+     .06349506148811, np.nan,  1.9599639845401,                0,
+     .89467323355669,   .0030739863865,  291.04658286247,                0,
+     .88864833095018,   .9006981361632, 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()
+
+
+results_trunc_poisson.margins_pr = Bunch(
+                table=table,
+                table_colnames=table_colnames,
+                table_rownames=table_rownames,
+                )
+
+est = dict(
+           k_aux = 1,
+           r2_p = .0215428784838031,
+           chi2_c = 6444.56377006125,
+           ll_c = -12557.82361740294,
+           p = 5.42079209773e-90,
+           chi2 = 411.0848328423963,
+           df_m = 2,
+           rank0 = 2,
+           ll_0 = -9541.08414879351,
+           k_eq_model = 1,
+           ll = -9335.541732372312,
+           k_autoCns = 0,
+           rc = 0,
+           converged = 1,
+           k_dv = 1,
+           k_eq = 2,
+           k = 4,
+           ic = 3,
+           N = 3237,
+           rank = 4,
+           alpha = .5883738271406221,
+           cmdline = "tnbreg docvis aget totchr if docvis > 0",
+           title = "Truncated negative binomial regression",
+           title2 = "Truncation points: 0",
+           llopt = "0",
+           cmd = "tnbreg",
+           predict = "tnbreg_p",
+           dispers = "mean",
+           diparm_opt2 = "noprob",
+           chi2_ct = "LR",
+           chi2type = "LR",
+           opt = "moptimize",
+           vce = "oim",
+           diparm1 = "lnalpha, exp label(",
+           user = "tnbreg_mean",
+           crittype = "log likelihood",
+           ml_method = "e2",
+           singularHmethod = "m-marquardt",
+           technique = "nr",
+           which = "max",
+           depvar = "docvis",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     .01798960271895,  .01237555909354,  1.4536395958332,  .14604625306297,
+    -.00626604739294,  .04224525283084, np.nan,  1.9599639845401,
+                   0,  .23731215078822,  .01166878414467,  20.337350305391,
+     6.009046369e-92,  .21444175412129,  .26018254745516, np.nan,
+     1.9599639845401,                0,  1.4035619564653,   .0366296524502,
+     38.317643291146,                0,  1.3317691568967,  1.4753547560339,
+    np.nan,  1.9599639845401,                0, -.53039277265033,
+     .04237842368148, -12.515632403809,  6.131755594e-36, -.61345295678762,
+    -.44733258851304, np.nan,  1.9599639845401,                0
+    ]).reshape(4,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'aget totchr _cons _cons'.split()
+
+cov = np.array([
+     .00015315446288, -8.133778629e-06,  -.0002727009794,  4.768991952e-06,
+    -8.133778629e-06,  .00013616052341, -.00027073391794,  .00003663514391,
+     -.0002727009794, -.00027073391794,  .00134173143862, -.00027563132595,
+     4.768991952e-06,  .00003663514391, -.00027563132595,  .00179593079373
+    ]).reshape(4,4)
+
+cov_colnames = 'aget totchr _cons _cons'.split()
+
+cov_rownames = 'aget totchr _cons _cons'.split()
+
+icr = np.array([
+                3237, -9541.0841487935, -9335.5417323723,                4,
+     18679.083464745,   18703.41307376])
+
+icr_colnames = 'N ll0 ll df AIC BIC'.split()
+
+icr_rownames = '.'.split()
+
+
+results_trunc_negbin = Bunch(
+                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,
+                icr=icr,
+                icr_colnames=icr_colnames,
+                icr_rownames=icr_rownames,
+                **est
+                )
+
+table = np.array([
+     7.5768532635168,  .11034667559901,  68.664082740929,                0,
+      7.360577753529,  7.7931287735046, np.nan,  1.9599639845401,
+                   0])
+
+table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+table_rownames = '_cons'.split()
+
+
+results_trunc_negbin.margins_cm = Bunch(
+                table=table,
+                table_colnames=table_colnames,
+                table_rownames=table_rownames,
+                )
+
+table = np.array([
+     7.1017589207455,  .11718653140646,  60.602177020781,                0,
+     6.8720775397156,  7.3314403017753, np.nan,  1.9599639845401,
+                   0])
+
+table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+table_rownames = '_cons'.split()
+
+
+results_trunc_negbin.margins_means = Bunch(
+                table=table,
+                table_colnames=table_colnames,
+                table_rownames=table_rownames,
+                )
+
+table = np.array([
+     6.7307995444045,  .10830267362473,  62.148046018945,                0,
+     6.5185302046706,  6.9430688841384, np.nan,  1.9599639845401,
+                   0])
+
+table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+table_rownames = '_cons'.split()
+
+
+results_trunc_negbin.margins_atmeans = Bunch(
+                table=table,
+                table_colnames=table_colnames,
+                table_rownames=table_rownames,
+                )
+
+table = np.array([
+     .10130558155035,  .00372164932482,  27.220614493326,  3.70370053e-163,
+     .09401128291061,  .10859988019009, np.nan,  1.9599639845401,
+                   0,  .10609694010369,  .00217062159601,  48.878597862821,
+                   0,  .10184259995145,  .11035128025594, np.nan,
+     1.9599639845401,                0,  .10187507062355,  .00125382749214,
+     81.251265634444,                0,  .09941761389613,  .10433252735097,
+    np.nan,  1.9599639845401,                0,  .69072240772241,
+     .00681119370137,  101.40989054297,                0,  .67737271337601,
+     .70407210206881, np.nan,  1.9599639845401,                0
+    ]).reshape(4,9)
+
+table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+table_rownames = '1bn._predict 2._predict 3._predict 4._predict'.split()
+
+
+results_trunc_negbin.margins_cpr = Bunch(
+                table=table,
+                table_colnames=table_colnames,
+                table_rownames=table_rownames,
+                )
+
+table = np.array([
+     .07074667643427,  .00449635579252,  15.734225603755,  8.812829317e-56,
+     .06193398101925,  .07955937184929, np.nan,  1.9599639845401,
+                   0,  .09312895729354,   .0029475364353,  31.595523698388,
+     4.25362024e-219,  .08735189203722,  .09890602254985, np.nan,
+     1.9599639845401,                0,  .09770317477771,  .00158068603995,
+     61.810614067951,                0,  .09460508706855,  .10080126248687,
+    np.nan,  1.9599639845401,                0,  .09397857365281,
+     .00100790260574,  93.241721092808,                0,  .09200312084564,
+     .09595402645998, np.nan,  1.9599639845401,                0,
+     .64444261784167,  .00909052984414,  70.891645359594,                0,
+     .62662550674676,  .66225972893657, 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()
+
+
+results_trunc_negbin.margins_pr = Bunch(
+                table=table,
+                table_colnames=table_colnames,
+                table_rownames=table_rownames,
+                )
+
+est = dict(
+           r2_p = .0631714878006528,
+           p = 4.9406564584e-324,
+           chi2 = 1489.753609191444,
+           df_m = 2,
+           ll_0 = -11791.34496477735,
+           k_eq_model = 1,
+           ll = -11046.46816018163,
+           k_autoCns = 0,
+           rc = 0,
+           converged = 1,
+           k_dv = 1,
+           k_eq = 1,
+           k = 3,
+           ic = 2,
+           N = 2925,
+           rank = 3,
+           cmdline = "tpoisson docvis aget totchr if docvis > 1, ll(1)",
+           title = "Truncated Poisson regression",
+           title2 = "Truncation points: 1",
+           llopt = "1",
+           cmd = "tpoisson",
+           predict = "tpoisson_p",
+           chi2type = "LR",
+           opt = "moptimize",
+           vce = "oim",
+           user = "tpoiss_d2",
+           crittype = "log likelihood",
+           ml_method = "e2",
+           singularHmethod = "m-marquardt",
+           technique = "nr",
+           which = "max",
+           depvar = "docvis",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     .00741559993424,  .00511787438189,  1.4489609124605,  .14734849869339,
+    -.00261524953166,  .01744644940014, np.nan,  1.9599639845401,
+                   0,  .18069681610939,  .00462967674542,  39.030115069715,
+                   0,   .1716228164283,  .18977081579048, np.nan,
+     1.9599639845401,                0,  1.6905464469665,  .01602194453256,
+     105.51443637387,                0,  1.6591440127204,  1.7219488812126,
+    np.nan,  1.9599639845401,                0]).reshape(3,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'aget totchr _cons'.split()
+
+cov = np.array([
+     .00002619263819, -9.943128998e-07, -.00004800030105, -9.943128998e-07,
+     .00002143390677, -.00005015292878, -.00004800030105, -.00005015292878,
+      .0002567027066]).reshape(3,3)
+
+cov_colnames = 'aget totchr _cons'.split()
+
+cov_rownames = 'aget totchr _cons'.split()
+
+icr = np.array([
+                2925, -11791.344964777, -11046.468160182,                3,
+     22098.936320363,  22116.879469642])
+
+icr_colnames = 'N ll0 ll df AIC BIC'.split()
+
+icr_rownames = '.'.split()
+
+
+results_trunc_poisson1 = Bunch(
+                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,
+                icr=icr,
+                icr_colnames=icr_colnames,
+                icr_rownames=icr_rownames,
+                **est
+                )
+
+table = np.array([
+     8.2615384672996,  .05245366864144,  157.50163298917,                0,
+     8.1587311659054,  8.3643457686938, np.nan,  1.9599639845401,
+                   0])
+
+table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+table_rownames = '_cons'.split()
+
+
+results_trunc_poisson1.margins_cm = Bunch(
+                table=table,
+                table_colnames=table_colnames,
+                table_rownames=table_rownames,
+                )
+
+table = np.array([
+     8.2234692879205,  .05360982028302,  153.39483035955,                0,
+     8.1183959709481,  8.3285426048929, np.nan,  1.9599639845401,
+                   0])
+
+table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+table_rownames = '_cons'.split()
+
+
+results_trunc_poisson1.margins_means = Bunch(
+                table=table,
+                table_colnames=table_colnames,
+                table_rownames=table_rownames,
+                )
+
+table = np.array([
+     7.9777789306415,  .05376558351155,  148.38077464422,                0,
+     7.8724003233511,  8.0831575379319, np.nan,  1.9599639845401,
+                   0])
+
+table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+table_rownames = '_cons'.split()
+
+
+results_trunc_poisson1.margins_atmeans = Bunch(
+                table=table,
+                table_colnames=table_colnames,
+                table_rownames=table_rownames,
+                )
+
+table = np.array([
+     .01903458968954,  .00079296931232,  24.004194606123,  2.51401100e-127,
+     .01748039839655,  .02058878098252, np.nan,  1.9599639845401,
+                   0,  .04148049972971,  .00131417109881,  31.564002409827,
+     1.15213452e-218,  .03890477170652,  .04405622775289, np.nan,
+     1.9599639845401,                0,   .0696144656319,  .00158464648625,
+     43.930596657447,                0,  .06650861559064,  .07272031567317,
+    np.nan,  1.9599639845401,                0,  .86987044494885,
+     .00367826110927,  236.48958546081,                0,  .86266118564894,
+     .87707970424876, np.nan,  1.9599639845401,                0
+    ]).reshape(4,9)
+
+table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+table_rownames = '1bn._predict 2._predict 3._predict 4._predict'.split()
+
+
+results_trunc_poisson1.margins_cpr = Bunch(
+                table=table,
+                table_colnames=table_colnames,
+                table_rownames=table_rownames,
+                )
+
+table = np.array([
+     .00093735269068,  .00005905857331,  15.871576946126,  9.970398969e-57,
+     .00082160001401,  .00105310536736, np.nan,  1.9599639845401,
+                   0,  .00586646311539,  .00030222083986,  19.411179977019,
+     6.208483313e-84,  .00527412115388,   .0064588050769, np.nan,
+     1.9599639845401,                0,  .01875199435581,  .00076464697042,
+       24.5237280485,  8.24915097e-133,  .01725331383289,  .02025067487873,
+    np.nan,  1.9599639845401,                0,  .04092373966247,
+     .00126585942523,  32.328818545641,  2.75392481e-229,  .03844270077953,
+     .04340477854541, np.nan,  1.9599639845401,                0,
+     .93352045017566,  .00238739224619,  391.02097766504,                0,
+     .92884124735615,  .93819965299516, 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()
+
+
+results_trunc_poisson1.margins_pr = Bunch(
+                table=table,
+                table_colnames=table_colnames,
+                table_rownames=table_rownames,
+                )
+
+est = dict(
+           k_aux = 1,
+           r2_p = .0176349216287439,
+           chi2_c = 5373.418644637513,
+           ll_c = -11046.46816018163,
+           p = 6.68889408978e-66,
+           chi2 = 300.1403351701738,
+           df_m = 2,
+           rank0 = 2,
+           ll_0 = -8509.829005447959,
+           k_eq_model = 1,
+           ll = -8359.758837862872,
+           k_autoCns = 0,
+           rc = 0,
+           converged = 1,
+           k_dv = 1,
+           k_eq = 2,
+           k = 4,
+           ic = 4,
+           N = 2925,
+           rank = 4,
+           alpha = .6463397153671251,
+           cmdline = "tnbreg docvis aget totchr if docvis > 1, ll(1)",
+           title = "Truncated negative binomial regression",
+           title2 = "Truncation points: 1",
+           llopt = "1",
+           cmd = "tnbreg",
+           predict = "tnbreg_p",
+           dispers = "mean",
+           diparm_opt2 = "noprob",
+           chi2_ct = "LR",
+           chi2type = "LR",
+           opt = "moptimize",
+           vce = "oim",
+           diparm1 = "lnalpha, exp label(",
+           user = "tnbreg_mean",
+           crittype = "log likelihood",
+           ml_method = "e1",
+           singularHmethod = "m-marquardt",
+           technique = "nr",
+           which = "max",
+           depvar = "docvis",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     .01248138313122,  .01373594189048,  .90866598233607,  .36352646578614,
+    -.01444056826785,  .03940333453029, np.nan,  1.9599639845401,
+                   0,  .22357307664024,  .01291927204045,   17.30539274506,
+     4.283502186e-67,  .19825176873449,    .248894384546, np.nan,
+     1.9599639845401,                0,  1.4174570100091,   .0439841469348,
+     32.226543170437,  7.49935100e-228,  1.3312496661262,   1.503664353892,
+    np.nan,  1.9599639845401,                0, -.43643003824212,
+     .05825731989132, -7.4914197744811,  6.813252519e-14, -.55061228706494,
+     -.3222477894193, np.nan,  1.9599639845401,                0
+    ]).reshape(4,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'aget totchr _cons _cons'.split()
+
+cov = np.array([
+     .00018867609962, -8.418931354e-06, -.00034269534637,  8.523991115e-06,
+    -8.418931354e-06,  .00016690759006,  -.0003648589293,  .00010167740775,
+    -.00034269534637,  -.0003648589293,  .00193460518158, -.00096544368473,
+     8.523991115e-06,  .00010167740775, -.00096544368473,  .00339391532092
+    ]).reshape(4,4)
+
+cov_colnames = 'aget totchr _cons _cons'.split()
+
+cov_rownames = 'aget totchr _cons _cons'.split()
+
+icr = np.array([
+                2925,  -8509.829005448, -8359.7588378629,                4,
+     16727.517675726,  16751.441874764])
+
+icr_colnames = 'N ll0 ll df AIC BIC'.split()
+
+icr_rownames = '.'.split()
+
+
+results_trunc_negbin1 = Bunch(
+                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,
+                icr=icr,
+                icr_colnames=icr_colnames,
+                icr_rownames=icr_rownames,
+                **est
+                )
+
+table = np.array([
+     8.2699639050783,  .11716249080665,   70.58542241754,                0,
+     8.0403296427583,  8.4995981673984, np.nan,  1.9599639845401,
+                   0])
+
+table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+table_rownames = '_cons'.split()
+
+
+results_trunc_negbin1.margins_cm = Bunch(
+                table=table,
+                table_colnames=table_colnames,
+                table_rownames=table_rownames,
+                )
+
+table = np.array([
+      7.017795994882,   .1490968794197,   47.06869803175,                0,
+     6.7255714810121,  7.3100205087519, np.nan,  1.9599639845401,
+                   0])
+
+table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+table_rownames = '_cons'.split()
+
+
+results_trunc_negbin1.margins_means = Bunch(
+                table=table,
+                table_colnames=table_colnames,
+                table_rownames=table_rownames,
+                )
+
+table = np.array([
+     6.6955630594846,  .14246748862917,  46.997129828773,                0,
+     6.4163319128035,  6.9747942061656, np.nan,  1.9599639845401,
+                   0])
+
+table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+table_rownames = '_cons'.split()
+
+
+results_trunc_negbin1.margins_atmeans = Bunch(
+                table=table,
+                table_colnames=table_colnames,
+                table_rownames=table_rownames,
+                )
+
+table = np.array([
+     .12259218620014,  .00421507068546,  29.084253942174,  5.67883794e-186,
+     .11433079946434,  .13085357293593, np.nan,  1.9599639845401,
+                   0,  .11517369272078,  .00228641261466,  50.373100630412,
+                   0,  .11069240634225,  .11965497909931, np.nan,
+     1.9599639845401,                0,  .10432064271736,  .00125159955387,
+     83.349856106274,                0,  .10186755266872,  .10677373276601,
+    np.nan,  1.9599639845401,                0,  .65791347836172,
+     .00742267145141,  88.635672839381,                0,  .64336530964788,
+     .67246164707557, np.nan,  1.9599639845401,                0
+    ]).reshape(4,9)
+
+table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+table_rownames = '1bn._predict 2._predict 3._predict 4._predict'.split()
+
+
+results_trunc_negbin1.margins_cpr = Bunch(
+                table=table,
+                table_colnames=table_colnames,
+                table_rownames=table_rownames,
+                )
+
+table = np.array([
+     .07948896451521,  .00728414519354,  10.912600230113,  1.003444098e-27,
+     .06521230227771,  .09376562675271, np.nan,  1.9599639845401,
+                   0,  .09752506791268,  .00432307282658,  22.559200787217,
+     1.09075552e-112,  .08905200087003,  .10599813495533, np.nan,
+     1.9599639845401,                0,  .09876742202689,  .00202841049952,
+     48.692028586009,                0,  .09479181050196,  .10274303355181,
+    np.nan,  1.9599639845401,                0,   .0931207168728,
+     .00101384753518,  91.848836872758,                0,  .09113361221802,
+     .09510782152757, np.nan,  1.9599639845401,                0,
+     .63109782867243,  .01370605085442,  46.045198239483,                0,
+     .60423446262748,  .65796119471738, 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()
+
+
+results_trunc_negbin1.margins_pr = Bunch(
+                table=table,
+                table_colnames=table_colnames,
+                table_rownames=table_rownames,
+                )
+

.venv/lib/python3.12/site-packages/statsmodels/discrete/tests/results/ships.csv

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