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alkaline-ml / statsmodels python
Repository URL to install this package:
|
Version:
0.10.2 ▾
|
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
)