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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(
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
)