import numpy as np
from statsmodels.tools.testing import ParamsTableTestBunch
est = dict(
rmse=.0136097497582343,
r2=.9741055881598619,
N=17,
df_r=14,
compat=.860625753607033,
vrank=2,
pvalue=.6503055973535645,
frac_sample=.7935370014985163,
frac_prior=.2064629985014838,
cmd="tgmixed",
predict="regres_p",
depvar="lconsump",
marginsok="XB default",
cmdline="tgmixed lconsump lincome lprice, prior(lprice -0.7 0.15 lincome 1 0.15) cov(lprice lincome -0.01)", # noqa:E501
prior="lprice -0.7 0.15 lincome 1 0.15",
properties="b V",
)
params_table = np.array([
1.0893571039001, .10338923727975, 10.53646523141, 4.871483239e-08,
.86760924410848, 1.3111049636916, 14, 2.1447866879178,
0, -.82054628653043, .03496499383295, -23.467651401591,
1.218701708e-12, -.89553873984647, -.74555383321439, 14,
2.1447866879178, 0, 1.4666439879147, .20347802665937,
7.2078740490733, 4.509300573e-06, 1.0302270250519, 1.9030609507775,
14, 2.1447866879178, 0]).reshape(3, 9)
params_table_colnames = 'b se t pvalue ll ul df crit eform'.split()
params_table_rownames = 'lincome lprice _cons'.split()
cov = np.array([
.01068933438529, -.00081953185523, -.0199747086722, -.00081953185523,
.00122255079374, -.00064024357954, -.0199747086722, -.00064024357954,
.04140330733319]).reshape(3, 3)
cov_colnames = 'lincome lprice _cons'.split()
cov_rownames = 'lincome lprice _cons'.split()
cov_prior = np.array([
.0225, -.01, 0, -.01,
.0225, 0, 0, 0,
0]).reshape(3, 3)
cov_prior_colnames = 'lincome lprice _cons'.split()
cov_prior_rownames = 'lincome lprice _cons'.split()
results_theil_textile = 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,
cov_prior=cov_prior,
cov_prior_colnames=cov_prior_colnames,
cov_prior_rownames=cov_prior_rownames,
**est
)