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 )