""" Results for test_glm.py. Hard-coded from R or Stata. Note that some of the remaining discrepancy vs. Stata may be because Stata uses ML by default unless you specifically ask for IRLS. """ import numpy as np import pandas as pd from statsmodels.compat.python import asbytes from . import glm_test_resids import os from statsmodels.api import add_constant, categorical # for genfromtxt changes import sys PY2 = (sys.version_info[0] < 3) from distutils.version import LooseVersion NUMPY_LT_113 = LooseVersion(np.__version__) < '1.13.0' # Test Precisions DECIMAL_4 = 4 DECIMAL_3 = 3 DECIMAL_2 = 2 DECIMAL_1 = 1 DECIMAL_0 = 0 class Longley(object): """ Longley used for TestGlmGaussian Results are from Stata and R. """ def __init__(self): self.resids = np.array([[ 267.34002976, 267.34002976, 267.34002976, 267.34002976, 267.34002976], [ -94.0139424 , -94.0139424 , -94.0139424 , -94.0139424 , -94.0139424 ], [ 46.28716776, 46.28716776, 46.28716776, 46.28716776, 46.28716776], [-410.11462193, -410.11462193, -410.11462193, -410.11462193, -410.11462193], [ 309.71459076, 309.71459076, 309.71459076, 309.71459076, 309.71459076], [-249.31121533, -249.31121533, -249.31121533, -249.31121533, -249.31121533], [-164.0489564 , -164.0489564 , -164.0489564 , -164.0489564 , -164.0489564 ], [ -13.18035687, -13.18035687, -13.18035687, -13.18035687, -13.18035687], [ 14.3047726 , 14.3047726 , 14.3047726 , 14.3047726 , 14.3047726 ], [ 455.39409455, 455.39409455, 455.39409455, 455.39409455, 455.39409455], [ -17.26892711, -17.26892711, -17.26892711, -17.26892711, -17.26892711], [ -39.05504252, -39.05504252, -39.05504252, -39.05504252, -39.05504252], [-155.5499736 , -155.5499736 , -155.5499736 , -155.5499736 , -155.5499736 ], [ -85.67130804, -85.67130804, -85.67130804, -85.67130804, -85.67130804], [ 341.93151396, 341.93151396, 341.93151396, 341.93151396, 341.93151396], [-206.75782519, -206.75782519, -206.75782519, -206.75782519, -206.75782519]]) self.null_deviance = 185008826 # taken from R. self.params = np.array([ 1.50618723e+01, -3.58191793e-02, -2.02022980e+00, -1.03322687e+00, -5.11041057e-02, 1.82915146e+03, -3.48225863e+06]) self.bse = np.array([8.49149258e+01, 3.34910078e-02, 4.88399682e-01, 2.14274163e-01, 2.26073200e-01, 4.55478499e+02, 8.90420384e+05]) self.aic_R = 235.23486961695903 # R adds 2 for dof to AIC self.aic_Stata = 14.57717943930524 # stata divides by nobs self.deviance = 836424.0555058046 # from R self.scale = 92936.006167311629 self.llf = -109.61743480847952 self.null_deviance = 185008826 # taken from R. Rpy bug self.bic_Stata = 836399.1760177979 # no bic in R? self.df_model = 6 self.df_resid = 9 self.chi2 = 1981.711859508729 #TODO: taken from Stata not available # in sm yet # self.pearson_chi2 = 836424.1293162981 # from Stata (?) self.fittedvalues = np.array([60055.659970240202, 61216.013942398131, 60124.71283224225, 61597.114621930756, 62911.285409240052, 63888.31121532945, 65153.048956395127, 63774.180356866214, 66004.695227399934, 67401.605905447621, 68186.268927114084, 66552.055042522494, 68810.549973595422, 69649.67130804155, 68989.068486039061, 70757.757825193927]) class GaussianLog(object): """ Uses generated data. These results are from R and Stata. """ def __init__(self): # self.resids = np.genfromtxt('./glm_gaussian_log_resid.csv', ',') self.resids = np.array([[3.20800000e-04, 3.20800000e-04, 8.72100000e-04, 3.20800000e-04, 3.20800000e-04], [ 8.12100000e-04, 8.12100000e-04, 2.16350000e-03, 8.12100000e-04, 8.12100000e-04], [ -2.94800000e-04, -2.94800000e-04, -7.69700000e-04, -2.94800000e-04, -2.94800000e-04], [ 1.40190000e-03, 1.40190000e-03, 3.58560000e-03, 1.40190000e-03, 1.40190000e-03], [ -2.30910000e-03, -2.30910000e-03, -5.78490000e-03, -2.30910000e-03, -2.30910000e-03], [ 1.10380000e-03, 1.10380000e-03, 2.70820000e-03, 1.10380000e-03, 1.10380000e-03], [ -5.14000000e-06, -5.14000000e-06, -1.23000000e-05, -5.14000000e-06, -5.14000000e-06], [ -1.65500000e-04, -1.65500000e-04, -3.89200000e-04, -1.65500000e-04, -1.65500000e-04], [ -7.55400000e-04, -7.55400000e-04, -1.73870000e-03, -7.55400000e-04, -7.55400000e-04], [ -1.39800000e-04, -1.39800000e-04, -3.14800000e-04, -1.39800000e-04, -1.39800000e-04], [ -7.17000000e-04, -7.17000000e-04, -1.58000000e-03, -7.17000000e-04, -7.17000000e-04], [ -1.12200000e-04, -1.12200000e-04, -2.41900000e-04, -1.12200000e-04, -1.12200000e-04], [ 3.22100000e-04, 3.22100000e-04, 6.79000000e-04, 3.22100000e-04, 3.22100000e-04], [ -3.78000000e-05, -3.78000000e-05, -7.79000000e-05, -3.78000000e-05, -3.78000000e-05], [ 5.54500000e-04, 5.54500000e-04, 1.11730000e-03, 5.54500000e-04, 5.54500000e-04], [ 3.38400000e-04, 3.38400000e-04, 6.66300000e-04, 3.38400000e-04, 3.38400000e-04], [ 9.72000000e-05, 9.72000000e-05, 1.87000000e-04, 9.72000000e-05, 9.72000000e-05], [ -7.92900000e-04, -7.92900000e-04, -1.49070000e-03, -7.92900000e-04, -7.92900000e-04], [ 3.33000000e-04, 3.33000000e-04, 6.11500000e-04, 3.33000000e-04, 3.33000000e-04], [ -8.35300000e-04, -8.35300000e-04, -1.49790000e-03, -8.35300000e-04, -8.35300000e-04], [ -3.99700000e-04, -3.99700000e-04, -6.99800000e-04, -3.99700000e-04, -3.99700000e-04], [ 1.41300000e-04, 1.41300000e-04, 2.41500000e-04, 1.41300000e-04, 1.41300000e-04], [ -8.50700000e-04, -8.50700000e-04, -1.41920000e-03, -8.50700000e-04, -8.50700000e-04], [ 1.43000000e-06, 1.43000000e-06, 2.33000000e-06, 1.43000000e-06, 1.43000000e-06], [ -9.12000000e-05, -9.12000000e-05, -1.44900000e-04, -9.12000000e-05, -9.12000000e-05], [ 6.75500000e-04, 6.75500000e-04, 1.04650000e-03, 6.75500000e-04, 6.75500000e-04], [ 3.97900000e-04, 3.97900000e-04, 6.01100000e-04, 3.97900000e-04, 3.97900000e-04], [ 1.07000000e-05, 1.07000000e-05, 1.57000000e-05, 1.07000000e-05, 1.07000000e-05], [ -8.15200000e-04, -8.15200000e-04, -1.17060000e-03, -8.15200000e-04, -8.15200000e-04], [ -8.46400000e-04, -8.46400000e-04, -1.18460000e-03, -8.46400000e-04, -8.46400000e-04], [ 9.91200000e-04, 9.91200000e-04, 1.35180000e-03, 9.91200000e-04, 9.91200000e-04], [ -5.07400000e-04, -5.07400000e-04, -6.74200000e-04, -5.07400000e-04, -5.07400000e-04], [ 1.08520000e-03, 1.08520000e-03, 1.40450000e-03, 1.08520000e-03, 1.08520000e-03], [ 9.56100000e-04, 9.56100000e-04, 1.20500000e-03, 9.56100000e-04, 9.56100000e-04], [ 1.87500000e-03, 1.87500000e-03, 2.30090000e-03, 1.87500000e-03, 1.87500000e-03], [ -1.93920000e-03, -1.93920000e-03, -2.31650000e-03, -1.93920000e-03, -1.93920000e-03], [ 8.16000000e-04, 8.16000000e-04, 9.48700000e-04, 8.16000000e-04, 8.16000000e-04], [ 1.01520000e-03, 1.01520000e-03, 1.14860000e-03, 1.01520000e-03, 1.01520000e-03], [ 1.04150000e-03, 1.04150000e-03, 1.14640000e-03, 1.04150000e-03, 1.04150000e-03], [ -3.88200000e-04, -3.88200000e-04, -4.15600000e-04, -3.88200000e-04, -3.88200000e-04], [ 9.95900000e-04, 9.95900000e-04, 1.03690000e-03, 9.95900000e-04, 9.95900000e-04], [ -6.82800000e-04, -6.82800000e-04, -6.91200000e-04, -6.82800000e-04, -6.82800000e-04], [ -8.11400000e-04, -8.11400000e-04, -7.98500000e-04, -8.11400000e-04, -8.11400000e-04], [ -1.79050000e-03, -1.79050000e-03, -1.71250000e-03, -1.79050000e-03, -1.79050000e-03], [ 6.10000000e-04, 6.10000000e-04, 5.66900000e-04, 6.10000000e-04, 6.10000000e-04], [ 2.52600000e-04, 2.52600000e-04, 2.28100000e-04, 2.52600000e-04, 2.52600000e-04], [ -8.62500000e-04, -8.62500000e-04, -7.56400000e-04, -8.62500000e-04, -8.62500000e-04], [ -3.47300000e-04, -3.47300000e-04, -2.95800000e-04, -3.47300000e-04, -3.47300000e-04], [ -7.79000000e-05, -7.79000000e-05, -6.44000000e-05, -7.79000000e-05, -7.79000000e-05], [ 6.72000000e-04, 6.72000000e-04, 5.39400000e-04, 6.72000000e-04, 6.72000000e-04], [ -3.72100000e-04, -3.72100000e-04, -2.89900000e-04, -3.72100000e-04, -3.72100000e-04], [ -1.22900000e-04, -1.22900000e-04, -9.29000000e-05, -1.22900000e-04, -1.22900000e-04], [ -1.63470000e-03, -1.63470000e-03, -1.19900000e-03, -1.63470000e-03, -1.63470000e-03], [ 2.64400000e-04, 2.64400000e-04, 1.88100000e-04, 2.64400000e-04, 2.64400000e-04], [ 1.79230000e-03, 1.79230000e-03, 1.23650000e-03, 1.79230000e-03, 1.79230000e-03], [ -1.40500000e-04, -1.40500000e-04, -9.40000000e-05, -1.40500000e-04, -1.40500000e-04], [ -2.98500000e-04, -2.98500000e-04, -1.93600000e-04, -2.98500000e-04, -2.98500000e-04], [ -9.33100000e-04, -9.33100000e-04, -5.86400000e-04, -9.33100000e-04, -9.33100000e-04], [ 9.11200000e-04, 9.11200000e-04, 5.54900000e-04, 9.11200000e-04, 9.11200000e-04], [ -1.31840000e-03, -1.31840000e-03, -7.77900000e-04, -1.31840000e-03, -1.31840000e-03], [ -1.30200000e-04, -1.30200000e-04, -7.44000000e-05, -1.30200000e-04, -1.30200000e-04], [ 9.09300000e-04, 9.09300000e-04, 5.03200000e-04, 9.09300000e-04, 9.09300000e-04], [ -2.39500000e-04, -2.39500000e-04, -1.28300000e-04, -2.39500000e-04, -2.39500000e-04], [ 7.15300000e-04, 7.15300000e-04, 3.71000000e-04, 7.15300000e-04, 7.15300000e-04], [ 5.45000000e-05, 5.45000000e-05, 2.73000000e-05, 5.45000000e-05, 5.45000000e-05], [ 2.85310000e-03, 2.85310000e-03, 1.38600000e-03, 2.85310000e-03, 2.85310000e-03], [ 4.63400000e-04, 4.63400000e-04, 2.17800000e-04, 4.63400000e-04, 4.63400000e-04], [ 2.80900000e-04, 2.80900000e-04, 1.27700000e-04, 2.80900000e-04, 2.80900000e-04], [ 5.42000000e-05, 5.42000000e-05, 2.38000000e-05, 5.42000000e-05, 5.42000000e-05], [ -3.62300000e-04, -3.62300000e-04, -1.54000000e-04, -3.62300000e-04, -3.62300000e-04], [ -1.11900000e-03, -1.11900000e-03, -4.59800000e-04, -1.11900000e-03, -1.11900000e-03], [ 1.28900000e-03, 1.28900000e-03, 5.11900000e-04, 1.28900000e-03, 1.28900000e-03], [ -1.40820000e-03, -1.40820000e-03, -5.40400000e-04, -1.40820000e-03, -1.40820000e-03], [ -1.69300000e-04, -1.69300000e-04, -6.28000000e-05, -1.69300000e-04, -1.69300000e-04], [ -1.03620000e-03, -1.03620000e-03, -3.71000000e-04, -1.03620000e-03, -1.03620000e-03], [ 1.49150000e-03, 1.49150000e-03, 5.15800000e-04, 1.49150000e-03, 1.49150000e-03], [ -7.22000000e-05, -7.22000000e-05, -2.41000000e-05, -7.22000000e-05, -7.22000000e-05], [ 5.49000000e-04, 5.49000000e-04, 1.76900000e-04, 5.49000000e-04, 5.49000000e-04], [ -2.12320000e-03, -2.12320000e-03, -6.60400000e-04, -2.12320000e-03, -2.12320000e-03], [ 7.84000000e-06, 7.84000000e-06, 2.35000000e-06, 7.84000000e-06, 7.84000000e-06], [ 1.15580000e-03, 1.15580000e-03, 3.34700000e-04, 1.15580000e-03, 1.15580000e-03], [ 4.83400000e-04, 4.83400000e-04, 1.35000000e-04, 4.83400000e-04, 4.83400000e-04], [ -5.26100000e-04, -5.26100000e-04, -1.41700000e-04, -5.26100000e-04, -5.26100000e-04], [ -1.75100000e-04, -1.75100000e-04, -4.55000000e-05, -1.75100000e-04, -1.75100000e-04], [ -1.84600000e-03, -1.84600000e-03, -4.62100000e-04, -1.84600000e-03, -1.84600000e-03], [ 2.07200000e-04, 2.07200000e-04, 5.00000000e-05, 2.07200000e-04, 2.07200000e-04], [ -8.54700000e-04, -8.54700000e-04, -1.98700000e-04, -8.54700000e-04, -8.54700000e-04], [ -9.20000000e-05, -9.20000000e-05, -2.06000000e-05, -9.20000000e-05, -9.20000000e-05], [ 5.35700000e-04, 5.35700000e-04, 1.15600000e-04, 5.35700000e-04, 5.35700000e-04], [ -7.67300000e-04, -7.67300000e-04, -1.59400000e-04, -7.67300000e-04, -7.67300000e-04], [ -1.79710000e-03, -1.79710000e-03, -3.59500000e-04, -1.79710000e-03, -1.79710000e-03], [ 1.10910000e-03, 1.10910000e-03, 2.13500000e-04, 1.10910000e-03, 1.10910000e-03], [ -5.53800000e-04, -5.53800000e-04, -1.02600000e-04, -5.53800000e-04, -5.53800000e-04], [ 7.48000000e-04, 7.48000000e-04, 1.33400000e-04, 7.48000000e-04, 7.48000000e-04], [ 4.23000000e-04, 4.23000000e-04, 7.26000000e-05, 4.23000000e-04, 4.23000000e-04], [ -3.16400000e-04, -3.16400000e-04, -5.22000000e-05, -3.16400000e-04, -3.16400000e-04], [ -6.63200000e-04, -6.63200000e-04, -1.05200000e-04, -6.63200000e-04, -6.63200000e-04], [ 1.33540000e-03, 1.33540000e-03, 2.03700000e-04, 1.33540000e-03, 1.33540000e-03], [ -7.81200000e-04, -7.81200000e-04, -1.14600000e-04, -7.81200000e-04, -7.81200000e-04], [ 1.67880000e-03, 1.67880000e-03, 2.36600000e-04, 1.67880000e-03, 1.67880000e-03]]) self.null_deviance = 56.691617808182208 self.params = np.array([9.99964386e-01,-1.99896965e-02, -1.00027232e-04]) self.bse = np.array([1.42119293e-04, 1.20276468e-05, 1.87347682e-07]) self.aic_R = -1103.8187213072656 # adds 2 for dof for scale self.aic_Stata = -11.05818072104212 # divides by nobs for e(aic) self.deviance = 8.68876986288542e-05 self.scale = 8.9574946938163984e-07 # from R but e(phi) in Stata self.llf = 555.9093606536328 self.bic_Stata = -446.7014211525822 self.df_model = 2 self.df_resid = 97 self.chi2 = 33207648.86501769 # from Stata not in sm self.fittedvalues = np.array([2.7181850213327747, 2.664122305869506, 2.6106125414084405, 2.5576658143523567, 2.5052916730829535, 2.4534991313100165, 2.4022966718815781, 2.3516922510411282, 2.3016933031175575, 2.2523067456332542, 2.2035389848154616, 2.1553959214958001, 2.107882957382607, 2.0610050016905817, 2.0147664781120667, 1.969171332114154, 1.9242230385457144, 1.8799246095383746, 1.8362786026854092, 1.7932871294825108, 1.7509518640143886, 1.7092740518711942, 1.6682545192788105, 1.6278936824271399, 1.5881915569806042, 1.5491477677552221, 1.5107615585467538, 1.4730318020945796, 1.4359570101661721, 1.3995353437472129, 1.3637646233226499, 1.3286423392342188, 1.2941656621002184, 1.2603314532836074, 1.2271362753947765, 1.1945764028156565, 1.162647832232141, 1.1313462931621328, 1.1006672584668622, 1.0706059548334832, 1.0411573732173065, 1.0123162792324054, 0.98407722347970683, 0.95643455180206194, 0.92938241545618494, 0.90291478119174029, 0.87702544122826565, 0.85170802312101246, 0.82695599950720078, 0.80276269772458597, 0.77912130929465073, 0.75602489926313921, 0.73346641539106316, 0.71143869718971686, 0.68993448479364294, 0.66894642766589496, 0.64846709313034534, 0.62848897472617915, 0.60900450038011367, 0.5900060403922629, 0.57148591523195513, 0.55343640314018494, 0.5358497475357491, 0.51871816422248385, 0.50203384839536769, 0.48578898144361343, 0.46997573754920047, 0.45458629007964013, 0.4396128177740814, 0.42504751072218311, 0.41088257613548018, 0.39711024391126759, 0.38372277198930843, 0.37071245150195081, 0.35807161171849949, 0.34579262478494655, 0.33386791026040569, 0.32228993945183393, 0.31105123954884056, 0.30014439756060574, 0.28956206405712448, 0.27929695671718968, 0.26934186368570684, 0.25968964674310463, 0.25033324428976694, 0.24126567414856051, 0.23248003618867552, 0.22396951477412205, 0.21572738104035141, 0.20774699500257574, 0.20002180749946474, 0.19254536197598673, 0.18531129610924435, 0.17831334328122878, 0.17154533390247831, 0.16500119659068577, 0.15867495920834204, 0.15256074976354628, 0.14665279717814039, 0.14094543192735109]) class GaussianInverse(object): """ This test uses generated data. Results are from R and Stata. """ def __init__(self): self.resids = np.array([[-5.15300000e-04, -5.15300000e-04, 5.14800000e-04, -5.15300000e-04, -5.15300000e-04], [ -2.12500000e-04, -2.12500000e-04, 2.03700000e-04, -2.12500000e-04, -2.12500000e-04], [ -1.71400000e-04, -1.71400000e-04, 1.57200000e-04, -1.71400000e-04, -1.71400000e-04], [ 1.94020000e-03, 1.94020000e-03, -1.69710000e-03, 1.94020000e-03, 1.94020000e-03], [ -6.81100000e-04, -6.81100000e-04, 5.66900000e-04, -6.81100000e-04, -6.81100000e-04], [ 1.21370000e-03, 1.21370000e-03, -9.58800000e-04, 1.21370000e-03, 1.21370000e-03], [ -1.51090000e-03, -1.51090000e-03, 1.13070000e-03, -1.51090000e-03, -1.51090000e-03], [ 3.21500000e-04, 3.21500000e-04, -2.27400000e-04, 3.21500000e-04, 3.21500000e-04], [ -3.18500000e-04, -3.18500000e-04, 2.12600000e-04, -3.18500000e-04, -3.18500000e-04], [ 3.75600000e-04, 3.75600000e-04, -2.36300000e-04, 3.75600000e-04, 3.75600000e-04], [ 4.82300000e-04, 4.82300000e-04, -2.85500000e-04, 4.82300000e-04, 4.82300000e-04], [ -1.41870000e-03, -1.41870000e-03, 7.89300000e-04, -1.41870000e-03, -1.41870000e-03], [ 6.75000000e-05, 6.75000000e-05, -3.52000000e-05, 6.75000000e-05, 6.75000000e-05], [ 4.06300000e-04, 4.06300000e-04, -1.99100000e-04, 4.06300000e-04, 4.06300000e-04], [ -3.61500000e-04, -3.61500000e-04, 1.66000000e-04, -3.61500000e-04, -3.61500000e-04], [ -2.97400000e-04, -2.97400000e-04, 1.28000000e-04, -2.97400000e-04, -2.97400000e-04], [ -9.32700000e-04, -9.32700000e-04, 3.75800000e-04, -9.32700000e-04, -9.32700000e-04], [ 1.16270000e-03, 1.16270000e-03, -4.38500000e-04, 1.16270000e-03, 1.16270000e-03], [ 6.77900000e-04, 6.77900000e-04, -2.39200000e-04, 6.77900000e-04, 6.77900000e-04], [ -1.29330000e-03, -1.29330000e-03, 4.27000000e-04, -1.29330000e-03, -1.29330000e-03], [ 2.24500000e-04, 2.24500000e-04, -6.94000000e-05, 2.24500000e-04, 2.24500000e-04], [ 1.05510000e-03, 1.05510000e-03, -3.04900000e-04, 1.05510000e-03, 1.05510000e-03], [ 2.50400000e-04, 2.50400000e-04, -6.77000000e-05, 2.50400000e-04, 2.50400000e-04], [ 4.08600000e-04, 4.08600000e-04, -1.03400000e-04, 4.08600000e-04, 4.08600000e-04], [ -1.67610000e-03, -1.67610000e-03, 3.96800000e-04, -1.67610000e-03, -1.67610000e-03], [ 7.47600000e-04, 7.47600000e-04, -1.65700000e-04, 7.47600000e-04, 7.47600000e-04], [ 2.08200000e-04, 2.08200000e-04, -4.32000000e-05, 2.08200000e-04, 2.08200000e-04], [ -8.00800000e-04, -8.00800000e-04, 1.55700000e-04, -8.00800000e-04, -8.00800000e-04], [ 5.81200000e-04, 5.81200000e-04, -1.05900000e-04, 5.81200000e-04, 5.81200000e-04], [ 1.00980000e-03, 1.00980000e-03, -1.72400000e-04, 1.00980000e-03, 1.00980000e-03], [ 2.77400000e-04, 2.77400000e-04, -4.44000000e-05, 2.77400000e-04, 2.77400000e-04], [ -5.02800000e-04, -5.02800000e-04, 7.55000000e-05, -5.02800000e-04, -5.02800000e-04], [ 2.69800000e-04, 2.69800000e-04, -3.80000000e-05, 2.69800000e-04, 2.69800000e-04], [ 2.01300000e-04, 2.01300000e-04, -2.67000000e-05, 2.01300000e-04, 2.01300000e-04], [ -1.19690000e-03, -1.19690000e-03, 1.48900000e-04, -1.19690000e-03, -1.19690000e-03], [ -6.94200000e-04, -6.94200000e-04, 8.12000000e-05, -6.94200000e-04, -6.94200000e-04], [ 5.65500000e-04, 5.65500000e-04, -6.22000000e-05, 5.65500000e-04, 5.65500000e-04], [ 4.93100000e-04, 4.93100000e-04, -5.10000000e-05, 4.93100000e-04, 4.93100000e-04], [ 3.25000000e-04, 3.25000000e-04, -3.17000000e-05, 3.25000000e-04, 3.25000000e-04], [ -7.70200000e-04, -7.70200000e-04, 7.07000000e-05, -7.70200000e-04, -7.70200000e-04], [ 2.58000000e-05, 2.58000000e-05, -2.23000000e-06, 2.58000000e-05, 2.58000000e-05], [ -1.52800000e-04, -1.52800000e-04, 1.25000000e-05, -1.52800000e-04, -1.52800000e-04], [ 4.52000000e-05, 4.52000000e-05, -3.48000000e-06, 4.52000000e-05, 4.52000000e-05], [ -6.83900000e-04, -6.83900000e-04, 4.97000000e-05, -6.83900000e-04, -6.83900000e-04], [ -7.77600000e-04, -7.77600000e-04, 5.34000000e-05, -7.77600000e-04, -7.77600000e-04], [ 1.03170000e-03, 1.03170000e-03, -6.70000000e-05, 1.03170000e-03, 1.03170000e-03], [ 1.20000000e-03, 1.20000000e-03, -7.37000000e-05, 1.20000000e-03, 1.20000000e-03], [ -7.71600000e-04, -7.71600000e-04, 4.48000000e-05, -7.71600000e-04, -7.71600000e-04], [ -3.37000000e-04, -3.37000000e-04, 1.85000000e-05, -3.37000000e-04, -3.37000000e-04], [ 1.19880000e-03, 1.19880000e-03, -6.25000000e-05, 1.19880000e-03, 1.19880000e-03], [ -1.54610000e-03, -1.54610000e-03, 7.64000000e-05, -1.54610000e-03, -1.54610000e-03], [ 9.11600000e-04, 9.11600000e-04, -4.27000000e-05, 9.11600000e-04, 9.11600000e-04], [ -4.70800000e-04, -4.70800000e-04, 2.09000000e-05, -4.70800000e-04, -4.70800000e-04], [ -1.21550000e-03, -1.21550000e-03, 5.13000000e-05, -1.21550000e-03, -1.21550000e-03], [ 1.09160000e-03, 1.09160000e-03, -4.37000000e-05, 1.09160000e-03, 1.09160000e-03], [ -2.72000000e-04, -2.72000000e-04, 1.04000000e-05, -2.72000000e-04, -2.72000000e-04], [ -7.84500000e-04, -7.84500000e-04, 2.84000000e-05, -7.84500000e-04, -7.84500000e-04], [ 1.53330000e-03, 1.53330000e-03, -5.28000000e-05, 1.53330000e-03, 1.53330000e-03], [ -1.84450000e-03, -1.84450000e-03, 6.05000000e-05, -1.84450000e-03, -1.84450000e-03], [ 1.68550000e-03, 1.68550000e-03, -5.26000000e-05, 1.68550000e-03, 1.68550000e-03], [ -3.06100000e-04, -3.06100000e-04, 9.10000000e-06, -3.06100000e-04, -3.06100000e-04], [ 1.00950000e-03, 1.00950000e-03, -2.86000000e-05, 1.00950000e-03, 1.00950000e-03], [ 5.22000000e-04, 5.22000000e-04, -1.41000000e-05, 5.22000000e-04, 5.22000000e-04], [ -2.18000000e-05, -2.18000000e-05, 5.62000000e-07, -2.18000000e-05, -2.18000000e-05], [ -7.80600000e-04, -7.80600000e-04, 1.92000000e-05, -7.80600000e-04, -7.80600000e-04], [ 6.81400000e-04, 6.81400000e-04, -1.60000000e-05, 6.81400000e-04, 6.81400000e-04], [ -1.43800000e-04, -1.43800000e-04, 3.23000000e-06, -1.43800000e-04, -1.43800000e-04], [ 7.76000000e-04, 7.76000000e-04, -1.66000000e-05, 7.76000000e-04, 7.76000000e-04], [ 2.54900000e-04, 2.54900000e-04, -5.22000000e-06, 2.54900000e-04, 2.54900000e-04], [ 5.77500000e-04, 5.77500000e-04, -1.13000000e-05, 5.77500000e-04, 5.77500000e-04], [ 7.58100000e-04, 7.58100000e-04, -1.42000000e-05, 7.58100000e-04, 7.58100000e-04], [ -8.31000000e-04, -8.31000000e-04, 1.49000000e-05, -8.31000000e-04, -8.31000000e-04], [ -2.10340000e-03, -2.10340000e-03, 3.62000000e-05, -2.10340000e-03, -2.10340000e-03], [ -8.89900000e-04, -8.89900000e-04, 1.47000000e-05, -8.89900000e-04, -8.89900000e-04], [ 1.08570000e-03, 1.08570000e-03, -1.71000000e-05, 1.08570000e-03, 1.08570000e-03], [ -1.88600000e-04, -1.88600000e-04, 2.86000000e-06, -1.88600000e-04, -1.88600000e-04], [ 9.10000000e-05, 9.10000000e-05, -1.32000000e-06, 9.10000000e-05, 9.10000000e-05], [ 1.07700000e-03, 1.07700000e-03, -1.50000000e-05, 1.07700000e-03, 1.07700000e-03], [ 9.04100000e-04, 9.04100000e-04, -1.21000000e-05, 9.04100000e-04, 9.04100000e-04], [ -2.20000000e-04, -2.20000000e-04, 2.83000000e-06, -2.20000000e-04, -2.20000000e-04], [ -1.64030000e-03, -1.64030000e-03, 2.02000000e-05, -1.64030000e-03, -1.64030000e-03], [ 2.20600000e-04, 2.20600000e-04, -2.62000000e-06, 2.20600000e-04, 2.20600000e-04], [ -2.78300000e-04, -2.78300000e-04, 3.17000000e-06, -2.78300000e-04, -2.78300000e-04], [ -4.93000000e-04, -4.93000000e-04, 5.40000000e-06, -4.93000000e-04, -4.93000000e-04], [ -1.85000000e-04, -1.85000000e-04, 1.95000000e-06, -1.85000000e-04, -1.85000000e-04], [ -7.64000000e-04, -7.64000000e-04, 7.75000000e-06, -7.64000000e-04, -7.64000000e-04], [ 7.79600000e-04, 7.79600000e-04, -7.61000000e-06, 7.79600000e-04, 7.79600000e-04], [ 2.88400000e-04, 2.88400000e-04, -2.71000000e-06, 2.88400000e-04, 2.88400000e-04], [ 1.09370000e-03, 1.09370000e-03, -9.91000000e-06, 1.09370000e-03, 1.09370000e-03], [ 3.07000000e-04, 3.07000000e-04, -2.68000000e-06, 3.07000000e-04, 3.07000000e-04], [ -8.76000000e-04, -8.76000000e-04, 7.37000000e-06, -8.76000000e-04, -8.76000000e-04], [ -1.85300000e-04, -1.85300000e-04, 1.50000000e-06, -1.85300000e-04, -1.85300000e-04], [ 3.24700000e-04, 3.24700000e-04, -2.54000000e-06, 3.24700000e-04, 3.24700000e-04], [ 4.59600000e-04, 4.59600000e-04, -3.47000000e-06, 4.59600000e-04, 4.59600000e-04], [ -2.73300000e-04, -2.73300000e-04, 1.99000000e-06, -2.73300000e-04, -2.73300000e-04], [ 1.32180000e-03, 1.32180000e-03, -9.29000000e-06, 1.32180000e-03, 1.32180000e-03], [ -1.32620000e-03, -1.32620000e-03, 9.00000000e-06, -1.32620000e-03, -1.32620000e-03], [ 9.62000000e-05, 9.62000000e-05, -6.31000000e-07, 9.62000000e-05, 9.62000000e-05], [ -6.04400000e-04, -6.04400000e-04, 3.83000000e-06, -6.04400000e-04, -6.04400000e-04], [ -6.66300000e-04, -6.66300000e-04, 4.08000000e-06, -6.66300000e-04, -6.66300000e-04]]) self.null_deviance = 6.8088354977561 # from R, Rpy bug self.params = np.array([ 1.00045997, 0.01991666, 0.00100126]) self.bse = np.array([ 4.55214070e-04, 7.00529313e-05, 1.84478509e-06]) self.aic_R = -1123.1528237643774 self.aic_Stata = -11.25152876811373 self.deviance = 7.1612915365488368e-05 self.scale = 7.3827747608449547e-07 self.llf = 565.57641188218872 self.bic_Stata = -446.7014364279675 self.df_model = 2 self.df_resid = 97 self.chi2 = 2704006.698904491 self.fittedvalues = np.array([ 0.99954024, 0.97906956, 0.95758077, 0.93526008, 0.91228657, 0.88882978, 0.8650479 , 0.84108646, 0.81707757, 0.79313958, 0.76937709, 0.74588129, 0.72273051, 0.69999099, 0.67771773, 0.65595543, 0.63473944, 0.61409675, 0.59404691, 0.57460297, 0.55577231, 0.53755742, 0.51995663, 0.50296478, 0.48657379, 0.47077316, 0.4555505 , 0.44089187, 0.42678213, 0.41320529, 0.40014475, 0.38758348, 0.37550428, 0.36388987, 0.35272306, 0.34198684, 0.33166446, 0.32173953, 0.31219604, 0.30301842, 0.29419156, 0.28570085, 0.27753216, 0.26967189, 0.26210695, 0.25482476, 0.24781324, 0.2410608 , 0.23455636, 0.22828931, 0.22224947, 0.21642715, 0.21081306, 0.20539835, 0.20017455, 0.19513359, 0.19026777, 0.18556972, 0.18103243, 0.17664922, 0.1724137 , 0.16831977, 0.16436164, 0.16053377, 0.15683086, 0.15324789, 0.14978003, 0.1464227 , 0.14317153, 0.14002232, 0.13697109, 0.13401403, 0.1311475 , 0.12836802, 0.12567228, 0.1230571 , 0.12051944, 0.11805642, 0.11566526, 0.1133433 , 0.11108802, 0.10889699, 0.10676788, 0.10469847, 0.10268664, 0.10073034, 0.09882763, 0.09697663, 0.09517555, 0.09342267, 0.09171634, 0.09005498, 0.08843707, 0.08686116, 0.08532585, 0.08382979, 0.0823717 , 0.08095035, 0.07956453, 0.07821311]) class Star98(object): """ Star98 class used with TestGlmBinomial """ def __init__(self): self.params = (-0.0168150366, 0.0099254766, -0.0187242148, -0.0142385609, 0.2544871730, 0.2406936644, 0.0804086739, -1.9521605027, -0.3340864748, -0.1690221685, 0.0049167021, -0.0035799644, -0.0140765648, -0.0040049918, -0.0039063958, 0.0917143006, 0.0489898381, 0.0080407389, 0.0002220095, -0.0022492486, 2.9588779262) self.bse = (4.339467e-04, 6.013714e-04, 7.435499e-04, 4.338655e-04, 2.994576e-02, 5.713824e-02, 1.392359e-02, 3.168109e-01, 6.126411e-02, 3.270139e-02, 1.253877e-03, 2.254633e-04, 1.904573e-03, 4.739838e-04, 9.623650e-04, 1.450923e-02, 7.451666e-03, 1.499497e-03, 2.988794e-05, 3.489838e-04, 1.546712e+00) self.null_deviance = 34345.3688931 self.df_null = 302 self.deviance = 4078.76541772 self.df_resid = 282 self.df_model = 20 self.aic_R = 6039.22511799 self.aic_Stata = 19.93143846737438 self.bic_Stata = 2467.493504191302 self.llf = -2998.61255899391 # from R self.llf_Stata = -2998.612927807218 self.scale = 1. self.pearson_chi2 = 4051.921614 self.resids = glm_test_resids.star98_resids self.fittedvalues = np.array([ 0.5833118 , 0.75144661, 0.50058272, 0.68534524, 0.32251021, 0.68693601, 0.33299827, 0.65624766, 0.49851481, 0.506736, 0.23954874, 0.86631452, 0.46432936, 0.44171873, 0.66797935, 0.73988491, 0.51966014, 0.42442446, 0.5649369 , 0.59251634, 0.34798337, 0.56415024, 0.49974355, 0.3565539 , 0.20752309, 0.18269097, 0.44932642, 0.48025128, 0.59965277, 0.58848671, 0.36264203, 0.33333196, 0.74253352, 0.5081886 , 0.53421878, 0.56291445, 0.60205239, 0.29174423, 0.2954348 , 0.32220414, 0.47977903, 0.23687535, 0.11776464, 0.1557423 , 0.27854799, 0.22699533, 0.1819439 , 0.32554433, 0.22681989, 0.15785389, 0.15268609, 0.61094772, 0.20743222, 0.51649059, 0.46502006, 0.41031788, 0.59523288, 0.65733285, 0.27835336, 0.2371213 , 0.25137045, 0.23953942, 0.27854519, 0.39652413, 0.27023163, 0.61411863, 0.2212025 , 0.42005842, 0.55940397, 0.35413774, 0.45724563, 0.57399437, 0.2168918 , 0.58308738, 0.17181104, 0.49873249, 0.22832683, 0.14846056, 0.5028073 , 0.24513863, 0.48202096, 0.52823155, 0.5086262 , 0.46295993, 0.57869402, 0.78363217, 0.21144435, 0.2298366 , 0.17954825, 0.32232586, 0.8343015 , 0.56217006, 0.47367315, 0.52535649, 0.60350746, 0.43210701, 0.44712008, 0.35858239, 0.2521347 , 0.19787004, 0.63256553, 0.51386532, 0.64997027, 0.13402072, 0.81756174, 0.74543642, 0.30825852, 0.23988707, 0.17273125, 0.27880599, 0.17395893, 0.32052828, 0.80467697, 0.18726218, 0.23842081, 0.19020381, 0.85835388, 0.58703615, 0.72415106, 0.64433695, 0.68766653, 0.32923663, 0.16352185, 0.38868816, 0.44980444, 0.74810044, 0.42973792, 0.53762581, 0.72714996, 0.61229484, 0.30267667, 0.24713253, 0.65086008, 0.48957265, 0.54955545, 0.5697156 , 0.36406211, 0.48906545, 0.45919413, 0.4930565 , 0.39785555, 0.5078719 , 0.30159626, 0.28524393, 0.34687707, 0.22522042, 0.52947159, 0.29277287, 0.8585002 , 0.60800389, 0.75830521, 0.35648175, 0.69508796, 0.45518355, 0.21567675, 0.39682985, 0.49042948, 0.47615798, 0.60588234, 0.62910299, 0.46005639, 0.71755165, 0.48852156, 0.47940661, 0.60128813, 0.16589699, 0.68512861, 0.46305199, 0.68832227, 0.7006721 , 0.56564937, 0.51753941, 0.54261733, 0.56072214, 0.34545715, 0.30226104, 0.3572956 , 0.40996287, 0.33517519, 0.36248407, 0.33937041, 0.34140691, 0.2627528 , 0.29955161, 0.38581683, 0.24840026, 0.15414272, 0.40415991, 0.53936252, 0.52111887, 0.28060168, 0.45600958, 0.51110589, 0.43757523, 0.46891953, 0.39425249, 0.5834369 , 0.55817308, 0.32051259, 0.43567448, 0.34134195, 0.43016545, 0.4885413 , 0.28478325, 0.2650776 , 0.46784606, 0.46265983, 0.42655938, 0.18972234, 0.60448491, 0.211896 , 0.37886032, 0.50727577, 0.39782309, 0.50427121, 0.35882898, 0.39596807, 0.49160806, 0.35618002, 0.6819922 , 0.36871093, 0.43079679, 0.67985516, 0.41270595, 0.68952767, 0.52587734, 0.32042126, 0.39120123, 0.56870985, 0.32962349, 0.32168989, 0.54076251, 0.4592907 , 0.48480182, 0.4408386 , 0.431178 , 0.47078232, 0.55911605, 0.30331618, 0.50310393, 0.65036038, 0.45078895, 0.62354291, 0.56435463, 0.50034281, 0.52693538, 0.57217285, 0.49221472, 0.40707122, 0.44226533, 0.3475959 , 0.54746396, 0.86385832, 0.48402233, 0.54313657, 0.61586824, 0.27097185, 0.69717808, 0.52156974, 0.50401189, 0.56724181, 0.6577178 , 0.42732047, 0.44808396, 0.65435634, 0.54766225, 0.38160648, 0.49890847, 0.50879037, 0.5875452 , 0.45101593, 0.5709704 , 0.3175516 , 0.39813159, 0.28305688, 0.40521062, 0.30120578, 0.26400428, 0.44205496, 0.40545798, 0.39366599, 0.55288196, 0.14104184, 0.17550155, 0.1949095 , 0.40255144, 0.21016822, 0.09712017, 0.63151487, 0.25885514, 0.57323748, 0.61836898, 0.43268601, 0.67008878, 0.75801989, 0.50353406, 0.64222315, 0.29925757, 0.32592036, 0.39634977, 0.39582747, 0.41037006, 0.34174944]) class Lbw(object): ''' The LBW data can be found here http://www.stata-press.com/data/r9/rmain.html ''' def __init__(self): # data set up for data not in datasets filename = os.path.join(os.path.dirname(os.path.abspath(__file__)), "stata_lbw_glm.csv") # https://github.com/statsmodels/statsmodels/pull/4432#issuecomment-379279617 if NUMPY_LT_113 or PY2: with open(filename, 'rb') as datafile: data=np.recfromcsv(datafile) vfunc = np.vectorize(lambda x: x.strip(asbytes("\""))) data['race'] = vfunc(data['race']) else: data = pd.read_csv(filename).to_records(index=False) # categorical does not work with pandas data = categorical(data, col='race', drop=True) self.endog = data.low design = np.column_stack((data['age'], data['lwt'], data['race_black'], data['race_other'], data['smoke'], data['ptl'], data['ht'], data['ui'])) self.exog = add_constant(design, prepend=False) # Results for Canonical Logit Link self.params = (-.02710031, -.01515082, 1.26264728, .86207916, .92334482, .54183656, 1.83251780, .75851348, .46122388) self.bse = (0.036449917, 0.006925765, 0.526405169, 0.439146744, 0.400820976, 0.346246857, 0.691623875, 0.459373871, 1.204574885) self.aic_R = 219.447991133 self.aic_Stata = 1.161100482182551 self.deviance = 201.4479911325021 self.scale = 1 self.llf = -100.7239955662511 self.chi2 = 25.65329337867037 # from Stata not used by sm self.null_deviance = 234.671996193219 self.bic_Stata = -742.0664715782335 self.df_resid = 180 self.df_model = 8 self.df_null = 188 self.pearson_chi2 = 182.023342493558 self.resids = glm_test_resids.lbw_resids self.fittedvalues = np.array([ 0.31217507, 0.12793027, 0.32119762, 0.48442686, 0.50853393, 0.24517662, 0.12755193, 0.33226988, 0.22013309, 0.26268069, 0.34729955, 0.18782188, 0.75404181, 0.54723527, 0.35016393, 0.35016393, 0.45824406, 0.25336683, 0.43087357, 0.23284101, 0.20146616, 0.24315597, 0.02725586, 0.22207692, 0.39800383, 0.05584178, 0.28403447, 0.06931188, 0.35371946, 0.3896279 , 0.3896279 , 0.47812002, 0.60043853, 0.07144772, 0.29995988, 0.17910031, 0.22773411, 0.22691015, 0.06221253, 0.2384528 , 0.32633864, 0.05131047, 0.2954536 , 0.07364416, 0.57241299, 0.57241299, 0.08272435, 0.23298882, 0.12658158, 0.58967487, 0.46989562, 0.22455631, 0.2348285 , 0.29571887, 0.28212464, 0.31499013, 0.68340511, 0.14090647, 0.31448425, 0.28082972, 0.28082972, 0.24918728, 0.27018297, 0.08175784, 0.64808999, 0.38252574, 0.25550797, 0.09113411, 0.40736693, 0.32644055, 0.54367425, 0.29606968, 0.47028421, 0.39972155, 0.25079125, 0.09678472, 0.08807264, 0.27467837, 0.5675742 , 0.045619 , 0.10719293, 0.04826292, 0.23934092, 0.24179618, 0.23802197, 0.49196179, 0.31379451, 0.10605469, 0.04047396, 0.11620849, 0.09937016, 0.21822964, 0.29770265, 0.83912829, 0.25079125, 0.08548557, 0.06550308, 0.2046457 , 0.2046457 , 0.08110349, 0.13519643, 0.47862055, 0.38891913, 0.1383964 , 0.26176764, 0.31594589, 0.11418612, 0.06324112, 0.28468594, 0.21663702, 0.03827107, 0.27237604, 0.20246694, 0.19042999, 0.15019447, 0.18759474, 0.12308435, 0.19700616, 0.11564002, 0.36595033, 0.07765727, 0.14119063, 0.13584627, 0.11012759, 0.10102472, 0.10002166, 0.07439288, 0.27919958, 0.12491598, 0.06774594, 0.72513764, 0.17714986, 0.67373352, 0.80679436, 0.52908941, 0.15695938, 0.49722003, 0.41970014, 0.62375224, 0.53695622, 0.25474238, 0.79135707, 0.2503871 , 0.25352337, 0.33474211, 0.19308929, 0.24658944, 0.25495092, 0.30867144, 0.41240259, 0.59412526, 0.16811226, 0.48282791, 0.36566756, 0.09279325, 0.75337353, 0.57128885, 0.52974123, 0.44548504, 0.77748843, 0.3224082 , 0.40054277, 0.29522468, 0.19673553, 0.73781774, 0.57680312, 0.44545573, 0.30242355, 0.38720223, 0.16632904, 0.30804092, 0.56385194, 0.60012179, 0.48324821, 0.24636345, 0.26153216, 0.2348285 , 0.29023669, 0.41011454, 0.36472083, 0.65922069, 0.30476903, 0.09986775, 0.70658332, 0.30713075, 0.36096386, 0.54962701, 0.71996086, 0.6633756 ]) class Scotvote(object): """ Scotvot class is used with TestGlmGamma. """ def __init__(self): self.params = (4.961768e-05, 2.034423e-03, -7.181429e-05, 1.118520e-04, -1.467515e-07, -5.186831e-04, -2.42717498e-06, -1.776527e-02) self.bse = (1.621577e-05, 5.320802e-04, 2.711664e-05, 4.057691e-05, 1.236569e-07, 2.402534e-04, 7.460253e-07, 1.147922e-02) self.null_deviance = 0.536072 self.df_null = 31 self.deviance = 0.087388516417 self.df_resid = 24 self.df_model = 7 self.aic_R = 182.947045954721 self.aic_Stata = 10.72212 self.bic_Stata = -83.09027 self.llf = -163.5539382 # from Stata, same as ours with scale = 1 # self.llf = -82.47352 # Very close to ours as is self.scale = 0.003584283 self.pearson_chi2 = .0860228056 self.resids = glm_test_resids.scotvote_resids self.fittedvalues = np.array([57.80431482, 53.2733447, 50.56347993, 58.33003783, 70.46562169, 56.88801284, 66.81878401, 66.03410393, 57.92937473, 63.23216907, 53.9914785 , 61.28993391, 64.81036393, 63.47546816, 60.69696114, 74.83508176, 56.56991106, 72.01804172, 64.35676519, 52.02445881, 64.24933079, 71.15070332, 45.73479688, 54.93318588, 66.98031261, 52.02479973, 56.18413736, 58.12267471, 67.37947398, 60.49162862, 73.82609217, 69.61515621]) class Cancer(object): ''' The Cancer data can be found here http://www.stata-press.com/data/r10/rmain.html ''' def __init__(self): filename = os.path.join(os.path.dirname(os.path.abspath(__file__)), "stata_cancer_glm.csv") data = np.recfromcsv(open(filename, 'rb')) self.endog = data.studytime design = np.column_stack((data.age,data.drug)) design = categorical(design, col=1, drop=True) design = np.delete(design, 1, axis=1) # drop first dummy self.exog = add_constant(design, prepend=False) class CancerLog(Cancer): """ CancerLog is used TestGlmGammaLog """ def __init__(self): super(CancerLog, self).__init__() self.resids = np.array([[-8.52598100e-01,-1.45739100e+00, -3.92408100e+01, -1.41526900e+00, -5.78417200e+00], [ -8.23683800e-01, -1.35040200e+00, -2.64957500e+01, -1.31777000e+00, -4.67162900e+00], [ -7.30450400e-01, -1.07754600e+00, -4.02136400e+01, -1.06208800e+00, -5.41978500e+00], [ -7.04471600e-01, -1.01441500e+00, -7.25951500e+01, -1.00172900e+00, -7.15130900e+00], [ -5.28668000e-01, -6.68617300e-01, -3.80758100e+01, -6.65304600e-01, -4.48658700e+00], [ -2.28658500e-01, -2.48859700e-01, -6.14913600e+00, -2.48707200e-01, -1.18577100e+00], [ -1.93939400e-01, -2.08119900e-01, -7.46226500e+00, -2.08031700e-01, -1.20300800e+00], [ -3.55635700e-01, -4.09525000e-01, -2.14132500e+01, -4.08815100e-01, -2.75958600e+00], [ -5.73360000e-02, -5.84700000e-02, -4.12946200e+00, -5.84681000e-02, -4.86586900e-01], [ 3.09828000e-02, 3.06685000e-02, 1.86551100e+00, 3.06682000e-02, 2.40413800e-01], [ -2.11924300e-01, -2.29071300e-01, -2.18386100e+01, -2.28953000e-01, -2.15130900e+00], [ -3.10989000e-01, -3.50739300e-01, -4.19249500e+01, -3.50300400e-01, -3.61084500e+00], [ -9.22250000e-03, -9.25100000e-03, -1.13679700e+00, -9.25100000e-03, -1.02392100e-01], [ 2.39402500e-01, 2.22589700e-01, 1.88577300e+01, 2.22493500e-01, 2.12475600e+00], [ 3.35166000e-02, 3.31493000e-02, 4.51842400e+00, 3.31489000e-02, 3.89155400e-01], [ 8.49829400e-01, 6.85180200e-01, 3.57627500e+01, 6.82689900e-01, 5.51291500e+00], [ 4.12934200e-01, 3.66785200e-01, 4.65392600e+01, 3.66370400e-01, 4.38379500e+00], [ 4.64148400e-01, 4.07123200e-01, 6.25726500e+01, 4.06561900e-01, 5.38915500e+00], [ 1.71104600e+00, 1.19474800e+00, 1.12676500e+02, 1.18311900e+00, 1.38850500e+01], [ 1.26571800e+00, 9.46389000e-01, 1.30431000e+02, 9.40244600e-01, 1.28486900e+01], [ -3.48532600e-01, -3.99988300e-01, -2.95638100e+01, -3.99328600e-01, -3.20997700e+00], [ -4.04340300e-01, -4.76960100e-01, -4.10254300e+01, -4.75818000e-01, -4.07286500e+00], [ -4.92057900e-01, -6.08818300e-01, -9.34509600e+01, -6.06357200e-01, -6.78109700e+00], [ -4.02876400e-01, -4.74878400e-01, -9.15226200e+01, -4.73751900e-01, -6.07225700e+00], [ -5.15056700e-01, -6.46013300e-01, -2.19014600e+02, -6.43043500e-01, -1.06209700e+01], [ -8.70423000e-02, -8.97043000e-02, -1.26361400e+01, -8.96975000e-02, -1.04875100e+00], [ 1.28362300e-01, 1.23247800e-01, 1.70383300e+01, 1.23231000e-01, 1.47887800e+00], [ -2.39271900e-01, -2.61562100e-01, -9.30283300e+01, -2.61384400e-01, -4.71795100e+00], [ 7.37246500e-01, 6.08186000e-01, 6.25359600e+01, 6.06409700e-01, 6.79002300e+00], [ -3.64110000e-02, -3.68626000e-02, -1.41565300e+01, -3.68621000e-02, -7.17951200e-01], [ 2.68833000e-01, 2.47933100e-01, 6.67934100e+01, 2.47801000e-01, 4.23748400e+00], [ 5.96389600e-01, 5.07237700e-01, 1.13265500e+02, 5.06180100e-01, 8.21890300e+00], [ 1.98218000e-02, 1.96923000e-02, 1.00820900e+01, 1.96923000e-02, 4.47040700e-01], [ 7.74936000e-01, 6.34305300e-01, 2.51883900e+02, 6.32303700e-01, 1.39711800e+01], [ -7.63925100e-01, -1.16591700e+00, -4.93461700e+02, -1.14588000e+00, -1.94156600e+01], [ -6.23771700e-01, -8.41174800e-01, -4.40679600e+02, -8.34266300e-01, -1.65796100e+01], [ -1.63272900e-01, -1.73115100e-01, -6.73975900e+01, -1.73064800e-01, -3.31725800e+00], [ -4.28562500e-01, -5.11932900e-01, -4.73787800e+02, -5.10507400e-01, -1.42494800e+01], [ 8.00693000e-02, 7.80269000e-02, 3.95353400e+01, 7.80226000e-02, 1.77920500e+00], [ -2.13674400e-01, -2.31127400e-01, -2.15987000e+02, -2.31005700e-01, -6.79344600e+00], [ -1.63544000e-02, -1.64444000e-02, -1.05642100e+01, -1.64444000e-02, -4.15657600e-01], [ 2.04900500e-01, 1.92372100e-01, 1.10651300e+02, 1.92309400e-01, 4.76156600e+00], [ -1.94758900e-01, -2.09067700e-01, -2.35484100e+02, -2.08978200e-01, -6.77219400e+00], [ 3.16727400e-01, 2.88367800e-01, 1.87065600e+02, 2.88162100e-01, 7.69732400e+00], [ 6.24234900e-01, 5.27632500e-01, 2.57678500e+02, 5.26448400e-01, 1.26827400e+01], [ 8.30241100e-01, 6.72002100e-01, 2.86513700e+02, 6.69644800e-01, 1.54232100e+01], [ 6.55140000e-03, 6.53710000e-03, 7.92130700e+00, 6.53710000e-03, 2.27805800e-01], [ 3.41595200e-01, 3.08985000e-01, 2.88667600e+02, 3.08733300e-01, 9.93012900e+00]]) self.null_deviance = 27.92207137420696 # From R (bug in rpy) self.params = np.array([-0.04477778, 0.57437126, 1.05210726, 4.64604002]) self.bse = np.array([ 0.0147328 , 0.19694727, 0.19772507, 0.83534671]) self.aic_R = 331.89022395372069 self.aic_Stata = 7.403608467857651 self.deviance = 16.174635536991005 self.scale = 0.31805268736385695 # self.llf = -160.94511197686035 # From R self.llf = -173.6866032285836 # from Staa self.bic_Stata = -154.1582089453923 # from Stata self.df_model = 3 self.df_resid = 44 self.chi2 = 36.77821448266359 # from Stata not in sm self.fittedvalues = np.array([ 6.78419193, 5.67167253, 7.41979002, 10.15123371, 8.48656317, 5.18582263, 6.20304079, 7.75958258, 8.48656317, 7.75958258, 10.15123371, 11.61071755, 11.10228357, 8.87520908, 11.61071755, 6.48711178, 10.61611394, 11.61071755, 8.11493609, 10.15123371, 9.21009116, 10.07296716, 13.78112366, 15.07225103, 20.62079147, 12.04881666, 11.5211983 , 19.71780584, 9.21009116, 19.71780584, 15.76249142, 13.78112366, 22.55271436, 18.02872842, 25.41575239, 26.579678 , 20.31745227, 33.24937131, 22.22095589, 31.79337946, 25.41575239, 23.23857437, 34.77204095, 24.30279515, 20.31745227, 18.57700761, 34.77204095, 29.06987768]) class CancerIdentity(Cancer): """ CancerIdentity is used with TestGlmGammaIdentity """ def __init__(self): super(CancerIdentity, self).__init__() self.resids = np.array([[ -8.52598100e-01, -1.45739100e+00, -3.92408100e+01, -1.41526900e+00, -5.78417200e+00], [ -8.23683800e-01, -1.35040200e+00, -2.64957500e+01, -1.31777000e+00, -4.67162900e+00], [ -7.30450400e-01, -1.07754600e+00, -4.02136400e+01, -1.06208800e+00, -5.41978500e+00], [ -7.04471600e-01, -1.01441500e+00, -7.25951500e+01, -1.00172900e+00, -7.15130900e+00], [ -5.28668000e-01, -6.68617300e-01, -3.80758100e+01, -6.65304600e-01, -4.48658700e+00], [ -2.28658500e-01, -2.48859700e-01, -6.14913600e+00, -2.48707200e-01, -1.18577100e+00], [ -1.93939400e-01, -2.08119900e-01, -7.46226500e+00, -2.08031700e-01, -1.20300800e+00], [ -3.55635700e-01, -4.09525000e-01, -2.14132500e+01, -4.08815100e-01, -2.75958600e+00], [ -5.73360000e-02, -5.84700000e-02, -4.12946200e+00, -5.84681000e-02, -4.86586900e-01], [ 3.09828000e-02, 3.06685000e-02, 1.86551100e+00, 3.06682000e-02, 2.40413800e-01], [ -2.11924300e-01, -2.29071300e-01, -2.18386100e+01, -2.28953000e-01, -2.15130900e+00], [ -3.10989000e-01, -3.50739300e-01, -4.19249500e+01, -3.50300400e-01, -3.61084500e+00], [ -9.22250000e-03, -9.25100000e-03, -1.13679700e+00, -9.25100000e-03, -1.02392100e-01], [ 2.39402500e-01, 2.22589700e-01, 1.88577300e+01, 2.22493500e-01, 2.12475600e+00], [ 3.35166000e-02, 3.31493000e-02, 4.51842400e+00, 3.31489000e-02, 3.89155400e-01], [ 8.49829400e-01, 6.85180200e-01, 3.57627500e+01, 6.82689900e-01, 5.51291500e+00], [ 4.12934200e-01, 3.66785200e-01, 4.65392600e+01, 3.66370400e-01, 4.38379500e+00], [ 4.64148400e-01, 4.07123200e-01, 6.25726500e+01, 4.06561900e-01, 5.38915500e+00], [ 1.71104600e+00, 1.19474800e+00, 1.12676500e+02, 1.18311900e+00, 1.38850500e+01], [ 1.26571800e+00, 9.46389000e-01, 1.30431000e+02, 9.40244600e-01, 1.28486900e+01], [ -3.48532600e-01, -3.99988300e-01, -2.95638100e+01, -3.99328600e-01, -3.20997700e+00], [ -4.04340300e-01, -4.76960100e-01, -4.10254300e+01, -4.75818000e-01, -4.07286500e+00], [ -4.92057900e-01, -6.08818300e-01, -9.34509600e+01, -6.06357200e-01, -6.78109700e+00], [ -4.02876400e-01, -4.74878400e-01, -9.15226200e+01, -4.73751900e-01, -6.07225700e+00], [ -5.15056700e-01, -6.46013300e-01, -2.19014600e+02, -6.43043500e-01, -1.06209700e+01], [ -8.70423000e-02, -8.97043000e-02, -1.26361400e+01, -8.96975000e-02, -1.04875100e+00], [ 1.28362300e-01, 1.23247800e-01, 1.70383300e+01, 1.23231000e-01, 1.47887800e+00], [ -2.39271900e-01, -2.61562100e-01, -9.30283300e+01, -2.61384400e-01, -4.71795100e+00], [ 7.37246500e-01, 6.08186000e-01, 6.25359600e+01, 6.06409700e-01, 6.79002300e+00], [ -3.64110000e-02, -3.68626000e-02, -1.41565300e+01, -3.68621000e-02, -7.17951200e-01], [ 2.68833000e-01, 2.47933100e-01, 6.67934100e+01, 2.47801000e-01, 4.23748400e+00], [ 5.96389600e-01, 5.07237700e-01, 1.13265500e+02, 5.06180100e-01, 8.21890300e+00], [ 1.98218000e-02, 1.96923000e-02, 1.00820900e+01, 1.96923000e-02, 4.47040700e-01], [ 7.74936000e-01, 6.34305300e-01, 2.51883900e+02, 6.32303700e-01, 1.39711800e+01], [ -7.63925100e-01, -1.16591700e+00, -4.93461700e+02, -1.14588000e+00, -1.94156600e+01], [ -6.23771700e-01, -8.41174800e-01, -4.40679600e+02, -8.34266300e-01, -1.65796100e+01], [ -1.63272900e-01, -1.73115100e-01, -6.73975900e+01, -1.73064800e-01, -3.31725800e+00], [ -4.28562500e-01, -5.11932900e-01, -4.73787800e+02, -5.10507400e-01, -1.42494800e+01], [ 8.00693000e-02, 7.80269000e-02, 3.95353400e+01, 7.80226000e-02, 1.77920500e+00], [ -2.13674400e-01, -2.31127400e-01, -2.15987000e+02, -2.31005700e-01, -6.79344600e+00], [ -1.63544000e-02, -1.64444000e-02, -1.05642100e+01, -1.64444000e-02, -4.15657600e-01], [ 2.04900500e-01, 1.92372100e-01, 1.10651300e+02, 1.92309400e-01, 4.76156600e+00], [ -1.94758900e-01, -2.09067700e-01, -2.35484100e+02, -2.08978200e-01, -6.77219400e+00], [ 3.16727400e-01, 2.88367800e-01, 1.87065600e+02, 2.88162100e-01, 7.69732400e+00], [ 6.24234900e-01, 5.27632500e-01, 2.57678500e+02, 5.26448400e-01, 1.26827400e+01], [ 8.30241100e-01, 6.72002100e-01, 2.86513700e+02, 6.69644800e-01, 1.54232100e+01], [ 6.55140000e-03, 6.53710000e-03, 7.92130700e+00, 6.53710000e-03, 2.27805800e-01], [ 3.41595200e-01, 3.08985000e-01, 2.88667600e+02, 3.08733300e-01, 9.93012900e+00]]) self.params = np.array([ -0.5369833, 6.47296332, 16.20336802, 38.96617431]) self.bse = np.array([ 0.13341238, 2.1349966 , 3.87411875, 8.19235553]) self.aic_R = 328.39209118952965 #TODO: the below will fail self.aic_Stata = 7.381090276021671 self.deviance = 15.093762327607557 self.scale = 0.29512089119443752 self.null_deviance = 27.92207137420696 # from R bug in RPy #NOTE: our scale is Stata's dispers_p (pearson?) #NOTE: if scale is analagous to Stata's dispersion, then this might be #where the discrepancies come from? # self.llf = -159.19604559476483 # From R self.llf = -173.1461666245201 # From Stata self.bic_Stata = -155.2390821535193 self.df_model = 3 self.df_resid = 44 self.chi2 = 51.56632068622578 self.fittedvalues = np.array([ 6.21019277, 4.06225956, 7.28415938, 11.04304251, 8.89510929, 2.98829295, 5.13622616, 7.82114268, 8.89510929, 7.82114268, 11.04304251, 12.65399242, 12.11700911, 9.43209259, 12.65399242, 5.67320947, 11.58002581, 12.65399242, 8.35812599, 11.04304251, 9.46125627, 10.53522287, 14.294106 , 15.36807261, 19.12695574, 12.68315609, 12.14617279, 18.58997243, 9.46125627, 18.58997243, 15.90505591, 14.294106 , 20.20092234, 17.51600582, 25.63546061, 26.17244391, 22.95054409, 28.85736043, 24.0245107 , 28.32037713, 25.63546061, 24.561494 , 29.39434374, 25.09847731, 22.95054409, 21.87657748, 29.39434374, 27.24641052]) class Cpunish(object): ''' The following are from the R script in models.datasets.cpunish Slightly different than published results, but should be correct Probably due to rounding in cleaning? ''' def __init__(self): self.params = (2.611017e-04, 7.781801e-02, -9.493111e-02, 2.969349e-01, 2.301183e+00, -1.872207e+01, -6.801480e+00) self.bse = (5.187132e-05, 7.940193e-02, 2.291926e-02, 4.375164e-01, 4.283826e-01, 4.283961e+00, 4.146850e+00) self.null_deviance = 136.57281747225 self.df_null = 16 self.deviance = 18.591641759528944 self.df_resid = 10 self.df_model = 6 self.aic_R = 77.8546573896503 # same as Stata self.aic_Stata = 4.579685683305706 self.bic_Stata = -9.740492454486446 self.chi2 = 128.8021169250578 # from Stata not in sm self.llf = -31.92732869482515 self.scale = 1 self.pearson_chi2 = 24.75374835 self.resids = glm_test_resids.cpunish_resids self.fittedvalues = np.array([35.2263655, 8.1965744, 1.3118966, 3.6862982, 2.0823003, 1.0650316, 1.9260424, 2.4171405, 1.8473219, 2.8643241, 3.1211989, 3.3382067, 2.5269969, 0.8972542, 0.9793332, 0.5346209, 1.9790936]) class Cpunish_offset(Cpunish): ''' Same model as Cpunish but with offset of 100. Many things do not change. ''' def __init__(self): super(Cpunish_offset, self).__init__() self.params = (-1.140665e+01, 2.611017e-04, 7.781801e-02, -9.493111e-02, 2.969349e-01, 2.301183e+00, -1.872207e+01) self.bse = (4.147e+00, 5.187e-05, 7.940e-02, 2.292e-02, 4.375e-01, 4.284e-01, 4.284e+00) class InvGauss(object): ''' Usef Data was generated by Hardin and Hilbe using Stata. Note only the first 5000 observations are used because the models code currently uses np.eye. ''' # np.random.seed(54321) # x1 = np.abs(stats.norm.ppf((np.random.random(5000)))) # x2 = np.abs(stats.norm.ppf((np.random.random(5000)))) # X = np.column_stack((x1,x2)) # X = add_constant(X) # params = np.array([.5, -.25, 1]) # eta = np.dot(X, params) # mu = 1/np.sqrt(eta) # sigma = .5 # This isn't correct. Errors need to be normally distributed # But Y needs to be Inverse Gaussian, so we could build it up # by throwing out data? # Refs: Lai (2009) Generating inverse Gaussian random variates by # approximation # Atkinson (1982) The simulation of generalized inverse gaussian and # hyperbolic random variables seems to be the canonical ref # Y = np.dot(X,params) + np.random.wald(mu, sigma, 1000) # model = GLM(Y, X, family=models.family.InverseGaussian(link=\ # models.family.links.identity)) def __init__(self): # set up data # filename = os.path.join(os.path.dirname(os.path.abspath(__file__)), "inv_gaussian.csv") data=np.genfromtxt(open(filename, 'rb'), delimiter=",", dtype=float)[1:] self.endog = data[:5000,0] self.exog = data[:5000,1:] self.exog = add_constant(self.exog, prepend=False) #class InvGaussDefault(InvGauss) # def __init__(self): # super(InvGaussDefault, self).__init__() # Results #NOTE: loglikelihood difference in R vs. Stata vs. Models # is the same situation as gamma self.params = (0.4519770, -0.2508288, 1.0359574) self.bse = (0.03148291, 0.02237211, 0.03429943) self.null_deviance = 1520.673165475461 self.df_null = 4999 self.deviance = 1423.943980407997 self.df_resid = 4997 self.df_model = 2 self.aic_R = 5059.41911646446 self.aic_Stata = 1.552280060977946 self.bic_Stata = -41136.47039418921 self.llf = -3877.700354 # Stata is same as ours with scale set to 1 # self.llf = -2525.70955823223 # from R, close to ours self.scale = 0.2867266359127567 self.pearson_chi2 = 1432.771536 self.resids = glm_test_resids.invgauss_resids self.fittedvalues = np.array([ 1.0404339 , 0.96831526, 0.81265833, 0.9958362 , 1.05433442, 1.09866137, 0.95548191, 1.38082105, 0.98942888, 0.96521958, 1.02684056, 0.91412576, 0.91492102, 0.92639676, 0.96763425, 0.80250852, 0.85281816, 0.90962261, 0.95550299, 0.86386815, 0.94760134, 0.94269533, 0.98960509, 0.84787252, 0.78949111, 0.76873582, 0.98933453, 0.95105574, 0.8489395 , 0.88962971, 0.84856357, 0.88567313, 0.84505405, 0.84626147, 0.77250421, 0.90175601, 1.15436378, 0.98375558, 0.83539542, 0.82845381, 0.90703971, 0.85546165, 0.96707286, 0.84127197, 0.82096543, 1.1311227 , 0.87617029, 0.91194419, 1.05125511, 0.95330314, 0.75556148, 0.82573228, 0.80424982, 0.83800144, 0.8203644 , 0.84423807, 0.98348433, 0.93165089, 0.83968706, 0.79256287, 1.0302839 , 0.90982028, 0.99471562, 0.70931825, 0.85471721, 1.02668021, 1.11308301, 0.80497105, 1.02708486, 1.07671424, 0.821108 , 0.86373486, 0.99104964, 1.06840593, 0.94947784, 0.80982122, 0.95778065, 1.0254212 , 1.03480946, 0.83942363, 1.17194944, 0.91772559, 0.92368795, 1.10410916, 1.12558875, 1.11290791, 0.87816503, 1.04299294, 0.89631173, 1.02093004, 0.86331723, 1.13134858, 1.01807861, 0.98441692, 0.72567667, 1.42760495, 0.78987436, 0.72734482, 0.81750166, 0.86451854, 0.90564264, 0.81022323, 0.98720325, 0.98263709, 0.99364823, 0.7264445 , 0.81632452, 0.7627845 , 1.10726938, 0.79195664, 0.86836774, 1.01558149, 0.82673675, 0.99529548, 0.97155636, 0.980696 , 0.85460503, 1.00460782, 0.77395244, 0.81229831, 0.94078297, 1.05910564, 0.95921954, 0.97841172, 0.93093166, 0.93009865, 0.89888111, 1.18714408, 0.98964763, 1.03388898, 1.67554215, 0.82998876, 1.34100687, 0.86766346, 0.96392316, 0.91371033, 0.76589296, 0.92329051, 0.82560326, 0.96758148, 0.8412995 , 1.02550678, 0.74911108, 0.8751611 , 1.01389312, 0.87865556, 1.24095868, 0.90678261, 0.85973204, 1.05617845, 0.94163038, 0.88087351, 0.95699844, 0.86083491, 0.89669384, 0.78646825, 1.0014202 , 0.82399199, 1.05313139, 1.06458324, 0.88501766, 1.19043294, 0.8458026 , 1.00231535, 0.72464305, 0.94790753, 0.7829744 , 1.1953009 , 0.85574035, 0.95433052, 0.96341484, 0.91362908, 0.94097713, 0.87273804, 0.81126399, 0.72715262, 0.85526116, 0.76015834, 0.8403826 , 0.9831501 , 1.17104665, 0.78862494, 1.01054909, 0.91511601, 1.0990797 , 0.91352124, 1.13671162, 0.98793866, 1.0300545 , 1.04490115, 0.85778231, 0.94824343, 1.14510618, 0.81305136, 0.88085051, 0.94743792, 0.94875465, 0.96206997, 0.94493612, 0.93547218, 1.09212018, 0.86934651, 0.90532353, 1.07066001, 1.26197714, 0.93858662, 0.9685039 , 0.7946546 , 1.03052031, 0.75395899, 0.87527062, 0.82156476, 0.949774 , 1.01000235, 0.82613526, 1.0224591 , 0.91529149, 0.91608832, 1.09418385, 0.8228272 , 1.06337472, 1.05533176, 0.93513063, 1.00055806, 0.95474743, 0.91329368, 0.88711836, 0.95584926, 0.9825458 , 0.74954073, 0.96964967, 0.88779583, 0.95321846, 0.95390055, 0.95369029, 0.94326714, 1.31881201, 0.71512263, 0.84526602, 0.92323824, 1.01993108, 0.85155992, 0.81416851, 0.98749128, 1.00034192, 0.98763473, 1.05974138, 1.05912658, 0.89772172, 0.97905626, 1.1534306 , 0.92304181, 1.16450278, 0.7142307 , 0.99846981, 0.79861247, 0.73939835, 0.93776385, 1.0072242 , 0.89159707, 1.05514263, 1.05254569, 0.81005146, 0.95179784, 1.00278795, 1.04910398, 0.88427798, 0.74394266, 0.92941178, 0.83622845, 0.84064958, 0.93426956, 1.03619314, 1.22439347, 0.73510451, 0.82997071, 0.90828036, 0.80866989, 1.34078212, 0.85079169, 0.88346039, 0.76871666, 0.96763454, 0.66936914, 0.94175741, 0.97127617, 1.00844382, 0.83449557, 0.88095564, 1.17711652, 1.0547188 , 1.04525593, 0.93817487, 0.77978294, 1.36143199, 1.16127997, 1.03792952, 1.03151637, 0.83837387, 0.94326066, 1.0054787 , 0.99656841, 1.05575689, 0.97641643, 0.85108163, 0.82631589, 0.77407305, 0.90566132, 0.91308164, 0.95560906, 1.04523011, 1.03773723, 0.97378685, 0.83999133, 1.06926871, 1.01073982, 0.9804959 , 1.06473061, 1.25315673, 0.969175 , 0.63443508, 0.84574684, 1.06031239, 0.93834605, 1.01784925, 0.93488249, 0.80240225, 0.88757274, 0.9224097 , 0.99158962, 0.87412592, 0.76418199, 0.78044069, 1.03117412, 0.82042521, 1.10272129, 1.09673757, 0.89626935, 1.01678612, 0.84911824, 0.95821431, 0.99169558, 0.86853864, 0.92172772, 0.94046199, 0.89750517, 1.09599258, 0.92387291, 1.07770118, 0.98831383, 0.86352396, 0.83079533, 0.94431185, 1.12424626, 1.02553104, 0.8357513 , 0.97019669, 0.76816092, 1.34011343, 0.86489527, 0.82156358, 1.25529129, 0.86820218, 0.96970237, 0.85850546, 0.97429559, 0.84826078, 1.02498396, 0.72478517, 0.993497 , 0.76918521, 0.91079198, 0.80988325, 0.75431095, 1.02918073, 0.88884197, 0.82625507, 0.78564563, 0.91505355, 0.88896863, 0.85882361, 0.81538316, 0.67656235, 0.8564822 , 0.82473022, 0.92928331, 0.98068415, 0.82605685, 1.0150412 , 1.00631678, 0.92405101, 0.88909552, 0.94873568, 0.87657342, 0.8280683 , 0.77596382, 0.96598811, 0.78922426, 0.87637606, 0.98698735, 0.92207026, 0.71487846, 1.03845478, 0.70749745, 1.08603388, 0.92697779, 0.86470448, 0.70119494, 1.00596847, 0.91426549, 1.05318838, 0.79621712, 0.96169742, 0.88053405, 0.98963934, 0.94152997, 0.88413591, 0.75035344, 0.86007123, 0.83713514, 0.91234911, 0.79562744, 0.84099675, 1.0334279 , 1.00272243, 0.95359383, 0.84292969, 0.94234155, 0.90190899, 0.97302022, 1.1009829 , 1.0148975 , 0.99082987, 0.75916515, 0.9204784 , 0.94477378, 1.01108683, 1.00038149, 0.9259798 , 1.19400436, 0.80191877, 0.79565851, 0.81865924, 0.79003506, 0.8995508 , 0.73137983, 0.88336018, 0.7855268 , 1.04478073, 0.90857981, 1.16076951, 0.76096486, 0.90004113, 0.83819665, 0.95295365, 1.09911441, 0.78498197, 0.95094991, 0.94333419, 0.95131688, 0.82961049, 1.08001761, 1.06426458, 0.94291798, 1.04381938, 0.90380364, 0.74060138, 0.98701862, 0.72250236, 0.86125293, 0.76488061, 0.9858051 , 0.98099677, 0.96849209, 0.90053351, 0.88469597, 0.80688516, 1.06396217, 1.02446023, 0.911863 , 0.98837746, 0.91102987, 0.92810392, 1.13526335, 1.00419541, 1.00866175, 0.74352261, 0.91051641, 0.81868428, 0.93538014, 0.87822651, 0.93278572, 1.0356074 , 1.25158731, 0.98372647, 0.81335741, 1.06441863, 0.80305786, 0.95201148, 0.90283451, 1.17319519, 0.8984894 , 0.88911288, 0.91474736, 0.94512294, 0.92956283, 0.86682085, 1.08937227, 0.94825713, 0.9787145 , 1.16747163, 0.80863682, 0.98314119, 0.91052823, 0.80913225, 0.78503169, 0.78751737, 1.08932193, 0.86859845, 0.96847458, 0.93468839, 1.10769915, 1.1769249 , 0.84916138, 1.00556408, 0.84508585, 0.92617942, 0.93985886, 1.17303268, 0.81172495, 0.93482682, 1.04082486, 1.03209348, 0.97220394, 0.90274672, 0.93686291, 0.91116431, 1.14814563, 0.83279158, 0.95853283, 1.0261179 , 0.95779432, 0.86995883, 0.78164915, 0.89946906, 0.9194465 , 0.97919367, 0.92719039, 0.89063569, 0.80847805, 0.81192101, 0.75044535, 0.86819023, 1.03420014, 0.8899434 , 0.94899544, 0.9860773 , 1.10047297, 1.00243849, 0.82153972, 1.14289945, 0.8604684 , 0.87187524, 1.00415032, 0.78460709, 0.86319884, 0.92818335, 1.08892111, 1.06841003, 1.00735918, 1.20775251, 0.72613554, 1.25768191, 1.08573511, 0.89671127, 0.91259535, 1.01414208, 0.87422903, 0.82720677, 0.9568079 , 1.00450416, 0.91043845, 0.84095709, 1.08010574, 0.69848293, 0.90769214, 0.94713501, 1.14808251, 1.0605676 , 1.21734482, 0.78578521, 1.01516235, 0.94330326, 0.98363817, 0.99650084, 0.74280796, 0.96227123, 0.95741454, 1.00980406, 0.93468092, 1.10098591, 1.18175828, 0.8553791 , 0.81713219, 0.82912143, 0.87599518, 1.15006511, 1.03151163, 0.8751847 , 1.15701331, 0.73394166, 0.91426368, 0.96953458, 1.13901709, 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1.03437641, 0.98567811, 0.78463682, 1.09573491, 0.89858959, 0.94056747, 1.16075317, 1.06296054, 0.85844006, 0.95475376, 0.67038747, 0.7924646 , 0.94009167, 0.88282093, 0.97711174, 0.9209607 , 1.03230176, 0.99981312, 1.12345314, 1.11705968, 1.02453864, 0.91724212, 0.98337942, 0.89195196, 0.83800177, 0.95044243, 0.76543521, 0.8613025 , 0.83907753, 0.69333275, 0.84411739, 0.68621941, 0.9847701 , 1.13328481, 1.1432074 , 0.97156328, 0.86464461, 0.74258211, 0.97319505, 1.11453917, 0.87344741, 0.91382664, 1.01635943, 1.38708812, 0.81377942, 1.3828856 , 0.74476285, 0.86657537, 1.1216954 , 0.91008346, 0.800862 , 0.98356936, 0.92409916, 1.13970543, 0.97547004, 0.99385865, 1.16476579, 0.78678084, 1.003947 , 0.81491463, 1.19724322, 0.9173622 , 0.93274116, 0.80047839, 0.86798029, 0.9433708 , 0.82376832, 1.01726905, 0.81914971, 0.73290844]) class Medpar1(object): ''' The medpar1 data can be found here. http://www.stata-press.com/data/hh2/medpar1 ''' def __init__(self): filename = os.path.join(os.path.dirname(os.path.abspath(__file__)), "stata_medpar1_glm.csv") data = pd.read_csv(filename).to_records() self.endog = data.los design = np.column_stack((data.admitype, data.codes)) design = categorical(design, col=0, drop=True) design = np.delete(design, 1, axis=1) # drop first dummy self.exog = add_constant(design, prepend=False) class InvGaussLog(Medpar1): """ InvGaussLog is used with TestGlmInvgaussLog """ def __init__(self): super(InvGaussLog, self).__init__() filename = os.path.join(os.path.dirname(os.path.abspath(__file__)), "medparlogresids.csv") self.resids = pd.read_csv(filename, sep=',', header=None).values self.null_deviance = 335.1539777981053 # from R, Rpy bug self.params = np.array([ 0.09927544, -0.19161722, 1.05712336]) self.bse = np.array([ 0.00600728, 0.02632126, 0.04915765]) self.aic_R = 18545.836421595981 self.aic_Stata = 6.619000588187141 self.deviance = 304.27188306012789 self.scale = 0.10240599519220173 # self.llf = -9268.9182107979905 # from R 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4.31095206, 4.31095206, 3.20058132, 4.31095206, 5.25778406, 4.31095206, 5.25778406, 3.90353806, 4.31095206, 5.80654132, 5.80654132, 5.80654132, 5.80654132, 3.90353806, 5.80654132, 5.80654132, 5.80654132, 4.31095206, 5.80654132, 5.80654132, 5.80654132, 3.90353806, 5.25778406, 3.90353806, 4.31095206, 4.76088805, 3.90353806, 5.80654132, 5.80654132, 5.80654132, 2.89810483, 5.80654132, 5.80654132, 5.80654132, 5.80654132, 5.80654132, 5.80654132, 5.80654132, 3.90353806, 3.20058132, 5.25778406, 4.76088805, 5.25778406]) class InvGaussIdentity(Medpar1): """ Accuracy is different for R vs Stata ML vs Stata IRLS, we are close. """ def __init__(self): super(InvGaussIdentity, self).__init__() self.params = np.array([ 0.44538838, -1.05872706, 2.83947966]) self.bse = np.array([ 0.02586783, 0.13830023, 0.20834864]) filename = os.path.join(os.path.dirname(os.path.abspath(__file__)), "igaussident_resids.csv") self.resids = pd.read_csv(filename, sep=',', header=None).values self.null_deviance = 335.1539777981053 # from R, Rpy bug self.df_null = 3675 self.deviance = 305.33661191013988 self.df_resid = 3673 self.df_model = 2 self.aic_R = 18558.677276882016 self.aic_Stata = 6.619290231464371 self.bic_Stata = -29848.45250412075 self.llf_stata = -12163.25544543151 self.chi2 = 567.1229375785638 # in Stata not sm # self.llf = -9275.3386384410078 # from R self.llf = -12163.25545 # from Stata, big diff with R self.scale = 0.10115387793455666 self.pearson_chi2 = 371.5346609292967 # deviance_p in Stata self.fittedvalues = np.array([ 6.84797506, 6.84797506, 6.84797506, 6.84797506, 5.9571983 , 6.84797506, 6.84797506, 6.40258668, 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5.34385962, 3.11691773, 4.45308287, 4.45308287, 3.11691773, 4.45308287, 5.34385962, 4.45308287, 5.34385962, 4.00769449, 4.45308287, 5.789248 , 5.789248 , 5.789248 , 5.789248 , 4.00769449, 5.789248 , 5.789248 , 5.789248 , 4.45308287, 5.789248 , 5.789248 , 5.789248 , 4.00769449, 5.34385962, 4.00769449, 4.45308287, 4.89847125, 4.00769449, 5.789248 , 5.789248 , 5.789248 , 2.67152936, 5.789248 , 5.789248 , 5.789248 , 5.789248 , 5.789248 , 5.789248 , 5.789248 , 4.00769449, 3.11691773, 5.34385962, 4.89847125, 5.34385962]) class Committee(object): def __init__(self): self.resids = np.array([[ -5.04950800e-01, -6.29721800e-01, -8.35499100e+01, -1.30628500e+00, -6.62028600e+00], [ -2.34152200e-01, -2.55423500e-01, -2.16830700e+02, -7.58866000e-01, -7.18370200e+00], [ 1.02423700e+00, 7.98775800e-01, 4.83736300e+02, 2.50351500e+00, 2.25135300e+01], [ -2.85061700e-01, -3.17796600e-01, -7.04115100e+04, -2.37991800e+00, -1.41745600e+02], [ 2.09902500e-01, 1.96787700e-01, 2.24751400e+03, 9.51945500e-01, 2.17724200e+01], [ -4.03483500e-01, -4.75741500e-01, -1.95633600e+04, -2.63502600e+00, -8.89461400e+01], [ -1.64413400e-01, -1.74401100e-01, -1.73310300e+04, -1.16235500e+00, -5.34213500e+01], [ -4.29607700e-01, -5.13466700e-01, -5.30037000e+03, -2.24496200e+00, -4.78260300e+01], [ 3.23713000e-01, 2.94184600e-01, 4.11079400e+03, 1.48684400e+00, 3.65598400e+01], [ 1.50367200e-01, 1.43429400e-01, 7.28532100e+03, 8.85542900e-01, 3.31355000e+01], [ 4.21288600e-01, 3.73428000e-01, 1.37315700e+03, 1.52133200e+00, 2.41570200e+01], [ 4.50658700e-01, 3.96586700e-01, 1.70146900e+03, 1.66177900e+00, 2.78032600e+01], [ 2.43537500e-01, 2.26174000e-01, 3.18402300e+03, 1.13656200e+00, 2.79073400e+01], [ 1.05182900e+00, 8.16205400e-01, 6.00135200e+03, 3.89079700e+00, 7.97131300e+01], [ -5.54450300e-01, -7.12749000e-01, -2.09485200e+03, -2.45496500e+00, -3.42189900e+01], [ -6.05750600e-01, -8.06411100e-01, -2.74738200e+02, -1.90774400e+00, -1.30510500e+01], [ -3.41215700e-01, -3.90244600e-01, -6.31138000e+02, -1.27022900e+00, -1.47600100e+01], [ 2.21898500e-01, 2.07328700e-01, 6.91135800e+02, 8.16876400e-01, 1.24392900e+01], [ 2.45592500e-01, 2.26639200e-01, 1.99250600e-01, 2.57948300e-01, 2.74723700e-01], [ -7.58952600e-01, -1.15300800e+00, -2.56739000e+02, -2.40716600e+00, -1.41474200e+01]]) self.null_deviance = 27.81104693643434 # from R, Rpy bug self.params = np.array([-0.0268147 , 1.25103364, 2.91070663, -0.34799563, 0.00659808, -0.31303026, -6.44847076]) self.bse = np.array([ 1.99956263e-02, 4.76820254e-01, 6.48362654e-01, 4.17956107e-01, 1.41512690e-03, 1.07770186e-01, 1.99557656e+00]) self.aic_R = 216.66573352377935 self.aic_Stata = 10.83328660860436 self.deviance = 5.615520158267981 self.scale = 0.38528595746569905 self.llf = -101.33286676188968 # from R self.llf_Stata = -101.3328660860436 # same as R self.bic_Stata = -33.32900074962649 self.chi2 = 5.008550263545408 self.df_model = 6 self.df_resid = 13 self.fittedvalues = np.array([12.62019383, 30.18289514, 21.48377849, 496.74068604, 103.23024673, 219.94693494, 324.4301163 , 110.82526477, 112.44244488, 219.86056381, 56.84399998, 61.19840382, 114.09290269, 75.29071944, 61.21994387, 21.05130889, 42.75939828, 55.56133536, 0.72532053, 18.14664665]) class Wfs(object): """ Wfs used for TestGlmPoissonOffset Results are from Stata and R. """ def __init__(self): self.resids = glm_test_resids.wfs_resids self.null_deviance = 3731.85161919 # from R self.params = [.9969348, 1.3693953, 1.6137574, 1.7849111, 1.9764051, .11241858, .15166023, .02297282, -.10127377, -.31014953, -.11709716] self.bse = [.0527437, .0510688, .0511949, .0512138, .0500341, .0324963, .0283292, .0226563, .0309871, .0552107, .0549118] self.aic_R = 522.14215776 # R adds 2 for dof to AIC self.aic_Stata = 7.459173652869477 # stata divides by nobs # self.deviance = 70.6652992116034 # from Stata self.deviance = 70.665301270867 # from R self.scale = 1.0 self.llf = -250.0710778504317 # from Stata, ours with scale=1 self.bic_Stata = -179.9959200693088 # no bic in R? self.df_model = 10 self.df_resid = 59 self.chi2 = 2699.138063147485 #TODO: taken from Stata not available # in sm yet self.fittedvalues = [7.11599,19.11356,33.76075,33.26743,11.94399, 27.49849,35.07923,37.22563,64.18037,108.0408,100.0948,35.67896, 24.10508,73.99577,52.2802,38.88975,35.06507,102.1198,107.251, 41.53885,196.3685,335.8434,205.3413,43.20131,41.98048,96.65113, 63.2286,30.78585,70.46306,172.2402,102.5898,43.06099,358.273, 549.8983,183.958,26.87062,62.53445,141.687,52.47494,13.10253, 114.9587,214.803,90.33611,18.32685,592.5995,457.4376,140.9273, 3.812064,111.3119,97.62744,57.48056,19.43552,130.4872, 151.7268,69.67963,13.04879,721.728,429.2136,128.2132,9.04735, 301.7067,177.3487,46.40818,4.707507,330.4211,330.7497,84.38604, 1456.757,451.005,67.51025] class CpunishTweediePower15(object): """ # From R setwd('c:/workspace') data <- read.csv('cpunish.csv', sep=",") library(statmod) library(tweedie) summary(glm(EXECUTIONS ~ INCOME + SOUTH - 1, family=tweedie(var.power=1.5, link.power=1), data=data)) """ def __init__(self): resid_resp = [28.90498242, 0.5714367394, 4.3135711827, -3.7417822942, -4.9544111888, 0.4666602184, 0.0747051827, -6.114236142, -1.0048540116, -6.9747602544, -0.7626907093, -0.5688093336, -6.9845579527, -1.1594503855, -0.6365453438, -0.3994222036, -0.732355528] resid_dev = [3.83881147757395, 0.113622743768915, 2.01981988071128, -0.938107751845672, -1.29607304923555, 0.316205676540778, 0.045273675744568, -1.69968893354602, -0.699080227540624, -2.1707839733642, -0.568738719015137, -0.451266938413727, -2.17218106358745, -0.774613533242944, -0.493831656345955, -0.336453094366771, -0.551210030548659] resid_pear = [6.02294407053171, 0.115516970886608, 2.9148208139849, -0.806210703943481, -1.04601155367613, 0.338668788938945, 0.045708693925888, -1.27176471794657, -0.5964031365026, -1.46974255264233, -0.498557360800493, -0.405777068096011, -1.47045242302365, -0.65086941662954, -0.439928270112046, -0.310433407220704, -0.485001313250992] resid_work = [28.9049727916181, 0.571427719513967, 4.31357425907762, -3.74179256698823, -4.9544210736226, 0.466663015515745, 0.0747086948013966, -6.114245735344, -1.00485035431368, -6.97477010217068, -0.76268749374494, -0.568806471745149, -6.98456778258272, -1.15944644619981, -0.636542358439925, -0.399419650775458, -0.732352367853816] self.resid_response = resid_resp self.resid_deviance = resid_dev self.resid_pearson = resid_pear self.resid_working = resid_work # self.null_deviance = 3731.85161919 # N/A self.params = [0.0000471043, 6.4721324886] self.bse = [0.0000246888, 3.5288126173] # self.aic_R = 522.14215776 # R adds 2 for dof to AIC # self.aic_Stata = 7.459173652869477 # stata divides by nobs # self.deviance = 70.6652992116034 # from Stata self.deviance = 36.087307138233 # from R # self.scale = 1.0 # self.llf = -250.0710778504317 # from Stata, ours with scale=1 # self.bic_Stata = -179.9959200693088 # no bic in R? self.df_model = 1 self.df_resid = 15 # self.chi2 = 2699.138063147485 #TODO: taken from Stata not available # in sm yet self.fittedvalues = [8.09501758000751, 8.42856326056927, 1.68642881732415, 7.74178229423817, 7.95441118875248, 1.53333978161934, 1.92529481734232, 8.11423614202829, 2.00485401159015, 7.97476025442155, 1.76269070926448, 1.56880933358418, 7.98455795270665, 2.15945038549266, 1.63654534384372, 1.39942220361664, 1.73235552803559] class CpunishTweediePower2(object): """ # From R setwd('c:/workspace') data <- read.csv('cpunish.csv', sep=",") library(statmod) library(tweedie) summary(glm(EXECUTIONS ~ INCOME + SOUTH - 1, family=tweedie(var.power=2, link.power=1), data=data)) """ def __init__(self): resid_resp = [28.9397568116168, 0.605199215492085, 4.30845487128123, -3.7059362524505, -4.91921022348665, 0.46200835064931, 0.068864196242604, -6.07952005594693, -1.01093636580438, -6.9396210244365, -0.768038385056284, -0.573568809339664, -6.94944844711606, -1.16600175635393, -0.641510318056987, -0.403667790321936, -0.737611172529194] resid_dev = [2.03295746713119, 0.0704291140028282, 1.60058476017728, -0.591230836989137, -0.836067997150736, 0.274690511542166, 0.0352446721149477, -1.13465831620614, -0.625909330466303, -1.5477830210949, -0.520517540529698, -0.421531194473357, -1.54848147513823, -0.684927882583903, -0.45784673829438, -0.320960880764019, -0.505992145923248] resid_pear = [3.59043221590711, 0.0720921473930558, 2.54705286789752, -0.480919661289957, -0.621174344999372, 0.300397177607798, 0.0356599448410699, -0.752460543924524, -0.502719222246499, -0.874049404005278, -0.434401419984914, -0.364501892726482, -0.874205109115113, -0.538319857282425, -0.390804925805356, -0.287580717535275, -0.424497254731367] resid_work = [28.9397568116168, 0.605199215492085, 4.30845487128123, -3.7059362524505, -4.91921022348665, 0.46200835064931, 0.068864196242604, -6.07952005594693, -1.01093636580438, -6.9396210244365, -0.768038385056284, -0.573568809339664, -6.94944844711606, -1.16600175635393, -0.641510318056987, -0.403667790321936, -0.737611172529194] self.resid_response = resid_resp self.resid_deviance = resid_dev self.resid_pearson = resid_pear self.resid_working = resid_work # self.null_deviance = 3731.85161919 # N/A self.params = [4.72472244209477e-05, 6.43243456540827] self.bse = [1.86839521185429e-05, 3.83231672422612] # self.aic_R = 522.14215776 # R adds 2 for dof to AIC # self.aic_Stata = 7.459173652869477 # stata divides by nobs # self.deviance = 70.6652992116034 # from Stata self.deviance = 15.7840685407599 # from R # self.scale = 1.0 # self.llf = -250.0710778504317 # from Stata, ours with scale=1 # self.bic_Stata = -179.9959200693088 # no bic in R? self.df_model = 1 self.df_resid = 15 # self.chi2 = 2699.138063147485 #TODO: taken from Stata not available # in sm yet self.fittedvalues = [8.06024318838318, 8.39480078450791, 1.69154512871877, 7.7059362524505, 7.91921022348665, 1.53799164935069, 1.9311358037574, 8.07952005594693, 2.01093636580438,7.9396210244365, 1.76803838505628, 1.57356880933966, 7.94944844711606, 2.16600175635393, 1.64151031805699, 1.40366779032194, 1.73761117252919] class CpunishTweedieLog1(object): """ # From R setwd('c:/workspace') data <- read.csv('cpunish.csv', sep=",") library(statmod) library(tweedie) summary(glm(EXECUTIONS ~ INCOME + SOUTH - 1, family=tweedie(var.power=1, link.power=0), data=data)) """ def __init__(self): resid_resp = [28.7231009386298, -0.307318358456484, 4.19015460156576, -3.30975297068573, -4.87746969906705, 0.285041779927669, 0.0315071085472043, -6.33304532673002, -1.02436294926752, -6.9340610414309, -0.859055122126197, -0.736490247380883, -6.96145354225969, -1.13750232106315, -0.778363801217565, -0.636042191521576, -0.839322392162821] resid_dev = [7.30513948467594, -0.101296157943519, 2.44987904003561, -1.34021826264378, -1.99062116973315, 0.212014827300475, 0.0223969676885324, -2.63775728156667, -0.798884085657077, -3.11862021596631, -0.691356293575324, -0.607658243497501, -3.12628915913493, -0.869326536299756, -0.636663290048755, -0.536212950673418, -0.67812263418512] resid_pear = [9.98383729954486, -0.100734032611758, 3.11465040934513, -1.22417704160631, -1.73780566805242, 0.217661565866984, 0.0224564769560215, -2.19386916576256, -0.719962160947025, -2.46172701579962, -0.630049829146329, -0.558895774299477, -2.4671965358931, -0.778034748813176, -0.583676657782738, -0.497265896656757, -0.61887064145702] resid_work = [3.47027319357873, -0.0330190014589175, 2.31520029566659, -0.452785885372436, -0.619167053050639, 0.166209168591668, 0.0160057009522403, -0.759991705123147, -0.506017436072008, -0.873961141113221, -0.46209233491888, -0.424125760851072, -0.874394795536774, -0.532164250702372, -0.437685360377137, -0.388768819543728, -0.456321521305397] self.resid_response = resid_resp self.resid_deviance = resid_dev self.resid_working = resid_work self.resid_pearson = resid_pear # self.null_deviance = 3731.85161919 # N/A self.params = [1.65700638623525e-05, 1.54257997850499] self.bse = [1.81044999017907e-05, 0.725739640176733] # self.aic_R = 522.14215776 # R adds 2 for dof to AIC # self.aic_Stata = 7.459173652869477 # stata divides by nobs # self.deviance = 70.6652992116034 # from Stata self.deviance = 95.0325613464258 # from R # self.scale = 1.0 # self.llf = -250.0710778504317 # from Stata, ours with scale=1 # self.bic_Stata = -179.9959200693088 # no bic in R? self.df_model = 1 self.df_resid = 15 # self.chi2 = 2699.138063147485 #TODO: taken from Stata not available # in sm yet self.fittedvalues = [8.27689906137016, 9.30731835845648, 1.80984539843424, 7.30975297068573, 7.87746969906705, 1.71495822007233, 1.9684928914528, 8.33304532673002, 2.02436294926752, 7.9340610414309, 1.8590551221262, 1.73649024738088, 7.96145354225969, 2.13750232106315, 1.77836380121756, 1.63604219152158, 1.83932239216282] class FairTweedieLog15(object): """ # From R setwd('c:/workspace') data <- read.csv('fair.csv', sep=",") library(statmod) library(tweedie) model <- glm(affairs ~ rate_marriage + age + yrs_married -1, data=data, family=tweedie(var.power=1.5, link.power = 0)) r <- resid(model, type='response') paste(as.character(r[1:17]), collapse=",") r <- resid(model, type='deviance') paste(as.character(r[1:17]), collapse=",") r <- resid(model, type='pearson') paste(as.character(r[1:17]), collapse=",") r <- resid(model, type='working') paste(as.character(r[1:17]), collapse=",") paste(as.character(model$coefficients[1:17]), collapse=",") s <- summary(model) paste(as.character(sqrt(diag(s$cov.scaled))), collapse=",") s$deviance paste(as.character(model$fitted.values[1:17]), collapse=",") """ def __init__(self): resid_resp = [-0.997868449815039, 2.69283106662728, 0.677397439981157, 0.220024942629269, 4.30244966465517, 4.12917275616972, 0.669303122309246, 1.64321562230925, 3.73361710426128, 0.271937359562684, 1.70030700747884, 1.55430573164611, -0.263723852468304, 1.51263973164611, 2.75223392654071, 0.310487741565721, 1.28077676333896 , -0.722602160018842] resid_dev = [-1.40274708439925, 2.48476334070913, 0.722690630291423, 0.333179337353702, 4.00781035212304, 3.33344591331998, 1.51543361886727, 2.82502498800952, 2.2795411865605, 0.245239170945663, 0.993721205729013, 1.74920359743562, -0.363141475997386, 1.71412357710318, 2.57445879456298, 0.279858474280908, 1.22953362433333, -1.84397406923697] resid_pear = [-0.923380371255914, 4.28706294677515, 0.864309147553743, 0.366063826152319, 9.17690493704408, 6.57783985712941, 2.39340023647571, 5.87607098775551, 3.55791152198837, 0.260052421285998, 1.21439278430259, 2.66470328868695, -0.327698246542009, 2.59327105694137, 4.53096038849505, 0.299198418236691, 1.6399313081981, -0.921987034618483] resid_work = [-0.899807800767353, 5.00583784559752, 0.937441759049674, 0.433762277766879, 11.8128959278604, 7.6822784352496, 3.65998654763585, 8.98568506862295, 3.50120010377224, 0.256207345500911, 1.08551656668241, 3.18923357641756, -0.352302468597673, 3.10374035363038, 5.35005901385941, 0.29552727652976, 1.78077778644209, -1] self.resid_response = resid_resp self.resid_deviance = resid_dev self.resid_working = resid_work self.resid_pearson = resid_pear # self.null_deviance = 3731.85161919 # N/A self.params = [-0.389168171340452, 0.0670222370664611, -0.0970852004566712] self.bse = [0.0323435784513691, 0.0063805300018014, 0.00893580175352525] # self.aic_R = 522.14215776 # R adds 2 for dof to AIC # self.aic_Stata = 7.459173652869477 # stata divides by nobs # self.deviance = 70.6652992116034 # from Stata self.deviance = 20741.82 # from R # self.scale = 1.0 # self.llf = -250.0710778504317 # from Stata, ours with scale=1 # self.bic_Stata = -179.9959200693088 # no bic in R? self.df_model = 2 self.df_resid = 6363 # self.chi2 = 2699.138063147485 #TODO: taken from Stata not available # in sm yet self.fittedvalues = [1.10897954981504, 0.537938133372725, 0.722602160018842, 0.507247757370731, 0.364216335344828, 0.537493243830281, 0.182870377690754, 0.182870377690754, 1.06638209573872, 1.06139564043732, 1.56635749252116, 0.487360268353893, 0.748572252468304, 0.487360268353893, 0.514430573459285, 1.05062295843428, 0.71922323666104, 0.722602160018842]