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"""
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 os
import numpy as np
import pandas as pd
from statsmodels.api import add_constant, categorical
from . import glm_test_resids
# 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
# TODO: taken from Stata; not available in sm yet
self.chi2 = 1981.711859508729
# 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.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
https://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")
data = pd.read_csv(filename)
dummies = pd.get_dummies(data.race, prefix="race", drop_first=False)
data = pd.concat([data, dummies], 1)
self.endog = data.low
design = data[["age", "lwt", "race_black", "race_other", "smoke",
"ptl", "ht", "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
https://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?)
# TODO: 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.
'''
# FIXME: do something with the commented-out code below
# 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 is not 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")
with open(filename, 'r') as fd:
data = np.genfromtxt(fd, delimiter=",", dtype=float)[1:]
self.endog = data[:5000, 0]
self.exog = data[:5000, 1:]
self.exog = add_constant(self.exog, prepend=False)
# 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, 0.83028721,
1.15742641, 0.9395442, 0.98118552, 0.89585426, 0.74147117,
0.8902096, 1.00212097, 0.97665858, 0.92624514, 0.98006601,
0.9507215, 1.00889825, 1.2406772, 0.88768719, 0.76587533,
1.0081044, 0.89608494, 1.00083526, 0.85594415, 0.76425576,
1.0286636, 1.13570272, 0.82020405, 0.81961271, 1.04586579,
1.26560245, 0.89721521, 1.19324037, 0.948205, 0.79414261,
0.85157002, 0.95155101, 0.91969239, 0.87699126, 1.03452982,
0.97093572, 1.14355781, 0.85088592, 0.79032079, 0.84521733,
0.99547581, 0.87593455, 0.8776799, 1.05531013, 0.94557017,
0.91538439, 0.79679863, 1.03398557, 0.88379021, 0.98850319,
1.05833423, 0.90055078, 0.92267584, 0.76273738, 0.98222632,
0.86392524, 0.78242646, 1.19417739, 0.89159895, 0.97565002,
0.85818308, 0.85334266, 1.85008011, 0.87199282, 0.77873231,
0.78036174, 0.96023918, 0.91574121, 0.89217979, 1.16421151,
1.29817786, 1.18683283, 0.96096225, 0.89964569, 1.00401442,
0.80758845, 0.89458758, 0.7994919, 0.85889356, 0.73147252,
0.7777221, 0.9148438, 0.72388117, 0.91134001, 1.0892724,
1.01736424, 0.86503014, 0.77344917, 1.04515616, 1.06677211,
0.93421936, 0.8821777, 0.91860774, 0.96381507, 0.70913689,
0.82354748, 1.12416046, 0.85989778, 0.90588737, 1.22832895,
0.65955579, 0.93828405, 0.88946418, 0.92152859, 0.83168025,
0.93346887, 0.96456078, 0.9039245, 1.03598695, 0.78405559,
1.21739525, 0.79019383, 0.84034646, 1.00273203, 0.96356393,
0.948103, 0.90279217, 1.0187839, 0.91630508, 1.15965854,
0.84203423, 0.98803156, 0.91604459, 0.90986512, 0.93384826,
0.76687038, 0.96251902, 0.80648134, 0.77336547, 0.85720164,
0.9351947, 0.88004728, 0.91083961, 1.06225829, 0.90230812,
0.72383932, 0.8343425, 0.8850996, 1.19037918, 0.93595522,
0.85061223, 0.84330949, 0.82397482, 0.92075047, 0.86129584,
0.99296756, 0.84912251, 0.8569699, 0.75252201, 0.80591772,
1.03902954, 1.04379139, 0.87360195, 0.97452318, 0.93240609,
0.85406409, 1.11717394, 0.95758536, 0.82772817, 0.67947416,
0.85957788, 0.93731268, 0.90349227, 0.79464185, 0.99148637,
0.8461071, 0.95399991, 1.04320664, 0.87290871, 0.96780849,
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0.91017317, 0.95323454, 1.29872357, 1.08851275, 1.01673108,
0.79536208, 0.84878371, 0.95165619, 0.87733936, 0.86319684,
0.96758495, 0.87763237, 0.95094713, 1.00143077, 1.0596993,
1.27278299, 0.82281481, 0.89765404, 0.94538181, 0.88161857,
0.77679456, 0.84274277, 0.89864342, 0.98705162, 0.95456512,
0.92712401, 0.77427128, 1.03292269, 0.87034158, 1.24316113,
0.98278702, 1.17325118, 1.18863971, 0.88678137, 0.90389731,
1.01740421, 0.80228624, 0.97742223, 0.82741518, 0.8359407,
0.7177401, 1.02297899, 0.81896048, 0.77127181, 0.83328601,
0.96939523, 0.94073198, 0.90356023, 1.12355064, 1.12811114,
0.92403138, 1.05423548, 0.70827734, 0.95891358, 0.89898027,
1.02318421, 0.93775375, 0.8245529, 0.80604304, 0.77555283,
0.92112699, 0.85662169, 0.92725859, 0.93599147, 0.78971931,
0.8337306, 0.93775212, 0.91025099, 0.75308822, 0.95391173,
0.96840576, 0.8394416, 0.89087015, 0.73703219, 0.97812386,
0.8787356, 0.93985266, 0.96406021, 0.88666152, 0.89242745,
0.97900374, 0.85697634, 0.8795755, 0.78581812, 0.87138735,
0.74602994, 0.96158936, 0.84529806, 0.85333232, 1.06116542,
1.05929382, 1.09720986, 1.28959453, 0.91541148, 0.87657407,
1.06514793, 0.8668096, 1.07325125, 0.85009534, 0.95542191,
0.86977409, 0.96249874, 0.97715908, 0.89360331, 0.98859647,
0.67560717, 0.90213348, 1.12051182, 0.99684949, 0.9863559,
1.32246221, 0.84632664, 0.89707447, 1.00486846, 0.90843649,
1.02399424, 0.97899017, 0.95693977, 0.8384806, 0.93927435,
0.79153251, 1.08694094, 1.01785553, 0.99674552, 0.898566,
0.94116882, 0.95224977, 0.99859129, 0.81125029, 0.85985586,
1.14418875, 0.96306241, 1.31398561, 0.77961419, 1.01958366,
0.9575668, 0.771084, 1.04473363, 1.01569517, 1.04560744,
0.9648178, 0.93466398, 1.09313672, 0.90349389, 1.00193114,
0.79991514, 0.91102351, 0.9795356, 0.89285193, 1.04898573,
0.93031782, 0.95087069, 1.15644699, 0.91155375, 0.93005986,
0.70098757, 0.82751625, 0.85462106, 1.34969332, 0.93382692,
1.05558387, 1.25417819, 1.0546501, 1.05217032, 0.86031346,
1.00864463, 0.73592482, 1.01899722, 1.00462831, 0.96882832,
0.81334751, 1.05102745, 0.82288113, 1.05798623, 0.77971966,
1.38584414, 1.0248193, 0.78951056, 0.76171823, 0.78407227,
1.14808104, 0.97890501, 0.99870905, 0.96006489, 0.78442704,
0.99315422, 0.83653213, 0.95210661, 0.97233777, 0.78140495,
0.95996216, 0.76318841, 0.82333311, 0.87123204, 0.79531258,
0.82681452, 1.00492217, 0.93549261, 1.00240153, 1.02086339,
1.00424549, 0.87437775, 0.84675564, 0.98014462, 0.77262117,
1.02620976, 0.91162462, 1.0275041, 1.1475431, 0.78167746,
0.86273856, 0.84499552, 0.99712362, 0.9694771, 0.94523806,
0.8450763, 0.93068519, 1.29362523, 1.0249628, 1.05522183,
1.13433408, 1.06981137, 0.85666419, 0.98203234, 0.75867592,
0.8844762, 0.89708521, 0.75482121, 0.80137918, 0.90412883,
0.88815714, 1.11497471, 0.77441965, 0.93853353, 0.8962444,
0.83055142, 0.99776183, 0.92581583, 0.78783745, 0.90934299,
0.81136457, 0.99000726, 0.9669203, 1.2890399, 1.01923088,
1.11076459, 1.01331706, 1.02470946, 0.92950448, 1.10298478,
1.03723287, 1.09129035, 0.95138186, 0.85764624, 0.86606803,
0.8141785, 1.0129293, 0.93267714, 0.95663734, 1.01940702,
0.8072268, 1.0707215, 0.90482063, 1.01546955, 0.84018308,
0.95938216, 0.96454054, 0.93114659, 1.09705112, 0.88720628,
0.81067916, 0.82667413, 0.89494027, 0.9173495, 0.73326273,
1.00209461, 0.9560545, 1.09126364, 0.95709908, 0.81314274,
0.8274943, 1.37605062, 0.99097917, 1.02221806, 0.90277482,
1.01611791, 0.79663017, 1.16686882, 1.19669266, 0.88366356,
0.77661102, 0.73467145, 1.15438391, 0.91439204, 0.78280849,
1.07238853, 1.03588797, 1.0438292, 0.75935005, 0.76200114,
0.81603429, 0.74402367, 1.1171573, 0.90227791, 0.94762351,
0.92462278, 0.8847803, 1.1343863, 0.8662186, 1.00410699,
1.05008842, 0.94783969, 0.89555844, 0.98278045, 0.80396855,
1.00483139, 0.82540491, 0.83284354, 0.93132265, 0.91191039,
0.95753995, 1.18260689, 0.84124197, 0.87429189, 0.67617592,
0.89495946, 0.92898357, 1.10528183, 1.06994417, 0.82259834,
0.74746328, 0.99070832, 1.07386274, 0.84007203, 0.89720099,
0.9670094, 1.02728082, 0.78001838, 0.97709347, 0.90602469,
1.49985196, 0.80256976, 1.05905677, 0.98298874, 0.94679703,
0.94305923, 0.98720786, 0.82091251, 0.91644161, 0.79576881,
0.98942172, 0.92974761, 0.99307545, 0.86959859, 0.88549807,
1.09246144, 0.87265047, 1.01449921, 0.74353851, 0.95029192,
0.94385304, 0.84779449, 1.00690543, 0.79727923, 0.92285822,
0.83164749, 1.06508941, 1.09757529, 0.9059649, 0.9146043,
0.74474669, 0.71306438, 0.77989422, 0.84965464, 0.9424323,
0.82492634, 0.85076686, 1.01110574, 1.01445751, 0.87929754,
0.8773275, 0.72314196, 0.92285502, 1.18173931, 0.86460799,
0.91795108, 1.16580482, 0.79880497, 0.72734786, 0.97579653,
0.76967834, 0.97543732, 1.04996964, 1.16439594, 1.08656546,
1.15644902, 0.98333436, 1.24374723, 0.95810117, 0.8488915,
1.06288523, 0.99055893, 0.75517736, 0.95856183, 0.85574796,
1.00426506, 1.25275675, 0.92735225, 0.83351314, 0.90216604,
0.87996386, 1.13312875, 1.00891523, 0.76513657, 0.85659621,
0.91142459, 1.05893495, 0.92253051, 0.87153684, 1.03190013,
0.92160845, 1.01768282, 0.80590054, 1.05172907, 0.92758177,
0.86902046, 0.93927127, 0.80389584, 0.96016014, 0.9720314,
0.93255573, 0.85792534, 0.97826842, 0.80506149, 0.97170364,
1.08397772, 1.01866333, 1.18898045, 1.02855427, 0.94848891,
0.94336541, 0.93119013, 0.92907817, 1.11806635, 0.88409637,
0.88809707, 1.06735612, 0.98447974, 0.88816438, 1.00099784,
0.92443453, 1.00325146, 0.86977836, 0.84621801, 0.92361073,
0.85573903, 0.77309241, 0.86717528, 1.19892035, 1.07497019,
1.02178857, 0.8718756, 0.90646803, 0.92912096, 1.04538692,
0.95245707, 0.99698525, 0.94583199, 0.92537599, 0.86720487,
0.89927054, 0.86111792, 0.94401208, 1.01130191, 1.03759681,
0.8177749, 1.07784373, 0.79823294, 1.00839713, 1.39409602,
0.87146241, 1.21218822, 0.84895926, 1.01742432, 0.8044077,
0.78632084, 1.07751744, 1.13147508, 0.90268302, 0.90024653,
0.92072578, 0.87763264, 1.00736787, 0.90978808, 0.90895492,
0.90766826, 0.98956566, 0.92075658, 0.77613105, 0.93815569,
0.95455546, 1.00607757, 0.82187828, 0.94197599, 0.867015,
0.90709762, 0.75604815, 0.91312261, 0.9286002, 0.74623204,
0.87368702, 0.83879278, 0.92224793, 0.81676402, 0.90355168,
0.92762955, 0.91784037, 0.82273304, 0.75947806, 0.92687078,
0.87971276, 1.15037445, 0.86707445, 0.8611453, 0.91921763,
1.07088129, 1.05150864, 1.02162325, 0.90305964, 0.99912687,
0.87693204, 0.6186911, 0.95526533, 1.15975655, 1.00061222,
0.74608861, 0.954568, 0.84965574, 0.79177899, 0.9741051,
1.0119514, 0.79147502, 0.81367071, 0.87757421, 1.01270813,
0.86044808, 0.9689615, 0.9577413, 0.79480242, 0.76073002,
0.83131288, 0.96379259, 0.84679732, 0.82508685, 0.89977283,
0.86766439, 1.12231836, 0.93058445, 1.04584181, 0.88838751,
0.96615893, 0.98731619, 1.05517799, 1.02860493, 0.98881473,
0.85210319, 0.91497438, 0.9275787, 0.97456134, 0.9011687,
0.69417417, 0.89661214, 0.79038577, 1.08118303, 1.0509366,
0.97813138, 0.85714945, 0.97330329, 0.83611871, 0.99772489,
0.83591193, 0.75592677, 0.85392601, 1.02734573, 0.72404609,
0.83534547, 0.91630472, 0.88463459, 1.12044562, 1.10991104,
0.96047701, 1.12342573, 0.72046647, 0.96852239, 0.89605698,
0.98310243, 0.92300659, 0.87794646, 0.83109321, 1.43297752,
0.80609029, 0.8692251, 0.90254649, 0.81647796, 1.07521371,
1.03942973, 0.96156488, 1.25225334, 1.0265727, 0.9518054,
0.87765718, 1.15552582, 0.79577766, 0.66849239, 0.87236017,
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.
https://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
self.llf = -12162.72308108797 # from Stata, big rounding diff with R
self.bic_Stata = -29849.51723280784
self.chi2 = 398.5465213008323 # from Stata not in sm
self.df_model = 2
self.df_resid = 3673
self.fittedvalues = np.array([
7.03292237, 7.03292237, 7.03292237, 7.03292237, 5.76642001,
7.03292237, 7.03292237, 6.36826384, 7.03292237, 7.03292237,
7.03292237, 7.03292237, 7.03292237, 5.76642001, 7.03292237,
5.22145448, 7.03292237, 5.22145448, 4.72799187, 4.72799187,
7.03292237, 7.03292237, 6.36826384, 7.03292237, 5.76642001,
7.03292237, 4.28116479, 7.03292237, 7.03292237, 7.03292237,
5.76642001, 7.03292237, 7.03292237, 7.03292237, 7.03292237,
7.03292237, 3.87656588, 7.03292237, 7.03292237, 4.28116479,
7.03292237, 7.03292237, 4.72799187, 7.03292237, 7.03292237,
7.03292237, 5.22145448, 6.36826384, 6.36826384, 4.28116479,
4.72799187, 7.03292237, 7.03292237, 7.03292237, 7.03292237,
5.22145448, 7.03292237, 7.03292237, 6.36826384, 7.03292237,
7.03292237, 7.03292237, 7.03292237, 5.76642001, 6.36826384,
6.36826384, 5.22145448, 7.03292237, 7.03292237, 7.03292237,
5.76642001, 7.03292237, 7.03292237, 3.87656588, 5.76642001,
7.03292237, 7.03292237, 7.03292237, 7.03292237, 7.03292237,
5.22145448, 5.22145448, 7.03292237, 6.36826384, 7.03292237,
7.03292237, 7.03292237, 4.72799187, 7.03292237, 6.36826384,
7.03292237, 6.36826384, 7.03292237, 7.03292237, 7.03292237,
7.03292237, 7.03292237, 7.03292237, 7.03292237, 7.03292237,
7.03292237, 7.03292237, 5.22145448, 6.36826384, 5.22145448,
7.03292237, 7.03292237, 4.72799187, 5.76642001, 7.03292237,
4.72799187, 6.36826384, 3.87656588, 7.03292237, 7.03292237,
5.22145448, 5.22145448, 6.36826384, 7.03292237, 7.03292237,
7.03292237, 7.03292237, 7.03292237, 6.36826384, 4.28116479,
7.03292237, 6.36826384, 6.36826384, 7.03292237, 7.03292237,
7.03292237, 7.03292237, 7.03292237, 6.36826384, 7.03292237,
6.36826384, 7.03292237, 7.03292237, 6.36826384, 7.03292237,
7.03292237, 7.03292237, 7.03292237, 7.03292237, 5.76642001,
7.03292237, 7.03292237, 7.03292237, 7.03292237, 7.03292237,
7.03292237, 7.03292237, 7.03292237, 7.03292237, 5.76642001,
7.03292237, 7.03292237, 6.36826384, 3.87656588, 7.03292237,
7.03292237, 5.22145448, 7.03292237, 5.76642001, 4.28116479,
5.76642001, 6.36826384, 6.36826384, 7.03292237, 7.03292237,
5.76642001, 7.03292237, 7.03292237, 4.28116479, 7.03292237,
6.36826384, 7.03292237, 6.36826384, 7.03292237, 5.22145448,
7.03292237, 4.28116479, 4.72799187, 7.03292237, 7.03292237,
7.03292237, 7.03292237, 7.03292237, 7.03292237, 6.36826384,
7.03292237, 4.28116479, 5.22145448, 7.03292237, 7.03292237,
7.03292237, 7.03292237, 4.28116479, 7.03292237, 7.03292237,
7.03292237, 6.36826384, 5.76642001, 7.03292237, 7.03292237,
7.03292237, 4.72799187, 7.03292237, 6.36826384, 7.03292237,
7.03292237, 7.03292237, 7.03292237, 6.36826384, 7.03292237,
6.36826384, 7.03292237, 6.36826384, 4.28116479, 5.76642001,
5.22145448, 6.36826384, 6.36826384, 7.03292237, 7.03292237,
7.03292237, 6.36826384, 7.03292237, 7.03292237, 6.36826384,
5.76642001, 7.03292237, 5.22145448, 5.76642001, 7.03292237,
7.03292237, 7.03292237, 7.03292237, 6.36826384, 7.03292237,
4.28116479, 7.03292237, 5.22145448, 7.03292237, 6.36826384,
5.76642001, 4.28116479, 4.28116479, 7.03292237, 6.36826384,
7.03292237, 7.03292237, 7.03292237, 6.36826384, 4.28116479,
7.03292237, 7.03292237, 7.03292237, 7.03292237, 5.22145448,
7.03292237, 7.03292237, 7.03292237, 5.22145448, 7.03292237,
5.76642001, 7.03292237, 4.72799187, 4.28116479, 6.36826384,
5.76642001, 7.03292237, 7.03292237, 7.03292237, 5.22145448,
7.03292237, 7.03292237, 7.03292237, 6.36826384, 7.03292237,
7.03292237, 7.03292237, 7.03292237, 6.36826384, 7.03292237,
3.87656588, 4.72799187, 7.03292237, 7.03292237, 7.03292237,
4.72799187, 7.03292237, 7.03292237, 6.36826384, 7.03292237,
7.03292237, 6.36826384, 3.87656588, 7.03292237, 7.03292237,
7.03292237, 7.03292237, 4.28116479, 7.03292237, 6.36826384,
7.03292237, 5.22145448, 5.22145448, 6.36826384, 7.03292237,
6.36826384, 6.36826384, 7.03292237, 4.28116479, 7.03292237,
7.03292237, 7.03292237, 5.22145448, 6.36826384, 7.03292237,
3.87656588, 6.36826384, 5.22145448, 5.76642001, 7.03292237,
7.03292237, 7.03292237, 7.03292237, 4.28116479, 7.03292237,
5.22145448, 7.03292237, 6.36826384, 5.22145448, 4.72799187,
7.03292237, 7.03292237, 7.03292237, 4.72799187, 6.36826384,
7.03292237, 6.36826384, 5.76642001, 7.03292237, 7.03292237,
7.03292237, 7.03292237, 5.22145448, 7.03292237, 7.03292237,
7.03292237, 7.03292237, 7.03292237, 3.87656588, 7.03292237,
7.03292237, 7.03292237, 7.03292237, 7.03292237, 7.03292237,
7.03292237, 7.03292237, 7.03292237, 4.72799187, 7.03292237,
7.03292237, 7.03292237, 7.03292237, 7.03292237, 7.03292237,
7.03292237, 7.03292237, 6.36826384, 7.03292237, 7.03292237,
6.36826384, 7.03292237, 6.36826384, 7.03292237, 5.22145448,
6.36826384, 7.03292237, 6.36826384, 7.03292237, 6.36826384,
7.03292237, 7.03292237, 7.03292237, 7.03292237, 7.03292237,
7.03292237, 7.03292237, 7.03292237, 7.03292237, 3.87656588,
7.03292237, 7.03292237, 7.03292237, 6.36826384, 7.03292237,
7.03292237, 6.36826384, 7.03292237, 7.03292237, 6.36826384,
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5.80654132, 5.80654132, 3.90353806, 4.76088805, 2.89810483,
5.80654132, 5.80654132, 5.80654132, 4.76088805, 5.25778406,
5.80654132, 5.80654132, 5.80654132, 5.80654132, 5.80654132,
5.80654132, 5.80654132, 3.90353806, 5.25778406, 4.76088805,
5.80654132, 4.76088805, 3.90353806, 5.80654132, 5.80654132,
4.76088805, 5.80654132, 5.25778406, 5.80654132, 2.89810483,
5.80654132, 5.25778406, 3.90353806, 3.90353806, 5.80654132,
5.25778406, 3.53462742, 5.80654132, 4.76088805, 5.25778406,
5.80654132, 3.90353806, 4.31095206, 5.80654132, 5.25778406,
3.90353806, 3.53462742, 5.25778406, 2.89810483, 5.80654132,
3.53462742, 4.76088805, 4.31095206, 5.80654132, 5.80654132,
5.80654132, 5.80654132, 5.80654132, 3.90353806, 5.80654132,
4.31095206, 5.80654132, 5.80654132, 5.25778406, 5.80654132,
5.80654132, 5.80654132, 5.80654132, 5.25778406, 5.25778406,
5.80654132, 5.25778406, 5.80654132, 5.80654132, 5.80654132,
5.80654132, 5.25778406, 4.31095206, 5.80654132, 5.25778406,
5.80654132, 5.25778406, 5.80654132, 5.80654132, 5.80654132,
5.80654132, 4.31095206, 5.25778406, 3.53462742, 2.89810483,
5.80654132, 5.80654132, 3.20058132, 5.80654132, 4.31095206,
5.80654132, 5.80654132, 5.80654132, 5.80654132, 3.90353806,
3.90353806, 3.90353806, 5.80654132, 5.80654132, 5.80654132,
5.80654132, 4.76088805, 3.20058132, 4.31095206, 5.80654132,
3.90353806, 5.80654132, 5.80654132, 5.80654132, 5.80654132,
5.80654132, 3.90353806, 5.80654132, 5.80654132, 5.80654132,
5.80654132, 3.90353806, 5.80654132, 3.90353806, 3.53462742,
5.80654132, 4.76088805, 5.80654132, 5.80654132, 5.80654132,
5.80654132, 5.80654132, 5.80654132, 5.80654132, 5.80654132,
4.76088805, 5.25778406, 5.80654132, 5.80654132, 5.80654132,
5.80654132, 5.80654132, 5.80654132, 5.80654132, 5.25778406,
3.53462742, 5.25778406, 5.80654132, 3.53462742, 5.80654132,
3.90353806, 5.80654132, 5.80654132, 5.80654132, 3.90353806,
3.20058132, 5.80654132, 5.80654132, 3.90353806, 5.80654132,
5.80654132, 5.80654132, 5.80654132, 5.80654132, 5.80654132,
5.80654132, 5.80654132, 5.80654132, 5.80654132, 5.80654132,
5.80654132, 5.80654132, 3.53462742, 5.80654132, 5.80654132,
5.80654132, 5.80654132, 3.53462742, 5.25778406, 3.90353806,
5.80654132, 4.76088805, 4.76088805, 3.90353806, 5.80654132,
5.80654132, 4.31095206, 2.89810483, 5.80654132, 5.80654132,
3.90353806, 5.80654132, 3.53462742, 3.90353806, 5.80654132,
5.80654132, 4.76088805, 5.80654132, 4.31095206, 5.25778406,
5.25778406, 3.20058132, 3.53462742, 5.80654132, 4.31095206,
5.80654132, 4.76088805, 3.90353806, 4.76088805, 4.76088805,
5.80654132, 5.80654132, 5.25778406, 3.90353806, 5.80654132,
5.80654132, 5.80654132, 5.80654132, 5.80654132, 5.80654132,
5.80654132, 3.53462742, 4.31095206, 3.90353806, 4.76088805,
4.31095206, 3.53462742, 3.90353806, 5.80654132, 5.80654132,
5.80654132, 5.80654132, 3.20058132, 5.80654132, 5.80654132,
5.80654132, 5.80654132, 5.80654132, 3.90353806, 4.76088805,
5.25778406, 3.53462742, 3.20058132, 5.80654132, 3.90353806,
5.80654132, 3.53462742, 5.80654132, 5.80654132, 3.90353806,
5.80654132, 3.90353806, 5.80654132, 5.80654132, 5.80654132,
5.80654132, 4.76088805, 3.90353806, 4.76088805, 5.25778406,
2.89810483, 5.80654132, 4.31095206, 5.80654132, 4.76088805,
5.80654132, 5.25778406, 5.80654132, 5.80654132, 5.80654132,
3.53462742, 2.89810483, 5.80654132, 5.80654132, 5.80654132,
3.90353806, 4.76088805, 5.80654132, 5.25778406, 4.76088805,
5.25778406, 5.80654132, 5.80654132, 5.25778406, 5.80654132,
5.80654132, 5.80654132, 2.89810483, 5.25778406, 5.80654132,
5.80654132, 4.76088805, 4.76088805, 5.25778406, 5.80654132,
5.80654132, 4.31095206, 3.20058132, 3.53462742, 5.80654132,
5.80654132, 5.80654132, 5.80654132, 5.80654132, 5.25778406,
5.80654132, 5.80654132, 3.90353806, 4.76088805, 5.80654132,
3.53462742, 5.80654132, 5.25778406, 2.89810483, 5.80654132,
5.25778406, 5.80654132, 5.80654132, 5.80654132, 5.25778406,
5.80654132, 5.80654132, 5.80654132, 5.80654132, 5.80654132,
5.80654132, 4.31095206, 5.80654132, 3.20058132, 5.80654132,
5.25778406, 4.76088805, 5.25778406, 5.80654132, 4.76088805,
5.80654132, 3.90353806, 4.31095206, 5.80654132, 5.80654132,
5.80654132, 5.80654132, 5.25778406, 5.80654132, 3.90353806,
4.76088805, 3.90353806, 5.80654132, 3.53462742, 5.80654132,
5.80654132, 5.80654132, 5.80654132, 3.53462742, 5.80654132,
4.76088805, 5.80654132, 5.80654132, 5.80654132, 5.80654132,
5.80654132, 5.80654132, 5.80654132, 5.80654132, 3.90353806,
2.6242144, 5.80654132, 5.80654132, 5.80654132, 5.80654132,
4.76088805, 5.80654132, 3.53462742, 5.80654132, 5.80654132,
3.90353806, 5.80654132, 5.80654132, 5.80654132, 5.80654132,
5.80654132, 5.80654132, 3.20058132, 3.20058132, 5.80654132,
5.80654132, 5.80654132, 3.90353806, 5.80654132, 5.25778406,
4.31095206, 5.25778406, 4.31095206, 4.31095206, 4.76088805,
5.80654132, 4.76088805, 5.80654132, 3.53462742, 5.80654132,
5.80654132, 5.80654132, 5.80654132, 5.80654132, 3.20058132,
5.80654132, 3.90353806, 5.80654132, 4.76088805, 5.80654132,
3.90353806, 5.80654132, 5.80654132, 5.80654132, 5.80654132,
5.80654132, 5.25778406, 5.80654132, 4.31095206, 5.25778406,
4.31095206, 5.80654132, 3.90353806, 5.80654132, 3.53462742,
5.25778406, 5.80654132, 5.80654132, 4.31095206, 3.90353806,
3.53462742, 5.80654132, 5.80654132, 5.80654132, 4.31095206,
5.80654132, 5.80654132, 5.25778406, 4.76088805, 4.31095206,
3.20058132, 5.80654132, 3.53462742, 3.20058132, 5.80654132,
5.80654132, 3.20058132, 3.20058132, 5.80654132, 4.31095206,
4.31095206, 5.80654132, 5.80654132, 3.90353806, 3.90353806,
3.53462742, 5.80654132, 3.90353806, 3.53462742, 5.80654132,
3.90353806, 5.25778406, 5.80654132, 3.53462742, 5.80654132,
5.25778406, 5.80654132, 4.31095206, 3.90353806, 5.80654132,
5.80654132, 4.31095206, 5.25778406, 5.80654132, 5.80654132,
5.80654132, 5.80654132, 5.80654132, 5.80654132, 5.25778406,
3.20058132, 5.25778406, 2.89810483, 3.90353806, 5.80654132,
3.53462742, 5.80654132, 5.25778406, 5.80654132, 2.89810483,
5.80654132, 5.80654132, 5.80654132, 5.80654132, 3.20058132,
5.80654132, 5.25778406, 3.53462742, 4.31095206, 4.76088805,
3.90353806, 5.80654132, 5.80654132, 5.25778406, 3.90353806,
4.76088805, 4.31095206, 5.80654132, 5.80654132, 5.80654132,
5.80654132, 5.80654132, 3.90353806, 5.80654132, 5.80654132,
5.80654132, 5.80654132, 5.80654132, 5.80654132, 5.25778406,
3.53462742, 5.80654132, 5.80654132, 5.25778406, 5.80654132,
3.20058132, 5.80654132, 4.76088805, 5.80654132, 4.76088805,
5.80654132, 5.25778406, 5.80654132, 5.80654132, 5.80654132,
5.80654132, 5.25778406, 2.89810483, 5.80654132, 5.80654132,
2.89810483, 3.53462742, 5.80654132, 5.80654132, 2.89810483,
4.31095206, 3.53462742, 4.31095206, 5.80654132, 5.80654132,
5.80654132, 5.80654132, 5.80654132, 5.80654132, 4.31095206,
4.76088805, 5.25778406, 5.80654132, 5.80654132, 5.80654132,
5.80654132, 5.25778406, 3.90353806, 5.80654132, 5.25778406,
5.80654132, 2.89810483, 2.89810483, 5.80654132, 3.53462742,
5.80654132, 3.53462742, 5.80654132, 4.31095206, 2.89810483,
5.80654132, 5.80654132, 2.89810483, 4.76088805, 5.80654132,
5.80654132, 3.20058132, 5.80654132, 3.90353806, 5.80654132,
5.80654132, 3.20058132, 3.90353806, 4.76088805, 4.76088805,
5.80654132, 3.90353806, 4.31095206, 5.80654132, 4.31095206,
5.80654132, 3.20058132, 4.31095206, 4.76088805, 3.53462742,
5.80654132, 5.80654132, 3.53462742, 3.53462742, 3.53462742,
5.80654132, 5.80654132, 3.90353806, 3.90353806, 3.20058132,
5.80654132, 5.80654132, 2.89810483, 3.90353806, 5.80654132,
2.89810483, 3.53462742, 3.53462742, 4.31095206, 5.80654132,
3.53462742, 5.80654132, 5.80654132, 5.80654132, 5.80654132,
5.80654132, 5.80654132, 5.25778406, 5.80654132, 5.80654132,
5.80654132, 5.80654132, 5.80654132, 5.80654132, 5.80654132,
5.80654132, 4.76088805, 5.80654132, 5.80654132, 4.76088805,
5.80654132, 5.80654132, 4.76088805, 4.76088805, 5.80654132,
5.25778406, 4.31095206, 5.80654132, 4.76088805, 3.90353806,
4.31095206, 5.80654132, 2.89810483, 4.31095206, 5.25778406,
5.80654132, 5.80654132, 5.80654132, 5.80654132, 3.20058132,
5.25778406, 5.80654132, 4.76088805, 5.80654132, 4.31095206,
5.80654132, 5.80654132, 4.76088805, 4.31095206, 5.80654132,
5.80654132, 5.80654132, 5.80654132, 5.80654132, 4.31095206,
4.31095206, 3.20058132, 4.76088805, 5.80654132, 3.20058132,
3.20058132, 5.80654132, 3.90353806, 5.25778406, 3.20058132,
4.76088805, 3.20058132, 3.53462742, 4.76088805, 5.80654132,
5.80654132, 4.31095206, 4.76088805, 5.80654132, 4.31095206,
5.80654132, 4.76088805, 4.31095206, 2.89810483, 5.80654132,
5.80654132, 5.80654132, 4.76088805, 5.80654132, 5.80654132,
5.80654132, 5.80654132, 4.76088805, 5.25778406, 4.31095206,
5.80654132, 3.90353806, 3.53462742, 4.76088805, 5.80654132,
4.31095206, 5.80654132, 5.80654132, 3.20058132, 5.80654132,
5.25778406, 5.80654132, 5.80654132, 5.80654132, 3.53462742,
2.6242144, 5.80654132, 5.80654132, 3.53462742, 5.25778406,
3.90353806, 5.80654132, 2.89810483, 5.80654132, 3.90353806,
5.80654132, 5.80654132, 3.90353806, 2.89810483, 5.80654132,
4.76088805, 4.31095206, 5.80654132, 5.25778406, 5.80654132,
5.80654132, 4.31095206, 5.80654132, 5.80654132, 5.80654132,
3.90353806, 4.76088805, 5.80654132, 4.76088805, 5.80654132,
4.76088805, 3.53462742, 3.90353806, 5.80654132, 5.80654132,
5.80654132, 5.25778406, 5.80654132, 5.80654132, 5.25778406,
5.80654132, 5.80654132, 5.80654132, 5.80654132, 5.80654132,
5.80654132, 5.80654132, 5.80654132, 5.80654132, 5.80654132,
3.53462742, 3.53462742, 3.90353806, 5.80654132, 4.31095206,
3.53462742, 5.80654132, 4.76088805, 4.76088805, 3.20058132,
3.90353806, 5.80654132, 5.25778406, 5.80654132, 5.80654132,
5.80654132, 5.80654132, 4.31095206, 5.25778406, 4.31095206,
5.80654132, 3.20058132, 5.80654132, 4.31095206, 4.31095206,
4.76088805, 5.80654132, 4.76088805, 4.31095206, 5.80654132,
5.25778406, 3.53462742, 3.53462742, 5.25778406, 5.80654132,
3.90353806, 5.25778406, 4.31095206, 4.31095206, 3.53462742,
5.80654132, 3.90353806, 5.80654132, 5.80654132, 4.76088805,
5.25778406, 3.20058132, 3.90353806, 5.80654132, 5.25778406,
5.80654132, 5.80654132, 5.25778406, 5.80654132, 4.31095206,
5.25778406, 4.76088805, 5.80654132, 5.80654132, 5.25778406,
3.53462742, 5.80654132, 5.80654132, 5.80654132, 5.25778406,
5.25778406, 5.80654132, 3.20058132, 5.80654132, 5.80654132,
3.53462742, 5.80654132, 5.80654132, 5.80654132, 4.31095206,
5.80654132, 4.76088805, 5.80654132, 5.80654132, 5.80654132,
3.90353806, 4.31095206, 5.25778406, 5.80654132, 3.53462742,
3.90353806, 5.25778406, 4.31095206, 5.80654132, 5.25778406,
5.25778406, 2.89810483, 5.80654132, 5.80654132, 5.80654132,
5.80654132, 5.80654132, 5.25778406, 5.80654132, 4.76088805,
5.80654132, 5.80654132, 5.80654132, 4.31095206, 5.80654132,
3.20058132, 3.90353806, 5.80654132, 5.80654132, 5.25778406,
5.80654132, 5.80654132, 5.80654132, 5.80654132, 5.80654132,
5.80654132, 5.80654132, 2.6242144, 5.80654132, 3.90353806,
5.25778406, 4.76088805, 5.80654132, 5.80654132, 3.90353806,
5.80654132, 3.53462742, 2.89810483, 5.80654132, 3.53462742,
2.89810483, 4.76088805, 5.80654132, 5.80654132, 5.80654132,
4.31095206, 5.80654132, 4.76088805, 3.90353806, 2.89810483,
4.76088805, 5.80654132, 2.6242144, 3.53462742, 4.31095206,
5.25778406, 5.25778406, 3.20058132, 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, 6.84797506, 6.84797506,
6.84797506, 6.84797506, 6.84797506, 5.9571983, 6.84797506,
5.51180993, 6.84797506, 5.51180993, 5.06642155, 5.06642155,
6.84797506, 6.84797506, 6.40258668, 6.84797506, 5.9571983,
6.84797506, 4.62103317, 6.84797506, 6.84797506, 6.84797506,
5.9571983, 6.84797506, 6.84797506, 6.84797506, 6.84797506,
6.84797506, 4.17564479, 6.84797506, 6.84797506, 4.62103317,
6.84797506, 6.84797506, 5.06642155, 6.84797506, 6.84797506,
6.84797506, 5.51180993, 6.40258668, 6.40258668, 4.62103317,
5.06642155, 6.84797506, 6.84797506, 6.84797506, 6.84797506,
5.51180993, 6.84797506, 6.84797506, 6.40258668, 6.84797506,
6.84797506, 6.84797506, 6.84797506, 5.9571983, 6.40258668,
6.40258668, 5.51180993, 6.84797506, 6.84797506, 6.84797506,
5.9571983, 6.84797506, 6.84797506, 4.17564479, 5.9571983,
6.84797506, 6.84797506, 6.84797506, 6.84797506, 6.84797506,
5.51180993, 5.51180993, 6.84797506, 6.40258668, 6.84797506,
6.84797506, 6.84797506, 5.06642155, 6.84797506, 6.40258668,
6.84797506, 6.40258668, 6.84797506, 6.84797506, 6.84797506,
6.84797506, 6.84797506, 6.84797506, 6.84797506, 6.84797506,
6.84797506, 6.84797506, 5.51180993, 6.40258668, 5.51180993,
6.84797506, 6.84797506, 5.06642155, 5.9571983, 6.84797506,
5.06642155, 6.40258668, 4.17564479, 6.84797506, 6.84797506,
5.51180993, 5.51180993, 6.40258668, 6.84797506, 6.84797506,
6.84797506, 6.84797506, 6.84797506, 6.40258668, 4.62103317,
6.84797506, 6.40258668, 6.40258668, 6.84797506, 6.84797506,
6.84797506, 6.84797506, 6.84797506, 6.40258668, 6.84797506,
6.40258668, 6.84797506, 6.84797506, 6.40258668, 6.84797506,
6.84797506, 6.84797506, 6.84797506, 6.84797506, 5.9571983,
6.84797506, 6.84797506, 6.84797506, 6.84797506, 6.84797506,
6.84797506, 6.84797506, 6.84797506, 6.84797506, 5.9571983,
6.84797506, 6.84797506, 6.40258668, 4.17564479, 6.84797506,
6.84797506, 5.51180993, 6.84797506, 5.9571983, 4.62103317,
5.9571983, 6.40258668, 6.40258668, 6.84797506, 6.84797506,
5.9571983, 6.84797506, 6.84797506, 4.62103317, 6.84797506,
6.40258668, 6.84797506, 6.40258668, 6.84797506, 5.51180993,
6.84797506, 4.62103317, 5.06642155, 6.84797506, 6.84797506,
6.84797506, 6.84797506, 6.84797506, 6.84797506, 6.40258668,
6.84797506, 4.62103317, 5.51180993, 6.84797506, 6.84797506,
6.84797506, 6.84797506, 4.62103317, 6.84797506, 6.84797506,
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5.789248, 5.34385962, 3.56230611, 3.11691773, 3.56230611,
4.00769449, 5.789248, 4.45308287, 4.89847125, 5.789248,
5.789248, 5.789248, 4.00769449, 4.89847125, 2.67152936,
5.789248, 5.789248, 5.789248, 4.89847125, 5.34385962,
5.789248, 5.789248, 5.789248, 5.789248, 5.789248,
5.789248, 5.789248, 4.00769449, 5.34385962, 4.89847125,
5.789248, 4.89847125, 4.00769449, 5.789248, 5.789248,
4.89847125, 5.789248, 5.34385962, 5.789248, 2.67152936,
5.789248, 5.34385962, 4.00769449, 4.00769449, 5.789248,
5.34385962, 3.56230611, 5.789248, 4.89847125, 5.34385962,
5.789248, 4.00769449, 4.45308287, 5.789248, 5.34385962,
4.00769449, 3.56230611, 5.34385962, 2.67152936, 5.789248,
3.56230611, 4.89847125, 4.45308287, 5.789248, 5.789248,
5.789248, 5.789248, 5.789248, 4.00769449, 5.789248,
4.45308287, 5.789248, 5.789248, 5.34385962, 5.789248,
5.789248, 5.789248, 5.789248, 5.34385962, 5.34385962,
5.789248, 5.34385962, 5.789248, 5.789248, 5.789248,
5.789248, 5.34385962, 4.45308287, 5.789248, 5.34385962,
5.789248, 5.34385962, 5.789248, 5.789248, 5.789248,
5.789248, 4.45308287, 5.34385962, 3.56230611, 2.67152936,
5.789248, 5.789248, 3.11691773, 5.789248, 4.45308287,
5.789248, 5.789248, 5.789248, 5.789248, 4.00769449,
4.00769449, 4.00769449, 5.789248, 5.789248, 5.789248,
5.789248, 4.89847125, 3.11691773, 4.45308287, 5.789248,
4.00769449, 5.789248, 5.789248, 5.789248, 5.789248,
5.789248, 4.00769449, 5.789248, 5.789248, 5.789248,
5.789248, 4.00769449, 5.789248, 4.00769449, 3.56230611,
5.789248, 4.89847125, 5.789248, 5.789248, 5.789248,
5.789248, 5.789248, 5.789248, 5.789248, 5.789248,
4.89847125, 5.34385962, 5.789248, 5.789248, 5.789248,
5.789248, 5.789248, 5.789248, 5.789248, 5.34385962,
3.56230611, 5.34385962, 5.789248, 3.56230611, 5.789248,
4.00769449, 5.789248, 5.789248, 5.789248, 4.00769449,
3.11691773, 5.789248, 5.789248, 4.00769449, 5.789248,
5.789248, 5.789248, 5.789248, 5.789248, 5.789248,
5.789248, 5.789248, 5.789248, 5.789248, 5.789248,
5.789248, 5.789248, 3.56230611, 5.789248, 5.789248,
5.789248, 5.789248, 3.56230611, 5.34385962, 4.00769449,
5.789248, 4.89847125, 4.89847125, 4.00769449, 5.789248,
5.789248, 4.45308287, 2.67152936, 5.789248, 5.789248,
4.00769449, 5.789248, 3.56230611, 4.00769449, 5.789248,
5.789248, 4.89847125, 5.789248, 4.45308287, 5.34385962,
5.34385962, 3.11691773, 3.56230611, 5.789248, 4.45308287,
5.789248, 4.89847125, 4.00769449, 4.89847125, 4.89847125,
5.789248, 5.789248, 5.34385962, 4.00769449, 5.789248,
5.789248, 5.789248, 5.789248, 5.789248, 5.789248,
5.789248, 3.56230611, 4.45308287, 4.00769449, 4.89847125,
4.45308287, 3.56230611, 4.00769449, 5.789248, 5.789248,
5.789248, 5.789248, 3.11691773, 5.789248, 5.789248,
5.789248, 5.789248, 5.789248, 4.00769449, 4.89847125,
5.34385962, 3.56230611, 3.11691773, 5.789248, 4.00769449,
5.789248, 3.56230611, 5.789248, 5.789248, 4.00769449,
5.789248, 4.00769449, 5.789248, 5.789248, 5.789248,
5.789248, 4.89847125, 4.00769449, 4.89847125, 5.34385962,
2.67152936, 5.789248, 4.45308287, 5.789248, 4.89847125,
5.789248, 5.34385962, 5.789248, 5.789248, 5.789248,
3.56230611, 2.67152936, 5.789248, 5.789248, 5.789248,
4.00769449, 4.89847125, 5.789248, 5.34385962, 4.89847125,
5.34385962, 5.789248, 5.789248, 5.34385962, 5.789248,
5.789248, 5.789248, 2.67152936, 5.34385962, 5.789248,
5.789248, 4.89847125, 4.89847125, 5.34385962, 5.789248,
5.789248, 4.45308287, 3.11691773, 3.56230611, 5.789248,
5.789248, 5.789248, 5.789248, 5.789248, 5.34385962,
5.789248, 5.789248, 4.00769449, 4.89847125, 5.789248,
3.56230611, 5.789248, 5.34385962, 2.67152936, 5.789248,
5.34385962, 5.789248, 5.789248, 5.789248, 5.34385962,
5.789248, 5.789248, 5.789248, 5.789248, 5.789248,
5.789248, 4.45308287, 5.789248, 3.11691773, 5.789248,
5.34385962, 4.89847125, 5.34385962, 5.789248, 4.89847125,
5.789248, 4.00769449, 4.45308287, 5.789248, 5.789248,
5.789248, 5.789248, 5.34385962, 5.789248, 4.00769449,
4.89847125, 4.00769449, 5.789248, 3.56230611, 5.789248,
5.789248, 5.789248, 5.789248, 3.56230611, 5.789248,
4.89847125, 5.789248, 5.789248, 5.789248, 5.789248,
5.789248, 5.789248, 5.789248, 5.789248, 4.00769449,
2.22614098, 5.789248, 5.789248, 5.789248, 5.789248,
4.89847125, 5.789248, 3.56230611, 5.789248, 5.789248,
4.00769449, 5.789248, 5.789248, 5.789248, 5.789248,
5.789248, 5.789248, 3.11691773, 3.11691773, 5.789248,
5.789248, 5.789248, 4.00769449, 5.789248, 5.34385962,
4.45308287, 5.34385962, 4.45308287, 4.45308287, 4.89847125,
5.789248, 4.89847125, 5.789248, 3.56230611, 5.789248,
5.789248, 5.789248, 5.789248, 5.789248, 3.11691773,
5.789248, 4.00769449, 5.789248, 4.89847125, 5.789248,
4.00769449, 5.789248, 5.789248, 5.789248, 5.789248,
5.789248, 5.34385962, 5.789248, 4.45308287, 5.34385962,
4.45308287, 5.789248, 4.00769449, 5.789248, 3.56230611,
5.34385962, 5.789248, 5.789248, 4.45308287, 4.00769449,
3.56230611, 5.789248, 5.789248, 5.789248, 4.45308287,
5.789248, 5.789248, 5.34385962, 4.89847125, 4.45308287,
3.11691773, 5.789248, 3.56230611, 3.11691773, 5.789248,
5.789248, 3.11691773, 3.11691773, 5.789248, 4.45308287,
4.45308287, 5.789248, 5.789248, 4.00769449, 4.00769449,
3.56230611, 5.789248, 4.00769449, 3.56230611, 5.789248,
4.00769449, 5.34385962, 5.789248, 3.56230611, 5.789248,
5.34385962, 5.789248, 4.45308287, 4.00769449, 5.789248,
5.789248, 4.45308287, 5.34385962, 5.789248, 5.789248,
5.789248, 5.789248, 5.789248, 5.789248, 5.34385962,
3.11691773, 5.34385962, 2.67152936, 4.00769449, 5.789248,
3.56230611, 5.789248, 5.34385962, 5.789248, 2.67152936,
5.789248, 5.789248, 5.789248, 5.789248, 3.11691773,
5.789248, 5.34385962, 3.56230611, 4.45308287, 4.89847125,
4.00769449, 5.789248, 5.789248, 5.34385962, 4.00769449,
4.89847125, 4.45308287, 5.789248, 5.789248, 5.789248,
5.789248, 5.789248, 4.00769449, 5.789248, 5.789248,
5.789248, 5.789248, 5.789248, 5.789248, 5.34385962,
3.56230611, 5.789248, 5.789248, 5.34385962, 5.789248,
3.11691773, 5.789248, 4.89847125, 5.789248, 4.89847125,
5.789248, 5.34385962, 5.789248, 5.789248, 5.789248,
5.789248, 5.34385962, 2.67152936, 5.789248, 5.789248,
2.67152936, 3.56230611, 5.789248, 5.789248, 2.67152936,
4.45308287, 3.56230611, 4.45308287, 5.789248, 5.789248,
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4.89847125, 5.34385962, 5.789248, 5.789248, 5.789248,
5.789248, 5.34385962, 4.00769449, 5.789248, 5.34385962,
5.789248, 2.67152936, 2.67152936, 5.789248, 3.56230611,
5.789248, 3.56230611, 5.789248, 4.45308287, 2.67152936,
5.789248, 5.789248, 2.67152936, 4.89847125, 5.789248,
5.789248, 3.11691773, 5.789248, 4.00769449, 5.789248,
5.789248, 3.11691773, 4.00769449, 4.89847125, 4.89847125,
5.789248, 4.00769449, 4.45308287, 5.789248, 4.45308287,
5.789248, 3.11691773, 4.45308287, 4.89847125, 3.56230611,
5.789248, 5.789248, 3.56230611, 3.56230611, 3.56230611,
5.789248, 5.789248, 4.00769449, 4.00769449, 3.11691773,
5.789248, 5.789248, 2.67152936, 4.00769449, 5.789248,
2.67152936, 3.56230611, 3.56230611, 4.45308287, 5.789248,
3.56230611, 5.789248, 5.789248, 5.789248, 5.789248,
5.789248, 5.789248, 5.34385962, 5.789248, 5.789248,
5.789248, 5.789248, 5.789248, 5.789248, 5.789248,
5.789248, 4.89847125, 5.789248, 5.789248, 4.89847125,
5.789248, 5.789248, 4.89847125, 4.89847125, 5.789248,
5.34385962, 4.45308287, 5.789248, 4.89847125, 4.00769449,
4.45308287, 5.789248, 2.67152936, 4.45308287, 5.34385962,
5.789248, 5.789248, 5.789248, 5.789248, 3.11691773,
5.34385962, 5.789248, 4.89847125, 5.789248, 4.45308287,
5.789248, 5.789248, 4.89847125, 4.45308287, 5.789248,
5.789248, 5.789248, 5.789248, 5.789248, 4.45308287,
4.45308287, 3.11691773, 4.89847125, 5.789248, 3.11691773,
3.11691773, 5.789248, 4.00769449, 5.34385962, 3.11691773,
4.89847125, 3.11691773, 3.56230611, 4.89847125, 5.789248,
5.789248, 4.45308287, 4.89847125, 5.789248, 4.45308287,
5.789248, 4.89847125, 4.45308287, 2.67152936, 5.789248,
5.789248, 5.789248, 4.89847125, 5.789248, 5.789248,
5.789248, 5.789248, 4.89847125, 5.34385962, 4.45308287,
5.789248, 4.00769449, 3.56230611, 4.89847125, 5.789248,
4.45308287, 5.789248, 5.789248, 3.11691773, 5.789248,
5.34385962, 5.789248, 5.789248, 5.789248, 3.56230611,
2.22614098, 5.789248, 5.789248, 3.56230611, 5.34385962,
4.00769449, 5.789248, 2.67152936, 5.789248, 4.00769449,
5.789248, 5.789248, 4.00769449, 2.67152936, 5.789248,
4.89847125, 4.45308287, 5.789248, 5.34385962, 5.789248,
5.789248, 4.45308287, 5.789248, 5.789248, 5.789248,
4.00769449, 4.89847125, 5.789248, 4.89847125, 5.789248,
4.89847125, 3.56230611, 4.00769449, 5.789248, 5.789248,
5.789248, 5.34385962, 5.789248, 5.789248, 5.34385962,
5.789248, 5.789248, 5.789248, 5.789248, 5.789248,
5.789248, 5.789248, 5.789248, 5.789248, 5.789248,
3.56230611, 3.56230611, 4.00769449, 5.789248, 4.45308287,
3.56230611, 5.789248, 4.89847125, 4.89847125, 3.11691773,
4.00769449, 5.789248, 5.34385962, 5.789248, 5.789248,
5.789248, 5.789248, 4.45308287, 5.34385962, 4.45308287,
5.789248, 3.11691773, 5.789248, 4.45308287, 4.45308287,
4.89847125, 5.789248, 4.89847125, 4.45308287, 5.789248,
5.34385962, 3.56230611, 3.56230611, 5.34385962, 5.789248,
4.00769449, 5.34385962, 4.45308287, 4.45308287, 3.56230611,
5.789248, 4.00769449, 5.789248, 5.789248, 4.89847125,
5.34385962, 3.11691773, 4.00769449, 5.789248, 5.34385962,
5.789248, 5.789248, 5.34385962, 5.789248, 4.45308287,
5.34385962, 4.89847125, 5.789248, 5.789248, 5.34385962,
3.56230611, 5.789248, 5.789248, 5.789248, 5.34385962,
5.34385962, 5.789248, 3.11691773, 5.789248, 5.789248,
3.56230611, 5.789248, 5.789248, 5.789248, 4.45308287,
5.789248, 4.89847125, 5.789248, 5.789248, 5.789248,
4.00769449, 4.45308287, 5.34385962, 5.789248, 3.56230611,
4.00769449, 5.34385962, 4.45308287, 5.789248, 5.34385962,
5.34385962, 2.67152936, 5.789248, 5.789248, 5.789248,
5.789248, 5.789248, 5.34385962, 5.789248, 4.89847125,
5.789248, 5.789248, 5.789248, 4.45308287, 5.789248,
3.11691773, 4.00769449, 5.789248, 5.789248, 5.34385962,
5.789248, 5.789248, 5.789248, 5.789248, 5.789248,
5.789248, 5.789248, 2.22614098, 5.789248, 4.00769449,
5.34385962, 4.89847125, 5.789248, 5.789248, 4.00769449,
5.789248, 3.56230611, 2.67152936, 5.789248, 3.56230611,
2.67152936, 4.89847125, 5.789248, 5.789248, 5.789248,
4.45308287, 5.789248, 4.89847125, 4.00769449, 2.67152936,
4.89847125, 5.789248, 2.22614098, 3.56230611, 4.45308287,
5.34385962, 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
# TODO: taken from Stata; not available in sm yet
self.chi2 = 2699.138063147485
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
# TODO: taken from Stata; not available in sm yet
# self.chi2 = 2699.138063147485
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
# TODO: taken from Stata; not available in sm yet
# self.chi2 = 2699.138063147485
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
# TODO: taken from Stata; not available in sm yet
# self.chi2 = 2699.138063147485
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
# TODO: taken from Stata; not available in sm yet
# self.chi2 = 2699.138063147485
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]