1""" 2Results for test_glm.py. 3 4Hard-coded from R or Stata. Note that some of the remaining discrepancy vs. 5Stata may be because Stata uses ML by default unless you specifically ask for 6IRLS. 7""" 8import os 9 10import numpy as np 11import pandas as pd 12 13from statsmodels.api import add_constant 14from statsmodels.genmod.tests.results import glm_test_resids 15 16# Test Precisions 17DECIMAL_4 = 4 18DECIMAL_3 = 3 19DECIMAL_2 = 2 20DECIMAL_1 = 1 21DECIMAL_0 = 0 22 23 24class Longley(object): 25 """ 26 Longley used for TestGlmGaussian 27 28 Results are from Stata and R. 29 """ 30 def __init__(self): 31 32 self.resids = np.array([ 33 [267.34002976, 267.34002976, 267.34002976, 34 267.34002976, 267.34002976], 35 [-94.0139424, -94.0139424, -94.0139424, -94.0139424, 36 -94.0139424], 37 [46.28716776, 46.28716776, 46.28716776, 46.28716776, 38 46.28716776], 39 [-410.11462193, -410.11462193, -410.11462193, -410.11462193, 40 -410.11462193], 41 [309.71459076, 309.71459076, 309.71459076, 309.71459076, 42 309.71459076], 43 [-249.31121533, -249.31121533, -249.31121533, -249.31121533, 44 -249.31121533], 45 [-164.0489564, -164.0489564, -164.0489564, -164.0489564, 46 -164.0489564], 47 [-13.18035687, -13.18035687, -13.18035687, -13.18035687, 48 -13.18035687], 49 [14.3047726, 14.3047726, 14.3047726, 14.3047726, 50 14.3047726], 51 [455.39409455, 455.39409455, 455.39409455, 455.39409455, 52 455.39409455], 53 [-17.26892711, -17.26892711, -17.26892711, -17.26892711, 54 -17.26892711], 55 [-39.05504252, -39.05504252, -39.05504252, -39.05504252, 56 -39.05504252], 57 [-155.5499736, -155.5499736, -155.5499736, -155.5499736, 58 -155.5499736], 59 [-85.67130804, -85.67130804, -85.67130804, -85.67130804, 60 -85.67130804], 61 [341.93151396, 341.93151396, 341.93151396, 341.93151396, 62 341.93151396], 63 [-206.75782519, -206.75782519, -206.75782519, -206.75782519, 64 -206.75782519]]) 65 self.null_deviance = 185008826 # taken from R. 66 self.params = np.array([ 67 1.50618723e+01, -3.58191793e-02, 68 -2.02022980e+00, -1.03322687e+00, -5.11041057e-02, 69 1.82915146e+03, -3.48225863e+06]) 70 self.bse = np.array([ 71 8.49149258e+01, 3.34910078e-02, 4.88399682e-01, 72 2.14274163e-01, 2.26073200e-01, 73 4.55478499e+02, 8.90420384e+05]) 74 self.aic_R = 235.23486961695903 # R adds 2 for dof to AIC 75 self.aic_Stata = 14.57717943930524 # stata divides by nobs 76 self.deviance = 836424.0555058046 # from R 77 self.scale = 92936.006167311629 78 self.llf = -109.61743480847952 79 self.null_deviance = 185008826 # taken from R. Rpy bug 80 81 self.bic_Stata = 836399.1760177979 # no bic in R? 82 self.df_model = 6 83 self.df_resid = 9 84 85 # TODO: taken from Stata; not available in sm yet 86 self.chi2 = 1981.711859508729 87 88 self.prsquared = 0.90 89 self.prsquared_cox_snell = 1.00 90 91 # self.pearson_chi2 = 836424.1293162981 # from Stata (?) 92 self.fittedvalues = np.array([ 93 60055.659970240202, 61216.013942398131, 94 60124.71283224225, 61597.114621930756, 62911.285409240052, 95 63888.31121532945, 65153.048956395127, 63774.180356866214, 96 66004.695227399934, 67401.605905447621, 97 68186.268927114084, 66552.055042522494, 98 68810.549973595422, 69649.67130804155, 68989.068486039061, 99 70757.757825193927]) 100 101 102class GaussianLog(object): 103 """ 104 Uses generated data. These results are from R and Stata. 105 """ 106 def __init__(self): 107 self.resids = np.array([ 108 [3.20800000e-04, 3.20800000e-04, 109 8.72100000e-04, 3.20800000e-04, 3.20800000e-04], 110 [8.12100000e-04, 8.12100000e-04, 2.16350000e-03, 111 8.12100000e-04, 8.12100000e-04], 112 [-2.94800000e-04, -2.94800000e-04, -7.69700000e-04, 113 -2.94800000e-04, -2.94800000e-04], 114 [1.40190000e-03, 1.40190000e-03, 3.58560000e-03, 115 1.40190000e-03, 1.40190000e-03], 116 [-2.30910000e-03, -2.30910000e-03, -5.78490000e-03, 117 -2.30910000e-03, -2.30910000e-03], 118 [1.10380000e-03, 1.10380000e-03, 2.70820000e-03, 119 1.10380000e-03, 1.10380000e-03], 120 [-5.14000000e-06, -5.14000000e-06, -1.23000000e-05, 121 -5.14000000e-06, -5.14000000e-06], 122 [-1.65500000e-04, -1.65500000e-04, -3.89200000e-04, 123 -1.65500000e-04, -1.65500000e-04], 124 [-7.55400000e-04, -7.55400000e-04, -1.73870000e-03, 125 -7.55400000e-04, -7.55400000e-04], 126 [-1.39800000e-04, -1.39800000e-04, -3.14800000e-04, 127 -1.39800000e-04, -1.39800000e-04], 128 [-7.17000000e-04, -7.17000000e-04, -1.58000000e-03, 129 -7.17000000e-04, -7.17000000e-04], 130 [-1.12200000e-04, -1.12200000e-04, -2.41900000e-04, 131 -1.12200000e-04, -1.12200000e-04], 132 [3.22100000e-04, 3.22100000e-04, 6.79000000e-04, 133 3.22100000e-04, 3.22100000e-04], 134 [-3.78000000e-05, -3.78000000e-05, -7.79000000e-05, 135 -3.78000000e-05, -3.78000000e-05], 136 [5.54500000e-04, 5.54500000e-04, 1.11730000e-03, 137 5.54500000e-04, 5.54500000e-04], 138 [3.38400000e-04, 3.38400000e-04, 6.66300000e-04, 139 3.38400000e-04, 3.38400000e-04], 140 [9.72000000e-05, 9.72000000e-05, 1.87000000e-04, 141 9.72000000e-05, 9.72000000e-05], 142 [-7.92900000e-04, -7.92900000e-04, -1.49070000e-03, 143 -7.92900000e-04, -7.92900000e-04], 144 [3.33000000e-04, 3.33000000e-04, 6.11500000e-04, 145 3.33000000e-04, 3.33000000e-04], 146 [-8.35300000e-04, -8.35300000e-04, -1.49790000e-03, 147 -8.35300000e-04, -8.35300000e-04], 148 [-3.99700000e-04, -3.99700000e-04, -6.99800000e-04, 149 -3.99700000e-04, -3.99700000e-04], 150 [1.41300000e-04, 1.41300000e-04, 2.41500000e-04, 151 1.41300000e-04, 1.41300000e-04], 152 [-8.50700000e-04, -8.50700000e-04, -1.41920000e-03, 153 -8.50700000e-04, -8.50700000e-04], 154 [1.43000000e-06, 1.43000000e-06, 2.33000000e-06, 155 1.43000000e-06, 1.43000000e-06], 156 [-9.12000000e-05, -9.12000000e-05, -1.44900000e-04, 157 -9.12000000e-05, -9.12000000e-05], 158 [6.75500000e-04, 6.75500000e-04, 1.04650000e-03, 159 6.75500000e-04, 6.75500000e-04], 160 [3.97900000e-04, 3.97900000e-04, 6.01100000e-04, 161 3.97900000e-04, 3.97900000e-04], 162 [1.07000000e-05, 1.07000000e-05, 1.57000000e-05, 163 1.07000000e-05, 1.07000000e-05], 164 [-8.15200000e-04, -8.15200000e-04, -1.17060000e-03, 165 -8.15200000e-04, -8.15200000e-04], 166 [-8.46400000e-04, -8.46400000e-04, -1.18460000e-03, 167 -8.46400000e-04, -8.46400000e-04], 168 [9.91200000e-04, 9.91200000e-04, 1.35180000e-03, 169 9.91200000e-04, 9.91200000e-04], 170 [-5.07400000e-04, -5.07400000e-04, -6.74200000e-04, 171 -5.07400000e-04, -5.07400000e-04], 172 [1.08520000e-03, 1.08520000e-03, 1.40450000e-03, 173 1.08520000e-03, 1.08520000e-03], 174 [9.56100000e-04, 9.56100000e-04, 1.20500000e-03, 175 9.56100000e-04, 9.56100000e-04], 176 [1.87500000e-03, 1.87500000e-03, 2.30090000e-03, 177 1.87500000e-03, 1.87500000e-03], 178 [-1.93920000e-03, -1.93920000e-03, -2.31650000e-03, 179 -1.93920000e-03, -1.93920000e-03], 180 [8.16000000e-04, 8.16000000e-04, 9.48700000e-04, 181 8.16000000e-04, 8.16000000e-04], 182 [1.01520000e-03, 1.01520000e-03, 1.14860000e-03, 183 1.01520000e-03, 1.01520000e-03], 184 [1.04150000e-03, 1.04150000e-03, 1.14640000e-03, 185 1.04150000e-03, 1.04150000e-03], 186 [-3.88200000e-04, -3.88200000e-04, -4.15600000e-04, 187 -3.88200000e-04, -3.88200000e-04], 188 [9.95900000e-04, 9.95900000e-04, 1.03690000e-03, 189 9.95900000e-04, 9.95900000e-04], 190 [-6.82800000e-04, -6.82800000e-04, -6.91200000e-04, 191 -6.82800000e-04, -6.82800000e-04], 192 [-8.11400000e-04, -8.11400000e-04, -7.98500000e-04, 193 -8.11400000e-04, -8.11400000e-04], 194 [-1.79050000e-03, -1.79050000e-03, -1.71250000e-03, 195 -1.79050000e-03, -1.79050000e-03], 196 [6.10000000e-04, 6.10000000e-04, 5.66900000e-04, 197 6.10000000e-04, 6.10000000e-04], 198 [2.52600000e-04, 2.52600000e-04, 2.28100000e-04, 199 2.52600000e-04, 2.52600000e-04], 200 [-8.62500000e-04, -8.62500000e-04, -7.56400000e-04, 201 -8.62500000e-04, -8.62500000e-04], 202 [-3.47300000e-04, -3.47300000e-04, -2.95800000e-04, 203 -3.47300000e-04, -3.47300000e-04], 204 [-7.79000000e-05, -7.79000000e-05, -6.44000000e-05, 205 -7.79000000e-05, -7.79000000e-05], 206 [6.72000000e-04, 6.72000000e-04, 5.39400000e-04, 207 6.72000000e-04, 6.72000000e-04], 208 [-3.72100000e-04, -3.72100000e-04, -2.89900000e-04, 209 -3.72100000e-04, -3.72100000e-04], 210 [-1.22900000e-04, -1.22900000e-04, -9.29000000e-05, 211 -1.22900000e-04, -1.22900000e-04], 212 [-1.63470000e-03, -1.63470000e-03, -1.19900000e-03, 213 -1.63470000e-03, -1.63470000e-03], 214 [2.64400000e-04, 2.64400000e-04, 1.88100000e-04, 215 2.64400000e-04, 2.64400000e-04], 216 [1.79230000e-03, 1.79230000e-03, 1.23650000e-03, 217 1.79230000e-03, 1.79230000e-03], 218 [-1.40500000e-04, -1.40500000e-04, -9.40000000e-05, 219 -1.40500000e-04, -1.40500000e-04], 220 [-2.98500000e-04, -2.98500000e-04, -1.93600000e-04, 221 -2.98500000e-04, -2.98500000e-04], 222 [-9.33100000e-04, -9.33100000e-04, -5.86400000e-04, 223 -9.33100000e-04, -9.33100000e-04], 224 [9.11200000e-04, 9.11200000e-04, 5.54900000e-04, 225 9.11200000e-04, 9.11200000e-04], 226 [-1.31840000e-03, -1.31840000e-03, -7.77900000e-04, 227 -1.31840000e-03, -1.31840000e-03], 228 [-1.30200000e-04, -1.30200000e-04, -7.44000000e-05, 229 -1.30200000e-04, -1.30200000e-04], 230 [9.09300000e-04, 9.09300000e-04, 5.03200000e-04, 231 9.09300000e-04, 9.09300000e-04], 232 [-2.39500000e-04, -2.39500000e-04, -1.28300000e-04, 233 -2.39500000e-04, -2.39500000e-04], 234 [7.15300000e-04, 7.15300000e-04, 3.71000000e-04, 235 7.15300000e-04, 7.15300000e-04], 236 [5.45000000e-05, 5.45000000e-05, 2.73000000e-05, 237 5.45000000e-05, 5.45000000e-05], 238 [2.85310000e-03, 2.85310000e-03, 1.38600000e-03, 239 2.85310000e-03, 2.85310000e-03], 240 [4.63400000e-04, 4.63400000e-04, 2.17800000e-04, 241 4.63400000e-04, 4.63400000e-04], 242 [2.80900000e-04, 2.80900000e-04, 1.27700000e-04, 243 2.80900000e-04, 2.80900000e-04], 244 [5.42000000e-05, 5.42000000e-05, 2.38000000e-05, 245 5.42000000e-05, 5.42000000e-05], 246 [-3.62300000e-04, -3.62300000e-04, -1.54000000e-04, 247 -3.62300000e-04, -3.62300000e-04], 248 [-1.11900000e-03, -1.11900000e-03, -4.59800000e-04, 249 -1.11900000e-03, -1.11900000e-03], 250 [1.28900000e-03, 1.28900000e-03, 5.11900000e-04, 251 1.28900000e-03, 1.28900000e-03], 252 [-1.40820000e-03, -1.40820000e-03, -5.40400000e-04, 253 -1.40820000e-03, -1.40820000e-03], 254 [-1.69300000e-04, -1.69300000e-04, -6.28000000e-05, 255 -1.69300000e-04, -1.69300000e-04], 256 [-1.03620000e-03, -1.03620000e-03, -3.71000000e-04, 257 -1.03620000e-03, -1.03620000e-03], 258 [1.49150000e-03, 1.49150000e-03, 5.15800000e-04, 259 1.49150000e-03, 1.49150000e-03], 260 [-7.22000000e-05, -7.22000000e-05, -2.41000000e-05, 261 -7.22000000e-05, -7.22000000e-05], 262 [5.49000000e-04, 5.49000000e-04, 1.76900000e-04, 263 5.49000000e-04, 5.49000000e-04], 264 [-2.12320000e-03, -2.12320000e-03, -6.60400000e-04, 265 -2.12320000e-03, -2.12320000e-03], 266 [7.84000000e-06, 7.84000000e-06, 2.35000000e-06, 267 7.84000000e-06, 7.84000000e-06], 268 [1.15580000e-03, 1.15580000e-03, 3.34700000e-04, 269 1.15580000e-03, 1.15580000e-03], 270 [4.83400000e-04, 4.83400000e-04, 1.35000000e-04, 271 4.83400000e-04, 4.83400000e-04], 272 [-5.26100000e-04, -5.26100000e-04, -1.41700000e-04, 273 -5.26100000e-04, -5.26100000e-04], 274 [-1.75100000e-04, -1.75100000e-04, -4.55000000e-05, 275 -1.75100000e-04, -1.75100000e-04], 276 [-1.84600000e-03, -1.84600000e-03, -4.62100000e-04, 277 -1.84600000e-03, -1.84600000e-03], 278 [2.07200000e-04, 2.07200000e-04, 5.00000000e-05, 279 2.07200000e-04, 2.07200000e-04], 280 [-8.54700000e-04, -8.54700000e-04, -1.98700000e-04, 281 -8.54700000e-04, -8.54700000e-04], 282 [-9.20000000e-05, -9.20000000e-05, -2.06000000e-05, 283 -9.20000000e-05, -9.20000000e-05], 284 [5.35700000e-04, 5.35700000e-04, 1.15600000e-04, 285 5.35700000e-04, 5.35700000e-04], 286 [-7.67300000e-04, -7.67300000e-04, -1.59400000e-04, 287 -7.67300000e-04, -7.67300000e-04], 288 [-1.79710000e-03, -1.79710000e-03, -3.59500000e-04, 289 -1.79710000e-03, -1.79710000e-03], 290 [1.10910000e-03, 1.10910000e-03, 2.13500000e-04, 291 1.10910000e-03, 1.10910000e-03], 292 [-5.53800000e-04, -5.53800000e-04, -1.02600000e-04, 293 -5.53800000e-04, -5.53800000e-04], 294 [7.48000000e-04, 7.48000000e-04, 1.33400000e-04, 295 7.48000000e-04, 7.48000000e-04], 296 [4.23000000e-04, 4.23000000e-04, 7.26000000e-05, 297 4.23000000e-04, 4.23000000e-04], 298 [-3.16400000e-04, -3.16400000e-04, -5.22000000e-05, 299 -3.16400000e-04, -3.16400000e-04], 300 [-6.63200000e-04, -6.63200000e-04, -1.05200000e-04, 301 -6.63200000e-04, -6.63200000e-04], 302 [1.33540000e-03, 1.33540000e-03, 2.03700000e-04, 303 1.33540000e-03, 1.33540000e-03], 304 [-7.81200000e-04, -7.81200000e-04, -1.14600000e-04, 305 -7.81200000e-04, -7.81200000e-04], 306 [1.67880000e-03, 1.67880000e-03, 2.36600000e-04, 307 1.67880000e-03, 1.67880000e-03]]) 308 309 self.null_deviance = 56.691617808182208 310 self.params = np.array([ 311 9.99964386e-01, -1.99896965e-02, -1.00027232e-04]) 312 self.bse = np.array([1.42119293e-04, 1.20276468e-05, 1.87347682e-07]) 313 self.aic_R = -1103.8187213072656 # adds 2 for dof for scale 314 315 self.aic_Stata = -11.05818072104212 # divides by nobs for e(aic) 316 self.deviance = 8.68876986288542e-05 317 self.scale = 8.9574946938163984e-07 # from R but e(phi) in Stata 318 self.llf = 555.9093606536328 319 self.bic_Stata = -446.7014211525822 320 self.df_model = 2 321 self.df_resid = 97 322 self.chi2 = 33207648.86501769 # from Stata not in sm 323 self.fittedvalues = np.array([ 324 2.7181850213327747, 2.664122305869506, 325 2.6106125414084405, 2.5576658143523567, 2.5052916730829535, 326 2.4534991313100165, 2.4022966718815781, 2.3516922510411282, 327 2.3016933031175575, 2.2523067456332542, 2.2035389848154616, 328 2.1553959214958001, 2.107882957382607, 2.0610050016905817, 329 2.0147664781120667, 1.969171332114154, 1.9242230385457144, 330 1.8799246095383746, 1.8362786026854092, 1.7932871294825108, 331 1.7509518640143886, 1.7092740518711942, 1.6682545192788105, 332 1.6278936824271399, 1.5881915569806042, 1.5491477677552221, 333 1.5107615585467538, 1.4730318020945796, 1.4359570101661721, 334 1.3995353437472129, 1.3637646233226499, 1.3286423392342188, 335 1.2941656621002184, 1.2603314532836074, 1.2271362753947765, 336 1.1945764028156565, 1.162647832232141, 1.1313462931621328, 337 1.1006672584668622, 1.0706059548334832, 1.0411573732173065, 338 1.0123162792324054, 0.98407722347970683, 0.95643455180206194, 339 0.92938241545618494, 0.90291478119174029, 0.87702544122826565, 340 0.85170802312101246, 0.82695599950720078, 0.80276269772458597, 341 0.77912130929465073, 0.75602489926313921, 0.73346641539106316, 342 0.71143869718971686, 0.68993448479364294, 0.66894642766589496, 343 0.64846709313034534, 0.62848897472617915, 0.60900450038011367, 344 0.5900060403922629, 0.57148591523195513, 0.55343640314018494, 345 0.5358497475357491, 0.51871816422248385, 0.50203384839536769, 346 0.48578898144361343, 0.46997573754920047, 0.45458629007964013, 347 0.4396128177740814, 0.42504751072218311, 0.41088257613548018, 348 0.39711024391126759, 0.38372277198930843, 0.37071245150195081, 349 0.35807161171849949, 0.34579262478494655, 0.33386791026040569, 350 0.32228993945183393, 0.31105123954884056, 0.30014439756060574, 351 0.28956206405712448, 0.27929695671718968, 0.26934186368570684, 352 0.25968964674310463, 0.25033324428976694, 0.24126567414856051, 353 0.23248003618867552, 0.22396951477412205, 0.21572738104035141, 354 0.20774699500257574, 0.20002180749946474, 0.19254536197598673, 355 0.18531129610924435, 0.17831334328122878, 0.17154533390247831, 356 0.16500119659068577, 0.15867495920834204, 0.15256074976354628, 357 0.14665279717814039, 0.14094543192735109]) 358 359 360class GaussianInverse(object): 361 """ 362 This test uses generated data. Results are from R and Stata. 363 """ 364 def __init__(self): 365 self.resids = np.array([ 366 [-5.15300000e-04, -5.15300000e-04, 367 5.14800000e-04, -5.15300000e-04, -5.15300000e-04], 368 [-2.12500000e-04, -2.12500000e-04, 2.03700000e-04, 369 -2.12500000e-04, -2.12500000e-04], 370 [-1.71400000e-04, -1.71400000e-04, 1.57200000e-04, 371 -1.71400000e-04, -1.71400000e-04], 372 [1.94020000e-03, 1.94020000e-03, -1.69710000e-03, 373 1.94020000e-03, 1.94020000e-03], 374 [-6.81100000e-04, -6.81100000e-04, 5.66900000e-04, 375 -6.81100000e-04, -6.81100000e-04], 376 [1.21370000e-03, 1.21370000e-03, -9.58800000e-04, 377 1.21370000e-03, 1.21370000e-03], 378 [-1.51090000e-03, -1.51090000e-03, 1.13070000e-03, 379 -1.51090000e-03, -1.51090000e-03], 380 [3.21500000e-04, 3.21500000e-04, -2.27400000e-04, 381 3.21500000e-04, 3.21500000e-04], 382 [-3.18500000e-04, -3.18500000e-04, 2.12600000e-04, 383 -3.18500000e-04, -3.18500000e-04], 384 [3.75600000e-04, 3.75600000e-04, -2.36300000e-04, 385 3.75600000e-04, 3.75600000e-04], 386 [4.82300000e-04, 4.82300000e-04, -2.85500000e-04, 387 4.82300000e-04, 4.82300000e-04], 388 [-1.41870000e-03, -1.41870000e-03, 7.89300000e-04, 389 -1.41870000e-03, -1.41870000e-03], 390 [6.75000000e-05, 6.75000000e-05, -3.52000000e-05, 391 6.75000000e-05, 6.75000000e-05], 392 [4.06300000e-04, 4.06300000e-04, -1.99100000e-04, 393 4.06300000e-04, 4.06300000e-04], 394 [-3.61500000e-04, -3.61500000e-04, 1.66000000e-04, 395 -3.61500000e-04, -3.61500000e-04], 396 [-2.97400000e-04, -2.97400000e-04, 1.28000000e-04, 397 -2.97400000e-04, -2.97400000e-04], 398 [-9.32700000e-04, -9.32700000e-04, 3.75800000e-04, 399 -9.32700000e-04, -9.32700000e-04], 400 [1.16270000e-03, 1.16270000e-03, -4.38500000e-04, 401 1.16270000e-03, 1.16270000e-03], 402 [6.77900000e-04, 6.77900000e-04, -2.39200000e-04, 403 6.77900000e-04, 6.77900000e-04], 404 [-1.29330000e-03, -1.29330000e-03, 4.27000000e-04, 405 -1.29330000e-03, -1.29330000e-03], 406 [2.24500000e-04, 2.24500000e-04, -6.94000000e-05, 407 2.24500000e-04, 2.24500000e-04], 408 [1.05510000e-03, 1.05510000e-03, -3.04900000e-04, 409 1.05510000e-03, 1.05510000e-03], 410 [2.50400000e-04, 2.50400000e-04, -6.77000000e-05, 411 2.50400000e-04, 2.50400000e-04], 412 [4.08600000e-04, 4.08600000e-04, -1.03400000e-04, 413 4.08600000e-04, 4.08600000e-04], 414 [-1.67610000e-03, -1.67610000e-03, 3.96800000e-04, 415 -1.67610000e-03, -1.67610000e-03], 416 [7.47600000e-04, 7.47600000e-04, -1.65700000e-04, 417 7.47600000e-04, 7.47600000e-04], 418 [2.08200000e-04, 2.08200000e-04, -4.32000000e-05, 419 2.08200000e-04, 2.08200000e-04], 420 [-8.00800000e-04, -8.00800000e-04, 1.55700000e-04, 421 -8.00800000e-04, -8.00800000e-04], 422 [5.81200000e-04, 5.81200000e-04, -1.05900000e-04, 423 5.81200000e-04, 5.81200000e-04], 424 [1.00980000e-03, 1.00980000e-03, -1.72400000e-04, 425 1.00980000e-03, 1.00980000e-03], 426 [2.77400000e-04, 2.77400000e-04, -4.44000000e-05, 427 2.77400000e-04, 2.77400000e-04], 428 [-5.02800000e-04, -5.02800000e-04, 7.55000000e-05, 429 -5.02800000e-04, -5.02800000e-04], 430 [2.69800000e-04, 2.69800000e-04, -3.80000000e-05, 431 2.69800000e-04, 2.69800000e-04], 432 [2.01300000e-04, 2.01300000e-04, -2.67000000e-05, 433 2.01300000e-04, 2.01300000e-04], 434 [-1.19690000e-03, -1.19690000e-03, 1.48900000e-04, 435 -1.19690000e-03, -1.19690000e-03], 436 [-6.94200000e-04, -6.94200000e-04, 8.12000000e-05, 437 -6.94200000e-04, -6.94200000e-04], 438 [5.65500000e-04, 5.65500000e-04, -6.22000000e-05, 439 5.65500000e-04, 5.65500000e-04], 440 [4.93100000e-04, 4.93100000e-04, -5.10000000e-05, 441 4.93100000e-04, 4.93100000e-04], 442 [3.25000000e-04, 3.25000000e-04, -3.17000000e-05, 443 3.25000000e-04, 3.25000000e-04], 444 [-7.70200000e-04, -7.70200000e-04, 7.07000000e-05, 445 -7.70200000e-04, -7.70200000e-04], 446 [2.58000000e-05, 2.58000000e-05, -2.23000000e-06, 447 2.58000000e-05, 2.58000000e-05], 448 [-1.52800000e-04, -1.52800000e-04, 1.25000000e-05, 449 -1.52800000e-04, -1.52800000e-04], 450 [4.52000000e-05, 4.52000000e-05, -3.48000000e-06, 451 4.52000000e-05, 4.52000000e-05], 452 [-6.83900000e-04, -6.83900000e-04, 4.97000000e-05, 453 -6.83900000e-04, -6.83900000e-04], 454 [-7.77600000e-04, -7.77600000e-04, 5.34000000e-05, 455 -7.77600000e-04, -7.77600000e-04], 456 [1.03170000e-03, 1.03170000e-03, -6.70000000e-05, 457 1.03170000e-03, 1.03170000e-03], 458 [1.20000000e-03, 1.20000000e-03, -7.37000000e-05, 459 1.20000000e-03, 1.20000000e-03], 460 [-7.71600000e-04, -7.71600000e-04, 4.48000000e-05, 461 -7.71600000e-04, -7.71600000e-04], 462 [-3.37000000e-04, -3.37000000e-04, 1.85000000e-05, 463 -3.37000000e-04, -3.37000000e-04], 464 [1.19880000e-03, 1.19880000e-03, -6.25000000e-05, 465 1.19880000e-03, 1.19880000e-03], 466 [-1.54610000e-03, -1.54610000e-03, 7.64000000e-05, 467 -1.54610000e-03, -1.54610000e-03], 468 [9.11600000e-04, 9.11600000e-04, -4.27000000e-05, 469 9.11600000e-04, 9.11600000e-04], 470 [-4.70800000e-04, -4.70800000e-04, 2.09000000e-05, 471 -4.70800000e-04, -4.70800000e-04], 472 [-1.21550000e-03, -1.21550000e-03, 5.13000000e-05, 473 -1.21550000e-03, -1.21550000e-03], 474 [1.09160000e-03, 1.09160000e-03, -4.37000000e-05, 475 1.09160000e-03, 1.09160000e-03], 476 [-2.72000000e-04, -2.72000000e-04, 1.04000000e-05, 477 -2.72000000e-04, -2.72000000e-04], 478 [-7.84500000e-04, -7.84500000e-04, 2.84000000e-05, 479 -7.84500000e-04, -7.84500000e-04], 480 [1.53330000e-03, 1.53330000e-03, -5.28000000e-05, 481 1.53330000e-03, 1.53330000e-03], 482 [-1.84450000e-03, -1.84450000e-03, 6.05000000e-05, 483 -1.84450000e-03, -1.84450000e-03], 484 [1.68550000e-03, 1.68550000e-03, -5.26000000e-05, 485 1.68550000e-03, 1.68550000e-03], 486 [-3.06100000e-04, -3.06100000e-04, 9.10000000e-06, 487 -3.06100000e-04, -3.06100000e-04], 488 [1.00950000e-03, 1.00950000e-03, -2.86000000e-05, 489 1.00950000e-03, 1.00950000e-03], 490 [5.22000000e-04, 5.22000000e-04, -1.41000000e-05, 491 5.22000000e-04, 5.22000000e-04], 492 [-2.18000000e-05, -2.18000000e-05, 5.62000000e-07, 493 -2.18000000e-05, -2.18000000e-05], 494 [-7.80600000e-04, -7.80600000e-04, 1.92000000e-05, 495 -7.80600000e-04, -7.80600000e-04], 496 [6.81400000e-04, 6.81400000e-04, -1.60000000e-05, 497 6.81400000e-04, 6.81400000e-04], 498 [-1.43800000e-04, -1.43800000e-04, 3.23000000e-06, 499 -1.43800000e-04, -1.43800000e-04], 500 [7.76000000e-04, 7.76000000e-04, -1.66000000e-05, 501 7.76000000e-04, 7.76000000e-04], 502 [2.54900000e-04, 2.54900000e-04, -5.22000000e-06, 503 2.54900000e-04, 2.54900000e-04], 504 [5.77500000e-04, 5.77500000e-04, -1.13000000e-05, 505 5.77500000e-04, 5.77500000e-04], 506 [7.58100000e-04, 7.58100000e-04, -1.42000000e-05, 507 7.58100000e-04, 7.58100000e-04], 508 [-8.31000000e-04, -8.31000000e-04, 1.49000000e-05, 509 -8.31000000e-04, -8.31000000e-04], 510 [-2.10340000e-03, -2.10340000e-03, 3.62000000e-05, 511 -2.10340000e-03, -2.10340000e-03], 512 [-8.89900000e-04, -8.89900000e-04, 1.47000000e-05, 513 -8.89900000e-04, -8.89900000e-04], 514 [1.08570000e-03, 1.08570000e-03, -1.71000000e-05, 515 1.08570000e-03, 1.08570000e-03], 516 [-1.88600000e-04, -1.88600000e-04, 2.86000000e-06, 517 -1.88600000e-04, -1.88600000e-04], 518 [9.10000000e-05, 9.10000000e-05, -1.32000000e-06, 519 9.10000000e-05, 9.10000000e-05], 520 [1.07700000e-03, 1.07700000e-03, -1.50000000e-05, 521 1.07700000e-03, 1.07700000e-03], 522 [9.04100000e-04, 9.04100000e-04, -1.21000000e-05, 523 9.04100000e-04, 9.04100000e-04], 524 [-2.20000000e-04, -2.20000000e-04, 2.83000000e-06, 525 -2.20000000e-04, -2.20000000e-04], 526 [-1.64030000e-03, -1.64030000e-03, 2.02000000e-05, 527 -1.64030000e-03, -1.64030000e-03], 528 [2.20600000e-04, 2.20600000e-04, -2.62000000e-06, 529 2.20600000e-04, 2.20600000e-04], 530 [-2.78300000e-04, -2.78300000e-04, 3.17000000e-06, 531 -2.78300000e-04, -2.78300000e-04], 532 [-4.93000000e-04, -4.93000000e-04, 5.40000000e-06, 533 -4.93000000e-04, -4.93000000e-04], 534 [-1.85000000e-04, -1.85000000e-04, 1.95000000e-06, 535 -1.85000000e-04, -1.85000000e-04], 536 [-7.64000000e-04, -7.64000000e-04, 7.75000000e-06, 537 -7.64000000e-04, -7.64000000e-04], 538 [7.79600000e-04, 7.79600000e-04, -7.61000000e-06, 539 7.79600000e-04, 7.79600000e-04], 540 [2.88400000e-04, 2.88400000e-04, -2.71000000e-06, 541 2.88400000e-04, 2.88400000e-04], 542 [1.09370000e-03, 1.09370000e-03, -9.91000000e-06, 543 1.09370000e-03, 1.09370000e-03], 544 [3.07000000e-04, 3.07000000e-04, -2.68000000e-06, 545 3.07000000e-04, 3.07000000e-04], 546 [-8.76000000e-04, -8.76000000e-04, 7.37000000e-06, 547 -8.76000000e-04, -8.76000000e-04], 548 [-1.85300000e-04, -1.85300000e-04, 1.50000000e-06, 549 -1.85300000e-04, -1.85300000e-04], 550 [3.24700000e-04, 3.24700000e-04, -2.54000000e-06, 551 3.24700000e-04, 3.24700000e-04], 552 [4.59600000e-04, 4.59600000e-04, -3.47000000e-06, 553 4.59600000e-04, 4.59600000e-04], 554 [-2.73300000e-04, -2.73300000e-04, 1.99000000e-06, 555 -2.73300000e-04, -2.73300000e-04], 556 [1.32180000e-03, 1.32180000e-03, -9.29000000e-06, 557 1.32180000e-03, 1.32180000e-03], 558 [-1.32620000e-03, -1.32620000e-03, 9.00000000e-06, 559 -1.32620000e-03, -1.32620000e-03], 560 [9.62000000e-05, 9.62000000e-05, -6.31000000e-07, 561 9.62000000e-05, 9.62000000e-05], 562 [-6.04400000e-04, -6.04400000e-04, 3.83000000e-06, 563 -6.04400000e-04, -6.04400000e-04], 564 [-6.66300000e-04, -6.66300000e-04, 4.08000000e-06, 565 -6.66300000e-04, -6.66300000e-04]]) 566 self.null_deviance = 6.8088354977561 # from R, Rpy bug 567 self.params = np.array([1.00045997, 0.01991666, 0.00100126]) 568 self.bse = np.array([4.55214070e-04, 7.00529313e-05, 1.84478509e-06]) 569 self.aic_R = -1123.1528237643774 570 self.aic_Stata = -11.25152876811373 571 self.deviance = 7.1612915365488368e-05 572 self.scale = 7.3827747608449547e-07 573 self.llf = 565.57641188218872 574 self.bic_Stata = -446.7014364279675 575 self.df_model = 2 576 self.df_resid = 97 577 self.chi2 = 2704006.698904491 578 self.fittedvalues = np.array([ 579 0.99954024, 0.97906956, 0.95758077, 0.93526008, 0.91228657, 580 0.88882978, 0.8650479, 0.84108646, 0.81707757, 0.79313958, 581 0.76937709, 0.74588129, 0.72273051, 0.69999099, 0.67771773, 582 0.65595543, 0.63473944, 0.61409675, 0.59404691, 0.57460297, 583 0.55577231, 0.53755742, 0.51995663, 0.50296478, 0.48657379, 584 0.47077316, 0.4555505, 0.44089187, 0.42678213, 0.41320529, 585 0.40014475, 0.38758348, 0.37550428, 0.36388987, 0.35272306, 586 0.34198684, 0.33166446, 0.32173953, 0.31219604, 0.30301842, 587 0.29419156, 0.28570085, 0.27753216, 0.26967189, 0.26210695, 588 0.25482476, 0.24781324, 0.2410608, 0.23455636, 0.22828931, 589 0.22224947, 0.21642715, 0.21081306, 0.20539835, 0.20017455, 590 0.19513359, 0.19026777, 0.18556972, 0.18103243, 0.17664922, 591 0.1724137, 0.16831977, 0.16436164, 0.16053377, 0.15683086, 592 0.15324789, 0.14978003, 0.1464227, 0.14317153, 0.14002232, 593 0.13697109, 0.13401403, 0.1311475, 0.12836802, 0.12567228, 594 0.1230571, 0.12051944, 0.11805642, 0.11566526, 0.1133433, 595 0.11108802, 0.10889699, 0.10676788, 0.10469847, 0.10268664, 596 0.10073034, 0.09882763, 0.09697663, 0.09517555, 0.09342267, 597 0.09171634, 0.09005498, 0.08843707, 0.08686116, 0.08532585, 598 0.08382979, 0.0823717, 0.08095035, 0.07956453, 0.07821311]) 599 600 601class Star98(object): 602 """ 603 Star98 class used with TestGlmBinomial 604 """ 605 def __init__(self): 606 self.params = ( 607 -0.0168150366, 0.0099254766, -0.0187242148, 608 -0.0142385609, 0.2544871730, 0.2406936644, 0.0804086739, 609 -1.9521605027, -0.3340864748, -0.1690221685, 0.0049167021, 610 -0.0035799644, -0.0140765648, -0.0040049918, -0.0039063958, 611 0.0917143006, 0.0489898381, 0.0080407389, 0.0002220095, 612 -0.0022492486, 2.9588779262) 613 self.bse = ( 614 4.339467e-04, 6.013714e-04, 7.435499e-04, 4.338655e-04, 615 2.994576e-02, 5.713824e-02, 1.392359e-02, 3.168109e-01, 616 6.126411e-02, 3.270139e-02, 1.253877e-03, 2.254633e-04, 617 1.904573e-03, 4.739838e-04, 9.623650e-04, 1.450923e-02, 618 7.451666e-03, 1.499497e-03, 2.988794e-05, 3.489838e-04, 619 1.546712e+00) 620 self.null_deviance = 34345.3688931 621 self.df_null = 302 622 self.deviance = 4078.76541772 623 self.df_resid = 282 624 self.df_model = 20 625 self.aic_R = 6039.22511799 626 self.aic_Stata = 19.93143846737438 627 self.bic_Stata = 2467.493504191302 628 self.llf = -2998.61255899391 # from R 629 self.llf_Stata = -2998.612927807218 630 self.scale = 1. 631 self.pearson_chi2 = 4051.921614 632 self.prsquared = 0.8346 633 self.prsquared_cox_snell = 1.0000 634 self.resids = glm_test_resids.star98_resids 635 self.fittedvalues = np.array([ 636 0.5833118, 0.75144661, 0.50058272, 0.68534524, 0.32251021, 637 0.68693601, 0.33299827, 0.65624766, 0.49851481, 0.506736, 638 0.23954874, 0.86631452, 0.46432936, 0.44171873, 0.66797935, 639 0.73988491, 0.51966014, 0.42442446, 0.5649369, 0.59251634, 640 0.34798337, 0.56415024, 0.49974355, 0.3565539, 0.20752309, 641 0.18269097, 0.44932642, 0.48025128, 0.59965277, 0.58848671, 642 0.36264203, 0.33333196, 0.74253352, 0.5081886, 0.53421878, 643 0.56291445, 0.60205239, 0.29174423, 0.2954348, 0.32220414, 644 0.47977903, 0.23687535, 0.11776464, 0.1557423, 0.27854799, 645 0.22699533, 0.1819439, 0.32554433, 0.22681989, 0.15785389, 646 0.15268609, 0.61094772, 0.20743222, 0.51649059, 0.46502006, 647 0.41031788, 0.59523288, 0.65733285, 0.27835336, 0.2371213, 648 0.25137045, 0.23953942, 0.27854519, 0.39652413, 0.27023163, 649 0.61411863, 0.2212025, 0.42005842, 0.55940397, 0.35413774, 650 0.45724563, 0.57399437, 0.2168918, 0.58308738, 0.17181104, 651 0.49873249, 0.22832683, 0.14846056, 0.5028073, 0.24513863, 652 0.48202096, 0.52823155, 0.5086262, 0.46295993, 0.57869402, 653 0.78363217, 0.21144435, 0.2298366, 0.17954825, 0.32232586, 654 0.8343015, 0.56217006, 0.47367315, 0.52535649, 0.60350746, 655 0.43210701, 0.44712008, 0.35858239, 0.2521347, 0.19787004, 656 0.63256553, 0.51386532, 0.64997027, 0.13402072, 0.81756174, 657 0.74543642, 0.30825852, 0.23988707, 0.17273125, 0.27880599, 658 0.17395893, 0.32052828, 0.80467697, 0.18726218, 0.23842081, 659 0.19020381, 0.85835388, 0.58703615, 0.72415106, 0.64433695, 660 0.68766653, 0.32923663, 0.16352185, 0.38868816, 0.44980444, 661 0.74810044, 0.42973792, 0.53762581, 0.72714996, 0.61229484, 662 0.30267667, 0.24713253, 0.65086008, 0.48957265, 0.54955545, 663 0.5697156, 0.36406211, 0.48906545, 0.45919413, 0.4930565, 664 0.39785555, 0.5078719, 0.30159626, 0.28524393, 0.34687707, 665 0.22522042, 0.52947159, 0.29277287, 0.8585002, 0.60800389, 666 0.75830521, 0.35648175, 0.69508796, 0.45518355, 0.21567675, 667 0.39682985, 0.49042948, 0.47615798, 0.60588234, 0.62910299, 668 0.46005639, 0.71755165, 0.48852156, 0.47940661, 0.60128813, 669 0.16589699, 0.68512861, 0.46305199, 0.68832227, 0.7006721, 670 0.56564937, 0.51753941, 0.54261733, 0.56072214, 0.34545715, 671 0.30226104, 0.3572956, 0.40996287, 0.33517519, 0.36248407, 672 0.33937041, 0.34140691, 0.2627528, 0.29955161, 0.38581683, 673 0.24840026, 0.15414272, 0.40415991, 0.53936252, 0.52111887, 674 0.28060168, 0.45600958, 0.51110589, 0.43757523, 0.46891953, 675 0.39425249, 0.5834369, 0.55817308, 0.32051259, 0.43567448, 676 0.34134195, 0.43016545, 0.4885413, 0.28478325, 0.2650776, 677 0.46784606, 0.46265983, 0.42655938, 0.18972234, 0.60448491, 678 0.211896, 0.37886032, 0.50727577, 0.39782309, 0.50427121, 679 0.35882898, 0.39596807, 0.49160806, 0.35618002, 0.6819922, 680 0.36871093, 0.43079679, 0.67985516, 0.41270595, 0.68952767, 681 0.52587734, 0.32042126, 0.39120123, 0.56870985, 0.32962349, 682 0.32168989, 0.54076251, 0.4592907, 0.48480182, 0.4408386, 683 0.431178, 0.47078232, 0.55911605, 0.30331618, 0.50310393, 684 0.65036038, 0.45078895, 0.62354291, 0.56435463, 0.50034281, 685 0.52693538, 0.57217285, 0.49221472, 0.40707122, 0.44226533, 686 0.3475959, 0.54746396, 0.86385832, 0.48402233, 0.54313657, 687 0.61586824, 0.27097185, 0.69717808, 0.52156974, 0.50401189, 688 0.56724181, 0.6577178, 0.42732047, 0.44808396, 0.65435634, 689 0.54766225, 0.38160648, 0.49890847, 0.50879037, 0.5875452, 690 0.45101593, 0.5709704, 0.3175516, 0.39813159, 0.28305688, 691 0.40521062, 0.30120578, 0.26400428, 0.44205496, 0.40545798, 692 0.39366599, 0.55288196, 0.14104184, 0.17550155, 0.1949095, 693 0.40255144, 0.21016822, 0.09712017, 0.63151487, 0.25885514, 694 0.57323748, 0.61836898, 0.43268601, 0.67008878, 0.75801989, 695 0.50353406, 0.64222315, 0.29925757, 0.32592036, 0.39634977, 696 0.39582747, 0.41037006, 0.34174944]) 697 698 699class Lbw(object): 700 ''' 701 The LBW data can be found here 702 703 https://www.stata-press.com/data/r9/rmain.html 704 ''' 705 def __init__(self): 706 # data set up for data not in datasets 707 filename = os.path.join(os.path.dirname(os.path.abspath(__file__)), 708 "stata_lbw_glm.csv") 709 710 data = pd.read_csv(filename) 711 dummies = pd.get_dummies(data.race, prefix="race", drop_first=False) 712 data = pd.concat([data, dummies], axis=1) 713 self.endog = data.low 714 design = data[["age", "lwt", "race_black", "race_other", "smoke", 715 "ptl", "ht", "ui"]] 716 self.exog = add_constant(design, prepend=False) 717 # Results for Canonical Logit Link 718 self.params = ( 719 -.02710031, -.01515082, 1.26264728, 720 .86207916, .92334482, .54183656, 1.83251780, 721 .75851348, .46122388) 722 self.bse = ( 723 0.036449917, 0.006925765, 0.526405169, 724 0.439146744, 0.400820976, 0.346246857, 0.691623875, 725 0.459373871, 1.204574885) 726 self.aic_R = 219.447991133 727 self.aic_Stata = 1.161100482182551 728 self.deviance = 201.4479911325021 729 self.scale = 1 730 self.llf = -100.7239955662511 731 self.chi2 = 25.65329337867037 # from Stata not used by sm 732 self.null_deviance = 234.671996193219 733 self.bic_Stata = -742.0664715782335 734 self.df_resid = 180 735 self.df_model = 8 736 self.df_null = 188 737 self.pearson_chi2 = 182.023342493558 738 self.resids = glm_test_resids.lbw_resids 739 self.fittedvalues = np.array([ 740 0.31217507, 0.12793027, 0.32119762, 0.48442686, 0.50853393, 741 0.24517662, 0.12755193, 0.33226988, 0.22013309, 0.26268069, 742 0.34729955, 0.18782188, 0.75404181, 0.54723527, 0.35016393, 743 0.35016393, 0.45824406, 0.25336683, 0.43087357, 0.23284101, 744 0.20146616, 0.24315597, 0.02725586, 0.22207692, 0.39800383, 745 0.05584178, 0.28403447, 0.06931188, 0.35371946, 0.3896279, 746 0.3896279, 0.47812002, 0.60043853, 0.07144772, 0.29995988, 747 0.17910031, 0.22773411, 0.22691015, 0.06221253, 0.2384528, 748 0.32633864, 0.05131047, 0.2954536, 0.07364416, 0.57241299, 749 0.57241299, 0.08272435, 0.23298882, 0.12658158, 0.58967487, 750 0.46989562, 0.22455631, 0.2348285, 0.29571887, 0.28212464, 751 0.31499013, 0.68340511, 0.14090647, 0.31448425, 0.28082972, 752 0.28082972, 0.24918728, 0.27018297, 0.08175784, 0.64808999, 753 0.38252574, 0.25550797, 0.09113411, 0.40736693, 0.32644055, 754 0.54367425, 0.29606968, 0.47028421, 0.39972155, 0.25079125, 755 0.09678472, 0.08807264, 0.27467837, 0.5675742, 0.045619, 756 0.10719293, 0.04826292, 0.23934092, 0.24179618, 0.23802197, 757 0.49196179, 0.31379451, 0.10605469, 0.04047396, 0.11620849, 758 0.09937016, 0.21822964, 0.29770265, 0.83912829, 0.25079125, 759 0.08548557, 0.06550308, 0.2046457, 0.2046457, 0.08110349, 760 0.13519643, 0.47862055, 0.38891913, 0.1383964, 0.26176764, 761 0.31594589, 0.11418612, 0.06324112, 0.28468594, 0.21663702, 762 0.03827107, 0.27237604, 0.20246694, 0.19042999, 0.15019447, 763 0.18759474, 0.12308435, 0.19700616, 0.11564002, 0.36595033, 764 0.07765727, 0.14119063, 0.13584627, 0.11012759, 0.10102472, 765 0.10002166, 0.07439288, 0.27919958, 0.12491598, 0.06774594, 766 0.72513764, 0.17714986, 0.67373352, 0.80679436, 0.52908941, 767 0.15695938, 0.49722003, 0.41970014, 0.62375224, 0.53695622, 768 0.25474238, 0.79135707, 0.2503871, 0.25352337, 0.33474211, 769 0.19308929, 0.24658944, 0.25495092, 0.30867144, 0.41240259, 770 0.59412526, 0.16811226, 0.48282791, 0.36566756, 0.09279325, 771 0.75337353, 0.57128885, 0.52974123, 0.44548504, 0.77748843, 772 0.3224082, 0.40054277, 0.29522468, 0.19673553, 0.73781774, 773 0.57680312, 0.44545573, 0.30242355, 0.38720223, 0.16632904, 774 0.30804092, 0.56385194, 0.60012179, 0.48324821, 0.24636345, 775 0.26153216, 0.2348285, 0.29023669, 0.41011454, 0.36472083, 776 0.65922069, 0.30476903, 0.09986775, 0.70658332, 0.30713075, 777 0.36096386, 0.54962701, 0.71996086, 0.6633756]) 778 779 780class Scotvote(object): 781 """ 782 Scotvot class is used with TestGlmGamma. 783 """ 784 def __init__(self): 785 self.params = ( 786 4.961768e-05, 2.034423e-03, -7.181429e-05, 1.118520e-04, 787 -1.467515e-07, -5.186831e-04, -2.42717498e-06, -1.776527e-02) 788 self.bse = ( 789 1.621577e-05, 5.320802e-04, 2.711664e-05, 4.057691e-05, 790 1.236569e-07, 2.402534e-04, 7.460253e-07, 1.147922e-02) 791 self.null_deviance = 0.536072 792 self.df_null = 31 793 self.deviance = 0.087388516417 794 self.df_resid = 24 795 self.df_model = 7 796 self.aic_R = 182.947045954721 797 self.aic_Stata = 10.72212 798 self.bic_Stata = -83.09027 799 self.llf = -163.5539382 # from Stata, same as ours with scale = 1 800 # self.llf = -82.47352 # Very close to ours as is 801 self.scale = 0.003584283 802 self.pearson_chi2 = .0860228056 803 self.prsquared = 0.429 804 self.prsquared_cox_snell = 0.97971 805 self.resids = glm_test_resids.scotvote_resids 806 self.fittedvalues = np.array([ 807 57.80431482, 53.2733447, 50.56347993, 58.33003783, 808 70.46562169, 56.88801284, 66.81878401, 66.03410393, 809 57.92937473, 63.23216907, 53.9914785, 61.28993391, 810 64.81036393, 63.47546816, 60.69696114, 74.83508176, 811 56.56991106, 72.01804172, 64.35676519, 52.02445881, 812 64.24933079, 71.15070332, 45.73479688, 54.93318588, 813 66.98031261, 52.02479973, 56.18413736, 58.12267471, 814 67.37947398, 60.49162862, 73.82609217, 69.61515621]) 815 816 817class Cancer(object): 818 ''' 819 The Cancer data can be found here 820 821 https://www.stata-press.com/data/r10/rmain.html 822 ''' 823 def __init__(self): 824 filename = os.path.join(os.path.dirname(os.path.abspath(__file__)), 825 "stata_cancer_glm.csv") 826 data = np.recfromcsv(open(filename, 'rb')) 827 self.endog = data.studytime 828 dummies = pd.get_dummies(pd.Series(data.drug, dtype="category"), 829 drop_first=True) 830 design = np.column_stack((data.age, dummies)).astype(float) 831 self.exog = add_constant(design, prepend=False) 832 833 834class CancerLog(Cancer): 835 """ 836 CancerLog is used TestGlmGammaLog 837 """ 838 def __init__(self): 839 super(CancerLog, self).__init__() 840 841 self.resids = np.array([ 842 [-8.52598100e-01, -1.45739100e+00, -3.92408100e+01, 843 -1.41526900e+00, -5.78417200e+00], 844 [-8.23683800e-01, -1.35040200e+00, -2.64957500e+01, 845 -1.31777000e+00, -4.67162900e+00], 846 [-7.30450400e-01, -1.07754600e+00, -4.02136400e+01, 847 -1.06208800e+00, -5.41978500e+00], 848 [-7.04471600e-01, -1.01441500e+00, -7.25951500e+01, 849 -1.00172900e+00, -7.15130900e+00], 850 [-5.28668000e-01, -6.68617300e-01, -3.80758100e+01, 851 -6.65304600e-01, -4.48658700e+00], 852 [-2.28658500e-01, -2.48859700e-01, -6.14913600e+00, 853 -2.48707200e-01, -1.18577100e+00], 854 [-1.93939400e-01, -2.08119900e-01, -7.46226500e+00, 855 -2.08031700e-01, -1.20300800e+00], 856 [-3.55635700e-01, -4.09525000e-01, -2.14132500e+01, 857 -4.08815100e-01, -2.75958600e+00], 858 [-5.73360000e-02, -5.84700000e-02, -4.12946200e+00, 859 -5.84681000e-02, -4.86586900e-01], 860 [3.09828000e-02, 3.06685000e-02, 1.86551100e+00, 861 3.06682000e-02, 2.40413800e-01], 862 [-2.11924300e-01, -2.29071300e-01, -2.18386100e+01, 863 -2.28953000e-01, -2.15130900e+00], 864 [-3.10989000e-01, -3.50739300e-01, -4.19249500e+01, 865 -3.50300400e-01, -3.61084500e+00], 866 [-9.22250000e-03, -9.25100000e-03, -1.13679700e+00, 867 -9.25100000e-03, -1.02392100e-01], 868 [2.39402500e-01, 2.22589700e-01, 1.88577300e+01, 869 2.22493500e-01, 2.12475600e+00], 870 [3.35166000e-02, 3.31493000e-02, 4.51842400e+00, 871 3.31489000e-02, 3.89155400e-01], 872 [8.49829400e-01, 6.85180200e-01, 3.57627500e+01, 873 6.82689900e-01, 5.51291500e+00], 874 [4.12934200e-01, 3.66785200e-01, 4.65392600e+01, 875 3.66370400e-01, 4.38379500e+00], 876 [4.64148400e-01, 4.07123200e-01, 6.25726500e+01, 877 4.06561900e-01, 5.38915500e+00], 878 [1.71104600e+00, 1.19474800e+00, 1.12676500e+02, 879 1.18311900e+00, 1.38850500e+01], 880 [1.26571800e+00, 9.46389000e-01, 1.30431000e+02, 881 9.40244600e-01, 1.28486900e+01], 882 [-3.48532600e-01, -3.99988300e-01, -2.95638100e+01, 883 -3.99328600e-01, -3.20997700e+00], 884 [-4.04340300e-01, -4.76960100e-01, -4.10254300e+01, 885 -4.75818000e-01, -4.07286500e+00], 886 [-4.92057900e-01, -6.08818300e-01, -9.34509600e+01, 887 -6.06357200e-01, -6.78109700e+00], 888 [-4.02876400e-01, -4.74878400e-01, -9.15226200e+01, 889 -4.73751900e-01, -6.07225700e+00], 890 [-5.15056700e-01, -6.46013300e-01, -2.19014600e+02, 891 -6.43043500e-01, -1.06209700e+01], 892 [-8.70423000e-02, -8.97043000e-02, -1.26361400e+01, 893 -8.96975000e-02, -1.04875100e+00], 894 [1.28362300e-01, 1.23247800e-01, 1.70383300e+01, 895 1.23231000e-01, 1.47887800e+00], 896 [-2.39271900e-01, -2.61562100e-01, -9.30283300e+01, 897 -2.61384400e-01, -4.71795100e+00], 898 [7.37246500e-01, 6.08186000e-01, 6.25359600e+01, 899 6.06409700e-01, 6.79002300e+00], 900 [-3.64110000e-02, -3.68626000e-02, -1.41565300e+01, 901 -3.68621000e-02, -7.17951200e-01], 902 [2.68833000e-01, 2.47933100e-01, 6.67934100e+01, 903 2.47801000e-01, 4.23748400e+00], 904 [5.96389600e-01, 5.07237700e-01, 1.13265500e+02, 905 5.06180100e-01, 8.21890300e+00], 906 [1.98218000e-02, 1.96923000e-02, 1.00820900e+01, 907 1.96923000e-02, 4.47040700e-01], 908 [7.74936000e-01, 6.34305300e-01, 2.51883900e+02, 909 6.32303700e-01, 1.39711800e+01], 910 [-7.63925100e-01, -1.16591700e+00, -4.93461700e+02, 911 -1.14588000e+00, -1.94156600e+01], 912 [-6.23771700e-01, -8.41174800e-01, -4.40679600e+02, 913 -8.34266300e-01, -1.65796100e+01], 914 [-1.63272900e-01, -1.73115100e-01, -6.73975900e+01, 915 -1.73064800e-01, -3.31725800e+00], 916 [-4.28562500e-01, -5.11932900e-01, -4.73787800e+02, 917 -5.10507400e-01, -1.42494800e+01], 918 [8.00693000e-02, 7.80269000e-02, 3.95353400e+01, 919 7.80226000e-02, 1.77920500e+00], 920 [-2.13674400e-01, -2.31127400e-01, -2.15987000e+02, 921 -2.31005700e-01, -6.79344600e+00], 922 [-1.63544000e-02, -1.64444000e-02, -1.05642100e+01, 923 -1.64444000e-02, -4.15657600e-01], 924 [2.04900500e-01, 1.92372100e-01, 1.10651300e+02, 925 1.92309400e-01, 4.76156600e+00], 926 [-1.94758900e-01, -2.09067700e-01, -2.35484100e+02, 927 -2.08978200e-01, -6.77219400e+00], 928 [3.16727400e-01, 2.88367800e-01, 1.87065600e+02, 929 2.88162100e-01, 7.69732400e+00], 930 [6.24234900e-01, 5.27632500e-01, 2.57678500e+02, 931 5.26448400e-01, 1.26827400e+01], 932 [8.30241100e-01, 6.72002100e-01, 2.86513700e+02, 933 6.69644800e-01, 1.54232100e+01], 934 [6.55140000e-03, 6.53710000e-03, 7.92130700e+00, 935 6.53710000e-03, 2.27805800e-01], 936 [3.41595200e-01, 3.08985000e-01, 2.88667600e+02, 937 3.08733300e-01, 9.93012900e+00]]) 938 self.null_deviance = 27.92207137420696 # From R (bug in rpy) 939 self.params = np.array([ 940 -0.04477778, 0.57437126, 1.05210726, 4.64604002]) 941 self.bse = np.array([0.0147328, 0.19694727, 0.19772507, 0.83534671]) 942 943 self.aic_R = 331.89022395372069 944 945 self.aic_Stata = 7.403608467857651 946 self.deviance = 16.174635536991005 947 self.scale = 0.31805268736385695 948 949 # self.llf = -160.94511197686035 # From R 950 self.llf = -173.6866032285836 # from Staa 951 self.bic_Stata = -154.1582089453923 # from Stata 952 self.df_model = 3 953 self.df_resid = 44 954 self.chi2 = 36.77821448266359 # from Stata not in sm 955 956 self.fittedvalues = np.array([ 957 6.78419193, 5.67167253, 7.41979002, 10.15123371, 958 8.48656317, 5.18582263, 6.20304079, 7.75958258, 959 8.48656317, 7.75958258, 10.15123371, 11.61071755, 960 11.10228357, 8.87520908, 11.61071755, 6.48711178, 961 10.61611394, 11.61071755, 8.11493609, 10.15123371, 962 9.21009116, 10.07296716, 13.78112366, 15.07225103, 963 20.62079147, 12.04881666, 11.5211983, 19.71780584, 964 9.21009116, 19.71780584, 15.76249142, 13.78112366, 965 22.55271436, 18.02872842, 25.41575239, 26.579678, 966 20.31745227, 33.24937131, 22.22095589, 31.79337946, 967 25.41575239, 23.23857437, 34.77204095, 24.30279515, 968 20.31745227, 18.57700761, 34.77204095, 29.06987768]) 969 970 971class CancerIdentity(Cancer): 972 """ 973 CancerIdentity is used with TestGlmGammaIdentity 974 """ 975 def __init__(self): 976 super(CancerIdentity, self).__init__() 977 978 self.resids = np.array([ 979 [-8.52598100e-01, -1.45739100e+00, -3.92408100e+01, 980 -1.41526900e+00, -5.78417200e+00], 981 [-8.23683800e-01, -1.35040200e+00, -2.64957500e+01, 982 -1.31777000e+00, -4.67162900e+00], 983 [-7.30450400e-01, -1.07754600e+00, -4.02136400e+01, 984 -1.06208800e+00, -5.41978500e+00], 985 [-7.04471600e-01, -1.01441500e+00, -7.25951500e+01, 986 -1.00172900e+00, -7.15130900e+00], 987 [-5.28668000e-01, -6.68617300e-01, -3.80758100e+01, 988 -6.65304600e-01, -4.48658700e+00], 989 [-2.28658500e-01, -2.48859700e-01, -6.14913600e+00, 990 -2.48707200e-01, -1.18577100e+00], 991 [-1.93939400e-01, -2.08119900e-01, -7.46226500e+00, 992 -2.08031700e-01, -1.20300800e+00], 993 [-3.55635700e-01, -4.09525000e-01, -2.14132500e+01, 994 -4.08815100e-01, -2.75958600e+00], 995 [-5.73360000e-02, -5.84700000e-02, -4.12946200e+00, 996 -5.84681000e-02, -4.86586900e-01], 997 [3.09828000e-02, 3.06685000e-02, 1.86551100e+00, 998 3.06682000e-02, 2.40413800e-01], 999 [-2.11924300e-01, -2.29071300e-01, -2.18386100e+01, 1000 -2.28953000e-01, -2.15130900e+00], 1001 [-3.10989000e-01, -3.50739300e-01, -4.19249500e+01, 1002 -3.50300400e-01, -3.61084500e+00], 1003 [-9.22250000e-03, -9.25100000e-03, -1.13679700e+00, 1004 -9.25100000e-03, -1.02392100e-01], 1005 [2.39402500e-01, 2.22589700e-01, 1.88577300e+01, 1006 2.22493500e-01, 2.12475600e+00], 1007 [3.35166000e-02, 3.31493000e-02, 4.51842400e+00, 1008 3.31489000e-02, 3.89155400e-01], 1009 [8.49829400e-01, 6.85180200e-01, 3.57627500e+01, 1010 6.82689900e-01, 5.51291500e+00], 1011 [4.12934200e-01, 3.66785200e-01, 4.65392600e+01, 1012 3.66370400e-01, 4.38379500e+00], 1013 [4.64148400e-01, 4.07123200e-01, 6.25726500e+01, 1014 4.06561900e-01, 5.38915500e+00], 1015 [1.71104600e+00, 1.19474800e+00, 1.12676500e+02, 1016 1.18311900e+00, 1.38850500e+01], 1017 [1.26571800e+00, 9.46389000e-01, 1.30431000e+02, 1018 9.40244600e-01, 1.28486900e+01], 1019 [-3.48532600e-01, -3.99988300e-01, -2.95638100e+01, 1020 -3.99328600e-01, -3.20997700e+00], 1021 [-4.04340300e-01, -4.76960100e-01, -4.10254300e+01, 1022 -4.75818000e-01, -4.07286500e+00], 1023 [-4.92057900e-01, -6.08818300e-01, -9.34509600e+01, 1024 -6.06357200e-01, -6.78109700e+00], 1025 [-4.02876400e-01, -4.74878400e-01, -9.15226200e+01, 1026 -4.73751900e-01, -6.07225700e+00], 1027 [-5.15056700e-01, -6.46013300e-01, -2.19014600e+02, 1028 -6.43043500e-01, -1.06209700e+01], 1029 [-8.70423000e-02, -8.97043000e-02, -1.26361400e+01, 1030 -8.96975000e-02, -1.04875100e+00], 1031 [1.28362300e-01, 1.23247800e-01, 1.70383300e+01, 1032 1.23231000e-01, 1.47887800e+00], 1033 [-2.39271900e-01, -2.61562100e-01, -9.30283300e+01, 1034 -2.61384400e-01, -4.71795100e+00], 1035 [7.37246500e-01, 6.08186000e-01, 6.25359600e+01, 1036 6.06409700e-01, 6.79002300e+00], 1037 [-3.64110000e-02, -3.68626000e-02, -1.41565300e+01, 1038 -3.68621000e-02, -7.17951200e-01], 1039 [2.68833000e-01, 2.47933100e-01, 6.67934100e+01, 1040 2.47801000e-01, 4.23748400e+00], 1041 [5.96389600e-01, 5.07237700e-01, 1.13265500e+02, 1042 5.06180100e-01, 8.21890300e+00], 1043 [1.98218000e-02, 1.96923000e-02, 1.00820900e+01, 1044 1.96923000e-02, 4.47040700e-01], 1045 [7.74936000e-01, 6.34305300e-01, 2.51883900e+02, 1046 6.32303700e-01, 1.39711800e+01], 1047 [-7.63925100e-01, -1.16591700e+00, -4.93461700e+02, 1048 -1.14588000e+00, -1.94156600e+01], 1049 [-6.23771700e-01, -8.41174800e-01, -4.40679600e+02, 1050 -8.34266300e-01, -1.65796100e+01], 1051 [-1.63272900e-01, -1.73115100e-01, -6.73975900e+01, 1052 -1.73064800e-01, -3.31725800e+00], 1053 [-4.28562500e-01, -5.11932900e-01, -4.73787800e+02, 1054 -5.10507400e-01, -1.42494800e+01], 1055 [8.00693000e-02, 7.80269000e-02, 3.95353400e+01, 1056 7.80226000e-02, 1.77920500e+00], 1057 [-2.13674400e-01, -2.31127400e-01, -2.15987000e+02, 1058 -2.31005700e-01, -6.79344600e+00], 1059 [-1.63544000e-02, -1.64444000e-02, -1.05642100e+01, 1060 -1.64444000e-02, -4.15657600e-01], 1061 [2.04900500e-01, 1.92372100e-01, 1.10651300e+02, 1062 1.92309400e-01, 4.76156600e+00], 1063 [-1.94758900e-01, -2.09067700e-01, -2.35484100e+02, 1064 -2.08978200e-01, -6.77219400e+00], 1065 [3.16727400e-01, 2.88367800e-01, 1.87065600e+02, 1066 2.88162100e-01, 7.69732400e+00], 1067 [6.24234900e-01, 5.27632500e-01, 2.57678500e+02, 1068 5.26448400e-01, 1.26827400e+01], 1069 [8.30241100e-01, 6.72002100e-01, 2.86513700e+02, 1070 6.69644800e-01, 1.54232100e+01], 1071 [6.55140000e-03, 6.53710000e-03, 7.92130700e+00, 1072 6.53710000e-03, 2.27805800e-01], 1073 [3.41595200e-01, 3.08985000e-01, 2.88667600e+02, 1074 3.08733300e-01, 9.93012900e+00]]) 1075 1076 self.params = np.array([ 1077 -0.5369833, 6.47296332, 16.20336802, 38.96617431]) 1078 self.bse = np.array([ 1079 0.13341238, 2.1349966, 3.87411875, 8.19235553]) 1080 1081 self.aic_R = 328.39209118952965 1082 1083 # TODO: the below will fail 1084 self.aic_Stata = 7.381090276021671 1085 self.deviance = 15.093762327607557 1086 self.scale = 0.29512089119443752 1087 self.null_deviance = 27.92207137420696 # from R bug in RPy 1088 # NOTE: our scale is Stata's dispers_p (pearson?) 1089 # TODO: if scale is analagous to Stata's dispersion, then this might be 1090 # where the discrepancies come from? 1091 # self.llf = -159.19604559476483 # From R 1092 self.llf = -173.1461666245201 # From Stata 1093 self.bic_Stata = -155.2390821535193 1094 self.df_model = 3 1095 self.df_resid = 44 1096 self.chi2 = 51.56632068622578 1097 self.fittedvalues = np.array([ 1098 6.21019277, 4.06225956, 1099 7.28415938, 11.04304251, 1100 8.89510929, 2.98829295, 5.13622616, 7.82114268, 1101 8.89510929, 7.82114268, 11.04304251, 12.65399242, 1102 12.11700911, 9.43209259, 12.65399242, 5.67320947, 1103 11.58002581, 12.65399242, 8.35812599, 11.04304251, 1104 9.46125627, 10.53522287, 14.294106, 15.36807261, 1105 19.12695574, 12.68315609, 12.14617279, 18.58997243, 1106 9.46125627, 18.58997243, 15.90505591, 14.294106, 1107 20.20092234, 17.51600582, 25.63546061, 26.17244391, 1108 22.95054409, 28.85736043, 24.0245107, 28.32037713, 1109 25.63546061, 24.561494, 29.39434374, 25.09847731, 1110 22.95054409, 21.87657748, 29.39434374, 27.24641052]) 1111 1112 1113class Cpunish(object): 1114 ''' 1115 The following are from the R script in models.datasets.cpunish 1116 Slightly different than published results, but should be correct 1117 Probably due to rounding in cleaning? 1118 ''' 1119 def __init__(self): 1120 self.params = ( 1121 2.611017e-04, 7.781801e-02, -9.493111e-02, 2.969349e-01, 1122 2.301183e+00, -1.872207e+01, -6.801480e+00) 1123 self.bse = ( 1124 5.187132e-05, 7.940193e-02, 2.291926e-02, 4.375164e-01, 1125 4.283826e-01, 4.283961e+00, 4.146850e+00) 1126 self.null_deviance = 136.57281747225 1127 self.df_null = 16 1128 self.deviance = 18.591641759528944 1129 self.df_resid = 10 1130 self.df_model = 6 1131 self.aic_R = 77.8546573896503 # same as Stata 1132 self.aic_Stata = 4.579685683305706 1133 self.bic_Stata = -9.740492454486446 1134 self.chi2 = 128.8021169250578 # from Stata not in sm 1135 self.llf = -31.92732869482515 1136 self.scale = 1 1137 self.pearson_chi2 = 24.75374835 1138 self.resids = glm_test_resids.cpunish_resids 1139 self.fittedvalues = np.array([ 1140 35.2263655, 8.1965744, 1.3118966, 1141 3.6862982, 2.0823003, 1.0650316, 1.9260424, 2.4171405, 1142 1.8473219, 2.8643241, 3.1211989, 3.3382067, 2.5269969, 1143 0.8972542, 0.9793332, 0.5346209, 1.9790936]) 1144 1145 1146class Cpunish_offset(Cpunish): 1147 ''' 1148 Same model as Cpunish but with offset of 100. Many things do not change. 1149 ''' 1150 def __init__(self): 1151 super(Cpunish_offset, self).__init__() 1152 1153 self.params = ( 1154 -1.140665e+01, 2.611017e-04, 7.781801e-02, 1155 -9.493111e-02, 2.969349e-01, 2.301183e+00, 1156 -1.872207e+01) 1157 self.bse = ( 1158 4.147e+00, 5.187e-05, 7.940e-02, 2.292e-02, 1159 4.375e-01, 4.284e-01, 4.284e+00) 1160 1161 1162class InvGauss(object): 1163 ''' 1164 Usef 1165 1166 Data was generated by Hardin and Hilbe using Stata. 1167 Note only the first 5000 observations are used because 1168 the models code currently uses np.eye. 1169 ''' 1170 # FIXME: do something with the commented-out code below 1171 # np.random.seed(54321) 1172 # x1 = np.abs(stats.norm.ppf((np.random.random(5000)))) 1173 # x2 = np.abs(stats.norm.ppf((np.random.random(5000)))) 1174 # X = np.column_stack((x1, x2)) 1175 # X = add_constant(X) 1176 # params = np.array([.5, -.25, 1]) 1177 # eta = np.dot(X, params) 1178 # mu = 1/np.sqrt(eta) 1179 # sigma = .5 1180 # This is not correct. Errors need to be normally distributed 1181 # But Y needs to be Inverse Gaussian, so we could build it up 1182 # by throwing out data? 1183 # Refs: 1184 # * Lai (2009) Generating inverse Gaussian random variates by 1185 # approximation 1186 # * Atkinson (1982) The simulation of generalized inverse gaussian 1187 # and hyperbolic random variables seems to be the canonical ref 1188 # Y = np.dot(X, params) + np.random.wald(mu, sigma, 1000) 1189 # model = GLM(Y, X, family=models.family.InverseGaussian(link=\ 1190 # models.family.links.identity())) 1191 1192 def __init__(self): 1193 # set up data # 1194 filename = os.path.join(os.path.dirname(os.path.abspath(__file__)), 1195 "inv_gaussian.csv") 1196 with open(filename, 'r') as fd: 1197 data = np.genfromtxt(fd, delimiter=",", dtype=float)[1:] 1198 self.endog = data[:5000, 0] 1199 self.exog = data[:5000, 1:] 1200 self.exog = add_constant(self.exog, prepend=False) 1201 1202 # Results 1203 # NOTE: loglikelihood difference in R vs. Stata vs. Models 1204 # is the same situation as gamma 1205 self.params = (0.4519770, -0.2508288, 1.0359574) 1206 self.bse = (0.03148291, 0.02237211, 0.03429943) 1207 self.null_deviance = 1520.673165475461 1208 self.df_null = 4999 1209 self.deviance = 1423.943980407997 1210 self.df_resid = 4997 1211 self.df_model = 2 1212 self.aic_R = 5059.41911646446 1213 self.aic_Stata = 1.552280060977946 1214 self.bic_Stata = -41136.47039418921 1215 self.llf = -3877.700354 # Stata is same as ours with scale set to 1 1216 # self.llf = -2525.70955823223 # from R, close to ours 1217 self.scale = 0.2867266359127567 1218 self.pearson_chi2 = 1432.771536 1219 self.resids = glm_test_resids.invgauss_resids 1220 self.fittedvalues = np.array([ 1221 1.0404339, 0.96831526, 0.81265833, 0.9958362, 1.05433442, 1222 1.09866137, 0.95548191, 1.38082105, 0.98942888, 0.96521958, 1223 1.02684056, 0.91412576, 0.91492102, 0.92639676, 0.96763425, 1224 0.80250852, 0.85281816, 0.90962261, 0.95550299, 0.86386815, 1225 0.94760134, 0.94269533, 0.98960509, 0.84787252, 0.78949111, 1226 0.76873582, 0.98933453, 0.95105574, 0.8489395, 0.88962971, 1227 0.84856357, 0.88567313, 0.84505405, 0.84626147, 0.77250421, 1228 0.90175601, 1.15436378, 0.98375558, 0.83539542, 0.82845381, 1229 0.90703971, 0.85546165, 0.96707286, 0.84127197, 0.82096543, 1230 1.1311227, 0.87617029, 0.91194419, 1.05125511, 0.95330314, 1231 0.75556148, 0.82573228, 0.80424982, 0.83800144, 0.8203644, 1232 0.84423807, 0.98348433, 0.93165089, 0.83968706, 0.79256287, 1233 1.0302839, 0.90982028, 0.99471562, 0.70931825, 0.85471721, 1234 1.02668021, 1.11308301, 0.80497105, 1.02708486, 1.07671424, 1235 0.821108, 0.86373486, 0.99104964, 1.06840593, 0.94947784, 1236 0.80982122, 0.95778065, 1.0254212, 1.03480946, 0.83942363, 1237 1.17194944, 0.91772559, 0.92368795, 1.10410916, 1.12558875, 1238 1.11290791, 0.87816503, 1.04299294, 0.89631173, 1.02093004, 1239 0.86331723, 1.13134858, 1.01807861, 0.98441692, 0.72567667, 1240 1.42760495, 0.78987436, 0.72734482, 0.81750166, 0.86451854, 1241 0.90564264, 0.81022323, 0.98720325, 0.98263709, 0.99364823, 1242 0.7264445, 0.81632452, 0.7627845, 1.10726938, 0.79195664, 1243 0.86836774, 1.01558149, 0.82673675, 0.99529548, 0.97155636, 1244 0.980696, 0.85460503, 1.00460782, 0.77395244, 0.81229831, 1245 0.94078297, 1.05910564, 0.95921954, 0.97841172, 0.93093166, 1246 0.93009865, 0.89888111, 1.18714408, 0.98964763, 1.03388898, 1247 1.67554215, 0.82998876, 1.34100687, 0.86766346, 0.96392316, 1248 0.91371033, 0.76589296, 0.92329051, 0.82560326, 0.96758148, 1249 0.8412995, 1.02550678, 0.74911108, 0.8751611, 1.01389312, 1250 0.87865556, 1.24095868, 0.90678261, 0.85973204, 1.05617845, 1251 0.94163038, 0.88087351, 0.95699844, 0.86083491, 0.89669384, 1252 0.78646825, 1.0014202, 0.82399199, 1.05313139, 1.06458324, 1253 0.88501766, 1.19043294, 0.8458026, 1.00231535, 0.72464305, 1254 0.94790753, 0.7829744, 1.1953009, 0.85574035, 0.95433052, 1255 0.96341484, 0.91362908, 0.94097713, 0.87273804, 0.81126399, 1256 0.72715262, 0.85526116, 0.76015834, 0.8403826, 0.9831501, 1257 1.17104665, 0.78862494, 1.01054909, 0.91511601, 1.0990797, 1258 0.91352124, 1.13671162, 0.98793866, 1.0300545, 1.04490115, 1259 0.85778231, 0.94824343, 1.14510618, 0.81305136, 0.88085051, 1260 0.94743792, 0.94875465, 0.96206997, 0.94493612, 0.93547218, 1261 1.09212018, 0.86934651, 0.90532353, 1.07066001, 1.26197714, 1262 0.93858662, 0.9685039, 0.7946546, 1.03052031, 0.75395899, 1263 0.87527062, 0.82156476, 0.949774, 1.01000235, 0.82613526, 1264 1.0224591, 0.91529149, 0.91608832, 1.09418385, 0.8228272, 1265 1.06337472, 1.05533176, 0.93513063, 1.00055806, 0.95474743, 1266 0.91329368, 0.88711836, 0.95584926, 0.9825458, 0.74954073, 1267 0.96964967, 0.88779583, 0.95321846, 0.95390055, 0.95369029, 1268 0.94326714, 1.31881201, 0.71512263, 0.84526602, 0.92323824, 1269 1.01993108, 0.85155992, 0.81416851, 0.98749128, 1.00034192, 1270 0.98763473, 1.05974138, 1.05912658, 0.89772172, 0.97905626, 1271 1.1534306, 0.92304181, 1.16450278, 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0.85101115, 1658 0.88859716, 0.88720344, 0.89291167, 1.10372601, 0.91132273, 1659 1.04156844, 0.94867703, 0.83546241, 0.84227545, 0.97043199, 1660 0.73281541, 0.74512501, 0.9128489, 0.99223543, 0.7319106, 1661 0.93065507, 1.07907995, 0.86895295, 0.84344015, 0.89394039, 1662 0.88802964, 1.00580322, 1.04286883, 0.82233574, 1.0279258, 1663 0.97550628, 1.03867605, 1.10231813, 0.9642628, 0.91684874, 1664 1.11066089, 0.99439688, 0.88595489, 0.88725073, 0.78921585, 1665 0.80397616, 0.71088468, 0.98316478, 0.72820659, 0.96964036, 1666 1.03825415, 1.01438989, 1.02763769, 1.29949298, 1.06450406, 1667 0.86198627, 0.85588074, 0.90445183, 1.01268187, 0.87927487, 1668 0.9263951, 0.93582126, 0.88738294, 1.20707424, 0.92887657, 1669 0.97891062, 0.92893689, 0.84846424, 0.96287008, 0.99565057, 1670 0.93483385, 1.21357183, 0.82369562, 0.65144728, 1.11249654, 1671 0.7785981, 0.88248898, 0.8953217, 0.95884666, 0.77538093, 1672 0.82272417, 0.91073072, 1.17185169, 0.99645708, 0.88693463, 1673 0.90293325, 0.93368474, 0.87575633, 1.01924242, 0.80011545, 1674 0.99762674, 0.75834671, 0.91952152, 0.86754419, 0.81073894, 1675 0.8880299, 0.74868718, 0.99979109, 0.90652154, 0.92463566, 1676 0.93894041, 0.92370595, 0.88766357, 1.04614978, 1.77193759, 1677 0.85480724, 0.85208602, 0.96154559, 0.95832935, 0.84210613, 1678 0.9604567, 0.88597666, 1.0010723, 0.91890105, 1.10529207, 1679 0.91123688, 0.88466788, 1.09759195, 0.8946647, 0.78066485, 1680 1.04376296, 1.02951755, 0.88455241, 0.99284282, 0.82423576, 1681 0.80612213, 0.80915541, 0.9482253, 0.8887192, 0.86163309, 1682 0.891385, 0.84850622, 1.03353375, 1.09248204, 1.05337218, 1683 0.85927317, 0.89167858, 1.04868715, 0.92933249, 1.1177299, 1684 0.99846776, 0.82418972, 0.86041965, 0.88015748, 0.89785813, 1685 0.85997945, 0.97102367, 0.86679181, 1.00848475, 0.9091588, 1686 0.92565039, 0.84019067, 0.86978485, 1.21977681, 1.14920817, 1687 1.05177219, 0.84202905, 0.85356083, 1.01379321, 0.93364219, 1688 1.01999942, 0.85906744, 0.98178266, 0.87218886, 0.93983742, 1689 0.79713053, 1.01123331, 0.86551625, 0.81983929, 0.86782985, 1690 0.86735664, 1.43316935, 0.8490094, 0.99909103, 0.85715326, 1691 0.89452366, 1.08380518, 0.74686847, 1.62233058, 0.81046611, 1692 0.83563461, 0.96925792, 0.82863186, 0.87147202, 0.92609558, 1693 0.8879082, 0.93933353, 0.90043906, 0.81677055, 0.78016427, 1694 0.68871014, 0.83329967, 0.81570171, 0.89780443, 0.81337668, 1695 1.00772749, 0.96220158, 0.90035459, 1.06031906, 0.85832752, 1696 0.93636203, 0.96336629, 0.94686138, 0.98499419, 0.87223701, 1697 0.96079992, 0.81302793, 0.99287479, 0.99369685, 1.21897038, 1698 0.94547481, 0.80785132, 1.02033902, 0.93270741, 0.90386512, 1699 1.05290969, 1.08873223, 0.81226537, 0.87185463, 0.96283379, 1700 0.95065022, 1.07603824, 1.22279786, 0.83749284, 0.93504869, 1701 0.93554565, 0.95255889, 0.96665227, 0.92370811, 0.76627742, 1702 1.14267254, 0.98268052, 1.10017739, 0.79569048, 0.86494449, 1703 1.17939799, 0.80655859, 0.76799971, 1.0018905, 0.83051793, 1704 1.37419036, 1.10424623, 0.93729691, 0.99655914, 0.94900303, 1705 1.157402, 0.93397459, 0.8133195, 0.8592273, 1.024661, 1706 0.83708977, 1.06537435, 0.93561942, 1.00402051, 0.68981047, 1707 0.92807172, 0.72192097, 1.232419, 0.97080757, 0.90350598, 1708 0.95122672, 1.04663207, 0.79080723, 0.8421381, 1.01956925, 1709 0.93307897, 0.88011784, 0.78674974, 0.97537097, 0.7582792, 1710 0.85704507, 0.97683858, 0.7739793, 0.96245444, 0.99506991, 1711 0.76853035, 0.90875698, 0.97951121, 0.93350388, 1.16380858, 1712 0.8154485, 1.16902243, 0.98644779, 0.969998, 0.73120517, 1713 1.19059456, 0.85953661, 0.99193867, 0.88144929, 0.99254885, 1714 1.02956121, 0.90689455, 0.89494433, 0.85625065, 0.86227273, 1715 0.99830845, 0.97635222, 0.83420327, 1.02359646, 0.93694813, 1716 0.88462353, 0.97040788, 1.02543309, 0.91904348, 1.2527365, 1717 0.82235812, 0.92026753, 0.93935859, 0.88919482, 1.00405208, 1718 1.06835782, 1.34738363, 0.97831176, 0.92053317, 1.09692339, 1719 0.86156677, 1.02455351, 1.25572326, 0.89721167, 0.95787106, 1720 0.85059479, 0.92044416, 0.99210399, 0.94334232, 0.76604642, 1721 0.8239008, 0.70790815, 1.06013034, 1.12729012, 0.88584074, 1722 0.91995677, 0.82002708, 0.91612106, 0.86556894, 0.88014564, 1723 0.95764757, 0.96559535, 0.97882426, 0.70725389, 0.9273384, 1724 0.86511581, 0.85436928, 1.26804081, 1.02018914, 0.95359667, 1725 0.89336753, 0.91851577, 0.78166458, 1.02673106, 1.01340992, 1726 1.34916703, 0.77389899, 1.12009884, 0.94523179, 0.87991868, 1727 0.82919239, 0.98198121, 0.83653977, 0.91748611, 1.0642761, 1728 0.86964263, 0.86304793, 1.11500797, 0.7234409, 1.00464282, 1729 1.01835251, 0.73389264, 0.88471293, 0.85754755, 1.05383962, 1730 0.73121546, 0.85445808, 0.768308, 0.81396206, 1.01261272, 1731 0.76696225, 1.01770784, 0.76742866, 0.98390583, 0.96277488, 1732 0.87998292, 0.85264282, 1.12704234, 0.79612317, 0.92206712, 1733 1.09846877, 0.99874997, 0.87707457, 1.03404785, 1.00726392, 1734 0.91613763, 0.74242708, 0.80247702, 0.90702146, 0.81638055, 1735 0.78507729, 1.00066404, 0.84687328, 0.76488847, 0.89697089, 1736 0.82524207, 0.84940145, 1.022041, 0.75856559, 1.15434195, 1737 1.09781849, 0.93256477, 0.96021119, 1.00796782, 0.88193493, 1738 0.87902107, 0.82245196, 1.04739362, 1.133521, 0.82969043, 1739 1.01007529, 1.07135903, 0.981338, 0.86178089, 0.77930618, 1740 0.82512349, 1.2017057, 1.30452154, 1.12652148, 1.03670177, 1741 0.90631643, 0.74222362, 0.84452965, 0.86366363, 0.79192948, 1742 1.10288297, 0.9554774, 1.00912465, 0.95545229, 0.93584303, 1743 0.91604017, 0.91681165, 0.76792072, 1.66615421, 0.99044246, 1744 1.05068209, 0.88197497, 0.91153792, 0.82702508, 0.95182748, 1745 1.05320356, 0.8466656, 1.01676717, 0.65881123, 1.02589358, 1746 1.03902555, 1.00199915, 1.03022137, 0.93427176, 0.94600332, 1747 0.94594696, 0.86465228, 0.91241272, 0.72232997, 0.93380167, 1748 1.1960032, 0.87463367, 0.78428202, 0.88088, 0.97202961, 1749 0.99425528, 0.89567214, 0.84908979, 0.81004889, 0.85484368, 1750 0.68478631, 0.96563032, 0.78298607, 0.71894276, 0.88632131, 1751 0.8885966, 0.99235811, 0.84002222, 0.91265424, 0.91999157, 1752 0.89786651, 1.18062511, 0.92378385, 0.82501238, 1.09009807, 1753 0.96787582, 1.12456979, 0.86339677, 0.8786218, 0.89865768, 1754 1.02943564, 0.98886502, 0.97135566, 0.95914954, 1.05080931, 1755 0.76554446, 0.80142172, 0.99661393, 1.14749469, 0.93695459, 1756 0.95769957, 1.00811373, 1.00352699, 0.98747546, 0.99436785, 1757 1.10256609, 0.84366101, 0.85931876, 0.90745126, 1.04928733, 1758 0.84499693, 1.14018589, 1.2337188, 0.90516077, 0.84991869, 1759 0.72984467, 0.9729476, 0.97483938, 0.88626286, 1.02838695, 1760 0.89750089, 0.80324802, 1.40726294, 0.91149383, 0.86837826, 1761 1.21798148, 0.96459285, 0.71897535, 0.76230781, 0.88042964, 1762 0.8205186, 1.0517869, 0.74269565, 0.98278109, 1.1454159, 1763 1.03806052, 0.75238659, 0.94224089, 0.94931526, 1.24018529, 1764 0.99048689, 0.88108251, 0.81008694, 0.95443294, 0.99975781, 1765 0.83336879, 0.74422074, 0.87934792, 0.81994499, 0.98684546, 1766 0.82176924, 0.91652824, 0.77571479, 0.77039071, 0.9951089, 1767 0.92896121, 0.96234268, 1.00295341, 1.01455466, 0.75014075, 1768 0.95568202, 0.80995874, 1.24671334, 0.89480962, 0.81300194, 1769 0.76967074, 0.92514927, 0.89610963, 0.97441759, 1.19354494, 1770 0.87041262, 0.97344039, 0.88983828, 0.91614149, 0.85782814, 1771 0.78403196, 0.96665254, 0.91000054, 0.78641804, 0.96920714, 1772 0.89670528, 0.79247817, 1.04189638, 0.86777037, 1.18686087, 1773 0.79506403, 0.92389297, 0.76211023, 0.93617759, 0.91879446, 1774 0.8207635, 0.78984486, 0.93005953, 0.78743101, 0.9814347, 1775 0.94882561, 0.9577075, 0.81121566, 1.01025446, 0.90587214, 1776 0.94842798, 0.8811194, 1.01942816, 0.94698308, 0.92603676, 1777 0.86119014, 0.97543551, 0.84730649, 0.77552262, 0.97536054, 1778 0.96944817, 0.8736804, 0.86809673, 0.98134953, 1.16303105, 1779 0.81534447, 1.35930512, 0.83221293, 0.94136243, 0.76926289, 1780 1.05844282, 0.87783288, 0.78921971, 0.84360428, 0.78722128, 1781 1.00022607, 0.96779519, 0.95891975, 0.91900001, 1.07307813, 1782 1.03713093, 0.96257742, 0.90363152, 0.88729834, 0.91929215, 1783 1.00508255, 0.80838454, 0.92165553, 0.94513005, 0.95429071, 1784 0.80829571, 0.79531708, 1.01317347, 0.75337253, 0.85965134, 1785 0.77014567, 0.77680991, 0.77158741, 0.88882588, 0.91466414, 1786 0.82815897, 0.80251251, 1.04901425, 1.03386161, 1.3267075, 1787 1.12457236, 0.8267327, 0.89313417, 0.85992512, 0.93482733, 1788 0.83456348, 0.87991138, 0.8110149, 0.77913188, 0.89391799, 1789 0.73646974, 0.87038816, 0.99533506, 0.90744083, 0.98175496, 1790 1.17458551, 0.86718975, 0.93125366, 0.76131575, 0.90419708, 1791 0.95122171, 0.97531776, 1.05955142, 0.94714906, 0.79360281, 1792 1.02765349, 0.85192628, 0.84680852, 0.85470655, 0.94950982, 1793 0.75868699, 0.89731933, 1.00736877, 1.05171121, 0.73336848, 1794 0.97323586, 0.9848978, 1.27418684, 0.83954394, 0.73979357, 1795 1.06785996, 0.97832832, 0.7903268, 0.76600605, 0.94906446, 1796 0.81383465, 0.83620612, 1.00573379, 0.86359645, 0.9962139, 1797 0.98779432, 1.13793814, 1.02764992, 0.9070168, 0.81340349, 1798 0.94807089, 0.90499083, 0.83805736, 0.99623054, 0.91875275, 1799 0.95603557, 0.93156095, 0.83858677, 1.03667466, 1.01436655, 1800 0.85551979, 0.76227045, 0.84743986, 0.88487423, 0.93800365, 1801 0.8984666, 0.92600404, 0.89230381, 1.34625848, 1.10026015, 1802 0.9314026, 0.82450724, 1.0299575, 0.98494286, 1.07564492, 1803 0.96565301, 0.89677015, 1.15236174, 0.85476951, 1.00169288, 1804 0.90520725, 1.06235248, 1.04267637, 0.8311949, 0.82017897, 1805 0.81635968, 0.97246582, 0.84554172, 0.85409644, 1.18006461, 1806 0.96488389, 0.69228637, 0.97812108, 0.91764623, 0.86250551, 1807 0.91067775, 1.04692847, 0.94594707, 1.04351374, 0.9861303, 1808 0.92192581, 0.835444, 0.84362223, 1.13770705, 0.8075574, 1809 1.02260109, 1.13786456, 0.80862839, 0.89291687, 0.90278047, 1810 1.11613951, 1.29900454, 1.5622857, 0.70999772, 0.99692653, 1811 0.89109939, 0.77506441, 0.86054356, 0.99498141, 0.84222293, 1812 0.95213508, 0.91438286, 0.89305591, 0.9716793, 0.88609491, 1813 1.00275797, 0.90086022, 0.75336995, 1.1572679, 0.75952094, 1814 0.89203313, 0.82115965, 0.81459913, 1.02943406, 0.67063452, 1815 1.08707079, 0.92139483, 0.89855103, 0.89910955, 1.07169531, 1816 0.93684641, 0.84893365, 1.08659966, 1.43385982, 0.94788914, 1817 0.95277539, 0.94709274, 1.08412066, 0.90274516, 0.85147284, 1818 0.89327944, 0.92176174, 0.83820774, 0.90981839, 0.82303984, 1819 0.95189716, 0.95154905, 0.73628819, 1.18956148, 1.20224654, 1820 0.97666968, 1.08057375, 0.90369444, 0.98589538, 0.81426873, 1821 0.75127684, 0.93200745, 0.833666, 0.79532088, 0.91965037, 1822 0.99540522, 0.75449668, 0.85698312, 0.79328453, 0.94667443, 1823 0.7637764, 0.77203985, 0.73841377, 0.98587851, 1.34642268, 1824 0.78002774, 1.04356217, 1.02266882, 1.08936378, 0.9794388, 1825 1.07623423, 0.78069571, 1.12194495, 0.8072132, 0.91672662, 1826 1.36102062, 0.86933509, 1.15282756, 1.06219505, 0.80295502, 1827 1.00999033, 0.69418333, 0.93678452, 1.13002256, 0.91465628, 1828 0.73558316, 1.1302073, 0.85856238, 0.89450543, 1.11812369, 1829 0.75891878, 0.66859534, 0.97445338, 0.82210227, 0.76292085, 1830 0.79289499, 1.04380135, 0.95586226, 0.87480096, 0.81244036, 1831 0.86097575, 0.84111811, 0.85369732, 0.99160655, 0.90911501, 1832 0.81315845, 0.74037745, 1.04369233, 1.03535223, 1.18886682, 1833 0.87092491, 0.93562683, 0.92555142, 0.95268616, 0.9653025, 1834 0.93447525, 0.9043932, 1.25701034, 1.10354218, 0.96588129, 1835 0.94717991, 0.97010307, 0.78264501, 0.80991731, 0.98540974, 1836 0.83174886, 0.66966351, 1.01747376, 1.21553117, 0.80527296, 1837 1.06556826, 1.00870321, 1.03316522, 0.88994006, 0.89092714, 1838 0.94119254, 0.83930854, 1.01500087, 1.03581272, 0.97608081, 1839 1.11919255, 1.16586474, 0.85064102, 1.06070274, 1.00679658, 1840 0.75848826, 0.97969353, 0.94834777, 1.64970724, 0.82448941, 1841 1.02236919, 0.95252025, 0.98638842, 0.89094895, 0.95522527, 1842 0.91533774, 0.83716951, 0.92612154, 0.8662328, 0.9675949, 1843 0.96758398, 0.84309291, 0.95071171, 1.0165785, 0.96628063, 1844 1.00096151, 0.83175371, 0.79063043, 0.97371271, 0.76009001, 1845 1.02409279, 0.97232166, 0.8480577, 0.8982739, 0.9959743, 1846 0.96604729, 0.8681602, 0.99850841, 0.96162481, 1.01259965, 1847 0.98580061, 0.82751273, 0.90469122, 0.98254028, 0.78012425, 1848 0.87023012, 0.96830515, 0.9415831, 0.8591063, 0.82961507, 1849 0.89166083, 0.88509907, 0.95987837, 1.12356244, 0.71406404, 1850 0.99047619, 0.93735587, 0.80540831, 1.0024624, 0.95179491, 1851 0.83602101, 0.90343297, 0.90510417, 0.96477126, 0.79995299, 1852 0.93123762, 0.73763362, 1.0619498, 0.80929865, 0.86110233, 1853 0.84552556, 0.9943, 0.97085623, 0.75751174, 0.9201456, 1854 1.02268858, 0.9642899, 0.79078558, 1.03160502, 0.85200219, 1855 1.02246639, 1.08771483, 0.81997868, 0.82499763, 0.92767703, 1856 1.06700018, 0.7882174, 0.7789828, 0.89096139, 0.73155973, 1857 1.01717651, 0.91889525, 0.93256065, 0.84716063, 1.00965969, 1858 0.74505112, 0.80104245, 0.76003901, 0.96662605, 0.96594583, 1859 1.04571121, 0.97700878, 0.85461917, 0.9150222, 0.89110471, 1860 1.11183096, 0.98143747, 1.02346975, 0.9059266, 1.00771483, 1861 0.96336096, 0.93783898, 0.90545613, 1.10404183, 0.75297691, 1862 0.92548654, 0.79889783, 0.88177552, 0.93896814, 0.87309811, 1863 0.80691061, 0.89725699, 1.16586955, 0.98948281, 0.94524894, 1864 0.86085608, 0.76716851, 0.85362573, 1.09936882, 0.9328761, 1865 0.74819673, 0.94331186, 0.81077304, 0.88610499, 1.01452015, 1866 0.91513953, 0.92846128, 0.93539081, 0.8946682, 0.9270336, 1867 0.96673629, 0.9897488, 1.11891899, 0.87551585, 0.85854576, 1868 1.13458763, 1.11450768, 0.79887951, 1.091154, 1.04180374, 1869 0.79252573, 0.90484245, 0.94221016, 0.95721137, 0.86776103, 1870 0.97167404, 0.83404166, 0.94634038, 0.98907413, 0.92321459, 1871 1.03547804, 0.79660212, 0.94870239, 0.70027204, 0.79841059, 1872 0.92563393, 1.4385341, 0.8331731, 0.844816, 0.97851389, 1873 1.24048695, 0.83765698, 0.83600835, 1.13901283, 1.05994936, 1874 0.84292427, 0.86759056, 0.9272156, 0.77375499, 0.99972839, 1875 0.95570976, 0.97879539, 0.95528351, 0.84555495, 0.95296134, 1876 0.87469056, 0.78862024, 0.793795, 0.8516853, 0.92816818, 1877 1.02492208, 0.8037345, 0.95481283, 0.75138828, 0.72110948, 1878 1.36815666, 0.9661646, 0.81651816, 0.87764538, 0.97397297, 1879 0.99845266, 0.77433798, 0.9266279, 1.92493013, 1.07588789, 1880 0.90412593, 1.03165475, 1.00826548, 0.75500744, 0.87198881, 1881 0.86871262, 0.97854606, 0.80954477, 0.84130266, 0.89674826, 1882 1.43926644, 0.74873088, 1.01894282, 0.93606154, 1.08241489, 1883 0.76626357, 0.97434747, 0.82824599, 1.00267494, 0.97168761, 1884 1.06433173, 1.22741978, 1.46998419, 0.9521923, 0.98276685, 1885 0.92422781, 1.14241216, 1.13339577, 1.05586816, 1.04923068, 1886 0.83364505, 0.98007268, 0.94322393, 0.84310173, 1.03481955, 1887 1.18281181, 0.79807678, 0.840274, 1.00344058, 1.09442855, 1888 0.88033836, 0.86189964, 1.1395012, 1.18808865, 0.78667714, 1889 1.09323293, 0.81511099, 0.95830848, 0.99637275, 0.9146258, 1890 0.96358155, 0.79048719, 0.80395604, 1.00828722, 0.92872342, 1891 0.98789363, 0.96720252, 0.80541021, 0.73697557, 0.86692999, 1892 0.86795696, 1.1516694, 0.95911714, 1.13981603, 1.02002866, 1893 0.90808456, 0.94208296, 0.93691739, 0.87653118, 0.72824225, 1894 0.78177906, 1.2139146, 0.83405505, 0.91764545, 0.83318595, 1895 0.77930256, 0.86499397, 0.95599882, 0.73850016, 0.9630604, 1896 0.97913407, 1.1790714, 0.94994057, 1.04379512, 0.80815459, 1897 1.16560205, 0.97486893, 1.02780804, 1.10633754, 0.78679252, 1898 0.94643528, 1.19999119, 0.98621069, 0.8899674, 0.89235261, 1899 0.8728921, 0.77089094, 0.8492628, 0.86905159, 0.90741875, 1900 0.81065291, 0.91208596, 1.04616696, 1.24291958, 0.98628605, 1901 0.99751975, 0.83249612, 0.96343385, 0.77862866, 0.72381238, 1902 1.17384381, 1.06013687, 0.73460652, 1.09554763, 0.82015886, 1903 0.90862905, 0.89037104, 0.7866143, 0.8570287, 0.75061334, 1904 0.94950855, 0.8091383, 1.04055212, 0.96679573, 0.78338675, 1905 0.75968533, 1.00495071, 0.6491633, 1.02802735, 1.00725883, 1906 0.89333988, 0.87539291, 0.99374251, 1.10241119, 1.14935785, 1907 0.9369769, 0.84772646, 1.05024743, 0.97411124, 0.76972352, 1908 0.92161017, 0.88689841, 0.78598549, 0.93400036, 1.14699647, 1909 0.98636563, 0.93051079, 1.00131515, 0.82749213, 0.96665447, 1910 0.84457933, 0.95172036, 0.86372572, 0.97034285, 0.99877807, 1911 0.8724721, 0.86281118, 0.96253742, 1.13485439, 1.03410559, 1912 0.83113167, 1.02644607, 1.0669284, 0.947969, 1.13373538, 1913 0.85495039, 1.15829218, 0.72662405, 0.81755747, 0.78381403, 1914 0.84360371, 1.10945791, 0.80215303, 0.8861351, 0.97484684, 1915 1.02996282, 0.86219328, 0.95675062, 1.10753315, 0.92496918, 1916 0.79323289, 0.76891191, 0.93106762, 0.94523682, 0.9534338, 1917 0.8954424, 0.81732651, 1.00443776, 0.96178195, 0.89727229, 1918 0.88917552, 0.88660003, 0.941933, 1.03900381, 0.75262915, 1919 0.94265862, 0.84472046, 1.09834757, 0.81516259, 0.90865634, 1920 0.9582531, 0.99819053, 0.8815072, 0.92425525, 0.79085083, 1921 0.98173446, 0.95199169, 0.71653726, 1.11863725, 0.97855807, 1922 0.87873181, 1.37925403, 0.8085008, 1.40027689, 0.79367826, 1923 0.82070449, 0.87039383, 0.95896081, 0.75617612, 1.3196712, 1924 0.9335008, 0.9461447, 1.0838461, 0.83347962, 0.69558254, 1925 0.92358528, 0.99423247, 0.94884494, 0.75094955, 0.90429063, 1926 1.13740548, 0.89354463, 1.13094104, 1.7373979, 0.87808028, 1927 0.72820621, 1.02995089, 0.80134468, 0.97511989, 0.93823103, 1928 0.98097787, 0.73179813, 0.93764192, 1.04399599, 0.95644709, 1929 0.80476939, 0.87463727, 0.83220517, 0.76978546, 0.97056432, 1930 1.1693819, 1.0368387, 0.98606478, 1.03538075, 0.88253058, 1931 0.91105775, 0.93745618, 0.80272442, 0.77045021, 0.8482449, 1932 1.04505306, 0.90427753, 0.706451, 1.02687396, 0.82931474, 1933 1.24255717, 0.91343217, 0.8692726, 0.98422894, 0.82142068, 1934 0.86854354, 0.77715916, 0.94490329, 0.97686366, 1.05198512, 1935 0.888989, 1.09252847, 0.8034292, 1.04727187, 0.87246831, 1936 0.89474556, 1.06031526, 0.93056174, 0.7747956, 0.87772054, 1937 1.1183045, 0.78938083, 0.82019511, 0.82553273, 1.04324276, 1938 0.7676436, 0.68914756, 0.88400598, 0.79611901, 0.77011016, 1939 0.76727015, 0.84523666, 1.09972447, 1.03942974, 1.07322466, 1940 1.01079248, 1.03469338, 0.90450148, 0.87367007, 0.88432601, 1941 0.85312482, 0.7328442, 1.12256832, 0.8837547, 0.81023384, 1942 0.87068285, 0.94466637, 1.13236695, 0.95958423, 0.8099625, 1943 1.07509372, 1.03306035, 0.99385633, 1.06433672, 1.07385915, 1944 0.92709455, 1.03502217, 0.88961476, 0.8307198, 0.98819038, 1945 1.09916368, 0.8919766, 0.90349117, 0.97554616, 0.98376763, 1946 0.89285893, 0.99941071, 1.16078972, 0.66336693, 1.16389515, 1947 1.10395069, 1.20381952, 0.98928899, 1.17155389, 0.81707565, 1948 0.82903836, 0.95892646, 0.8437454, 0.79017432, 0.81562954, 1949 0.65169124, 0.87950793, 0.9017879, 0.82160564, 0.87079127, 1950 0.88100146, 1.00783979, 0.84102603, 1.16817499, 0.97697533, 1951 0.89115235, 0.77254376, 0.7679024, 0.97093775, 1.13881665, 1952 0.90348632, 1.14654277, 1.08625707, 0.98787902, 1.49057495, 1953 0.99639001, 0.97623973, 0.74807856, 0.76656108, 0.79095998, 1954 1.04583503, 0.95124469, 0.90228738, 1.03129265, 1.02663212, 1955 0.67704952, 0.95335397, 1.01726294, 0.78765385, 0.91140255, 1956 1.04097119, 0.71881619, 1.14572601, 0.79708798, 1.07104057, 1957 0.95925248, 0.72556831, 0.92256392, 1.08702165, 0.95977251, 1958 0.99670254, 0.95276505, 1.15268752, 0.68215678, 1.05573208, 1959 0.89672437, 0.89396611, 1.01814905, 0.81969778, 0.74390457, 1960 1.20909881, 0.82388701, 1.00574083, 1.01348114, 1.01492015, 1961 0.94759788, 0.99758684, 1.19912008, 0.92749943, 1.16660441, 1962 0.97646538, 0.8189475, 0.97464158, 1.01050799, 0.94368665, 1963 0.70995047, 0.94469581, 1.02534612, 1.3513094, 0.88081968, 1964 1.00576693, 0.9695495, 1.0549135, 1.29993316, 0.91050559, 1965 0.95543198, 1.02161725, 0.76895773, 1.03685293, 0.88201449, 1966 0.90345561, 1.02793048, 1.00267831, 0.84653161, 0.9217411, 1967 0.94666576, 0.94946561, 0.77482488, 0.94358305, 0.89779666, 1968 1.01462131, 1.05829923, 1.13217729, 1.12260175, 0.89810828, 1969 0.96305689, 0.90466377, 0.8091617, 0.93070824, 1.03997521, 1970 1.04076373, 0.95858477, 0.94382748, 0.7585222, 1.22890096, 1971 0.97300529, 0.87424719, 0.90435141, 0.91894865, 0.97819677, 1972 0.80300175, 1.03729016, 1.19305569, 0.81633791, 0.7930351, 1973 0.8141721, 0.86764479, 0.89207142, 0.89691482, 0.86243171, 1974 0.91184679, 0.94284352, 1.01357831, 1.03806277, 0.92000143, 1975 0.91018767, 0.90555137, 0.89089532, 1.3530331, 0.96933587, 1976 0.82350429, 0.71549154, 1.13399156, 0.87838533, 0.99177078, 1977 0.93296992, 1.43078263, 0.90278792, 0.85789581, 0.93531789, 1978 0.84948314, 0.95778101, 0.80962713, 0.88865859, 1.15297165, 1979 0.85695093, 0.88601982, 0.96665296, 0.9320964, 1.04193558, 1980 1.006005, 0.78939639, 0.79344784, 0.87012624, 0.8532022, 1981 0.93351167, 0.91705323, 0.74384626, 0.84219843, 0.78265573, 1982 1.07759963, 1.0236098, 1.00202257, 1.18687122, 1.00869294, 1983 0.8809502, 0.76397598, 0.81845324, 0.97439912, 1.10466318, 1984 1.10678275, 0.96692316, 0.84120323, 1.13151276, 0.72574077, 1985 0.82457571, 0.8179266, 1.01118196, 0.84303742, 0.86255339, 1986 1.03927791, 0.82302701, 1.03586066, 0.75785864, 0.9186558, 1987 0.97139449, 0.92424514, 1.00415659, 1.08544681, 0.80940032, 1988 0.9073428, 0.83621672, 1.04027879, 0.79447936, 0.94829305, 1989 1.16176292, 1.11185195, 0.88652664, 0.98676451, 0.89310091, 1990 0.72272527, 0.79963233, 0.94651986, 0.91540761, 1.0498236, 1991 0.84938647, 1.15539602, 1.03118991, 0.86565049, 0.77764016, 1992 0.77866522, 0.78008955, 0.89062575, 0.81285464, 0.92554114, 1993 1.08747324, 0.84338687, 0.76746516, 0.99205474, 0.86649541, 1994 0.97586166, 0.9721711, 1.14895298, 1.04659345, 1.0605085, 1995 1.06392238, 1.08286448, 0.93612266, 0.82545354, 0.84305431, 1996 0.83650404, 1.11073704, 0.91760695, 0.83281572, 0.84244131, 1997 1.05843708, 0.94695861, 0.95469608, 0.96038612, 0.81373042, 1998 0.94943303, 1.00824522, 0.86416102, 0.87121008, 1.04208739, 1999 0.81171276, 1.12798927, 0.99122576, 0.80626996, 1.07103151, 2000 0.99809277, 1.08490135, 0.9441509, 0.98766371, 1.33205139, 2001 0.92145678, 0.88112784, 0.9297591, 1.17549838, 0.8481953, 2002 0.96359948, 0.98478935, 0.77028684, 0.86408555, 0.92863805, 2003 0.94593549, 0.78705212, 1.1923026, 0.9983487, 0.99152533, 2004 0.95313678, 1.01847515, 1.05728959, 0.88009142, 1.00351951, 2005 1.00549552, 0.81671365, 0.90545602, 0.77895202, 0.82217088, 2006 0.94838645, 0.85928327, 0.90729044, 0.92975916, 0.91946285, 2007 0.80537364, 1.11885357, 0.84691232, 0.85356231, 0.85102988, 2008 1.06499659, 1.0242127, 0.91245632, 0.83131215, 0.72151085, 2009 0.9295769, 0.89549018, 0.87914839, 0.93541175, 0.97319188, 2010 0.791944, 1.08008186, 0.79549907, 0.90967683, 0.80506028, 2011 1.1206821, 0.91258859, 1.24855319, 0.96112955, 1.14305514, 2012 0.79327927, 0.84209204, 0.94494251, 0.89573237, 1.0571304, 2013 0.94504292, 0.84446547, 0.92060829, 0.82347072, 0.86280426, 2014 0.85516098, 0.78649432, 0.89522516, 0.94529795, 0.90322825, 2015 0.9616288, 0.77439126, 1.0130917, 0.84021262, 0.97337238, 2016 0.93206526, 0.93809914, 0.87626441, 0.92706652, 0.86819358, 2017 0.74060652, 0.84046045, 0.94130171, 0.92537388, 0.80485074, 2018 0.81633347, 0.76401825, 0.81300784, 0.8052467, 1.27234895, 2019 0.92674704, 1.12106762, 0.91743016, 0.94694287, 0.87309918, 2020 0.99163895, 0.83777703, 0.89713459, 0.88208343, 0.90205904, 2021 0.9708827, 0.94965009, 0.81446019, 0.89512677, 0.97025135, 2022 1.02314481, 0.88399736, 1.01059963, 0.86193889, 0.94621507, 2023 0.97334837, 0.90122433, 0.71015398, 1.17491792, 1.13869784, 2024 1.03908735, 0.85480742, 0.98971408, 1.04147459, 0.85170846, 2025 0.94861439, 0.7778831, 0.73445723, 0.89587488, 0.88627975, 2026 0.98253057, 0.86159356, 1.06559385, 0.90852704, 0.86562284, 2027 0.92122779, 0.98233847, 0.94989946, 0.97171474, 0.92428639, 2028 1.03712828, 0.88170861, 0.86802004, 0.79670394, 0.85606075, 2029 1.09636421, 0.85048902, 0.99393971, 1.10510884, 0.80515088, 2030 0.95559246, 0.96803475, 0.98115871, 0.94603995, 0.8654312, 2031 0.90759845, 0.9010954, 0.77979965, 0.83322032, 0.8485444, 2032 0.89217626, 0.78817966, 1.03815705, 0.84076982, 0.93362471, 2033 1.06173045, 0.82612852, 0.8336989, 0.93943901, 0.91775212, 2034 1.00501856, 1.04269442, 0.93195426, 0.78377288, 1.03372915, 2035 0.8415154, 1.02888978, 0.93202174, 0.78683383, 0.85106996, 2036 0.9724203, 0.93409182, 0.97876305, 1.17153649, 0.9434591, 2037 0.81361398, 1.09554602, 1.48193137, 0.96349931, 0.93586569, 2038 1.0210303, 0.88980694, 0.88890459, 1.05330284, 1.09511186, 2039 0.91202441, 0.78753378, 0.98074421, 1.04268892, 1.14265114, 2040 0.86482628, 0.87233851, 1.18915875, 0.82556032, 0.87461473, 2041 1.08396187, 0.69206719, 0.88113605, 0.96951674, 0.89248729, 2042 0.909926, 0.82966779, 0.8261611, 0.9551228, 0.79879533, 2043 1.09416042, 1.01020839, 1.04133795, 1.09654304, 0.84060693, 2044 1.02612223, 1.00177693, 0.90510435, 1.2091018, 1.03290288, 2045 0.80529305, 0.74332311, 1.04728164, 1.04647891, 0.83707027, 2046 0.81648396, 1.07180239, 0.7926372, 0.99855278, 1.16851397, 2047 0.94566149, 0.75612408, 0.94975744, 0.92924923, 1.03215206, 2048 0.82394984, 0.84142091, 0.88028348, 1.11036047, 0.82451341, 2049 0.83694112, 0.84207459, 0.94095384, 1.00173733, 1.10241786, 2050 0.86609134, 0.86859604, 1.1211537, 0.84188088, 0.89023025, 2051 0.99062899, 0.96828743, 0.80106184, 0.86745454, 0.99013196, 2052 0.91838615, 0.86400837, 0.95679525, 0.78893711, 1.03753175, 2053 0.97177648, 0.88685941, 0.9441012, 0.69289996, 0.84219432, 2054 1.01050959, 0.83578317, 0.79907595, 1.21281139, 0.91613925, 2055 1.00202544, 0.95293036, 0.84583258, 0.84574886, 0.76470341, 2056 1.23606485, 1.10063291, 0.93852084, 0.97201415, 0.68523403, 2057 0.94560108, 0.81903039, 1.14332074, 0.80914367, 1.46398921, 2058 0.85155227, 1.41106313, 0.85740937, 0.91107708, 0.9003576, 2059 0.94132363, 0.85710825, 0.74805485, 1.2521402, 0.95307547, 2060 0.94274593, 0.86732331, 0.83850172, 0.96835288, 1.09443821, 2061 0.68532627, 0.84736457, 1.06989165, 0.81424504, 1.02942437, 2062 0.80255995, 0.89258275, 0.93560962, 1.04192911, 1.13498644, 2063 1.24409985, 0.93295415, 1.08360355, 1.16468059, 0.81482388, 2064 0.92387137, 1.07508578, 0.86564567, 1.0142773, 0.86143907, 2065 0.91214944, 0.9757589, 0.90588817, 0.74168224, 0.91222552, 2066 0.96119617, 0.95431519, 0.78080736, 1.0327991, 1.05112022, 2067 0.92761155, 1.0183631, 0.73188757, 0.85617225, 0.93341155, 2068 0.95106173, 0.9481304, 0.92996766, 1.08092599, 0.96485228, 2069 0.97964284, 0.94224551, 1.00654477, 1.01367565, 0.89785325, 2070 0.80725703, 0.7495798, 0.78240339, 1.04479122, 0.88200252, 2071 1.0664992, 1.05951775, 0.82508097, 0.81201381, 0.81860218, 2072 1.07561763, 1.02830358, 0.87348993, 1.0081337, 0.87470565, 2073 1.45597242, 0.77540871, 0.8036279, 0.80514427, 0.92688461, 2074 0.88152328, 1.56288788, 0.87251203, 0.92808414, 1.03548911, 2075 0.65226699, 0.81243827, 1.03103554, 1.11995602, 0.78956176, 2076 0.96734427, 0.91600861, 0.8246106, 1.09390498, 0.98187349, 2077 0.8919928, 0.98746862, 0.96298125, 0.93854424, 0.83060031, 2078 0.74692856, 0.99757209, 0.78888849, 1.17517182, 1.06657933, 2079 1.1244446, 0.93608433, 0.88898472, 0.96823218, 0.87496056, 2080 0.81776683, 0.98863687, 0.82962648, 1.02395766, 0.99622674, 2081 1.07138771, 0.86669915, 0.98172208, 0.8787271, 0.86125353, 2082 0.79554881, 0.93382729, 1.00706175, 1.08386454, 0.69664542, 2083 0.77316657, 0.79978147, 0.80764736, 0.9969375, 0.83554928, 2084 0.91017317, 0.95323454, 1.29872357, 1.08851275, 1.01673108, 2085 0.79536208, 0.84878371, 0.95165619, 0.87733936, 0.86319684, 2086 0.96758495, 0.87763237, 0.95094713, 1.00143077, 1.0596993, 2087 1.27278299, 0.82281481, 0.89765404, 0.94538181, 0.88161857, 2088 0.77679456, 0.84274277, 0.89864342, 0.98705162, 0.95456512, 2089 0.92712401, 0.77427128, 1.03292269, 0.87034158, 1.24316113, 2090 0.98278702, 1.17325118, 1.18863971, 0.88678137, 0.90389731, 2091 1.01740421, 0.80228624, 0.97742223, 0.82741518, 0.8359407, 2092 0.7177401, 1.02297899, 0.81896048, 0.77127181, 0.83328601, 2093 0.96939523, 0.94073198, 0.90356023, 1.12355064, 1.12811114, 2094 0.92403138, 1.05423548, 0.70827734, 0.95891358, 0.89898027, 2095 1.02318421, 0.93775375, 0.8245529, 0.80604304, 0.77555283, 2096 0.92112699, 0.85662169, 0.92725859, 0.93599147, 0.78971931, 2097 0.8337306, 0.93775212, 0.91025099, 0.75308822, 0.95391173, 2098 0.96840576, 0.8394416, 0.89087015, 0.73703219, 0.97812386, 2099 0.8787356, 0.93985266, 0.96406021, 0.88666152, 0.89242745, 2100 0.97900374, 0.85697634, 0.8795755, 0.78581812, 0.87138735, 2101 0.74602994, 0.96158936, 0.84529806, 0.85333232, 1.06116542, 2102 1.05929382, 1.09720986, 1.28959453, 0.91541148, 0.87657407, 2103 1.06514793, 0.8668096, 1.07325125, 0.85009534, 0.95542191, 2104 0.86977409, 0.96249874, 0.97715908, 0.89360331, 0.98859647, 2105 0.67560717, 0.90213348, 1.12051182, 0.99684949, 0.9863559, 2106 1.32246221, 0.84632664, 0.89707447, 1.00486846, 0.90843649, 2107 1.02399424, 0.97899017, 0.95693977, 0.8384806, 0.93927435, 2108 0.79153251, 1.08694094, 1.01785553, 0.99674552, 0.898566, 2109 0.94116882, 0.95224977, 0.99859129, 0.81125029, 0.85985586, 2110 1.14418875, 0.96306241, 1.31398561, 0.77961419, 1.01958366, 2111 0.9575668, 0.771084, 1.04473363, 1.01569517, 1.04560744, 2112 0.9648178, 0.93466398, 1.09313672, 0.90349389, 1.00193114, 2113 0.79991514, 0.91102351, 0.9795356, 0.89285193, 1.04898573, 2114 0.93031782, 0.95087069, 1.15644699, 0.91155375, 0.93005986, 2115 0.70098757, 0.82751625, 0.85462106, 1.34969332, 0.93382692, 2116 1.05558387, 1.25417819, 1.0546501, 1.05217032, 0.86031346, 2117 1.00864463, 0.73592482, 1.01899722, 1.00462831, 0.96882832, 2118 0.81334751, 1.05102745, 0.82288113, 1.05798623, 0.77971966, 2119 1.38584414, 1.0248193, 0.78951056, 0.76171823, 0.78407227, 2120 1.14808104, 0.97890501, 0.99870905, 0.96006489, 0.78442704, 2121 0.99315422, 0.83653213, 0.95210661, 0.97233777, 0.78140495, 2122 0.95996216, 0.76318841, 0.82333311, 0.87123204, 0.79531258, 2123 0.82681452, 1.00492217, 0.93549261, 1.00240153, 1.02086339, 2124 1.00424549, 0.87437775, 0.84675564, 0.98014462, 0.77262117, 2125 1.02620976, 0.91162462, 1.0275041, 1.1475431, 0.78167746, 2126 0.86273856, 0.84499552, 0.99712362, 0.9694771, 0.94523806, 2127 0.8450763, 0.93068519, 1.29362523, 1.0249628, 1.05522183, 2128 1.13433408, 1.06981137, 0.85666419, 0.98203234, 0.75867592, 2129 0.8844762, 0.89708521, 0.75482121, 0.80137918, 0.90412883, 2130 0.88815714, 1.11497471, 0.77441965, 0.93853353, 0.8962444, 2131 0.83055142, 0.99776183, 0.92581583, 0.78783745, 0.90934299, 2132 0.81136457, 0.99000726, 0.9669203, 1.2890399, 1.01923088, 2133 1.11076459, 1.01331706, 1.02470946, 0.92950448, 1.10298478, 2134 1.03723287, 1.09129035, 0.95138186, 0.85764624, 0.86606803, 2135 0.8141785, 1.0129293, 0.93267714, 0.95663734, 1.01940702, 2136 0.8072268, 1.0707215, 0.90482063, 1.01546955, 0.84018308, 2137 0.95938216, 0.96454054, 0.93114659, 1.09705112, 0.88720628, 2138 0.81067916, 0.82667413, 0.89494027, 0.9173495, 0.73326273, 2139 1.00209461, 0.9560545, 1.09126364, 0.95709908, 0.81314274, 2140 0.8274943, 1.37605062, 0.99097917, 1.02221806, 0.90277482, 2141 1.01611791, 0.79663017, 1.16686882, 1.19669266, 0.88366356, 2142 0.77661102, 0.73467145, 1.15438391, 0.91439204, 0.78280849, 2143 1.07238853, 1.03588797, 1.0438292, 0.75935005, 0.76200114, 2144 0.81603429, 0.74402367, 1.1171573, 0.90227791, 0.94762351, 2145 0.92462278, 0.8847803, 1.1343863, 0.8662186, 1.00410699, 2146 1.05008842, 0.94783969, 0.89555844, 0.98278045, 0.80396855, 2147 1.00483139, 0.82540491, 0.83284354, 0.93132265, 0.91191039, 2148 0.95753995, 1.18260689, 0.84124197, 0.87429189, 0.67617592, 2149 0.89495946, 0.92898357, 1.10528183, 1.06994417, 0.82259834, 2150 0.74746328, 0.99070832, 1.07386274, 0.84007203, 0.89720099, 2151 0.9670094, 1.02728082, 0.78001838, 0.97709347, 0.90602469, 2152 1.49985196, 0.80256976, 1.05905677, 0.98298874, 0.94679703, 2153 0.94305923, 0.98720786, 0.82091251, 0.91644161, 0.79576881, 2154 0.98942172, 0.92974761, 0.99307545, 0.86959859, 0.88549807, 2155 1.09246144, 0.87265047, 1.01449921, 0.74353851, 0.95029192, 2156 0.94385304, 0.84779449, 1.00690543, 0.79727923, 0.92285822, 2157 0.83164749, 1.06508941, 1.09757529, 0.9059649, 0.9146043, 2158 0.74474669, 0.71306438, 0.77989422, 0.84965464, 0.9424323, 2159 0.82492634, 0.85076686, 1.01110574, 1.01445751, 0.87929754, 2160 0.8773275, 0.72314196, 0.92285502, 1.18173931, 0.86460799, 2161 0.91795108, 1.16580482, 0.79880497, 0.72734786, 0.97579653, 2162 0.76967834, 0.97543732, 1.04996964, 1.16439594, 1.08656546, 2163 1.15644902, 0.98333436, 1.24374723, 0.95810117, 0.8488915, 2164 1.06288523, 0.99055893, 0.75517736, 0.95856183, 0.85574796, 2165 1.00426506, 1.25275675, 0.92735225, 0.83351314, 0.90216604, 2166 0.87996386, 1.13312875, 1.00891523, 0.76513657, 0.85659621, 2167 0.91142459, 1.05893495, 0.92253051, 0.87153684, 1.03190013, 2168 0.92160845, 1.01768282, 0.80590054, 1.05172907, 0.92758177, 2169 0.86902046, 0.93927127, 0.80389584, 0.96016014, 0.9720314, 2170 0.93255573, 0.85792534, 0.97826842, 0.80506149, 0.97170364, 2171 1.08397772, 1.01866333, 1.18898045, 1.02855427, 0.94848891, 2172 0.94336541, 0.93119013, 0.92907817, 1.11806635, 0.88409637, 2173 0.88809707, 1.06735612, 0.98447974, 0.88816438, 1.00099784, 2174 0.92443453, 1.00325146, 0.86977836, 0.84621801, 0.92361073, 2175 0.85573903, 0.77309241, 0.86717528, 1.19892035, 1.07497019, 2176 1.02178857, 0.8718756, 0.90646803, 0.92912096, 1.04538692, 2177 0.95245707, 0.99698525, 0.94583199, 0.92537599, 0.86720487, 2178 0.89927054, 0.86111792, 0.94401208, 1.01130191, 1.03759681, 2179 0.8177749, 1.07784373, 0.79823294, 1.00839713, 1.39409602, 2180 0.87146241, 1.21218822, 0.84895926, 1.01742432, 0.8044077, 2181 0.78632084, 1.07751744, 1.13147508, 0.90268302, 0.90024653, 2182 0.92072578, 0.87763264, 1.00736787, 0.90978808, 0.90895492, 2183 0.90766826, 0.98956566, 0.92075658, 0.77613105, 0.93815569, 2184 0.95455546, 1.00607757, 0.82187828, 0.94197599, 0.867015, 2185 0.90709762, 0.75604815, 0.91312261, 0.9286002, 0.74623204, 2186 0.87368702, 0.83879278, 0.92224793, 0.81676402, 0.90355168, 2187 0.92762955, 0.91784037, 0.82273304, 0.75947806, 0.92687078, 2188 0.87971276, 1.15037445, 0.86707445, 0.8611453, 0.91921763, 2189 1.07088129, 1.05150864, 1.02162325, 0.90305964, 0.99912687, 2190 0.87693204, 0.6186911, 0.95526533, 1.15975655, 1.00061222, 2191 0.74608861, 0.954568, 0.84965574, 0.79177899, 0.9741051, 2192 1.0119514, 0.79147502, 0.81367071, 0.87757421, 1.01270813, 2193 0.86044808, 0.9689615, 0.9577413, 0.79480242, 0.76073002, 2194 0.83131288, 0.96379259, 0.84679732, 0.82508685, 0.89977283, 2195 0.86766439, 1.12231836, 0.93058445, 1.04584181, 0.88838751, 2196 0.96615893, 0.98731619, 1.05517799, 1.02860493, 0.98881473, 2197 0.85210319, 0.91497438, 0.9275787, 0.97456134, 0.9011687, 2198 0.69417417, 0.89661214, 0.79038577, 1.08118303, 1.0509366, 2199 0.97813138, 0.85714945, 0.97330329, 0.83611871, 0.99772489, 2200 0.83591193, 0.75592677, 0.85392601, 1.02734573, 0.72404609, 2201 0.83534547, 0.91630472, 0.88463459, 1.12044562, 1.10991104, 2202 0.96047701, 1.12342573, 0.72046647, 0.96852239, 0.89605698, 2203 0.98310243, 0.92300659, 0.87794646, 0.83109321, 1.43297752, 2204 0.80609029, 0.8692251, 0.90254649, 0.81647796, 1.07521371, 2205 1.03942973, 0.96156488, 1.25225334, 1.0265727, 0.9518054, 2206 0.87765718, 1.15552582, 0.79577766, 0.66849239, 0.87236017, 2207 1.03437641, 0.98567811, 0.78463682, 1.09573491, 0.89858959, 2208 0.94056747, 1.16075317, 1.06296054, 0.85844006, 0.95475376, 2209 0.67038747, 0.7924646, 0.94009167, 0.88282093, 0.97711174, 2210 0.9209607, 1.03230176, 0.99981312, 1.12345314, 1.11705968, 2211 1.02453864, 0.91724212, 0.98337942, 0.89195196, 0.83800177, 2212 0.95044243, 0.76543521, 0.8613025, 0.83907753, 0.69333275, 2213 0.84411739, 0.68621941, 0.9847701, 1.13328481, 1.1432074, 2214 0.97156328, 0.86464461, 0.74258211, 0.97319505, 1.11453917, 2215 0.87344741, 0.91382664, 1.01635943, 1.38708812, 0.81377942, 2216 1.3828856, 0.74476285, 0.86657537, 1.1216954, 0.91008346, 2217 0.800862, 0.98356936, 0.92409916, 1.13970543, 0.97547004, 2218 0.99385865, 1.16476579, 0.78678084, 1.003947, 0.81491463, 2219 1.19724322, 0.9173622, 0.93274116, 0.80047839, 0.86798029, 2220 0.9433708, 0.82376832, 1.01726905, 0.81914971, 0.73290844]) 2221 2222 2223class Medpar1(object): 2224 ''' 2225 The medpar1 data can be found here. 2226 2227 https://www.stata-press.com/data/hh2/medpar1 2228 ''' 2229 def __init__(self): 2230 filename = os.path.join(os.path.dirname(os.path.abspath(__file__)), 2231 "stata_medpar1_glm.csv") 2232 data = pd.read_csv(filename).to_records() 2233 self.endog = data.los 2234 dummies = pd.get_dummies(data.admitype, prefix="race", drop_first=True) 2235 design = np.column_stack((data.codes, dummies)).astype(float) 2236 self.exog = add_constant(design, prepend=False) 2237 2238 2239class InvGaussLog(Medpar1): 2240 """ 2241 InvGaussLog is used with TestGlmInvgaussLog 2242 """ 2243 def __init__(self): 2244 super(InvGaussLog, self).__init__() 2245 filename = os.path.join(os.path.dirname(os.path.abspath(__file__)), 2246 "medparlogresids.csv") 2247 self.resids = pd.read_csv(filename, sep=',', header=None).values 2248 self.null_deviance = 335.1539777981053 # from R, Rpy bug 2249 self.params = np.array([0.09927544, -0.19161722, 1.05712336]) 2250 self.bse = np.array([0.00600728, 0.02632126, 0.04915765]) 2251 self.aic_R = 18545.836421595981 2252 self.aic_Stata = 6.619000588187141 2253 self.deviance = 304.27188306012789 2254 self.scale = 0.10240599519220173 2255 # self.llf = -9268.9182107979905 # from R 2256 self.llf = -12162.72308108797 # from Stata, big rounding diff with R 2257 self.bic_Stata = -29849.51723280784 2258 self.chi2 = 398.5465213008323 # from Stata not in sm 2259 self.df_model = 2 2260 self.df_resid = 3673 2261 self.fittedvalues = np.array([ 2262 7.03292237, 7.03292237, 7.03292237, 7.03292237, 5.76642001, 2263 7.03292237, 7.03292237, 6.36826384, 7.03292237, 7.03292237, 2264 7.03292237, 7.03292237, 7.03292237, 5.76642001, 7.03292237, 2265 5.22145448, 7.03292237, 5.22145448, 4.72799187, 4.72799187, 2266 7.03292237, 7.03292237, 6.36826384, 7.03292237, 5.76642001, 2267 7.03292237, 4.28116479, 7.03292237, 7.03292237, 7.03292237, 2268 5.76642001, 7.03292237, 7.03292237, 7.03292237, 7.03292237, 2269 7.03292237, 3.87656588, 7.03292237, 7.03292237, 4.28116479, 2270 7.03292237, 7.03292237, 4.72799187, 7.03292237, 7.03292237, 2271 7.03292237, 5.22145448, 6.36826384, 6.36826384, 4.28116479, 2272 4.72799187, 7.03292237, 7.03292237, 7.03292237, 7.03292237, 2273 5.22145448, 7.03292237, 7.03292237, 6.36826384, 7.03292237, 2274 7.03292237, 7.03292237, 7.03292237, 5.76642001, 6.36826384, 2275 6.36826384, 5.22145448, 7.03292237, 7.03292237, 7.03292237, 2276 5.76642001, 7.03292237, 7.03292237, 3.87656588, 5.76642001, 2277 7.03292237, 7.03292237, 7.03292237, 7.03292237, 7.03292237, 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5.80654132, 2953 5.80654132, 4.31095206, 5.80654132, 5.80654132, 5.80654132, 2954 3.90353806, 4.76088805, 5.80654132, 4.76088805, 5.80654132, 2955 4.76088805, 3.53462742, 3.90353806, 5.80654132, 5.80654132, 2956 5.80654132, 5.25778406, 5.80654132, 5.80654132, 5.25778406, 2957 5.80654132, 5.80654132, 5.80654132, 5.80654132, 5.80654132, 2958 5.80654132, 5.80654132, 5.80654132, 5.80654132, 5.80654132, 2959 3.53462742, 3.53462742, 3.90353806, 5.80654132, 4.31095206, 2960 3.53462742, 5.80654132, 4.76088805, 4.76088805, 3.20058132, 2961 3.90353806, 5.80654132, 5.25778406, 5.80654132, 5.80654132, 2962 5.80654132, 5.80654132, 4.31095206, 5.25778406, 4.31095206, 2963 5.80654132, 3.20058132, 5.80654132, 4.31095206, 4.31095206, 2964 4.76088805, 5.80654132, 4.76088805, 4.31095206, 5.80654132, 2965 5.25778406, 3.53462742, 3.53462742, 5.25778406, 5.80654132, 2966 3.90353806, 5.25778406, 4.31095206, 4.31095206, 3.53462742, 2967 5.80654132, 3.90353806, 5.80654132, 5.80654132, 4.76088805, 2968 5.25778406, 3.20058132, 3.90353806, 5.80654132, 5.25778406, 2969 5.80654132, 5.80654132, 5.25778406, 5.80654132, 4.31095206, 2970 5.25778406, 4.76088805, 5.80654132, 5.80654132, 5.25778406, 2971 3.53462742, 5.80654132, 5.80654132, 5.80654132, 5.25778406, 2972 5.25778406, 5.80654132, 3.20058132, 5.80654132, 5.80654132, 2973 3.53462742, 5.80654132, 5.80654132, 5.80654132, 4.31095206, 2974 5.80654132, 4.76088805, 5.80654132, 5.80654132, 5.80654132, 2975 3.90353806, 4.31095206, 5.25778406, 5.80654132, 3.53462742, 2976 3.90353806, 5.25778406, 4.31095206, 5.80654132, 5.25778406, 2977 5.25778406, 2.89810483, 5.80654132, 5.80654132, 5.80654132, 2978 5.80654132, 5.80654132, 5.25778406, 5.80654132, 4.76088805, 2979 5.80654132, 5.80654132, 5.80654132, 4.31095206, 5.80654132, 2980 3.20058132, 3.90353806, 5.80654132, 5.80654132, 5.25778406, 2981 5.80654132, 5.80654132, 5.80654132, 5.80654132, 5.80654132, 2982 5.80654132, 5.80654132, 2.6242144, 5.80654132, 3.90353806, 2983 5.25778406, 4.76088805, 5.80654132, 5.80654132, 3.90353806, 2984 5.80654132, 3.53462742, 2.89810483, 5.80654132, 3.53462742, 2985 2.89810483, 4.76088805, 5.80654132, 5.80654132, 5.80654132, 2986 4.31095206, 5.80654132, 4.76088805, 3.90353806, 2.89810483, 2987 4.76088805, 5.80654132, 2.6242144, 3.53462742, 4.31095206, 2988 5.25778406, 5.25778406, 3.20058132, 4.31095206, 4.31095206, 2989 3.20058132, 4.31095206, 5.25778406, 4.31095206, 5.25778406, 2990 3.90353806, 4.31095206, 5.80654132, 5.80654132, 5.80654132, 2991 5.80654132, 3.90353806, 5.80654132, 5.80654132, 5.80654132, 2992 4.31095206, 5.80654132, 5.80654132, 5.80654132, 3.90353806, 2993 5.25778406, 3.90353806, 4.31095206, 4.76088805, 3.90353806, 2994 5.80654132, 5.80654132, 5.80654132, 2.89810483, 5.80654132, 2995 5.80654132, 5.80654132, 5.80654132, 5.80654132, 5.80654132, 2996 5.80654132, 3.90353806, 3.20058132, 5.25778406, 4.76088805, 2997 5.25778406]) 2998 2999 3000class InvGaussIdentity(Medpar1): 3001 """ 3002 Accuracy is different for R vs Stata ML vs Stata IRLS, we are close. 3003 """ 3004 def __init__(self): 3005 super(InvGaussIdentity, self).__init__() 3006 self.params = np.array([0.44538838, -1.05872706, 2.83947966]) 3007 self.bse = np.array([0.02586783, 0.13830023, 0.20834864]) 3008 filename = os.path.join(os.path.dirname(os.path.abspath(__file__)), 3009 "igaussident_resids.csv") 3010 self.resids = pd.read_csv(filename, sep=',', header=None).values 3011 self.null_deviance = 335.1539777981053 # from R, Rpy bug 3012 self.df_null = 3675 3013 self.deviance = 305.33661191013988 3014 self.df_resid = 3673 3015 self.df_model = 2 3016 self.aic_R = 18558.677276882016 3017 self.aic_Stata = 6.619290231464371 3018 self.bic_Stata = -29848.45250412075 3019 self.llf_stata = -12163.25544543151 3020 self.chi2 = 567.1229375785638 # in Stata not sm 3021 # self.llf = -9275.3386384410078 # from R 3022 self.llf = -12163.25545 # from Stata, big diff with R 3023 self.scale = 0.10115387793455666 3024 self.pearson_chi2 = 371.5346609292967 # deviance_p in Stata 3025 self.fittedvalues = np.array([ 3026 6.84797506, 6.84797506, 6.84797506, 6.84797506, 5.9571983, 3027 6.84797506, 6.84797506, 6.40258668, 6.84797506, 6.84797506, 3028 6.84797506, 6.84797506, 6.84797506, 5.9571983, 6.84797506, 3029 5.51180993, 6.84797506, 5.51180993, 5.06642155, 5.06642155, 3030 6.84797506, 6.84797506, 6.40258668, 6.84797506, 5.9571983, 3031 6.84797506, 4.62103317, 6.84797506, 6.84797506, 6.84797506, 3032 5.9571983, 6.84797506, 6.84797506, 6.84797506, 6.84797506, 3033 6.84797506, 4.17564479, 6.84797506, 6.84797506, 4.62103317, 3034 6.84797506, 6.84797506, 5.06642155, 6.84797506, 6.84797506, 3035 6.84797506, 5.51180993, 6.40258668, 6.40258668, 4.62103317, 3036 5.06642155, 6.84797506, 6.84797506, 6.84797506, 6.84797506, 3037 5.51180993, 6.84797506, 6.84797506, 6.40258668, 6.84797506, 3038 6.84797506, 6.84797506, 6.84797506, 5.9571983, 6.40258668, 3039 6.40258668, 5.51180993, 6.84797506, 6.84797506, 6.84797506, 3040 5.9571983, 6.84797506, 6.84797506, 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3752 5.34385962, 5.34385962, 3.11691773, 4.45308287, 4.45308287, 3753 3.11691773, 4.45308287, 5.34385962, 4.45308287, 5.34385962, 3754 4.00769449, 4.45308287, 5.789248, 5.789248, 5.789248, 3755 5.789248, 4.00769449, 5.789248, 5.789248, 5.789248, 3756 4.45308287, 5.789248, 5.789248, 5.789248, 4.00769449, 3757 5.34385962, 4.00769449, 4.45308287, 4.89847125, 4.00769449, 3758 5.789248, 5.789248, 5.789248, 2.67152936, 5.789248, 3759 5.789248, 5.789248, 5.789248, 5.789248, 5.789248, 3760 5.789248, 4.00769449, 3.11691773, 5.34385962, 4.89847125, 3761 5.34385962]) 3762 3763 3764class Committee(object): 3765 def __init__(self): 3766 self.resids = np.array([ 3767 [-5.04950800e-01, -6.29721800e-01, -8.35499100e+01, 3768 -1.30628500e+00, -6.62028600e+00], 3769 [-2.34152200e-01, -2.55423500e-01, -2.16830700e+02, 3770 -7.58866000e-01, -7.18370200e+00], 3771 [1.02423700e+00, 7.98775800e-01, 4.83736300e+02, 3772 2.50351500e+00, 2.25135300e+01], 3773 [-2.85061700e-01, -3.17796600e-01, -7.04115100e+04, 3774 -2.37991800e+00, -1.41745600e+02], 3775 [2.09902500e-01, 1.96787700e-01, 2.24751400e+03, 3776 9.51945500e-01, 2.17724200e+01], 3777 [-4.03483500e-01, -4.75741500e-01, -1.95633600e+04, 3778 -2.63502600e+00, -8.89461400e+01], 3779 [-1.64413400e-01, -1.74401100e-01, -1.73310300e+04, 3780 -1.16235500e+00, -5.34213500e+01], 3781 [-4.29607700e-01, -5.13466700e-01, -5.30037000e+03, 3782 -2.24496200e+00, -4.78260300e+01], 3783 [3.23713000e-01, 2.94184600e-01, 4.11079400e+03, 3784 1.48684400e+00, 3.65598400e+01], 3785 [1.50367200e-01, 1.43429400e-01, 7.28532100e+03, 3786 8.85542900e-01, 3.31355000e+01], 3787 [4.21288600e-01, 3.73428000e-01, 1.37315700e+03, 3788 1.52133200e+00, 2.41570200e+01], 3789 [4.50658700e-01, 3.96586700e-01, 1.70146900e+03, 3790 1.66177900e+00, 2.78032600e+01], 3791 [2.43537500e-01, 2.26174000e-01, 3.18402300e+03, 3792 1.13656200e+00, 2.79073400e+01], 3793 [1.05182900e+00, 8.16205400e-01, 6.00135200e+03, 3794 3.89079700e+00, 7.97131300e+01], 3795 [-5.54450300e-01, -7.12749000e-01, -2.09485200e+03, 3796 -2.45496500e+00, -3.42189900e+01], 3797 [-6.05750600e-01, -8.06411100e-01, -2.74738200e+02, 3798 -1.90774400e+00, -1.30510500e+01], 3799 [-3.41215700e-01, -3.90244600e-01, -6.31138000e+02, 3800 -1.27022900e+00, -1.47600100e+01], 3801 [2.21898500e-01, 2.07328700e-01, 6.91135800e+02, 3802 8.16876400e-01, 1.24392900e+01], 3803 [2.45592500e-01, 2.26639200e-01, 1.99250600e-01, 3804 2.57948300e-01, 2.74723700e-01], 3805 [-7.58952600e-01, -1.15300800e+00, -2.56739000e+02, 3806 -2.40716600e+00, -1.41474200e+01]]) 3807 self.null_deviance = 27.81104693643434 # from R, Rpy bug 3808 self.params = np.array([ 3809 -0.0268147, 1.25103364, 2.91070663, 3810 -0.34799563, 0.00659808, -0.31303026, -6.44847076]) 3811 self.bse = np.array([ 3812 1.99956263e-02, 4.76820254e-01, 3813 6.48362654e-01, 4.17956107e-01, 1.41512690e-03, 1.07770186e-01, 3814 1.99557656e+00]) 3815 self.aic_R = 216.66573352377935 3816 self.aic_Stata = 10.83328660860436 3817 self.deviance = 5.615520158267981 3818 self.scale = 0.38528595746569905 3819 self.llf = -101.33286676188968 # from R 3820 self.llf_Stata = -101.3328660860436 # same as R 3821 self.bic_Stata = -33.32900074962649 3822 self.chi2 = 5.008550263545408 3823 self.df_model = 6 3824 self.df_resid = 13 3825 self.fittedvalues = np.array([ 3826 12.62019383, 30.18289514, 21.48377849, 496.74068604, 3827 103.23024673, 219.94693494, 324.4301163, 110.82526477, 3828 112.44244488, 219.86056381, 56.84399998, 61.19840382, 3829 114.09290269, 75.29071944, 61.21994387, 21.05130889, 3830 42.75939828, 55.56133536, 0.72532053, 18.14664665]) 3831 3832 3833class Wfs(object): 3834 """ 3835 Wfs used for TestGlmPoissonOffset 3836 3837 Results are from Stata and R. 3838 """ 3839 def __init__(self): 3840 3841 self.resids = glm_test_resids.wfs_resids 3842 self.null_deviance = 3731.85161919 # from R 3843 self.params = [ 3844 .9969348, 1.3693953, 1.6137574, 1.7849111, 1.9764051, 3845 .11241858, .15166023, .02297282, -.10127377, -.31014953, 3846 -.11709716] 3847 self.bse = [ 3848 .0527437, .0510688, .0511949, .0512138, .0500341, 3849 .0324963, .0283292, .0226563, .0309871, .0552107, .0549118] 3850 self.aic_R = 522.14215776 # R adds 2 for dof to AIC 3851 self.aic_Stata = 7.459173652869477 # stata divides by nobs 3852 # self.deviance = 70.6652992116034 # from Stata 3853 self.deviance = 70.665301270867 # from R 3854 self.scale = 1.0 3855 self.llf = -250.0710778504317 # from Stata, ours with scale=1 3856 self.bic_Stata = -179.9959200693088 # no bic in R? 3857 self.df_model = 10 3858 self.df_resid = 59 3859 3860 # TODO: taken from Stata; not available in sm yet 3861 self.chi2 = 2699.138063147485 3862 3863 self.fittedvalues = [ 3864 7.11599, 19.11356, 33.76075, 33.26743, 11.94399, 3865 27.49849, 35.07923, 37.22563, 64.18037, 108.0408, 3866 100.0948, 35.67896, 24.10508, 73.99577, 52.2802, 3867 38.88975, 35.06507, 102.1198, 107.251, 41.53885, 3868 196.3685, 335.8434, 205.3413, 43.20131, 41.98048, 3869 96.65113, 63.2286, 30.78585, 70.46306, 172.2402, 3870 102.5898, 43.06099, 358.273, 549.8983, 183.958, 3871 26.87062, 62.53445, 141.687, 52.47494, 13.10253, 3872 114.9587, 214.803, 90.33611, 18.32685, 592.5995, 3873 457.4376, 140.9273, 3.812064, 111.3119, 97.62744, 3874 57.48056, 19.43552, 130.4872, 151.7268, 69.67963, 3875 13.04879, 721.728, 429.2136, 128.2132, 9.04735, 3876 301.7067, 177.3487, 46.40818, 4.707507, 330.4211, 3877 330.7497, 84.38604, 1456.757, 451.005, 67.51025] 3878 3879 3880class CpunishTweediePower15(object): 3881 """ 3882 # From R 3883 setwd('c:/workspace') 3884 data <- read.csv('cpunish.csv', sep=",") 3885 3886 library(statmod) 3887 library(tweedie) 3888 3889 summary(glm(EXECUTIONS ~ INCOME + SOUTH - 1, 3890 family=tweedie(var.power=1.5, link.power=1), 3891 data=data)) 3892 """ 3893 def __init__(self): 3894 3895 resid_resp = [ 3896 28.90498242, 0.5714367394, 4.3135711827, -3.7417822942, 3897 -4.9544111888, 0.4666602184, 0.0747051827, -6.114236142, 3898 -1.0048540116, -6.9747602544, -0.7626907093, 3899 -0.5688093336, -6.9845579527, -1.1594503855, 3900 -0.6365453438, -0.3994222036, -0.732355528] 3901 resid_dev = [ 3902 3.83881147757395, 0.113622743768915, 2.01981988071128, 3903 -0.938107751845672, -1.29607304923555, 0.316205676540778, 3904 0.045273675744568, -1.69968893354602, -0.699080227540624, 3905 -2.1707839733642, -0.568738719015137, -0.451266938413727, 3906 -2.17218106358745, -0.774613533242944, -0.493831656345955, 3907 -0.336453094366771, -0.551210030548659] 3908 resid_pear = [ 3909 6.02294407053171, 0.115516970886608, 2.9148208139849, 3910 -0.806210703943481, -1.04601155367613, 0.338668788938945, 3911 0.045708693925888, -1.27176471794657, -0.5964031365026, 3912 -1.46974255264233, -0.498557360800493, 3913 -0.405777068096011, -1.47045242302365, -0.65086941662954, 3914 -0.439928270112046, -0.310433407220704, 3915 -0.485001313250992] 3916 resid_work = [ 3917 28.9049727916181, 0.571427719513967, 4.31357425907762, 3918 -3.74179256698823, -4.9544210736226, 0.466663015515745, 3919 0.0747086948013966, -6.114245735344, -1.00485035431368, 3920 -6.97477010217068, -0.76268749374494, -0.568806471745149, 3921 -6.98456778258272, -1.15944644619981, -0.636542358439925, 3922 -0.399419650775458, -0.732352367853816] 3923 self.resid_response = resid_resp 3924 self.resid_deviance = resid_dev 3925 self.resid_pearson = resid_pear 3926 self.resid_working = resid_work 3927 # self.null_deviance = 3731.85161919 # N/A 3928 self.params = [0.0000471043, 6.4721324886] 3929 self.bse = [0.0000246888, 3.5288126173] 3930 # self.aic_R = 522.14215776 # R adds 2 for dof to AIC 3931 # self.aic_Stata = 7.459173652869477 # stata divides by nobs 3932 # self.deviance = 70.6652992116034 # from Stata 3933 self.deviance = 36.087307138233 # from R 3934 # self.scale = 1.0 3935 # self.llf = -250.0710778504317 # from Stata, ours with scale=1 3936 # self.bic_Stata = -179.9959200693088 # no bic in R? 3937 self.df_model = 1 3938 self.df_resid = 15 3939 3940 # TODO: taken from Stata; not available in sm yet 3941 # self.chi2 = 2699.138063147485 3942 3943 self.fittedvalues = [ 3944 8.09501758000751, 8.42856326056927, 3945 1.68642881732415, 7.74178229423817, 3946 7.95441118875248, 1.53333978161934, 3947 1.92529481734232, 8.11423614202829, 3948 2.00485401159015, 7.97476025442155, 3949 1.76269070926448, 1.56880933358418, 3950 7.98455795270665, 2.15945038549266, 3951 1.63654534384372, 1.39942220361664, 3952 1.73235552803559] 3953 3954 3955class CpunishTweediePower2(object): 3956 """ 3957 # From R 3958 setwd('c:/workspace') 3959 data <- read.csv('cpunish.csv', sep=",") 3960 3961 library(statmod) 3962 library(tweedie) 3963 3964 summary(glm(EXECUTIONS ~ INCOME + SOUTH - 1, 3965 family=tweedie(var.power=2, link.power=1), 3966 data=data)) 3967 """ 3968 def __init__(self): 3969 resid_resp = [ 3970 28.9397568116168, 0.605199215492085, 4.30845487128123, 3971 -3.7059362524505, -4.91921022348665, 0.46200835064931, 3972 0.068864196242604, -6.07952005594693, -1.01093636580438, 3973 -6.9396210244365, -0.768038385056284, -0.573568809339664, 3974 -6.94944844711606, -1.16600175635393, -0.641510318056987, 3975 -0.403667790321936, -0.737611172529194] 3976 resid_dev = [ 3977 2.03295746713119, 0.0704291140028282, 1.60058476017728, 3978 -0.591230836989137, -0.836067997150736, 0.274690511542166, 3979 0.0352446721149477, -1.13465831620614, -0.625909330466303, 3980 -1.5477830210949, -0.520517540529698, -0.421531194473357, 3981 -1.54848147513823, -0.684927882583903, -0.45784673829438, 3982 -0.320960880764019, -0.505992145923248] 3983 resid_pear = [ 3984 3.59043221590711, 0.0720921473930558, 2.54705286789752, 3985 -0.480919661289957, -0.621174344999372, 3986 0.300397177607798, 0.0356599448410699, 3987 -0.752460543924524, -0.502719222246499, 3988 -0.874049404005278, -0.434401419984914, 3989 -0.364501892726482, -0.874205109115113, 3990 -0.538319857282425, -0.390804925805356, 3991 -0.287580717535275, -0.424497254731367] 3992 resid_work = [ 3993 28.9397568116168, 0.605199215492085, 4.30845487128123, 3994 -3.7059362524505, -4.91921022348665, 0.46200835064931, 3995 0.068864196242604, -6.07952005594693, -1.01093636580438, 3996 -6.9396210244365, -0.768038385056284, -0.573568809339664, 3997 -6.94944844711606, -1.16600175635393, -0.641510318056987, 3998 -0.403667790321936, -0.737611172529194] 3999 self.resid_response = resid_resp 4000 self.resid_deviance = resid_dev 4001 self.resid_pearson = resid_pear 4002 self.resid_working = resid_work 4003 # self.null_deviance = 3731.85161919 # N/A 4004 self.params = [4.72472244209477e-05, 6.43243456540827] 4005 self.bse = [1.86839521185429e-05, 3.83231672422612] 4006 # self.aic_R = 522.14215776 # R adds 2 for dof to AIC 4007 # self.aic_Stata = 7.459173652869477 # stata divides by nobs 4008 # self.deviance = 70.6652992116034 # from Stata 4009 self.deviance = 15.7840685407599 # from R 4010 # self.scale = 1.0 4011 # self.llf = -250.0710778504317 # from Stata, ours with scale=1 4012 # self.bic_Stata = -179.9959200693088 # no bic in R? 4013 self.df_model = 1 4014 self.df_resid = 15 4015 4016 # TODO: taken from Stata; not available in sm yet 4017 # self.chi2 = 2699.138063147485 4018 4019 self.fittedvalues = [ 4020 8.06024318838318, 8.39480078450791, 4021 1.69154512871877, 7.7059362524505, 4022 7.91921022348665, 1.53799164935069, 4023 1.9311358037574, 8.07952005594693, 4024 2.01093636580438, 7.9396210244365, 4025 1.76803838505628, 1.57356880933966, 4026 7.94944844711606, 2.16600175635393, 4027 1.64151031805699, 1.40366779032194, 4028 1.73761117252919] 4029 4030 4031class CpunishTweedieLog1(object): 4032 """ 4033 # From R 4034 setwd('c:/workspace') 4035 data <- read.csv('cpunish.csv', sep=",") 4036 4037 library(statmod) 4038 library(tweedie) 4039 4040 summary(glm(EXECUTIONS ~ INCOME + SOUTH - 1, 4041 family=tweedie(var.power=1, link.power=0), 4042 data=data)) 4043 """ 4044 def __init__(self): 4045 resid_resp = [ 4046 28.7231009386298, -0.307318358456484, 4.19015460156576, 4047 -3.30975297068573, -4.87746969906705, 0.285041779927669, 4048 0.0315071085472043, -6.33304532673002, -1.02436294926752, 4049 -6.9340610414309, -0.859055122126197, -0.736490247380883, 4050 -6.96145354225969, -1.13750232106315, -0.778363801217565, 4051 -0.636042191521576, -0.839322392162821] 4052 resid_dev = [ 4053 7.30513948467594, -0.101296157943519, 2.44987904003561, 4054 -1.34021826264378, -1.99062116973315, 0.212014827300475, 4055 0.0223969676885324, -2.63775728156667, -0.798884085657077, 4056 -3.11862021596631, -0.691356293575324, -0.607658243497501, 4057 -3.12628915913493, -0.869326536299756, -0.636663290048755, 4058 -0.536212950673418, -0.67812263418512] 4059 resid_pear = [ 4060 9.98383729954486, -0.100734032611758, 3.11465040934513, 4061 -1.22417704160631, -1.73780566805242, 0.217661565866984, 4062 0.0224564769560215, -2.19386916576256, 4063 -0.719962160947025, -2.46172701579962, 4064 -0.630049829146329, -0.558895774299477, 4065 -2.4671965358931, -0.778034748813176, 4066 -0.583676657782738, -0.497265896656757, 4067 -0.61887064145702] 4068 resid_work = [ 4069 3.47027319357873, -0.0330190014589175, 2.31520029566659, 4070 -0.452785885372436, -0.619167053050639, 4071 0.166209168591668, 0.0160057009522403, 4072 -0.759991705123147, -0.506017436072008, 4073 -0.873961141113221, -0.46209233491888, 4074 -0.424125760851072, -0.874394795536774, 4075 -0.532164250702372, -0.437685360377137, 4076 -0.388768819543728, -0.456321521305397] 4077 self.resid_response = resid_resp 4078 self.resid_deviance = resid_dev 4079 self.resid_working = resid_work 4080 self.resid_pearson = resid_pear 4081 # self.null_deviance = 3731.85161919 # N/A 4082 self.params = [1.65700638623525e-05, 1.54257997850499] 4083 self.bse = [1.81044999017907e-05, 0.725739640176733] 4084 # self.aic_R = 522.14215776 # R adds 2 for dof to AIC 4085 # self.aic_Stata = 7.459173652869477 # stata divides by nobs 4086 # self.deviance = 70.6652992116034 # from Stata 4087 self.deviance = 95.0325613464258 # from R 4088 # self.scale = 1.0 4089 # self.llf = -250.0710778504317 # from Stata, ours with scale=1 4090 # self.bic_Stata = -179.9959200693088 # no bic in R? 4091 self.df_model = 1 4092 self.df_resid = 15 4093 4094 # TODO: taken from Stata; not available in sm yet 4095 # self.chi2 = 2699.138063147485 4096 4097 self.fittedvalues = [ 4098 8.27689906137016, 9.30731835845648, 4099 1.80984539843424, 7.30975297068573, 4100 7.87746969906705, 1.71495822007233, 4101 1.9684928914528, 8.33304532673002, 4102 2.02436294926752, 7.9340610414309, 4103 1.8590551221262, 1.73649024738088, 4104 7.96145354225969, 2.13750232106315, 4105 1.77836380121756, 1.63604219152158, 4106 1.83932239216282] 4107 4108 4109class FairTweedieLog15(object): 4110 """ 4111 # From R 4112 setwd('c:/workspace') 4113 data <- read.csv('fair.csv', sep=",") 4114 4115 library(statmod) 4116 library(tweedie) 4117 4118 model <- glm(affairs ~ rate_marriage + age + yrs_married -1, data=data, 4119 family=tweedie(var.power=1.5, link.power = 0)) 4120 r <- resid(model, type='response') 4121 paste(as.character(r[1:17]), collapse=",") 4122 r <- resid(model, type='deviance') 4123 paste(as.character(r[1:17]), collapse=",") 4124 r <- resid(model, type='pearson') 4125 paste(as.character(r[1:17]), collapse=",") 4126 r <- resid(model, type='working') 4127 paste(as.character(r[1:17]), collapse=",") 4128 paste(as.character(model$coefficients[1:17]), collapse=",") 4129 s <- summary(model) 4130 paste(as.character(sqrt(diag(s$cov.scaled))), collapse=",") 4131 s$deviance 4132 paste(as.character(model$fitted.values[1:17]), collapse=",") 4133 """ 4134 def __init__(self): 4135 resid_resp = [ 4136 -0.997868449815039, 2.69283106662728, 0.677397439981157, 4137 0.220024942629269, 4.30244966465517, 4.12917275616972, 4138 0.669303122309246, 1.64321562230925, 3.73361710426128, 4139 0.271937359562684, 1.70030700747884, 1.55430573164611, 4140 -0.263723852468304, 1.51263973164611, 2.75223392654071, 4141 0.310487741565721, 1.28077676333896, -0.722602160018842] 4142 resid_dev = [ 4143 -1.40274708439925, 2.48476334070913, 0.722690630291423, 4144 0.333179337353702, 4.00781035212304, 3.33344591331998, 4145 1.51543361886727, 2.82502498800952, 2.2795411865605, 4146 0.245239170945663, 0.993721205729013, 1.74920359743562, 4147 -0.363141475997386, 1.71412357710318, 2.57445879456298, 4148 0.279858474280908, 1.22953362433333, -1.84397406923697] 4149 resid_pear = [ 4150 -0.923380371255914, 4.28706294677515, 0.864309147553743, 4151 0.366063826152319, 9.17690493704408, 6.57783985712941, 4152 2.39340023647571, 5.87607098775551, 3.55791152198837, 4153 0.260052421285998, 1.21439278430259, 2.66470328868695, 4154 -0.327698246542009, 2.59327105694137, 4.53096038849505, 4155 0.299198418236691, 1.6399313081981, -0.921987034618483] 4156 resid_work = [ 4157 -0.899807800767353, 5.00583784559752, 0.937441759049674, 4158 0.433762277766879, 11.8128959278604, 7.6822784352496, 4159 3.65998654763585, 8.98568506862295, 3.50120010377224, 4160 0.256207345500911, 1.08551656668241, 3.18923357641756, 4161 -0.352302468597673, 3.10374035363038, 5.35005901385941, 4162 0.29552727652976, 1.78077778644209, -1] 4163 self.resid_response = resid_resp 4164 self.resid_deviance = resid_dev 4165 self.resid_working = resid_work 4166 self.resid_pearson = resid_pear 4167 # self.null_deviance = 3731.85161919 # N/A 4168 self.params = [ 4169 -0.389168171340452, 0.0670222370664611, -0.0970852004566712] 4170 self.bse = [ 4171 0.0323435784513691, 0.0063805300018014, 0.00893580175352525] 4172 # self.aic_R = 522.14215776 # R adds 2 for dof to AIC 4173 # self.aic_Stata = 7.459173652869477 # stata divides by nobs 4174 # self.deviance = 70.6652992116034 # from Stata 4175 self.deviance = 20741.82 # from R 4176 # self.scale = 1.0 4177 # self.llf = -250.0710778504317 # from Stata, ours with scale=1 4178 # self.bic_Stata = -179.9959200693088 # no bic in R? 4179 self.df_model = 2 4180 self.df_resid = 6363 4181 4182 # TODO: taken from Stata; not available in sm yet 4183 # self.chi2 = 2699.138063147485 4184 4185 self.fittedvalues = [ 4186 1.10897954981504, 0.537938133372725, 4187 0.722602160018842, 0.507247757370731, 4188 0.364216335344828, 0.537493243830281, 4189 0.182870377690754, 0.182870377690754, 4190 1.06638209573872, 1.06139564043732, 4191 1.56635749252116, 0.487360268353893, 4192 0.748572252468304, 0.487360268353893, 4193 0.514430573459285, 1.05062295843428, 4194 0.71922323666104, 0.722602160018842] 4195