import numpy as np class Bunch(dict): def __init__(self, **kw): dict.__init__(self, kw) self.__dict__ = self epanechnikov_hsheather_q75 = Bunch() epanechnikov_hsheather_q75.table = np.array([ [.6440143 , .0122001 , 52.79 , 0.000 , .6199777 , .6680508], [62.39648 , 13.5509 , 4.60 , 0.000 , 35.69854 , 89.09443] ]) epanechnikov_hsheather_q75.psrsquared = 0.6966 epanechnikov_hsheather_q75.rank = 2 epanechnikov_hsheather_q75.sparsity = 223.784434936344 epanechnikov_hsheather_q75.bwidth = .1090401129546568 #epanechnikov_hsheather_q75.kbwidth = 59.62067927472172 # Stata 12 results epanechnikov_hsheather_q75.kbwidth = 59.30 # TODO: why do we need lower tolerance? epanechnikov_hsheather_q75.df_m = 1 epanechnikov_hsheather_q75.df_r = 233 epanechnikov_hsheather_q75.f_r = .0044685860313942 epanechnikov_hsheather_q75.N = 235 epanechnikov_hsheather_q75.q_v = 745.2352905273438 epanechnikov_hsheather_q75.q = .75 epanechnikov_hsheather_q75.sum_rdev = 43036.06956481934 epanechnikov_hsheather_q75.sum_adev = 13058.50008841318 epanechnikov_hsheather_q75.convcode = 0 biweight_bofinger = Bunch() biweight_bofinger.table = np.array([ [ .5601805 , .0136491 , 41.04 , 0.000 , .533289 , .5870719], [ 81.48233 , 15.1604 , 5.37 , 0.000 , 51.61335 , 111.3513] ]) biweight_bofinger.psrsquared = 0.6206 biweight_bofinger.rank = 2 biweight_bofinger.sparsity = 216.8218989750115 biweight_bofinger.bwidth = .2173486679767846 biweight_bofinger.kbwidth = 91.50878448104551 biweight_bofinger.df_m = 1 biweight_bofinger.df_r = 233 biweight_bofinger.f_r = .0046120802590851 biweight_bofinger.N = 235 biweight_bofinger.q_v = 582.541259765625 biweight_bofinger.q = .5 biweight_bofinger.sum_rdev = 46278.05667114258 biweight_bofinger.sum_adev = 17559.93220318131 biweight_bofinger.convcode = 0 biweight_hsheather = Bunch() biweight_hsheather.table = np.array([ [.5601805 , .0128449 , 43.61 , 0.000 , .5348735 , .5854875], [81.48233 , 14.26713 , 5.71 , 0.000 , 53.37326 , 109.5914] ]) biweight_hsheather.psrsquared = 0.6206 biweight_hsheather.rank = 2 biweight_hsheather.sparsity = 204.0465407204423 biweight_hsheather.bwidth = .1574393314202373 biweight_hsheather.kbwidth = 64.53302151153288 biweight_hsheather.df_m = 1 biweight_hsheather.df_r = 233 biweight_hsheather.f_r = .0049008427022052 biweight_hsheather.N = 235 biweight_hsheather.q_v = 582.541259765625 biweight_hsheather.q = .5 biweight_hsheather.sum_rdev = 46278.05667114258 biweight_hsheather.sum_adev = 17559.93220318131 biweight_hsheather.convcode = 0 biweight_chamberlain = Bunch() biweight_chamberlain.table = np.array([ [ .5601805 , .0114969 , 48.72 , 0.000 , .5375294 , .5828315], [ 81.48233 , 12.76983 , 6.38 , 0.000 , 56.32325 , 106.6414] ]) biweight_chamberlain.psrsquared = 0.6206 biweight_chamberlain.rank = 2 biweight_chamberlain.sparsity = 182.6322495257494 biweight_chamberlain.bwidth = .063926976464458 biweight_chamberlain.kbwidth = 25.61257055690209 biweight_chamberlain.df_m = 1 biweight_chamberlain.df_r = 233 biweight_chamberlain.f_r = .005475484218131 biweight_chamberlain.N = 235 biweight_chamberlain.q_v = 582.541259765625 biweight_chamberlain.q = .5 biweight_chamberlain.sum_rdev = 46278.05667114258 biweight_chamberlain.sum_adev = 17559.93220318131 biweight_chamberlain.convcode = 0 epanechnikov_bofinger = Bunch() epanechnikov_bofinger.table = np.array([ [ .5601805 , .0209663 , 26.72 , 0.000 , .5188727 , .6014882], [ 81.48233 , 23.28774 , 3.50 , 0.001 , 35.60088 , 127.3638] ]) epanechnikov_bofinger.psrsquared = 0.6206 epanechnikov_bofinger.rank = 2 epanechnikov_bofinger.sparsity = 333.0579553401614 epanechnikov_bofinger.bwidth = .2173486679767846 epanechnikov_bofinger.kbwidth = 91.50878448104551 epanechnikov_bofinger.df_m = 1 epanechnikov_bofinger.df_r = 233 epanechnikov_bofinger.f_r = .0030024804511235 epanechnikov_bofinger.N = 235 epanechnikov_bofinger.q_v = 582.541259765625 epanechnikov_bofinger.q = .5 epanechnikov_bofinger.sum_rdev = 46278.05667114258 epanechnikov_bofinger.sum_adev = 17559.93220318131 epanechnikov_bofinger.convcode = 0 epanechnikov_hsheather = Bunch() epanechnikov_hsheather.table = np.array([ [.5601805 , .0170484 , 32.86 , 0.000 , .5265918 , .5937692], [81.48233 , 18.93605 , 4.30 , 0.000 , 44.17457 , 118.7901] ]) epanechnikov_hsheather.psrsquared = 0.6206 epanechnikov_hsheather.rank = 2 epanechnikov_hsheather.sparsity = 270.8207209067576 epanechnikov_hsheather.bwidth = .1574393314202373 epanechnikov_hsheather.kbwidth = 64.53302151153288 epanechnikov_hsheather.df_m = 1 epanechnikov_hsheather.df_r = 233 epanechnikov_hsheather.f_r = .0036924796472434 epanechnikov_hsheather.N = 235 epanechnikov_hsheather.q_v = 582.541259765625 epanechnikov_hsheather.q = .5 epanechnikov_hsheather.sum_rdev = 46278.05667114258 epanechnikov_hsheather.sum_adev = 17559.93220318131 epanechnikov_hsheather.convcode = 0 epanechnikov_chamberlain = Bunch() epanechnikov_chamberlain.table = np.array([ [.5601805 , .0130407 , 42.96 , 0.000 , .5344876 , .5858733], [81.48233 , 14.48467 , 5.63 , 0.000 , 52.94468 , 110.02] ]) epanechnikov_chamberlain.psrsquared = 0.6206 epanechnikov_chamberlain.rank = 2 epanechnikov_chamberlain.sparsity = 207.1576340635951 epanechnikov_chamberlain.bwidth = .063926976464458 epanechnikov_chamberlain.kbwidth = 25.61257055690209 epanechnikov_chamberlain.df_m = 1 epanechnikov_chamberlain.df_r = 233 epanechnikov_chamberlain.f_r = .0048272418466269 epanechnikov_chamberlain.N = 235 epanechnikov_chamberlain.q_v = 582.541259765625 epanechnikov_chamberlain.q = .5 epanechnikov_chamberlain.sum_rdev = 46278.05667114258 epanechnikov_chamberlain.sum_adev = 17559.93220318131 epanechnikov_chamberlain.convcode = 0 epan2_bofinger = Bunch() epan2_bofinger.table = np.array([ [.5601805 , .0143484 , 39.04 , 0.000 , .5319113 , .5884496], [81.48233 , 15.93709 , 5.11 , 0.000 , 50.08313 , 112.8815] ]) epan2_bofinger.psrsquared = 0.6206 epan2_bofinger.rank = 2 epan2_bofinger.sparsity = 227.9299402797656 epan2_bofinger.bwidth = .2173486679767846 epan2_bofinger.kbwidth = 91.50878448104551 epan2_bofinger.df_m = 1 epan2_bofinger.df_r = 233 epan2_bofinger.f_r = .0043873130435281 epan2_bofinger.N = 235 epan2_bofinger.q_v = 582.541259765625 epan2_bofinger.q = .5 epan2_bofinger.sum_rdev = 46278.05667114258 epan2_bofinger.sum_adev = 17559.93220318131 epan2_bofinger.convcode = 0 epan2_hsheather = Bunch() epan2_hsheather.table = np.array([ [.5601805 , .0131763 , 42.51 , 0.000 , .5342206 , .5861403], [81.48233 , 14.63518 , 5.57 , 0.000 , 52.64815 , 110.3165] ]) epan2_hsheather.psrsquared = 0.6206 epan2_hsheather.rank = 2 epan2_hsheather.sparsity = 209.3102085912557 epan2_hsheather.bwidth = .1574393314202373 epan2_hsheather.kbwidth = 64.53302151153288 epan2_hsheather.df_m = 1 epan2_hsheather.df_r = 233 epan2_hsheather.f_r = .0047775978378236 epan2_hsheather.N = 235 epan2_hsheather.q_v = 582.541259765625 epan2_hsheather.q = .5 epan2_hsheather.sum_rdev = 46278.05667114258 epan2_hsheather.sum_adev = 17559.93220318131 epan2_hsheather.convcode = 0 epan2_chamberlain = Bunch() epan2_chamberlain.table = np.array([ [.5601805 , .0117925 , 47.50 , 0.000 , .5369469 , .583414], [81.48233 , 13.0982 , 6.22 , 0.000 , 55.67629 , 107.2884] ]) epan2_chamberlain.psrsquared = 0.6206 epan2_chamberlain.rank = 2 epan2_chamberlain.sparsity = 187.3286437436797 epan2_chamberlain.bwidth = .063926976464458 epan2_chamberlain.kbwidth = 25.61257055690209 epan2_chamberlain.df_m = 1 epan2_chamberlain.df_r = 233 epan2_chamberlain.f_r = .0053382119253919 epan2_chamberlain.N = 235 epan2_chamberlain.q_v = 582.541259765625 epan2_chamberlain.q = .5 epan2_chamberlain.sum_rdev = 46278.05667114258 epan2_chamberlain.sum_adev = 17559.93220318131 epan2_chamberlain.convcode = 0 rectangle_bofinger = Bunch() rectangle_bofinger.table = np.array([ [.5601805 , .0158331 , 35.38 , 0.000 , .5289861 , .5913748], [81.48233 , 17.5862 , 4.63 , 0.000 , 46.83404 , 116.1306] ]) rectangle_bofinger.psrsquared = 0.6206 rectangle_bofinger.rank = 2 rectangle_bofinger.sparsity = 251.515372550242 rectangle_bofinger.bwidth = .2173486679767846 rectangle_bofinger.kbwidth = 91.50878448104551 rectangle_bofinger.df_m = 1 rectangle_bofinger.df_r = 233 rectangle_bofinger.f_r = .0039759001203803 rectangle_bofinger.N = 235 rectangle_bofinger.q_v = 582.541259765625 rectangle_bofinger.q = .5 rectangle_bofinger.sum_rdev = 46278.05667114258 rectangle_bofinger.sum_adev = 17559.93220318131 rectangle_bofinger.convcode = 0 rectangle_hsheather = Bunch() rectangle_hsheather.table = np.array([ [.5601805 , .0137362 , 40.78 , 0.000 , .5331174 , .5872435], [81.48233 , 15.25712 , 5.34 , 0.000 , 51.42279 , 111.5419] ]) rectangle_hsheather.psrsquared = 0.6206 rectangle_hsheather.rank = 2 rectangle_hsheather.sparsity = 218.2051806505069 rectangle_hsheather.bwidth = .1574393314202373 rectangle_hsheather.kbwidth = 64.53302151153288 rectangle_hsheather.df_m = 1 rectangle_hsheather.df_r = 233 rectangle_hsheather.f_r = .004582842611797 rectangle_hsheather.N = 235 rectangle_hsheather.q_v = 582.541259765625 rectangle_hsheather.q = .5 rectangle_hsheather.sum_rdev = 46278.05667114258 rectangle_hsheather.sum_adev = 17559.93220318131 rectangle_hsheather.convcode = 0 rectangle_chamberlain = Bunch() rectangle_chamberlain.table = np.array([ [.5601805 , .0118406 , 47.31 , 0.000 , .5368522 , .5835087], [81.48233 , 13.1516 , 6.20 , 0.000 , 55.57108 , 107.3936] ]) rectangle_chamberlain.psrsquared = 0.6206 rectangle_chamberlain.rank = 2 rectangle_chamberlain.sparsity = 188.0923150272497 rectangle_chamberlain.bwidth = .063926976464458 rectangle_chamberlain.kbwidth = 25.61257055690209 rectangle_chamberlain.df_m = 1 rectangle_chamberlain.df_r = 233 rectangle_chamberlain.f_r = .0053165383171297 rectangle_chamberlain.N = 235 rectangle_chamberlain.q_v = 582.541259765625 rectangle_chamberlain.q = .5 rectangle_chamberlain.sum_rdev = 46278.05667114258 rectangle_chamberlain.sum_adev = 17559.93220318131 rectangle_chamberlain.convcode = 0 triangle_bofinger = Bunch() triangle_bofinger.table = np.array([ [.5601805 , .0138712 , 40.38 , 0.000 , .5328515 , .5875094], [81.48233 , 15.40706 , 5.29 , 0.000 , 51.12738 , 111.8373] ]) triangle_bofinger.psrsquared = 0.6206 triangle_bofinger.rank = 2 triangle_bofinger.sparsity = 220.3495620604223 triangle_bofinger.bwidth = .2173486679767846 triangle_bofinger.kbwidth = 91.50878448104551 triangle_bofinger.df_m = 1 triangle_bofinger.df_r = 233 triangle_bofinger.f_r = .0045382436463649 triangle_bofinger.N = 235 triangle_bofinger.q_v = 582.541259765625 triangle_bofinger.q = .5 triangle_bofinger.sum_rdev = 46278.05667114258 triangle_bofinger.sum_adev = 17559.93220318131 triangle_bofinger.convcode = 0 triangle_hsheather = Bunch() triangle_hsheather.table = np.array([ [.5601805 , .0128874 , 43.47 , 0.000 , .5347898 , .5855711], [81.48233 , 14.31431 , 5.69 , 0.000 , 53.2803 , 109.6844] ]) triangle_hsheather.psrsquared = 0.6206 triangle_hsheather.rank = 2 triangle_hsheather.sparsity = 204.7212998199564 triangle_hsheather.bwidth = .1574393314202373 triangle_hsheather.kbwidth = 64.53302151153288 triangle_hsheather.df_m = 1 triangle_hsheather.df_r = 233 triangle_hsheather.f_r = .004884689579831 triangle_hsheather.N = 235 triangle_hsheather.q_v = 582.541259765625 triangle_hsheather.q = .5 triangle_hsheather.sum_rdev = 46278.05667114258 triangle_hsheather.sum_adev = 17559.93220318131 triangle_hsheather.convcode = 0 triangle_chamberlain = Bunch() triangle_chamberlain.table = np.array([ [.5601805 , .0115725 , 48.41 , 0.000 , .5373803 , .5829806], [81.48233 , 12.85389 , 6.34 , 0.000 , 56.15764 , 106.807] ]) triangle_chamberlain.psrsquared = 0.6206 triangle_chamberlain.rank = 2 triangle_chamberlain.sparsity = 183.8344452913298 triangle_chamberlain.bwidth = .063926976464458 triangle_chamberlain.kbwidth = 25.61257055690209 triangle_chamberlain.df_m = 1 triangle_chamberlain.df_r = 233 triangle_chamberlain.f_r = .0054396769790083 triangle_chamberlain.N = 235 triangle_chamberlain.q_v = 582.541259765625 triangle_chamberlain.q = .5 triangle_chamberlain.sum_rdev = 46278.05667114258 triangle_chamberlain.sum_adev = 17559.93220318131 triangle_chamberlain.convcode = 0 gaussian_bofinger = Bunch() gaussian_bofinger.table = np.array([ [.5601805 , .0197311 , 28.39 , 0.000 , .5213062 , .5990547], [81.48233 , 21.91582 , 3.72 , 0.000 , 38.30383 , 124.6608] ]) gaussian_bofinger.psrsquared = 0.6206 gaussian_bofinger.rank = 2 gaussian_bofinger.sparsity = 313.4370075776719 gaussian_bofinger.bwidth = .2173486679767846 gaussian_bofinger.kbwidth = 91.50878448104551 gaussian_bofinger.df_m = 1 gaussian_bofinger.df_r = 233 gaussian_bofinger.f_r = .0031904337261521 gaussian_bofinger.N = 235 gaussian_bofinger.q_v = 582.541259765625 gaussian_bofinger.q = .5 gaussian_bofinger.sum_rdev = 46278.05667114258 gaussian_bofinger.sum_adev = 17559.93220318131 gaussian_bofinger.convcode = 0 gaussian_hsheather = Bunch() gaussian_hsheather.table = np.array([ [.5601805 , .016532 , 33.88 , 0.000 , .5276092 , .5927518], [81.48233 , 18.36248 , 4.44 , 0.000 , 45.30462 , 117.66] ]) gaussian_hsheather.psrsquared = 0.6206 gaussian_hsheather.rank = 2 gaussian_hsheather.sparsity = 262.6175743002715 gaussian_hsheather.bwidth = .1574393314202373 gaussian_hsheather.kbwidth = 64.53302151153288 gaussian_hsheather.df_m = 1 gaussian_hsheather.df_r = 233 gaussian_hsheather.f_r = .0038078182797341 gaussian_hsheather.N = 235 gaussian_hsheather.q_v = 582.541259765625 gaussian_hsheather.q = .5 gaussian_hsheather.sum_rdev = 46278.05667114258 gaussian_hsheather.sum_adev = 17559.93220318131 gaussian_hsheather.convcode = 0 gaussian_chamberlain = Bunch() gaussian_chamberlain.table = np.array([ [.5601805 , .0128123 , 43.72 , 0.000 , .5349378 , .5854232], [81.48233 , 14.23088 , 5.73 , 0.000 , 53.44468 , 109.52] ]) gaussian_chamberlain.psrsquared = 0.6206 gaussian_chamberlain.rank = 2 gaussian_chamberlain.sparsity = 203.5280962791137 gaussian_chamberlain.bwidth = .063926976464458 gaussian_chamberlain.kbwidth = 25.61257055690209 gaussian_chamberlain.df_m = 1 gaussian_chamberlain.df_r = 233 gaussian_chamberlain.f_r = .004913326554328 gaussian_chamberlain.N = 235 gaussian_chamberlain.q_v = 582.541259765625 gaussian_chamberlain.q = .5 gaussian_chamberlain.sum_rdev = 46278.05667114258 gaussian_chamberlain.sum_adev = 17559.93220318131 gaussian_chamberlain.convcode = 0 cosine_bofinger = Bunch() cosine_bofinger.table = np.array([ [.5601805 , .0121011 , 46.29 , 0.000 , .536339 , .5840219], [81.48233 , 13.44092 , 6.06 , 0.000 , 55.00106 , 107.9636] ]) cosine_bofinger.psrsquared = 0.6206 cosine_bofinger.rank = 2 cosine_bofinger.sparsity = 192.2302014415605 cosine_bofinger.bwidth = .2173486679767846 cosine_bofinger.kbwidth = 91.50878448104551 cosine_bofinger.df_m = 1 cosine_bofinger.df_r = 233 cosine_bofinger.f_r = .0052020961976883 cosine_bofinger.N = 235 cosine_bofinger.q_v = 582.541259765625 cosine_bofinger.q = .5 cosine_bofinger.sum_rdev = 46278.05667114258 cosine_bofinger.sum_adev = 17559.93220318131 cosine_bofinger.convcode = 0 cosine_hsheather = Bunch() cosine_hsheather.table = np.array([ [.5601805 , .0116679 , 48.01 , 0.000 , .5371924 , .5831685], [81.48233 , 12.9598 , 6.29 , 0.000 , 55.94897 , 107.0157] ]) cosine_hsheather.psrsquared = 0.6206 cosine_hsheather.rank = 2 cosine_hsheather.sparsity = 185.349198428224 cosine_hsheather.bwidth = .1574393314202373 cosine_hsheather.kbwidth = 64.53302151153288 cosine_hsheather.df_m = 1 cosine_hsheather.df_r = 233 cosine_hsheather.f_r = .0053952216059205 cosine_hsheather.N = 235 cosine_hsheather.q_v = 582.541259765625 cosine_hsheather.q = .5 cosine_hsheather.sum_rdev = 46278.05667114258 cosine_hsheather.sum_adev = 17559.93220318131 cosine_hsheather.convcode = 0 cosine_chamberlain = Bunch() cosine_chamberlain.table = np.array([ [.5601805 , .0106479 , 52.61 , 0.000 , .539202 , .5811589], [81.48233 , 11.82688 , 6.89 , 0.000 , 58.18104 , 104.7836] ]) cosine_chamberlain.psrsquared = 0.6206 cosine_chamberlain.rank = 2 cosine_chamberlain.sparsity = 169.1463943762948 cosine_chamberlain.bwidth = .063926976464458 cosine_chamberlain.kbwidth = 25.61257055690209 cosine_chamberlain.df_m = 1 cosine_chamberlain.df_r = 233 cosine_chamberlain.f_r = .0059120385254878 cosine_chamberlain.N = 235 cosine_chamberlain.q_v = 582.541259765625 cosine_chamberlain.q = .5 cosine_chamberlain.sum_rdev = 46278.05667114258 cosine_chamberlain.sum_adev = 17559.93220318131 cosine_chamberlain.convcode = 0 parzen_bofinger = Bunch() parzen_bofinger.table = np.array([ [.5601805 , .012909 , 43.39 , 0.000 , .5347471 , .5856138], [81.48233 , 14.33838 , 5.68 , 0.000 , 53.23289 , 109.7318] ]) parzen_bofinger.psrsquared = 0.6206 parzen_bofinger.rank = 2 parzen_bofinger.sparsity = 205.0654663067616 parzen_bofinger.bwidth = .2173486679767846 parzen_bofinger.kbwidth = 91.50878448104551 parzen_bofinger.df_m = 1 parzen_bofinger.df_r = 233 parzen_bofinger.f_r = .0048764914834762 parzen_bofinger.N = 235 parzen_bofinger.q_v = 582.541259765625 parzen_bofinger.q = .5 parzen_bofinger.sum_rdev = 46278.05667114258 parzen_bofinger.sum_adev = 17559.93220318131 parzen_bofinger.convcode = 0 parzen_hsheather = Bunch() parzen_hsheather.table = np.array([ [.5601805 , .0122688 , 45.66 , 0.000 , .5360085 , .5843524], [81.48233 , 13.62723 , 5.98 , 0.000 , 54.63401 , 108.3307] ]) parzen_hsheather.psrsquared = 0.6206 parzen_hsheather.rank = 2 parzen_hsheather.sparsity = 194.8946558099188 parzen_hsheather.bwidth = .1574393314202373 parzen_hsheather.kbwidth = 64.53302151153288 parzen_hsheather.df_m = 1 parzen_hsheather.df_r = 233 parzen_hsheather.f_r = .0051309770185556 parzen_hsheather.N = 235 parzen_hsheather.q_v = 582.541259765625 parzen_hsheather.q = .5 parzen_hsheather.sum_rdev = 46278.05667114258 parzen_hsheather.sum_adev = 17559.93220318131 parzen_hsheather.convcode = 0 parzen_chamberlain = Bunch() parzen_chamberlain.table = np.array([ [.5601805 , .0110507 , 50.69 , 0.000 , .5384084 , .5819526], [81.48233 , 12.2743 , 6.64 , 0.000 , 57.29954 , 105.6651] ]) parzen_chamberlain.psrsquared = 0.6206 parzen_chamberlain.rank = 2 parzen_chamberlain.sparsity = 175.5452813763412 parzen_chamberlain.bwidth = .063926976464458 parzen_chamberlain.kbwidth = 25.61257055690209 parzen_chamberlain.df_m = 1 parzen_chamberlain.df_r = 233 parzen_chamberlain.f_r = .0056965359146063 parzen_chamberlain.N = 235 parzen_chamberlain.q_v = 582.541259765625 parzen_chamberlain.q = .5 parzen_chamberlain.sum_rdev = 46278.05667114258 parzen_chamberlain.sum_adev = 17559.93220318131 parzen_chamberlain.convcode = 0 Rquantreg = Bunch() Rquantreg.fittedvalues = np.array([ 278.946531823426, 327.662259651587, 472.195784028597, 366.902127539958, 411.817682123087, 490.131199885949, 443.36524597881, 503.536477958636, 636.406081281679, 709.736288922034, 312.165058899648, 357.917286612496, 427.907157504212, 333.474578745265, 396.777813086185, 447.125068738706, 325.117049130677, 349.771067249961, 481.598886608367, 306.106158691415, 388.420502955027, 511.05437589194, 313.836609745169, 372.960145596262, 485.358358918327, 284.379882747628, 346.21761302202, 470.314386890694, 292.735362869831, 345.174109237497, 431.875199165716, 312.003504742171, 396.809344806674, 474.141604734191, 463.93526593027, 430.280150030025, 453.602705891221, 579.151509166254, 320.586493222875, 379.637682965454, 261.63071606774, 452.262394881918, 560.558633285135, 361.453261675451, 433.779038355879, 334.560374744198, 465.46340752116, 615.361560833631, 934.235038902725, 699.263962186247, 403.470311834164, 431.875199165716, 610.619729234566, 592.662336978523, 365.021855935831, 494.3226340628, 571.610146001548, 820.79435094965, 1244.13870203673, 480.7121636877, 1031.46758784013, 362.238578597709, 467.705425607653, 577.073721157792, 591.087071047729, 323.397708458532, 569.0193777981, 547.264281981988, 304.861831677351, 727.261856258699, 382.899300447742, 380.080019200391, 387.548077543462, 455.939284414524, 461.007422390708, 469.504909066202, 571.628605113602, 482.528691661418, 447.239662606701, 443.405235218729, 617.975233440748, 888.754433927184, 390.908239427091, 479.934781986261, 872.520684097057, 468.389025193617, 467.583874563025, 600.891141536243, 328.94286096288, 524.54251098332, 697.904660068404, 443.782036008026, 501.879849409525, 647.703493205875, 458.730704476452, 401.721440320659, 507.49016247142, 722.834547664808, 380.206920088912, 481.131145133749, 299.904199685339, 347.488751302111, 488.171723133137, 368.620444647759, 1135.31179977255, 831.440717300088, 578.509405496803, 437.83023460596, 618.056493326843, 550.238043601025, 289.910800377653, 583.813030650723, 502.185524461957, 519.079736225537, 280.441976767315, 334.638935071642, 489.10661171308, 651.842445716676, 1050.52148262534, 346.050058523346, 729.252186278533, 558.451123252156, 529.859949563712, 668.51276154189, 1113.66644452647, 747.093352541566, 858.247029841287, 917.230577685989, 390.29683477012, 339.650965997876, 350.536705877117, 295.394850812194, 502.711215489629, 465.29644549865, 398.753783734842, 328.660789278315, 747.883662637697, 2102.02108846944, 499.727307212846, 278.882398125705, 335.933513242034, 387.198765462081, 672.019128653175, 439.917678218736, 461.755563134383, 669.580269655421, 614.886877182468, 657.691667171562, 913.373865558921, 596.066409235456, 562.142375003233, 844.328711460744, 517.593907165227, 463.853902738299, 1087.39298812569, 583.363256443676, 601.337926899021, 720.657238588974, 386.421327566452, 493.072243052398, 493.072243052398, 493.072243052398, 487.416418391789, 625.768736956952, 717.442211022949, 649.545102325439, 315.764483006998, 387.279123698926, 725.770230770932, 534.438688677752, 265.753297513836, 265.753297513836, 275.266770483504, 444.714192650306, 357.226955834115, 466.694950404995, 521.878226495306, 514.492832530541, 401.82351113515, 308.781536524607, 410.920025494164, 506.942199600954, 426.771590593594, 444.329655290455, 567.951437149727, 314.044484679614, 341.637888287124, 390.011222091572, 371.617953454683, 491.650387255751, 309.86707973756, 339.215312155678, 401.313514123063, 274.198577993648, 366.737853359169, 602.621623875386, 397.5527766612, 431.743315417498, 502.103203876367, 758.493699184236, 354.616793574895, 495.299694296035, 445.994704272822, 461.179388784645, 492.398877810457, 300.177654706392, 351.604018662754, 397.001476569931, 443.325261537312, 495.754449130328, 597.467889923833, 495.384438403965, 563.913157758648, 890.789267794131, 326.949960612346, 296.399786939819, 336.191062827908, 406.145865402583, 678.81933687482, 997.557647439337, 365.6649642219, 415.242904378452, 543.581822640472, 310.924419326878, 519.951702825405, 751.022676264054, 422.151172626615, 604.684498888169, 836.51478813311, 277.051441764913, 287.126391607546, 327.57744312456, 343.712072517474, 408.684566875422, 535.079861284096 ]) Rquantreg.residuals = np.array([ -23.1071072288498, -16.7035925924416, 13.4842301424879, 36.0952280042049, 83.7430928106889, 143.666615246444, 187.391321726407, 196.904426307396, 194.552540223671, 105.623928368427, 25.8363284299391, 54.4440518161423, 92.0934604769094, 118.926894162067, 115.9422447507, 211.714461564889, 67.4824476183351, 93.7875665496341, 158.517491105043, 27.7332282843317, 78.537815573821, 32.3425283712905, 3.88323401131549, 51.360750756311, 33.6032971350797, 53.6215045819588, 73.423561609355, 6.00566204286901, 93.6248004402887, 78.1042405680565, 71.4819720245138, 42.635363803128, 100.508824022316, 114.377859224531, 190.661878666847, 120.44727756794, 74.7742708230821, 61.3298388891191, 80.7338623237625, 56.3613389354645, 14.9298935780977, 136.086423177107, 103.639168918795, 83.406904735325, 29.1204765079443, 43.2188638018142, 87.68702809519, 195.534593927816, 133.719017238015, 350.614829501073, 119.230897841979, 140.205463451968, 296.7772153462, 218.915256717207, 62.7756642652107, 155.675830060721, 288.990008558354, 322.6267348787, 788.540488171593, 109.906163322267, 538.923550476946, 121.241446063999, 132.774973479468, 119.128384587689, 183.709108010048, 67.2007219409504, 43.5425230980599, 161.49786961119, -7.94264590491014, 344.200840210711, 113.698279589573, 123.317422174319, -29.9069666643728, -25.6016916976065, 163.691619008174, 113.036341875648, 8.59293713333579, 61.3520511228119, 141.397517015987, 184.594660239251, 94.1259408216085, 79.6405061341515, 91.6733481942127, 113.234603595283, 161.045070172193, 225.290448255665, 226.095598886257, 160.387924286533, 32.4551953759993, 103.909707216036, 73.543904350086, 313.336630439861, 319.717144678425, 374.616677214425, 220.710022883649, 137.027675366142, 172.507934775539, 254.168723294869, 180.994568087336, 247.26860148984, 72.4144228992704, 14.0322012670994, 29.7478676180694, 91.1972063421171, -271.391948301552, -1.13686837721616e-13, -43.7483617057195, -45.7799925843755, 316.918702117259, 263.070052700293, -26.2008042070246, 185.270818370404, 128.400761958987, 126.90766445377, 39.11640958216, 13.8128950328002, 125.400191450337, 10.1671163220818, 453.849292287238, 60.1679571929374, -37.0832869573327, 29.6859273527971, -18.5990635840888, 32.0472277998125, 187.478653019356, 131.972664872163, 54.6380484088667, 592.550594160211, 93.7637141660844, 60.0193510971532, 93.5633996863289, -46.5847395899585, 25.0901440883857, 35.3349035566588, 38.0569510057356, 46.1382548463761, -21.4915275364559, -274.821124029843, 23.7637774954742, 56.1174237129451, 137.267370647069, 194.004180243594, 257.734838779675, 151.279738459292, 175.792712085095, 5.37066578891586, 161.87201799729, 301.825301306357, 337.590468355399, 141.753670144823, 248.534867350933, 138.672153434971, 191.302922030722, 169.266111420092, 337.411668163301, 247.595365061674, 324.241547334534, 441.345158140916, -2.96336484193375, 128.045086149367, 128.045086149367, 128.045086149367, 61.183812841238, 119.466557499153, 120.358284525061, 145.795139311381, 102.833084929086, 121.51832618868, 157.507780852421, 208.08888035649, -23.4330955930956, -23.4330955930956, -9.26578861762408, 170.044609958764, 28.0914420512634, 48.9250186205624, 186.600476930648, 219.742798279756, 31.1774726408891, 18.6372355131512, 18.1199081442213, 112.698628091063, -25.9726125403696, 176.470984835994, 252.045003846191, 46.8335329758523, 54.1229163256514, 51.9888300300891, 32.4204776768067, 179.14892185478, -12.2969284011601, 14.2728509760057, -17.3759292437432, 10.6022252751131, 64.362111397076, 198.730134737533, 50.898482335236, 146.167755331756, 68.4178084237137, 106.826836694182, 89.9409707510827, 185.120132493866, 130.283190180665, 170.618786347098, 116.24297250849, 0.822265604206791, 26.3943953963694, 0, 145.19420242141, 186.007125906439, 209.892380176326, 201.416658681816, 247.283084092749, 414.930873548859, 115.050091509315, 57.2015105045924, 131.809734683313, 120.611481397197, 211.419693391706, 321.245634797383, -34.6644264712713, 1.15862100546684, 53.2587319600932, 97.5747986822971, 255.069199707627, 387.139369759176, 63.3685935821613, 168.076643464964, 157.44822913826, 28.387531965303, 19.3926869633912, -28.3781151570223, 124.288724993746, 113.917339005042, 215.240302135105 ])