Source code for naginterfaces.​library.​examples.​correg.​quantile_linreg_ex

#!/usr/bin/env python
"``naginterface.library.correg.quantile_linreg`` Python Example."

# NAG Copyright 2017-2019.

# pylint: disable=invalid-name

import numpy as np

from naginterfaces.library import correg

[docs]def main(): """ Example for :func:`naginterfaces.library.correg.quantile_linreg`. Multiple linear quantile regression (comprehensive interface.) >>> main() naginterfaces.library.correg.quantile_linreg Python Example Results. Quantile regression model fitted to Engels' 1857 study of household expenditure on food. Quantile: 0.100 Lower Parameter Upper Limit Estimate Limit 0 74.946 110.142 145.337 1 0.370 0.402 0.433 Covariance matrix: [ 3.191e+02 -2.541e-01, 2.587e-04 ] Quantile: 0.250 Lower Parameter Upper Limit Estimate Limit 0 64.232 95.483 126.735 1 0.446 0.474 0.502 Covariance matrix: [ 2.516e+02 -2.004e-01, 2.039e-04 ] Quantile: 0.500 Lower Parameter Upper Limit Estimate Limit 0 55.399 81.482 107.566 1 0.537 0.560 0.584 Covariance matrix: [ 1.753e+02 -1.396e-01, 1.421e-04 ] Quantile: 0.750 Lower Parameter Upper Limit Estimate Limit 0 41.372 62.396 83.421 1 0.625 0.644 0.663 Covariance matrix: [ 1.139e+02 -9.068e-02, 9.230e-05 ] Quantile: 0.900 Lower Parameter Upper Limit Estimate Limit 0 26.829 67.351 107.873 1 0.650 0.686 0.723 Covariance matrix: [ 4.230e+02 -3.369e-01, 3.429e-04 ] First 10 residuals: Quantile Obs. 0.10000 0.25000 0.50000 0.75000 0.90000 1 -23.10718 -38.84219 -61.00711 -77.14462 -99.86551 2 -16.70358 -41.20981 -73.81193 -100.11463 -127.96277 3 13.48419 -37.04518 -100.61322 -157.07478 -200.13481 4 36.09526 4.52393 -36.48522 -70.97584 -102.95390 5 83.74310 44.08476 -6.54743 -50.41028 -87.11562 6 143.66660 89.90799 22.49734 -37.70668 -82.65437 7 187.39134 142.05288 84.66171 34.21603 -5.80963 8 196.90443 140.73220 70.44951 7.44831 -38.91027 9 194.55254 114.45726 15.70761 -75.01861 -135.36147 10 105.62394 12.32563 -102.13482 -208.16238 -276.22311 """ print( 'naginterfaces.library.correg.quantile_linreg Python Example Results.' ) print('Quantile regression model fitted to Engels\' 1857 study of') print('household expenditure on food.') # Retrieve the data set for this example: sorder, dat, y = get_data() # The independent variables to include: isx = [1]*dat.shape[1] # The quantiles of interest: tau = [0.10, 0.25, 0.50, 0.75, 0.90] # Initialize the routine and set some optional parameters: comm = {} correg.optset('Initialize = quantile_linreg', comm) correg.optset('Return Residuals = Yes', comm) correg.optset('Matrix Returned = Covariance', comm) correg.optset('Interval Method = IID', comm) regn = correg.quantile_linreg( sorder, dat, isx, y, tau, comm=comm, ) for l, tau_l in enumerate(tau): print('Quantile: {:6.3f}'.format(tau_l)) print(' Lower Parameter Upper') print(' Limit Estimate Limit') for j in range(regn.bl.shape[0]): print( '{:3d} {:7.3f} {:7.3f} {:7.3f}'.format( j, regn.bl[j, l], regn.b[j, l], regn.bu[j, l], ) ) print('Covariance matrix:') print('[') for i in range(regn.ch.shape[0]): print( ' ' + ', '.join(['{:10.3e}']*(i+1)).format(*regn.ch[:, i, l]) ) print(']') print('First 10 residuals:') print(' Quantile') print('Obs. ' + ' '.join(['{:10.5f}']*(len(tau))).format(*tau)) for i in range(min(regn.res.shape[0], 10)): print( '{:2d}'.format(i+1) + ' ' + ' '.join(['{:10.5f}']*(len(tau))).format(*regn.res[i, :]) )
def get_data(): """ The data set for this example. """ # The variate values (there is a single # variate in this example): dat = [ 420.1577, 541.4117, 901.1575, 639.0802, 750.8756, 945.7989, 829.3979, 979.1648, 1309.8789, 1492.3987, 502.8390, 616.7168, 790.9225, 555.8786, 713.4412, 838.7561, 535.0766, 596.4408, 924.5619, 487.7583, 692.6397, 997.8770, 506.9995, 654.1587, 933.9193, 433.6813, 587.5962, 896.4746, 454.4782, 584.9989, 800.7990, 502.4369, 713.5197, 906.0006, 880.5969, 796.8289, 854.8791, 1167.3716, 523.8000, 670.7792, 377.0584, 851.5430, 1121.0937, 625.5179, 805.5377, 558.5812, 884.4005, 1257.4989, 2051.1789, 1466.3330, 730.0989, 2432.3910, 940.9218, 1177.8547, 1222.5939, 1519.5811, 687.6638, 953.1192, 953.1192, 953.1192, 939.0418, 1283.4025, 1511.5789, 1342.5821, 511.7980, 689.7988, 1532.3074, 1056.0808, 387.3195, 387.3195, 410.9987, 499.7510, 832.7554, 614.9986, 887.4658, 1595.1611, 1807.9520, 541.2006, 1057.6767, 800.7990, 1245.6964, 1201.0002, 634.4002, 956.2315, 1148.6010, 1768.8236, 2822.5330, 922.3548, 2293.1920, 627.4726, 889.9809, 1162.2000, 1197.0794, 530.7972, 1142.1526, 1088.0039, 484.6612, 1536.0201, 678.8974, 671.8802, 690.4683, 860.6948, 873.3095, 894.4598, 1148.6470, 926.8762, 839.0414, 829.4974, 1264.0043, 1937.9771, 698.8317, 920.4199, 1897.5711, 891.6824, 889.6784, 1221.4818, 544.5991, 1031.4491, 1462.9497, 830.4353, 975.0415, 1337.9983, 867.6427, 725.7459, 989.0056, 1525.0005, 672.1960, 923.3977, 472.3215, 590.7601, 831.7983, 1139.4945, 507.5169, 576.1972, 696.5991, 650.8180, 949.5802, 497.1193, 570.1674, 724.7306, 408.3399, 638.6713, 1225.7890, 715.3701, 800.4708, 975.5974, 1613.7565, 608.5019, 958.6634, 835.9426, 1024.8177, 1006.4353, 726.0000, 494.4174, 776.5958, 415.4407, 581.3599, 643.3571, 2551.6615, 1795.3226, 1165.7734, 815.6212, 1264.2066, 1095.4056, 447.4479, 1178.9742, 975.8023, 1017.8522, 423.8798, 558.7767, 943.2487, 1348.3002, 2340.6174, 587.1792, 1540.9741, 1115.8481, 1044.6843, 1389.7929, 2497.7860, 1585.3809, 1862.0438, 2008.8546, 697.3099, 571.2517, 598.3465, 461.0977, 977.1107, 883.9849, 718.3594, 543.8971, 1587.3480, 4957.8130, 969.6838, 419.9980, 561.9990, 689.5988, 1398.5203, 820.8168, 875.1716, 1392.4499, 1256.3174, 1362.8590, 1999.2552, 1209.4730, 1125.0356, 1827.4010, 1014.1540, 880.3944, 873.7375, 951.4432, 473.0022, 601.0030, 713.9979, 829.2984, 959.7953, 1212.9613, 958.8743, 1129.4431, 1943.0419, 539.6388, 463.5990, 562.6400, 736.7584, 1415.4461, 2208.7897, 636.0009, 759.4010, 1078.8382, 748.6413, 987.6417, 788.0961, 1020.0225, 1230.9235, 440.5174, 743.0772, ] # The observations on the dependent variable: y = [ 255.8394, 310.9587, 485.6800, 402.9974, 495.5608, 633.7978, 630.7566, 700.4409, 830.9586, 815.3602, 338.0014, 412.3613, 520.0006, 452.4015, 512.7201, 658.8395, 392.5995, 443.5586, 640.1164, 333.8394, 466.9583, 543.3969, 317.7198, 424.3209, 518.9617, 338.0014, 419.6412, 476.3200, 386.3602, 423.2783, 503.3572, 354.6389, 497.3182, 588.5195, 654.5971, 550.7274, 528.3770, 640.4813, 401.3204, 435.9990, 276.5606, 588.3488, 664.1978, 444.8602, 462.8995, 377.7792, 553.1504, 810.8962, 1067.9541, 1049.8788, 522.7012, 1424.8047, 517.9196, 830.9586, 925.5795, 1162.0024, 383.4580, 621.1173, 621.1173, 621.1173, 548.6002, 745.2353, 837.8005, 795.3402, 418.5976, 508.7974, 883.2780, 742.5276, 242.3202, 242.3202, 266.0010, 408.4992, 614.7588, 385.3184, 515.6200, 1138.1620, 993.9630, 299.1993, 750.3202, 572.0807, 907.3969, 811.5776, 427.7975, 649.9985, 860.6002, 1143.4211, 2032.6792, 590.6183, 1570.3911, 483.4800, 600.4804, 696.2021, 774.7962, 390.5984, 612.5619, 708.7622, 296.9192, 1071.4627, 496.5976, 503.3974, 357.6411, 430.3376, 624.6990, 582.5413, 580.2215, 543.8807, 588.6372, 627.9999, 712.1012, 968.3949, 482.5816, 593.1694, 1033.5658, 693.6795, 693.6795, 761.2791, 361.3981, 628.4522, 771.4486, 757.1187, 821.5970, 1022.3202, 679.4407, 538.7491, 679.9981, 977.0033, 561.2015, 728.3997, 372.3186, 361.5210, 620.8006, 819.9964, 360.8780, 395.7608, 442.0001, 404.0384, 670.7993, 297.5702, 353.4882, 383.9376, 284.8008, 431.1000, 801.3518, 448.4513, 577.9111, 570.5210, 865.3205, 444.5578, 680.4198, 576.2779, 708.4787, 734.2356, 433.0010, 327.4188, 485.5198, 305.4390, 468.0008, 459.8177, 863.9199, 831.4407, 534.7610, 392.0502, 934.9752, 813.3081, 263.7100, 769.0838, 630.5863, 645.9874, 319.5584, 348.4518, 614.5068, 662.0096, 1504.3708, 406.2180, 692.1689, 588.1371, 511.2609, 700.5600, 1301.1451, 879.0660, 912.8851, 1509.7812, 484.0605, 399.6703, 444.1001, 248.8101, 527.8014, 500.6313, 436.8107, 374.7990, 726.3921, 1827.2000, 523.4911, 334.9998, 473.2009, 581.2029, 929.7540, 591.1974, 637.5483, 674.9509, 776.7589, 959.5170, 1250.9643, 737.8201, 810.6772, 983.0009, 708.8968, 633.1200, 631.7982, 608.6419, 300.9999, 377.9984, 397.0015, 588.5195, 681.7616, 807.3603, 696.8011, 811.1962, 1305.7201, 442.0001, 353.6013, 468.0008, 526.7573, 890.2390, 1318.8033, 331.0005, 416.4015, 596.8406, 429.0399, 619.6408, 400.7990, 775.0209, 772.7611, 306.5191, 522.6019, ] # Store the values by columns: dat = np.reshape(dat, (len(y), 1)) sorder = 1 return sorder, dat, y if __name__ == '__main__': import doctest import sys sys.exit( doctest.testmod( None, verbose=True, report=False, optionflags=doctest.REPORT_NDIFF, ).failed )