diff --git a/.travis.yml b/.travis.yml index 094c210..746c9de 100644 --- a/.travis.yml +++ b/.travis.yml @@ -4,8 +4,8 @@ branches: only: - master python: - - "3.5" - - "3.6" + - 3.5 + - 3.6 env: - PYSAL_PLUS=false @@ -19,7 +19,7 @@ matrix: env: PYSAL_PYPI=false before_install: - - wget https://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh + - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh - chmod +x miniconda.sh - ./miniconda.sh -b -p ./miniconda - export PATH=`pwd`/miniconda/bin:$PATH @@ -49,6 +49,7 @@ notifications: email: recipients: - tayoshan@gmail.com + - jgaboardi@gmail.com on_change: change on_failure: always diff --git a/mgwr/tests/georgia/.DS_Store b/mgwr/tests/georgia/.DS_Store deleted file mode 100644 index 5008ddf..0000000 Binary files a/mgwr/tests/georgia/.DS_Store and /dev/null differ diff --git a/mgwr/tests/georgia/GData_utm.csv b/mgwr/tests/georgia/GData_utm.csv deleted file mode 100755 index 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mode 100755 index 6256b7e..0000000 Binary files a/mgwr/tests/georgia/G_utm.sbn and /dev/null differ diff --git a/mgwr/tests/georgia/G_utm.sbx b/mgwr/tests/georgia/G_utm.sbx deleted file mode 100755 index 277149b..0000000 Binary files a/mgwr/tests/georgia/G_utm.sbx and /dev/null differ diff --git a/mgwr/tests/georgia/G_utm.shp b/mgwr/tests/georgia/G_utm.shp deleted file mode 100755 index 6d17128..0000000 Binary files a/mgwr/tests/georgia/G_utm.shp and /dev/null differ diff --git a/mgwr/tests/georgia/G_utm.shx b/mgwr/tests/georgia/G_utm.shx deleted file mode 100755 index 5d3b1f9..0000000 Binary files a/mgwr/tests/georgia/G_utm.shx and /dev/null differ diff --git a/mgwr/tests/georgia/georgia_BS_F.ctl b/mgwr/tests/georgia/georgia_BS_F.ctl deleted file mode 100755 index 5dc124d..0000000 --- a/mgwr/tests/georgia/georgia_BS_F.ctl +++ /dev/null @@ -1,45 +0,0 @@ - -C:\Users\IEUser\Desktop\georgia\georgia\GData_utm.csv -FORMAT/DELIMITER: 1 -Number_of_fields: 13 -Number_of_areas: 159 -Fields -AreaKey 001 AreaKey -X 012 X -Y 013 Y -Gmetric 0 -Dependent 006 PctBach -Offset -Independent_geo 4 -000 Intercept -005 PctRural -009 PctPov -010 PctBlack -Independent_fix 0 -Unused_fields 6 -002 Latitude -003 Longitud -004 TotPop90 -007 PctEld -008 PctFB -011 ID -MODELTYPE: 0 -STANDARDISATION: 0 -GTEST: 0 -VSL2G: 0 -VSG2L: 0 -KERNELTYPE: 1 -BANDSELECTIONMETHOD: 1 -Goldrangeflag: 0 -Goldenmax: -Goldenmin: -Fixedbandsize: -IntervalMax: -IntervalMin: -IntervalStep: -Criteria: -summary_output: C:\Users\IEUser\Desktop\georgia_BS_F_summary.txt -listwise_output: C:\Users\IEUser\Desktop\georgia_BS_F_listwise.csv -predictflag: 0 -prediction_def: -prediction_output: diff --git a/mgwr/tests/georgia/georgia_BS_F_listwise.csv b/mgwr/tests/georgia/georgia_BS_F_listwise.csv deleted file mode 100755 index ec525a4..0000000 --- a/mgwr/tests/georgia/georgia_BS_F_listwise.csv +++ /dev/null @@ -1,160 +0,0 @@ -Area_num, Area_key, x_coord, y_coord, est_Intercept, se_Intercept, t_Intercept, est_PctRural, se_PctRural, t_PctRural, est_PctPov, se_PctPov, t_PctPov, est_PctBlack, se_PctBlack, t_PctBlack, y, yhat, residual, std_residual, localR2, influence, CooksD - 0, 13001, 941396.6, 3521764, 17.773084, 2.613925, 6.799385, -0.084447, 0.022534, -3.747519, -0.206895, 0.123152, -1.680000, 0.072218, 0.053829, 1.341633, 8.200000, 8.770904, -0.570904, -0.152628, 0.534242, 0.046417, 0.000068 - 1, 13003, 895553, 3471916, 17.909007, 2.851004, 6.281650, -0.075912, 0.023937, -3.171273, -0.300513, 0.134796, -2.229398, 0.116286, 0.057385, 2.026436, 6.400000, 5.627943, 0.772057, 0.212853, 0.559361, 0.103315, 0.000312 - 2, 13005, 930946.4, 3502787, 17.741664, 2.738235, 6.479233, -0.081922, 0.023223, -3.527632, -0.235500, 0.126826, -1.856878, 0.088548, 0.057898, 1.529380, 6.600000, 8.376916, -1.776916, -0.496464, 0.547517, 0.126915, 0.002142 - 3, 13007, 745398.6, 3474765, 18.862030, 2.594357, 7.270406, -0.068144, 0.023305, -2.924023, -0.355610, 0.133412, -2.665500, 0.115038, 0.060624, 1.897563, 9.400000, 9.172561, 0.227439, 0.064599, 0.566464, 0.155149, 0.000046 - 4, 13009, 849431.3, 3665553, 25.837943, 1.440975, 17.930872, -0.132573, 0.016150, -8.208601, -0.238408, 0.086974, -2.741124, -0.014170, 0.039810, -0.355929, 13.300000, 15.404309, -2.104309, -0.567856, 0.575601, 0.064075, 0.001320 - 5, 13011, 819317.3, 3807616, 29.452459, 1.621412, 18.164695, -0.188534, 0.020954, -8.997310, -0.113731, 0.134442, -0.845946, -0.041081, 0.043226, -0.950360, 6.400000, 8.738397, -2.338397, -0.630888, 0.541123, 0.063666, 0.001618 - 6, 13013, 803747.1, 3769623, 28.770962, 1.518404, 18.948166, -0.180582, 0.019082, -9.463370, -0.138754, 0.123232, -1.125951, -0.032265, 0.039831, -0.810044, 9.200000, 14.696568, -5.496568, -1.463365, 0.558627, 0.038440, 0.005119 - 7, 13015, 699011.5, 3793408, 26.813145, 1.908661, 14.048145, -0.139906, 0.022388, -6.249145, -0.445255, 0.144918, -3.072471, 0.110329, 0.048948, 2.253984, 9.000000, 12.544111, -3.544111, -0.944225, 0.621363, 0.039798, 0.002210 - 8, 13017, 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0.417192, 0.564821, 0.112748, 0.001323 - 17, 13035, 782448.2, 3684504, 26.883788, 1.356073, 19.824738, -0.150977, 0.016149, -9.349208, -0.241435, 0.097165, -2.484778, 0.000226, 0.038312, 0.005889, 7.200000, 12.043680, -4.843680, -1.288907, 0.594877, 0.037487, 0.003869 - 18, 13037, 724741.2, 3492653, 19.002626, 2.539986, 7.481390, -0.069926, 0.022830, -3.062963, -0.338172, 0.129720, -2.606949, 0.103909, 0.062809, 1.654362, 10.100000, 7.375325, 2.724675, 0.761889, 0.585250, 0.128345, 0.005111 - 19, 13039, 1008480, 3437933, 19.613265, 4.084949, 4.801349, -0.100208, 0.032522, -3.081243, -0.296955, 0.192167, -1.545297, 0.124032, 0.094322, 1.314988, 13.500000, 13.982697, -0.482697, -0.151688, 0.773166, 0.309848, 0.000618 - 20, 13043, 964264.9, 3598842, 19.841663, 2.269700, 8.741978, -0.094625, 0.022214, -4.259627, -0.190744, 0.119946, -1.590251, 0.023944, 0.045757, 0.523293, 9.900000, 11.055578, -1.155578, -0.311170, 0.556492, 0.060056, 0.000370 - 21, 13045, 678778.6, 3713250, 26.696030, 1.597285, 16.713379, -0.134240, 0.020277, -6.620453, -0.564322, 0.139328, -4.050317, 0.135815, 0.048051, 2.826501, 12.000000, 11.474032, 0.525968, 0.140163, 0.636072, 0.040273, 0.000049 - 22, 13047, 670055.9, 3862318, 22.743482, 2.793802, 8.140691, -0.063164, 0.037651, -1.677638, -0.681787, 0.219722, -3.102956, 0.294595, 0.100431, 2.933325, 8.100000, 12.076153, -3.976153, -1.217296, 0.657638, 0.272836, 0.033247 - 23, 13049, 962612.3, 3432769, 19.075145, 3.741817, 5.097830, -0.092524, 0.031391, -2.947426, -0.316625, 0.182674, -1.733280, 0.134073, 0.088866, 1.508707, 6.400000, 7.655186, -1.255186, -0.375373, 0.709966, 0.237940, 0.002631 - 24, 13051, 1059706, 3556747, 18.990865, 3.735673, 5.083653, -0.107228, 0.031900, -3.361352, -0.101299, 0.189184, -0.535450, 0.029855, 0.080420, 0.371233, 18.600000, 17.836743, 0.763257, 0.257606, 0.586796, 0.401690, 0.002664 - 25, 13053, 704959.2, 3577608, 20.896180, 1.796002, 11.634829, -0.090270, 0.019345, -4.666330, -0.332885, 0.109990, -3.026520, 0.087573, 0.058660, 1.492885, 20.200000, 18.906977, 1.293023, 0.373348, 0.629131, 0.182508, 0.001861 - 26, 13055, 653026.6, 3813760, 25.293384, 2.281356, 11.086998, -0.093700, 0.031341, -2.989693, -0.723140, 0.193378, -3.739520, 0.232752, 0.073528, 3.165481, 5.900000, 9.487181, -3.587181, -0.966545, 0.666937, 0.061225, 0.003643 - 27, 13057, 734240.9, 3794110, 27.473015, 1.747669, 15.719800, -0.157980, 0.020569, -7.680380, -0.309053, 0.133275, -2.318914, 0.054192, 0.042857, 1.264487, 18.400000, 16.552460, 1.847540, 0.501873, 0.592194, 0.076370, 0.001245 - 28, 13059, 832508.6, 3762905, 29.069629, 1.507976, 19.277245, -0.181979, 0.019101, -9.527381, -0.106946, 0.122360, -0.874034, -0.055090, 0.041226, -1.336292, 37.500000, 21.534224, 15.965776, 5.352787, 0.552284, 0.393657, 1.112367 - 29, 13061, 695793.9, 3495219, 19.140866, 2.707916, 7.068485, -0.068142, 0.023743, -2.870047, -0.339158, 0.139936, -2.423673, 0.099905, 0.069720, 1.432932, 11.200000, 6.288878, 4.911122, 1.403680, 0.595006, 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3584821, 21.293824, 1.597952, 13.325694, -0.094980, 0.017175, -5.530035, -0.310747, 0.086959, -3.573476, 0.068075, 0.043594, 1.561558, 10.100000, 9.986675, 0.113325, 0.030822, 0.605532, 0.078626, 0.000005 - 96, 13195, 848638.8, 3785405, 29.665459, 1.585784, 18.707124, -0.188785, 0.020553, -9.185429, -0.082299, 0.130669, -0.629829, -0.066689, 0.043549, -1.531330, 9.700000, 8.940051, 0.759949, 0.204907, 0.537671, 0.062535, 0.000167 - 97, 13197, 732876.8, 3584393, 21.287860, 1.663691, 12.795564, -0.094840, 0.018186, -5.215065, -0.333359, 0.098240, -3.393310, 0.084270, 0.051742, 1.628653, 4.600000, 5.885159, -1.285159, -0.348489, 0.628953, 0.073097, 0.000573 - 98, 13199, 715359.8, 3660275, 25.278260, 1.412006, 17.902376, -0.132555, 0.017564, -7.547125, -0.388190, 0.112278, -3.457384, 0.082447, 0.048052, 1.715792, 6.700000, 9.352300, -2.652300, -0.711387, 0.630927, 0.052604, 0.001680 - 99, 13201, 716369.8, 3451034, 18.856279, 3.036399, 6.210080, -0.061187, 0.026390, -2.318620, -0.382287, 0.158717, -2.408610, 0.122488, 0.071505, 1.713013, 8.200000, 7.654967, 0.545033, 0.157866, 0.540716, 0.187604, 0.000344 - 100, 13205, 766238.6, 3453930, 18.703792, 2.743232, 6.818159, -0.066072, 0.024377, -2.710476, -0.376084, 0.142703, -2.635428, 0.128400, 0.060834, 2.110661, 7.800000, 10.348551, -2.548551, -0.688157, 0.542703, 0.065218, 0.001976 - 101, 13207, 790338.7, 3660608, 25.979778, 1.339185, 19.399696, -0.138009, 0.015635, -8.826765, -0.267208, 0.090104, -2.965537, 0.009781, 0.038797, 0.252108, 12.900000, 12.238686, 0.661314, 0.176343, 0.601412, 0.041491, 0.000080 - 102, 13209, 920887.4, 3568473, 18.687533, 2.125727, 8.791127, -0.087084, 0.019800, -4.398202, -0.204125, 0.102363, -1.994123, 0.046965, 0.041954, 1.119441, 10.100000, 6.427709, 3.672291, 0.992285, 0.525070, 0.066531, 0.004196 - 103, 13211, 825920.1, 3717990, 28.020536, 1.413213, 19.827533, -0.164606, 0.017079, -9.637685, -0.164677, 0.104271, -1.579310, -0.038710, 0.039128, -0.989332, 11.000000, 12.189318, 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863291.8, 3756777, 29.030992, 1.567298, 18.522953, -0.175749, 0.019313, -9.099909, -0.112366, 0.121016, -0.928522, -0.065436, 0.042854, -1.526964, 12.800000, 8.016853, 4.783147, 1.283999, 0.547215, 0.054206, 0.005650 - 109, 13223, 695329.2, 3758093, 27.274120, 1.744758, 15.632036, -0.144442, 0.020583, -7.017471, -0.468677, 0.136337, -3.437638, 0.099586, 0.044417, 2.242072, 7.600000, 10.007867, -2.407867, -0.657937, 0.619379, 0.087160, 0.002472 - 110, 13225, 798061.4, 3609091, 22.911456, 1.455086, 15.745774, -0.107172, 0.016129, -6.644700, -0.303000, 0.083959, -3.608887, 0.044849, 0.040208, 1.115426, 15.200000, 11.201480, 3.998520, 1.064377, 0.602027, 0.038153, 0.002687 - 111, 13227, 733846.7, 3812828, 27.067149, 1.848969, 14.639047, -0.152597, 0.021661, -7.044803, -0.314593, 0.139070, -2.262122, 0.069847, 0.046673, 1.496519, 9.000000, 7.883990, 1.116010, 0.300089, 0.595538, 0.057383, 0.000328 - 112, 13229, 953533.8, 3482044, 18.064377, 3.131710, 5.768216, -0.085386, 0.026297, 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11.700000, 12.927678, -1.227678, -0.324666, 0.574894, 0.025475, 0.000165 - 117, 13239, 686875.4, 3524124, 19.553040, 2.461852, 7.942410, -0.073096, 0.022501, -3.248588, -0.316420, 0.131341, -2.409151, 0.084088, 0.068300, 1.231144, 7.300000, 6.000066, 1.299934, 0.361955, 0.619147, 0.120915, 0.001078 - 118, 13241, 824645.5, 3864805, 30.124548, 1.999281, 15.067687, -0.193143, 0.024769, -7.797799, -0.123725, 0.159090, -0.777704, -0.034983, 0.053204, -0.657519, 11.600000, 9.115350, 2.484650, 0.676215, 0.514212, 0.079848, 0.002373 - 119, 13243, 712437.1, 3519627, 19.313328, 2.338038, 8.260484, -0.073983, 0.021723, -3.405788, -0.316976, 0.120998, -2.619688, 0.089673, 0.062608, 1.432302, 6.000000, 9.192126, -3.192126, -0.906941, 0.605231, 0.155693, 0.009070 - 120, 13245, 954272.3, 3697862, 24.864332, 2.487806, 9.994480, -0.119045, 0.023857, -4.989919, -0.183935, 0.123709, -1.486838, -0.046512, 0.051179, -0.908802, 17.300000, 18.386537, -1.086537, -0.341191, 0.543252, 0.308818, 0.003110 - 121, 13247, 777759, 3729605, 27.879124, 1.438940, 19.374767, -0.167090, 0.017502, -9.546728, -0.205515, 0.111669, -1.840400, -0.007612, 0.038057, -0.200024, 18.100000, 16.652079, 1.447921, 0.389372, 0.578975, 0.057549, 0.000554 - 122, 13249, 752973.1, 3570222, 20.504909, 1.740140, 11.783482, -0.088584, 0.018211, -4.864341, -0.312258, 0.093067, -3.355177, 0.078859, 0.049053, 1.607638, 8.000000, 8.120892, -0.120892, -0.032584, 0.609678, 0.061800, 0.000004 - 123, 13251, 1004028, 3641918, 21.528192, 2.675468, 8.046516, -0.106300, 0.025539, -4.162287, -0.167044, 0.146753, -1.138270, -0.008355, 0.053691, -0.155619, 8.600000, 8.899895, -0.299895, -0.082244, 0.586512, 0.093784, 0.000042 - 124, 13253, 704495.6, 3422002, 18.260183, 3.617098, 5.048297, -0.054520, 0.030333, -1.797368, -0.389261, 0.191659, -2.031005, 0.131016, 0.083971, 1.560263, 7.800000, 7.438489, 0.361511, 0.107363, 0.479643, 0.227263, 0.000203 - 125, 13255, 754916.2, 3685029, 26.693402, 1.380677, 19.333559, -0.149572, 0.016612, 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7.100000, 8.142710, -1.042710, -0.299561, 0.633879, 0.174236, 0.001132 - 130, 13265, 884376.9, 3717493, 27.498067, 1.629302, 16.877208, -0.151074, 0.018424, -8.199904, -0.168619, 0.104139, -1.619169, -0.051847, 0.042346, -1.224358, 5.600000, 3.830396, 1.769604, 0.494437, 0.553520, 0.126970, 0.002126 - 131, 13267, 963427.8, 3560039, 18.601329, 2.424675, 7.671679, -0.090755, 0.022539, -4.026638, -0.180378, 0.124426, -1.449677, 0.042975, 0.049046, 0.876211, 6.500000, 8.708577, -2.208577, -0.587336, 0.538511, 0.036283, 0.000777 - 132, 13269, 759410.8, 3608179, 22.891730, 1.466371, 15.611142, -0.108439, 0.016736, -6.479269, -0.322737, 0.090475, -3.567130, 0.061804, 0.044415, 1.391496, 7.100000, 5.197665, 1.902335, 0.516480, 0.622518, 0.075373, 0.001300 - 133, 13271, 882069.4, 3534470, 17.875565, 2.262005, 7.902532, -0.080869, 0.019152, -4.222387, -0.226674, 0.101421, -2.234977, 0.072027, 0.040720, 1.768811, 8.600000, 8.297619, 0.302381, 0.080615, 0.510849, 0.041087, 0.000017 - 134, 13273, 743031.8, 3522636, 19.123657, 2.180093, 8.771946, -0.075765, 0.020559, -3.685199, -0.306216, 0.108446, -2.823665, 0.088795, 0.055982, 1.586128, 9.200000, 11.720643, -2.520643, -0.701640, 0.590563, 0.120385, 0.004029 - 135, 13275, 795506.2, 3421725, 18.575044, 3.313021, 5.606679, -0.065338, 0.027022, -2.417989, -0.403287, 0.165376, -2.438605, 0.147919, 0.065955, 2.242739, 13.400000, 11.464667, 1.935333, 0.526130, 0.514137, 0.077804, 0.001397 - 136, 13277, 831682.3, 3487715, 18.002384, 2.421168, 7.435414, -0.073093, 0.020605, -3.547363, -0.308960, 0.115034, -2.685805, 0.111867, 0.047686, 2.345887, 14.000000, 10.176769, 3.823231, 1.025109, 0.547638, 0.051976, 0.003445 - 137, 13279, 941734.4, 3567586, 18.734097, 2.205759, 8.493265, -0.088922, 0.020893, -4.256132, -0.194247, 0.110649, -1.755519, 0.043521, 0.044402, 0.980154, 11.400000, 11.915166, -0.515166, -0.143572, 0.534286, 0.122489, 0.000172 - 138, 13281, 797981.7, 3872640, 28.260718, 2.095470, 13.486574, -0.171439, 0.024762, 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6.125084, -1.325084, -0.355689, 0.581536, 0.054102, 0.000433 - 143, 13291, 777040.1, 3858779, 27.355973, 1.992492, 13.729526, -0.160435, 0.023575, -6.805350, -0.215422, 0.153188, -1.406258, 0.047016, 0.053449, 0.879646, 10.100000, 7.374916, 2.725084, 0.765841, 0.564511, 0.137059, 0.005570 - 144, 13293, 752165.2, 3639192, 24.694933, 1.374212, 17.970253, -0.126240, 0.016353, -7.719480, -0.318947, 0.095073, -3.354770, 0.048502, 0.043181, 1.123237, 9.000000, 13.110363, -4.110363, -1.087819, 0.621716, 0.026927, 0.001958 - 145, 13295, 658870.4, 3842167, 23.797346, 2.574103, 9.244909, -0.073567, 0.035603, -2.066340, -0.718646, 0.210264, -3.417834, 0.275347, 0.089879, 3.063513, 8.400000, 12.329924, -3.929924, -1.145116, 0.665567, 0.197272, 0.019270 - 146, 13297, 800384.3, 3742691, 28.359668, 1.454704, 19.495145, -0.173781, 0.017973, -9.668790, -0.158902, 0.114986, -1.381919, -0.028886, 0.038847, -0.743582, 9.400000, 15.096119, -5.696119, -1.504976, 0.568110, 0.023669, 0.003283 - 147, 13299, 938349.6, 3446675, 18.490102, 3.417095, 5.411059, -0.084783, 0.029304, -2.893239, -0.312518, 0.168008, -1.860136, 0.130292, 0.083147, 1.567015, 10.400000, 10.672681, -0.272681, -0.073377, 0.643221, 0.058794, 0.000020 - 148, 13301, 902471.1, 3699878, 26.263211, 1.793029, 14.647396, -0.135919, 0.019314, -7.037350, -0.185363, 0.101791, -1.821017, -0.044920, 0.044065, -1.019424, 4.200000, 3.922913, 0.277087, 0.077494, 0.546399, 0.128636, 0.000053 - 149, 13303, 894704.3, 3648583, 23.561458, 1.780554, 13.232656, -0.110584, 0.018676, -5.921070, -0.228893, 0.090454, -2.530499, -0.009672, 0.042947, -0.225197, 9.800000, 10.695591, -0.895591, -0.243482, 0.537175, 0.077885, 0.000299 - 150, 13305, 986832.8, 3494323, 18.297991, 3.326648, 5.500429, -0.091330, 0.027475, -3.324157, -0.227218, 0.156553, -1.451380, 0.093881, 0.077548, 1.210625, 9.600000, 9.836261, -0.236261, -0.064049, 0.621505, 0.072615, 0.000019 - 151, 13307, 731576.3, 3544716, 19.661101, 2.010838, 9.777565, -0.079654, 0.019877, -4.007338, -0.303385, 0.104883, -2.892595, 0.080652, 0.056418, 1.429528, 5.500000, 8.918201, -3.418201, -0.950050, 0.599234, 0.117732, 0.007202 - 152, 13309, 898776.3, 3563384, 18.501153, 2.113110, 8.755415, -0.085034, 0.019080, -4.456837, -0.212335, 0.097675, -2.173892, 0.052857, 0.040097, 1.318242, 8.600000, 5.152843, 3.447157, 0.958650, 0.511966, 0.118747, 0.007405 - 153, 13311, 796905.6, 3841086, 28.733091, 1.787983, 16.070111, -0.179033, 0.022232, -8.052889, -0.160660, 0.144555, -1.111412, -0.003425, 0.046862, -0.073091, 13.600000, 8.812706, 4.787294, 1.289709, 0.547887, 0.060936, 0.006454 - 154, 13313, 686891.4, 3855274, 23.394471, 2.554012, 9.159890, -0.083855, 0.032799, -2.556635, -0.572061, 0.189459, -3.019437, 0.255288, 0.086126, 2.964126, 12.000000, 12.211185, -0.211185, -0.056625, 0.650040, 0.052012, 0.000011 - 155, 13315, 838551.5, 3538547, 18.192778, 2.093412, 8.690492, -0.079700, 0.018426, -4.325377, -0.252670, 0.094685, -2.668518, 0.078927, 0.040585, 1.944708, 7.600000, 5.503119, 2.096881, 0.573156, 0.532468, 0.087779, 0.001890 - 156, 13317, 891228.5, 3749769, 28.509057, 1.735328, 16.428630, -0.164395, 0.019996, -8.221384, -0.132861, 0.119490, -1.111898, -0.066138, 0.044269, -1.494004, 10.400000, 12.670101, -2.270101, -0.611560, 0.541756, 0.060902, 0.001450 - 157, 13319, 858796.9, 3637891, 24.021985, 1.546444, 15.533688, -0.114677, 0.016624, -6.898087, -0.258557, 0.084212, -3.070304, 0.004043, 0.040248, 0.100446, 8.800000, 8.768099, 0.031901, 0.008848, 0.564146, 0.113973, 0.000001 - 158, 13321, 801018.1, 3487328, 18.349546, 2.358175, 7.781248, -0.072543, 0.020883, -3.473737, -0.323199, 0.114810, -2.815070, 0.112093, 0.049993, 2.242190, 6.300000, 8.166285, -1.866285, -0.499215, 0.558495, 0.047469, 0.000743 diff --git a/mgwr/tests/georgia/georgia_BS_F_summary.txt b/mgwr/tests/georgia/georgia_BS_F_summary.txt deleted file mode 100755 index 4333fb2..0000000 --- a/mgwr/tests/georgia/georgia_BS_F_summary.txt +++ /dev/null @@ -1,170 +0,0 @@ -***************************************************************************** -* Semiparametric Geographically Weighted Regression * -* Release 1.0.90 (GWR 4.0.90) * -* 12 May 2015 * -* (Originally coded by T. Nakaya: 1 Nov 2009) * -* * -* Tomoki Nakaya(1), Martin Charlton(2), Chris Brunsdon (2) * -* Paul Lewis (2), Jing Yao (3), A Stewart Fotheringham (4) * -* (c) GWR4 development team * -* (1) Ritsumeikan University, (2) National University of Ireland, Maynooth, * -* (3) University of Glasgow, (4) Arizona State University * -***************************************************************************** - -Program began at 7/25/2016 2:04:40 AM - -***************************************************************************** -Session: -Session control file: C:\Users\IEUser\Desktop\georgia_BS_F.ctl -***************************************************************************** -Data filename: C:\Users\IEUser\Desktop\georgia\georgia\GData_utm.csv -Number of areas/points: 159 - -Model settings--------------------------------- -Model type: Gaussian -Geographic kernel: fixed bi-square -Method for optimal bandwidth search: Golden section search -Criterion for optimal bandwidth: AICc -Number of varying coefficients: 4 -Number of fixed coefficients: 0 - -Modelling options--------------------------------- -Standardisation of independent variables: OFF -Testing geographical variability of local coefficients: OFF -Local to Global Variable selection: OFF -Global to Local Variable selection: OFF -Prediction at non-regression points: OFF - -Variable settings--------------------------------- -Area key: field1: AreaKey -Easting (x-coord): field12 : X -Northing (y-coord): field13: Y -Cartesian coordinates: Euclidean distance -Dependent variable: field6: PctBach -Offset variable is not specified -Intercept: varying (Local) intercept -Independent variable with varying (Local) coefficient: field5: PctRural -Independent variable with varying (Local) coefficient: field9: PctPov -Independent variable with varying (Local) coefficient: field10: PctBlack -***************************************************************************** - -***************************************************************************** - Global regression result -***************************************************************************** - < Diagnostic information > -Residual sum of squares: 2639.559476 -Number of parameters: 4 - (Note: this num does not include an error variance term for a Gaussian model) -ML based global sigma estimate: 4.074433 -Unbiased global sigma estimate: 4.126671 --2 log-likelihood: 897.927089 -Classic AIC: 907.927089 -AICc: 908.319245 -BIC/MDL: 923.271610 -CV: 18.100197 -R square: 0.485273 -Adjusted R square: 0.471903 - -Variable Estimate Standard Error t(Est/SE) --------------------- --------------- --------------- --------------- -Intercept 23.854615 1.173043 20.335661 -PctRural -0.111395 0.012878 -8.649661 -PctPov -0.345778 0.070863 -4.879540 -PctBlack 0.058331 0.029187 1.998499 - -***************************************************************************** - GWR (Geographically weighted regression) bandwidth selection -***************************************************************************** - -Bandwidth search - Limits: 108972.626308445, 558903.094487309 - Golden section search begins... - Initial values - pL Bandwidth: 108972.626 Criterion: 948.618 - p1 Bandwidth: 280830.773 Criterion: 898.912 - p2 Bandwidth: 387044.948 Criterion: 903.831 - pU Bandwidth: 558903.094 Criterion: 906.526 - iter 1 (p1) Bandwidth: 280830.773 Criterion: 898.912 Diff: 106214.176 - iter 2 (p1) Bandwidth: 215186.802 Criterion: 895.030 Diff: 65643.971 - iter 3 (p2) Bandwidth: 215186.802 Criterion: 895.030 Diff: 40570.205 - iter 4 (p1) Bandwidth: 215186.802 Criterion: 895.030 Diff: 25073.766 - iter 5 (p2) Bandwidth: 215186.802 Criterion: 895.030 Diff: 15496.439 - iter 6 (p1) Bandwidth: 215186.802 Criterion: 895.030 Diff: 9577.326 - iter 7 (p1) Bandwidth: 209267.689 Criterion: 894.983 Diff: 5919.113 - iter 8 (p2) Bandwidth: 209267.689 Criterion: 894.983 Diff: 3658.213 -Best bandwidth size 209267.689 -Minimum AICc 894.983 - -***************************************************************************** - GWR (Geographically weighted regression) result -***************************************************************************** - Bandwidth and geographic ranges -Bandwidth size: 209267.688808 -Coordinate Min Max Range ---------------- --------------- --------------- --------------- -X-coord 635964.300000 1059706.000000 423741.700000 -Y-coord 3401148.000000 3872640.000000 471492.000000 - - Diagnostic information -Residual sum of squares: 2012.563924 -Effective number of parameters (model: trace(S)): 16.722876 -Effective number of parameters (variance: trace(S'S)): 11.612295 -Degree of freedom (model: n - trace(S)): 142.277124 -Degree of freedom (residual: n - 2trace(S) + trace(S'S)): 137.166544 -ML based sigma estimate: 3.557757 -Unbiased sigma estimate: 3.830458 --2 log-likelihood: 854.805884 -Classic AIC: 890.251635 -AICc: 894.982602 -BIC/MDL: 944.641443 -CV: 18.254062 -R square: 0.607540 -Adjusted R square: 0.544612 - -*********************************************************** - << Geographically varying (Local) coefficients >> -*********************************************************** -Estimates of varying coefficients have been saved in the following file. - Listwise output file: C:\Users\IEUser\Desktop\georgia_BS_F_listwise.csv - -Summary statistics for varying (Local) coefficients -Variable Mean STD --------------------- --------------- --------------- -Intercept 23.100425 4.102437 -PctRural -0.115556 0.038728 -PctPov -0.284599 0.131956 -PctBlack 0.058275 0.075808 - -Variable Min Max Range --------------------- --------------- --------------- --------------- -Intercept 17.723650 30.349333 12.625683 -PctRural -0.197305 -0.054520 0.142786 -PctPov -0.772356 -0.072209 0.700147 -PctBlack -0.075941 0.308952 0.384893 - -Variable Lwr Quartile Median Upr Quartile --------------------- --------------- --------------- --------------- -Intercept 18.856279 23.103493 27.099926 -PctRural -0.150482 -0.106458 -0.082746 -PctPov -0.339158 -0.271117 -0.198485 -PctBlack 0.000226 0.057840 0.110329 - -Variable Interquartile R Robust STD --------------------- --------------- --------------- -Intercept 8.243647 6.110932 -PctRural 0.067736 0.050212 -PctPov 0.140673 0.104280 -PctBlack 0.110103 0.081618 - (Note: Robust STD is given by (interquartile range / 1.349) ) - -***************************************************************************** - GWR ANOVA Table -***************************************************************************** -Source SS DF MS F ------------------ ------------------- ---------- --------------- ---------- -Global Residuals 2639.559 155.000 -GWR Improvement 626.996 17.833 35.158 -GWR Residuals 2012.564 137.167 14.672 2.396224 - -***************************************************************************** -Program terminated at 7/25/2016 2:04:40 AM diff --git a/mgwr/tests/georgia/georgia_BS_NN.ctl b/mgwr/tests/georgia/georgia_BS_NN.ctl deleted file mode 100755 index 9aec16d..0000000 --- a/mgwr/tests/georgia/georgia_BS_NN.ctl +++ /dev/null @@ -1,45 +0,0 @@ - -C:\Users\IEUser\Desktop\georgia\georgia\GData_utm.csv -FORMAT/DELIMITER: 1 -Number_of_fields: 13 -Number_of_areas: 159 -Fields -AreaKey 001 AreaKey -X 012 X -Y 013 Y -Gmetric 0 -Dependent 006 PctBach -Offset -Independent_geo 4 -000 Intercept -005 PctRural -009 PctPov -010 PctBlack -Independent_fix 0 -Unused_fields 6 -002 Latitude -003 Longitud -004 TotPop90 -007 PctEld -008 PctFB -011 ID -MODELTYPE: 0 -STANDARDISATION: 0 -GTEST: 0 -VSL2G: 0 -VSG2L: 0 -KERNELTYPE: 2 -BANDSELECTIONMETHOD: 1 -Goldrangeflag: 0 -Goldenmax: -Goldenmin: -Fixedbandsize: -IntervalMax: -IntervalMin: -IntervalStep: -Criteria: -summary_output: C:\Users\IEUser\Desktop\georgia_BS_NN_summary.txt -listwise_output: C:\Users\IEUser\Desktop\georgia_BS_NN_listwise.csv -predictflag: 0 -prediction_def: -prediction_output: diff --git a/mgwr/tests/georgia/georgia_BS_NN_listwise.csv b/mgwr/tests/georgia/georgia_BS_NN_listwise.csv deleted file mode 100755 index f51d270..0000000 --- a/mgwr/tests/georgia/georgia_BS_NN_listwise.csv +++ /dev/null @@ -1,160 +0,0 @@ -Area_num, Area_key, x_coord, y_coord, est_Intercept, se_Intercept, t_Intercept, est_PctRural, se_PctRural, t_PctRural, est_PctPov, se_PctPov, t_PctPov, est_PctBlack, se_PctBlack, t_PctBlack, y, yhat, residual, std_residual, localR2, influence, CooksD - 0, 13001, 941396.6, 3521764, 18.375924, 2.414905, 7.609379, -0.087919, 0.021113, -4.164093, -0.218522, 0.115485, -1.892203, 0.069101, 0.048422, 1.427054, 8.200000, 8.815245, -0.615245, -0.162278, 0.551117, 0.041718, 0.000077 - 1, 13003, 895553, 3471916, 18.039692, 2.693495, 6.697503, -0.077996, 0.022571, -3.455642, -0.291285, 0.126975, -2.294038, 0.109652, 0.053382, 2.054089, 6.400000, 5.611921, 0.788079, 0.213714, 0.557455, 0.093454, 0.000315 - 2, 13005, 930946.4, 3502787, 18.173904, 2.525672, 7.195672, -0.085464, 0.021449, -3.984466, -0.235007, 0.118227, -1.987757, 0.081621, 0.050307, 1.622470, 6.600000, 8.495724, -1.895724, -0.518796, 0.553851, 0.109830, 0.002225 - 3, 13007, 745398.6, 3474765, 18.612431, 2.254469, 8.255795, -0.072676, 0.020748, -3.502894, -0.325567, 0.109910, -2.962119, 0.107978, 0.052233, 2.067223, 9.400000, 8.849965, 0.550035, 0.151238, 0.571077, 0.118198, 0.000205 - 4, 13009, 849431.3, 3665553, 25.027931, 1.767947, 14.156497, -0.128431, 0.019962, -6.433839, -0.188146, 0.106755, -1.762416, -0.028756, 0.050361, -0.571001, 13.300000, 15.032420, -1.732420, -0.470868, 0.559486, 0.097548, 0.001606 - 5, 13011, 819317.3, 3807616, 28.868732, 1.568279, 18.407904, -0.180965, 0.020098, -9.004150, -0.137907, 0.130001, -1.060813, -0.031319, 0.041694, -0.751165, 6.400000, 8.580509, -2.180509, -0.580528, 0.551175, 0.059443, 0.001427 - 6, 13013, 803747.1, 3769623, 29.126594, 1.578247, 18.455024, -0.185670, 0.019899, -9.330583, -0.119547, 0.128153, -0.932843, -0.039784, 0.041354, -0.962027, 9.200000, 14.919817, -5.719817, -1.508125, 0.558752, 0.041031, 0.006520 - 7, 13015, 699011.5, 3793408, 26.738740, 1.663068, 16.077957, -0.143921, 0.019787, -7.273569, -0.379195, 0.129687, -2.923921, 0.074043, 0.041423, 1.787476, 9.000000, 12.540446, -3.540446, -0.929355, 0.571809, 0.032462, 0.001942 - 8, 13017, 863020.8, 3520432, 17.332852, 2.862878, 6.054346, -0.072048, 0.022398, -3.216792, -0.259626, 0.122903, -2.112442, 0.093798, 0.048378, 1.938852, 7.600000, 11.173490, -3.573490, -0.950910, 0.513439, 0.058498, 0.003764 - 9, 13019, 859915.8, 3466377, 18.009999, 2.499664, 7.204967, -0.074505, 0.021286, -3.500257, -0.310736, 0.120344, -2.582057, 0.116161, 0.049213, 2.360389, 7.500000, 8.430319, -0.930319, -0.253303, 0.550571, 0.100714, 0.000481 - 10, 13021, 809736.9, 3636468, 23.331917, 1.754697, 13.296838, -0.117008, 0.019905, -5.878384, -0.244202, 0.105854, -2.306955, 0.017538, 0.051088, 0.343293, 17.000000, 17.490415, -0.490415, -0.139052, 0.578390, 0.170747, 0.000267 - 11, 13023, 844270.1, 3595691, 18.575691, 2.534320, 7.329656, -0.087278, 0.023528, -3.709493, -0.206076, 0.108188, -1.904795, 0.051458, 0.050376, 1.021487, 10.300000, 10.901700, -0.601700, -0.162155, 0.545373, 0.082058, 0.000157 - 12, 13025, 979288.9, 3463849, 18.853338, 2.745800, 6.866246, -0.091904, 0.023206, -3.960341, -0.248679, 0.131652, -1.888905, 0.086563, 0.057140, 1.514925, 5.800000, 5.533408, 0.266592, 0.076204, 0.604611, 0.184081, 0.000088 - 13, 13027, 827822, 3421638, 18.212539, 2.372760, 7.675677, -0.073817, 0.021056, -3.505663, -0.334842, 0.119039, -2.812877, 0.126093, 0.049579, 2.543305, 9.100000, 9.926865, -0.826865, -0.217609, 0.563673, 0.037431, 0.000123 - 14, 13029, 1023145, 3554982, 20.021869, 2.358372, 8.489700, -0.099557, 0.022023, -4.520597, -0.215786, 0.120983, -1.783596, 0.045851, 0.047733, 0.960576, 11.800000, 9.830106, 1.969894, 0.545752, 0.606627, 0.131419, 0.003019 - 15, 13031, 994903.4, 3600493, 20.563701, 2.266851, 9.071485, -0.098698, 0.021867, -4.513625, -0.210540, 0.118868, -1.771213, 0.026472, 0.045694, 0.579333, 19.900000, 9.223105, 10.676895, 2.926313, 0.579241, 0.112510, 0.072736 - 16, 13033, 971593.8, 3671394, 23.303807, 2.330530, 9.999358, -0.108670, 0.022178, -4.899843, -0.199793, 0.116824, -1.710202, -0.024031, 0.048206, -0.498492, 9.600000, 8.139072, 1.460928, 0.397864, 0.547193, 0.101114, 0.001193 - 17, 13035, 782448.2, 3684504, 27.304692, 1.530430, 17.841186, -0.160167, 0.018818, -8.511440, -0.192097, 0.124573, -1.542034, -0.017182, 0.048881, -0.351502, 7.200000, 11.942142, -4.742142, -1.254875, 0.584010, 0.047942, 0.005313 - 18, 13037, 724741.2, 3492653, 18.937685, 2.200939, 8.604366, -0.073158, 0.020785, -3.519730, -0.322443, 0.109529, -2.943902, 0.099296, 0.054756, 1.813430, 10.100000, 7.215765, 2.884235, 0.790774, 0.578040, 0.113107, 0.005343 - 19, 13039, 1008480, 3437933, 19.091389, 2.850729, 6.697020, -0.094154, 0.024029, -3.918325, -0.253757, 0.137167, -1.849990, 0.088445, 0.060000, 1.474091, 13.500000, 13.524229, -0.024229, -0.006914, 0.622744, 0.181309, 0.000001 - 20, 13043, 964264.9, 3598842, 20.107213, 2.214298, 9.080628, -0.094601, 0.021563, -4.387163, -0.206276, 0.114816, -1.796571, 0.026868, 0.044953, 0.597696, 9.900000, 11.038573, -1.138573, -0.302560, 0.554506, 0.055910, 0.000363 - 21, 13045, 678778.6, 3713250, 26.584070, 1.581426, 16.810189, -0.135079, 0.019980, -6.760826, -0.534973, 0.135058, -3.961050, 0.126713, 0.046902, 2.701643, 12.000000, 11.586549, 0.413451, 0.108831, 0.616314, 0.037814, 0.000031 - 22, 13047, 670055.9, 3862318, 25.993181, 1.713271, 15.171670, -0.133974, 0.020694, -6.474081, -0.384352, 0.134571, -2.856124, 0.083942, 0.043006, 1.951858, 8.100000, 15.616062, -7.516062, -2.056611, 0.553322, 0.109586, 0.034878 - 23, 13049, 962612.3, 3432769, 18.861404, 2.759415, 6.835291, -0.089420, 0.023149, -3.862817, -0.274742, 0.132733, -2.069892, 0.099041, 0.057079, 1.735149, 6.400000, 7.570655, -1.170655, -0.324220, 0.610492, 0.130853, 0.001060 - 24, 13051, 1059706, 3556747, 20.315761, 2.423043, 8.384399, -0.101838, 0.022656, -4.494979, -0.216591, 0.125196, -1.730021, 0.043496, 0.048872, 0.890002, 18.600000, 17.724733, 0.875267, 0.258772, 0.618849, 0.237285, 0.001396 - 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80, 13163, 924108.1, 3668080, 23.757825, 2.206260, 10.768372, -0.109965, 0.022121, -4.971070, -0.202582, 0.109758, -1.845709, -0.029277, 0.049365, -0.593063, 6.200000, 4.783389, 1.416611, 0.390222, 0.539692, 0.121394, 0.001410 - 81, 13165, 970465.7, 3640263, 21.960048, 2.257751, 9.726515, -0.101055, 0.021632, -4.671502, -0.214505, 0.114253, -1.877457, 0.000130, 0.046377, 0.002792, 7.700000, 10.565422, -2.865422, -0.774466, 0.553734, 0.087385, 0.003848 - 82, 13167, 908636.7, 3624562, 20.800860, 2.231355, 9.322075, -0.090609, 0.022445, -4.036906, -0.217432, 0.106067, -2.049956, 0.009159, 0.048207, 0.189985, 4.900000, 7.223319, -2.323319, -0.624740, 0.519140, 0.077991, 0.002212 - 83, 13169, 821367.1, 3660143, 25.164234, 1.703418, 14.772789, -0.134897, 0.020264, -6.657152, -0.180505, 0.112681, -1.601903, -0.022814, 0.051755, -0.440808, 12.000000, 11.582661, 0.417339, 0.113205, 0.565167, 0.093922, 0.000089 - 84, 13171, 766461.7, 3663959, 26.024579, 1.497784, 17.375394, -0.143216, 0.018286, -7.832129, -0.284279, 0.119755, -2.373838, 0.026241, 0.050068, 0.524102, 10.000000, 13.175302, -3.175302, -0.833277, 0.607287, 0.031930, 0.001534 - 85, 13173, 873804.3, 3439981, 18.276315, 2.490184, 7.339344, -0.077016, 0.021570, -3.570442, -0.319038, 0.123240, -2.588749, 0.120036, 0.050653, 2.369783, 5.400000, 5.502197, -0.102197, -0.027681, 0.568180, 0.091298, 0.000005 - 86, 13175, 884830.4, 3599291, 18.598483, 2.431209, 7.649891, -0.082642, 0.023564, -3.507163, -0.196772, 0.109178, -1.802296, 0.035151, 0.047949, 0.733080, 12.000000, 11.364096, 0.635904, 0.168806, 0.491248, 0.053937, 0.000109 - 87, 13177, 770455.5, 3520161, 18.808846, 2.158241, 8.714896, -0.076247, 0.020082, -3.796826, -0.299851, 0.103299, -2.902752, 0.091913, 0.051330, 1.790638, 13.700000, 10.834768, 2.865232, 0.791532, 0.580526, 0.126429, 0.006075 - 88, 13179, 1014742, 3537225, 19.712953, 2.408576, 8.184484, -0.098465, 0.022114, -4.452569, -0.216323, 0.122218, -1.769972, 0.052522, 0.049083, 1.070060, 13.400000, 14.808946, -1.408946, -0.390956, 0.605081, 0.134140, 0.001587 - 89, 13181, 919396.5, 3752562, 27.921286, 1.942379, 14.374787, -0.155026, 0.021024, -7.373823, -0.148542, 0.117998, -1.258854, -0.063926, 0.044645, -1.431878, 8.200000, 7.333349, 0.866651, 0.236217, 0.551278, 0.102608, 0.000427 - 90, 13183, 1004544, 3517834, 19.433808, 2.463849, 7.887580, -0.096848, 0.022131, -4.376163, -0.221665, 0.123027, -1.801754, 0.060652, 0.050489, 1.201306, 5.200000, 5.814789, -0.614789, -0.165374, 0.603162, 0.078631, 0.000156 - 91, 13185, 864781.1, 3419313, 18.350950, 2.445420, 7.504212, -0.076702, 0.021489, -3.569278, -0.329237, 0.123298, -2.670262, 0.124459, 0.050633, 2.458042, 16.300000, 12.115865, 4.184135, 1.118755, 0.573075, 0.067484, 0.006069 - 92, 13187, 772600, 3832429, 27.530540, 1.601738, 17.187921, -0.162008, 0.019726, -8.212728, -0.235629, 0.128914, -1.827804, 0.019242, 0.040368, 0.476659, 11.100000, 11.218728, -0.118728, -0.031656, 0.551941, 0.062220, 0.000004 - 93, 13189, 917730.9, 3716368, 26.556594, 2.049086, 12.960213, -0.138472, 0.021353, -6.485034, -0.167632, 0.113454, -1.477533, -0.055326, 0.046813, -1.181850, 10.400000, 11.797658, -1.397658, -0.367670, 0.550795, 0.036613, 0.000344 - 94, 13191, 1030500, 3500535, 19.585650, 2.544855, 7.696175, -0.098386, 0.022670, -4.339861, -0.226774, 0.126838, -1.787908, 0.063397, 0.052340, 1.211263, 8.700000, 7.437642, 1.262358, 0.356947, 0.617381, 0.166179, 0.001701 - 95, 13193, 777055.3, 3584821, 20.302644, 1.973180, 10.289304, -0.091171, 0.019705, -4.626838, -0.265047, 0.103437, -2.562410, 0.057636, 0.056291, 1.023896, 10.100000, 9.966811, 0.133189, 0.036409, 0.608093, 0.107871, 0.000011 - 96, 13195, 848638.8, 3785405, 29.590949, 1.593987, 18.564107, -0.187790, 0.020620, -9.107394, -0.086238, 0.131109, -0.657756, -0.065090, 0.043751, -1.487726, 9.700000, 8.916465, 0.783535, 0.208857, 0.550716, 0.061720, 0.000192 - 97, 13197, 732876.8, 3584393, 21.013811, 1.777595, 11.821484, -0.093080, 0.019026, -4.892205, -0.318679, 0.105346, -3.025058, 0.079295, 0.056776, 1.396634, 4.600000, 5.995525, -1.395525, -0.375831, 0.627532, 0.080811, 0.000832 - 98, 13199, 715359.8, 3660275, 25.224396, 1.488370, 16.947670, -0.131121, 0.018846, -6.957310, -0.423909, 0.126330, -3.355567, 0.097420, 0.054586, 1.784685, 6.700000, 9.284445, -2.584445, -0.688536, 0.634592, 0.060716, 0.002053 - 99, 13201, 716369.8, 3451034, 18.655811, 2.280386, 8.180987, -0.071531, 0.021084, -3.392620, -0.334852, 0.113005, -2.963173, 0.111371, 0.053690, 2.074325, 8.200000, 7.162908, 1.037092, 0.281743, 0.569713, 0.096678, 0.000569 - 100, 13205, 766238.6, 3453930, 18.368840, 2.383153, 7.707789, -0.071437, 0.021302, -3.353496, -0.335002, 0.117147, -2.859663, 0.118058, 0.051930, 2.273394, 7.800000, 10.395689, -2.595689, -0.689616, 0.559710, 0.055490, 0.001872 - 101, 13207, 790338.7, 3660608, 25.731843, 1.562314, 16.470342, -0.141435, 0.019015, -7.438071, -0.222813, 0.119013, -1.872183, -0.001812, 0.051624, -0.035106, 12.900000, 11.977673, 0.922327, 0.244942, 0.589995, 0.054726, 0.000233 - 102, 13209, 920887.4, 3568473, 17.978451, 2.322577, 7.740734, -0.082891, 0.021648, -3.829113, -0.183316, 0.111667, -1.641636, 0.044016, 0.044863, 0.981109, 10.100000, 6.558461, 3.541539, 0.951440, 0.481273, 0.076288, 0.005009 - 103, 13211, 825920.1, 3717990, 28.693695, 1.569305, 18.284333, -0.177965, 0.019473, -9.139108, -0.067768, 0.130562, -0.519046, -0.073499, 0.047448, -1.549049, 11.000000, 12.132376, -1.132376, -0.300273, 0.557708, 0.051877, 0.000331 - 104, 13213, 707834.3, 3854188, 26.386878, 1.696040, 15.557935, -0.142118, 0.020440, -6.952847, -0.338818, 0.133131, -2.544991, 0.066919, 0.042099, 1.589590, 5.500000, 9.927145, -4.427145, -1.170075, 0.552413, 0.045589, 0.004382 - 105, 13215, 700833.7, 3598228, 21.843074, 1.659262, 13.164332, -0.099834, 0.019010, -5.251575, -0.366487, 0.112803, -3.248908, 0.099910, 0.058191, 1.716912, 16.600000, 18.498512, -1.898512, -0.538318, 0.644127, 0.170792, 0.003999 - 106, 13217, 793263.9, 3719734, 28.847315, 1.589465, 18.149077, -0.182570, 0.019717, -9.259420, -0.102857, 0.130715, -0.786878, -0.050833, 0.045691, -1.112541, 9.500000, 12.354765, -2.854765, -0.748598, 0.563542, 0.030476, 0.001180 - 107, 13219, 830735.9, 3750903, 29.460662, 1.585724, 18.578680, -0.188049, 0.020178, -9.319456, -0.064893, 0.131731, -0.492614, -0.071948, 0.044437, -1.619114, 28.400000, 10.515515, 17.884485, 4.859650, 0.552622, 0.097062, 0.170092 - 108, 13221, 863291.8, 3756777, 29.177378, 1.620291, 18.007492, -0.178213, 0.020046, -8.890033, -0.096511, 0.126917, -0.760425, -0.071130, 0.044577, -1.595640, 12.800000, 8.032877, 4.767123, 1.267608, 0.554574, 0.057117, 0.006522 - 109, 13223, 695329.2, 3758093, 26.994593, 1.653437, 16.326350, -0.144304, 0.019681, -7.332245, -0.426293, 0.129851, -3.282933, 0.085506, 0.042140, 2.029086, 7.600000, 10.058864, -2.458864, -0.661149, 0.588412, 0.077883, 0.002474 - 110, 13225, 798061.4, 3609091, 21.207599, 1.931336, 10.980793, -0.099787, 0.020005, -4.988105, -0.262799, 0.103970, -2.527649, 0.046023, 0.053326, 0.863052, 15.200000, 10.970992, 4.229008, 1.124549, 0.591719, 0.057165, 0.005137 - 111, 13227, 733846.7, 3812828, 27.084437, 1.647418, 16.440536, -0.153281, 0.019750, -7.761060, -0.301715, 0.128916, -2.340399, 0.047284, 0.040489, 1.167835, 9.000000, 7.964322, 1.035678, 0.274469, 0.560910, 0.050752, 0.000270 - 112, 13229, 953533.8, 3482044, 18.612524, 2.601788, 7.153743, -0.089241, 0.022051, -4.047026, -0.245085, 0.123930, -1.977606, 0.084538, 0.052861, 1.599251, 6.300000, 7.740923, -1.440923, -0.391676, 0.584391, 0.097715, 0.001113 - 113, 13231, 744180.8, 3665561, 25.907755, 1.497189, 17.304261, -0.140148, 0.018450, -7.595919, -0.354426, 0.123720, -2.864736, 0.059245, 0.051687, 1.146231, 9.300000, 8.330884, 0.969116, 0.261042, 0.619238, 0.081150, 0.000403 - 114, 13233, 668031.4, 3764766, 26.504604, 1.635503, 16.205781, -0.135427, 0.019829, -6.829612, -0.464528, 0.131301, -3.537897, 0.101547, 0.042796, 2.372841, 6.800000, 11.379041, -4.579041, -1.211531, 0.587011, 0.047653, 0.004921 - 115, 13235, 833819.6, 3567447, 18.394926, 2.553523, 7.203743, -0.083630, 0.021858, -3.826044, -0.230167, 0.108238, -2.126485, 0.066148, 0.048223, 1.371699, 10.700000, 10.223950, 0.476050, 0.126055, 0.573500, 0.049173, 0.000055 - 116, 13237, 840169.1, 3695254, 27.466413, 1.601400, 17.151505, -0.158342, 0.019109, -8.286358, -0.116111, 0.119382, -0.972603, -0.061461, 0.048837, -1.258477, 11.700000, 13.017184, -1.317184, -0.345827, 0.563046, 0.032860, 0.000272 - 117, 13239, 686875.4, 3524124, 19.674459, 2.048805, 9.602895, -0.076800, 0.020589, -3.730161, -0.327569, 0.111591, -2.935453, 0.091916, 0.058596, 1.568643, 7.300000, 5.774051, 1.525949, 0.415296, 0.596610, 0.099923, 0.001283 - 118, 13241, 824645.5, 3864805, 28.095647, 1.581450, 17.765752, -0.169776, 0.020283, -8.370470, -0.179779, 0.132555, -1.356259, -0.009479, 0.042367, -0.223734, 11.600000, 8.669737, 2.930263, 0.780979, 0.541754, 0.061466, 0.002676 - 119, 13243, 712437.1, 3519627, 19.400471, 2.095997, 9.255965, -0.075752, 0.020517, -3.692142, -0.317680, 0.108586, -2.925619, 0.089892, 0.056893, 1.580033, 6.000000, 9.172055, -3.172055, -0.881768, 0.589691, 0.137244, 0.008287 - 120, 13245, 954272.3, 3697862, 24.815945, 2.300383, 10.787743, -0.120551, 0.022525, -5.351946, -0.180967, 0.116635, -1.551575, -0.044908, 0.048682, -0.922473, 17.300000, 18.444544, -1.144544, -0.346488, 0.545224, 0.272550, 0.003014 - 121, 13247, 777759, 3729605, 28.996670, 1.635103, 17.733848, -0.184296, 0.020004, -9.213078, -0.141807, 0.130817, -1.084007, -0.032054, 0.044234, -0.724635, 18.100000, 16.949769, 1.150231, 0.308225, 0.568126, 0.071571, 0.000491 - 122, 13249, 752973.1, 3570222, 20.143981, 1.923033, 10.475110, -0.086448, 0.019432, -4.448672, -0.290041, 0.102402, -2.832382, 0.070816, 0.055933, 1.266086, 8.000000, 8.141504, -0.141504, -0.037905, 0.608463, 0.070927, 0.000007 - 123, 13251, 1004028, 3641918, 22.000899, 2.283359, 9.635323, -0.103888, 0.021736, -4.779433, -0.211583, 0.118168, -1.790533, 0.002808, 0.046173, 0.060804, 8.600000, 9.042787, -0.442787, -0.118149, 0.570086, 0.063635, 0.000064 - 124, 13253, 704495.6, 3422002, 18.552729, 2.331305, 7.958089, -0.070842, 0.021334, -3.320568, -0.340959, 0.116027, -2.938625, 0.116789, 0.053802, 2.170711, 7.800000, 7.538033, 0.261967, 0.071239, 0.566278, 0.098482, 0.000037 - 125, 13255, 754916.2, 3685029, 27.046720, 1.537997, 17.585678, -0.154438, 0.018841, -8.197081, -0.303840, 0.129284, -2.350179, 0.032794, 0.049959, 0.656430, 11.100000, 14.982577, -3.882577, -1.020007, 0.601112, 0.034063, 0.002458 - 126, 13257, 842085.9, 3827075, 28.950415, 1.568949, 18.452111, -0.180631, 0.020403, -8.853278, -0.131568, 0.131295, -1.002078, -0.037893, 0.042715, -0.887132, 13.100000, 14.615563, -1.515563, -0.404594, 0.546778, 0.064541, 0.000757 - 127, 13259, 703256.8, 3552857, 20.171000, 1.923869, 10.484603, -0.082675, 0.019898, -4.155026, -0.324660, 0.108254, -2.999061, 0.087485, 0.057894, 1.511118, 8.000000, 7.260976, 0.739024, 0.206144, 0.611534, 0.143175, 0.000476 - 128, 13261, 763457.1, 3551752, 19.436785, 2.046619, 9.497019, -0.081094, 0.019795, -4.096627, -0.283650, 0.101675, -2.789759, 0.074573, 0.054198, 1.375946, 15.900000, 12.190501, 3.709499, 0.987719, 0.594733, 0.059673, 0.004148 - 129, 13263, 734217.9, 3623162, 23.078763, 1.586403, 14.547858, -0.112567, 0.018725, -6.011687, -0.360965, 0.115852, -3.115739, 0.084650, 0.057540, 1.471151, 7.100000, 8.347484, -1.247484, -0.364614, 0.640115, 0.219599, 0.002506 - 130, 13265, 884376.9, 3717493, 27.520861, 1.772296, 15.528370, -0.151714, 0.019767, -7.675126, -0.151989, 0.114644, -1.325746, -0.058766, 0.046272, -1.270003, 5.600000, 3.895132, 1.704868, 0.474766, 0.559500, 0.140317, 0.002465 - 131, 13267, 963427.8, 3560039, 19.256298, 2.233654, 8.620986, -0.093360, 0.020979, -4.450236, -0.206422, 0.113088, -1.825317, 0.046954, 0.045435, 1.033424, 6.500000, 8.702775, -2.202775, -0.577719, 0.564123, 0.030782, 0.000710 - 132, 13269, 759410.8, 3608179, 22.034483, 1.698501, 12.972900, -0.103733, 0.018699, -5.547404, -0.312460, 0.106211, -2.941886, 0.066035, 0.055455, 1.190796, 7.100000, 5.296954, 1.803046, 0.491319, 0.626729, 0.102154, 0.001840 - 133, 13271, 882069.4, 3534470, 16.895516, 2.852481, 5.923095, -0.072526, 0.022747, -3.188337, -0.218596, 0.119920, -1.822841, 0.078357, 0.048073, 1.629970, 8.600000, 8.361885, 0.238115, 0.063130, 0.473791, 0.051550, 0.000015 - 134, 13273, 743031.8, 3522636, 19.123574, 2.168708, 8.817958, -0.075826, 0.020568, -3.686576, -0.305719, 0.107638, -2.840247, 0.088406, 0.055670, 1.588052, 9.200000, 11.708678, -2.508678, -0.689780, 0.587694, 0.118173, 0.004272 - 135, 13275, 795506.2, 3421725, 18.208195, 2.418624, 7.528327, -0.071792, 0.021418, -3.351909, -0.341804, 0.120813, -2.829192, 0.127193, 0.050858, 2.500930, 13.400000, 11.344938, 2.055062, 0.543157, 0.557015, 0.045636, 0.000945 - 136, 13277, 831682.3, 3487715, 18.006901, 2.486725, 7.241212, -0.072662, 0.021087, -3.445811, -0.311581, 0.118432, -2.630888, 0.112785, 0.048796, 2.311368, 14.000000, 10.167793, 3.832207, 1.016772, 0.548050, 0.052966, 0.003874 - 137, 13279, 941734.4, 3567586, 18.743279, 2.227902, 8.412973, -0.088969, 0.021102, -4.216177, -0.194575, 0.111740, -1.741319, 0.043583, 0.044856, 0.971615, 11.400000, 11.916248, -0.516248, -0.142276, 0.527237, 0.122250, 0.000189 - 138, 13281, 797981.7, 3872640, 27.490506, 1.586879, 17.323629, -0.161521, 0.019954, -8.094660, -0.219397, 0.130834, -1.676917, 0.011283, 0.041348, 0.272884, 11.400000, 8.266897, 3.133103, 0.834189, 0.543414, 0.059551, 0.002952 - 139, 13283, 919077.6, 3595170, 18.866189, 2.283136, 8.263278, -0.085392, 0.022390, -3.813858, -0.189617, 0.110938, -1.709216, 0.029085, 0.045268, 0.642506, 6.300000, 10.138875, -3.838875, -1.035981, 0.493954, 0.084581, 0.006644 - 140, 13285, 682616.8, 3660254, 24.730344, 1.492810, 16.566306, -0.123629, 0.019693, -6.277743, -0.491018, 0.132968, -3.692752, 0.133664, 0.057257, 2.334434, 13.600000, 15.300990, -1.700990, -0.450355, 0.643286, 0.048937, 0.000699 - 141, 13287, 819399.6, 3514927, 18.225195, 2.349879, 7.755803, -0.075361, 0.020383, -3.697309, -0.292708, 0.109450, -2.674345, 0.098907, 0.047627, 2.076718, 7.200000, 9.731427, -2.531427, -0.696808, 0.563722, 0.120127, 0.004442 - 142, 13289, 832935, 3623868, 20.965009, 2.112019, 9.926525, -0.098113, 0.022476, -4.365304, -0.229657, 0.105169, -2.183700, 0.027869, 0.052411, 0.531731, 4.800000, 6.462592, -1.662592, -0.449433, 0.537914, 0.087659, 0.001300 - 143, 13291, 777040.1, 3858779, 27.320591, 1.609147, 16.978309, -0.158970, 0.019948, -7.969116, -0.239476, 0.130640, -1.833093, 0.022034, 0.041008, 0.537312, 10.100000, 7.043390, 3.056610, 0.834905, 0.546488, 0.106444, 0.005564 - 144, 13293, 752165.2, 3639192, 24.200809, 1.564276, 15.470937, -0.123605, 0.018710, -6.606297, -0.344539, 0.119565, -2.881611, 0.065065, 0.056060, 1.160625, 9.000000, 12.872171, -3.872171, -1.017570, 0.628564, 0.034626, 0.002488 - 145, 13295, 658870.4, 3842167, 25.991901, 1.717976, 15.129371, -0.131963, 0.020741, -6.362486, -0.411469, 0.135161, -3.044283, 0.092313, 0.043488, 2.122713, 8.400000, 15.157496, -6.757496, -1.835909, 0.559385, 0.096799, 0.024203 - 146, 13297, 800384.3, 3742691, 29.279015, 1.595726, 18.348400, -0.188348, 0.019983, -9.425633, -0.091658, 0.129666, -0.706881, -0.053861, 0.043092, -1.249899, 9.400000, 15.552791, -6.152791, -1.612155, 0.560098, 0.028940, 0.005190 - 147, 13299, 938349.6, 3446675, 18.656967, 2.676737, 6.970040, -0.086523, 0.022520, -3.841971, -0.278733, 0.128515, -2.168882, 0.101281, 0.054819, 1.847558, 10.400000, 10.707326, -0.307326, -0.080997, 0.595942, 0.040209, 0.000018 - 148, 13301, 902471.1, 3699878, 26.151850, 1.941109, 13.472636, -0.134341, 0.020585, -6.526246, -0.177206, 0.108749, -1.629500, -0.049077, 0.047066, -1.042728, 4.200000, 3.984952, 0.215048, 0.059813, 0.553755, 0.138238, 0.000038 - 149, 13303, 894704.3, 3648583, 22.653152, 2.139217, 10.589460, -0.101212, 0.021973, -4.606206, -0.216008, 0.105576, -2.045982, -0.013325, 0.050849, -0.262048, 9.800000, 10.505011, -0.705011, -0.191521, 0.534857, 0.096610, 0.000263 - 150, 13305, 986832.8, 3494323, 19.058441, 2.563838, 7.433559, -0.094024, 0.022310, -4.214534, -0.231460, 0.125065, -1.850712, 0.072389, 0.052717, 1.373168, 9.600000, 9.927395, -0.327395, -0.086895, 0.598634, 0.053615, 0.000029 - 151, 13307, 731576.3, 3544716, 19.682721, 2.013145, 9.777100, -0.079862, 0.019978, -3.997518, -0.304909, 0.104974, -2.904624, 0.081356, 0.056410, 1.442209, 5.500000, 8.920127, -3.420127, -0.939373, 0.599113, 0.116263, 0.007778 - 152, 13309, 898776.3, 3563384, 16.844017, 2.682408, 6.279439, -0.074241, 0.023436, -3.167862, -0.172899, 0.117933, -1.466082, 0.050370, 0.046776, 1.076834, 8.600000, 5.695227, 2.904773, 0.818087, 0.423182, 0.159493, 0.008509 - 153, 13311, 796905.6, 3841086, 27.838898, 1.574679, 17.679091, -0.166676, 0.019761, -8.434538, -0.203986, 0.129239, -1.578360, 0.003179, 0.040768, 0.077966, 13.600000, 8.629694, 4.970306, 1.317270, 0.548624, 0.050856, 0.006229 - 154, 13313, 686891.4, 3855274, 26.164642, 1.710298, 15.298295, -0.137364, 0.020596, -6.669445, -0.367377, 0.134044, -2.740722, 0.077726, 0.042667, 1.821677, 12.000000, 12.786831, -0.786831, -0.206834, 0.553793, 0.035205, 0.000105 - 155, 13315, 838551.5, 3538547, 18.025600, 2.660378, 6.775579, -0.076497, 0.021987, -3.479159, -0.261962, 0.115963, -2.259014, 0.084694, 0.048373, 1.750865, 7.600000, 5.573625, 2.026375, 0.558896, 0.557453, 0.123618, 0.002952 - 156, 13317, 891228.5, 3749769, 28.516555, 1.762358, 16.180906, -0.164552, 0.020298, -8.106824, -0.131495, 0.121461, -1.082611, -0.066626, 0.044950, -1.482242, 10.400000, 12.676648, -2.276648, -0.606735, 0.555125, 0.061337, 0.001612 - 157, 13319, 858796.9, 3637891, 22.169471, 1.996974, 11.101535, -0.101861, 0.021349, -4.771271, -0.224866, 0.101262, -2.220636, 0.003954, 0.050613, 0.078119, 8.800000, 8.708923, 0.091077, 0.025605, 0.538754, 0.156479, 0.000008 - 158, 13321, 801018.1, 3487328, 18.263625, 2.314566, 7.890733, -0.073520, 0.020449, -3.595321, -0.314540, 0.110659, -2.842413, 0.109955, 0.048770, 2.254575, 6.300000, 8.172088, -1.872088, -0.494558, 0.561673, 0.044714, 0.000767 diff --git a/mgwr/tests/georgia/georgia_BS_NN_summary.txt b/mgwr/tests/georgia/georgia_BS_NN_summary.txt deleted file mode 100755 index 52cbf74..0000000 --- a/mgwr/tests/georgia/georgia_BS_NN_summary.txt +++ /dev/null @@ -1,170 +0,0 @@ -***************************************************************************** -* Semiparametric Geographically Weighted Regression * -* Release 1.0.90 (GWR 4.0.90) * -* 12 May 2015 * -* (Originally coded by T. Nakaya: 1 Nov 2009) * -* * -* Tomoki Nakaya(1), Martin Charlton(2), Chris Brunsdon (2) * -* Paul Lewis (2), Jing Yao (3), A Stewart Fotheringham (4) * -* (c) GWR4 development team * -* (1) Ritsumeikan University, (2) National University of Ireland, Maynooth, * -* (3) University of Glasgow, (4) Arizona State University * -***************************************************************************** - -Program began at 7/25/2016 2:04:17 AM - -***************************************************************************** -Session: -Session control file: C:\Users\IEUser\Desktop\georgia_BS_NN.ctl -***************************************************************************** -Data filename: C:\Users\IEUser\Desktop\georgia\georgia\GData_utm.csv -Number of areas/points: 159 - -Model settings--------------------------------- -Model type: Gaussian -Geographic kernel: adaptive bi-square -Method for optimal bandwidth search: Golden section search -Criterion for optimal bandwidth: AICc -Number of varying coefficients: 4 -Number of fixed coefficients: 0 - -Modelling options--------------------------------- -Standardisation of independent variables: OFF -Testing geographical variability of local coefficients: OFF -Local to Global Variable selection: OFF -Global to Local Variable selection: OFF -Prediction at non-regression points: OFF - -Variable settings--------------------------------- -Area key: field1: AreaKey -Easting (x-coord): field12 : X -Northing (y-coord): field13: Y -Cartesian coordinates: Euclidean distance -Dependent variable: field6: PctBach -Offset variable is not specified -Intercept: varying (Local) intercept -Independent variable with varying (Local) coefficient: field5: PctRural -Independent variable with varying (Local) coefficient: field9: PctPov -Independent variable with varying (Local) coefficient: field10: PctBlack -***************************************************************************** - -***************************************************************************** - Global regression result -***************************************************************************** - < Diagnostic information > -Residual sum of squares: 2639.559476 -Number of parameters: 4 - (Note: this num does not include an error variance term for a Gaussian model) -ML based global sigma estimate: 4.074433 -Unbiased global sigma estimate: 4.126671 --2 log-likelihood: 897.927089 -Classic AIC: 907.927089 -AICc: 908.319245 -BIC/MDL: 923.271610 -CV: 18.100197 -R square: 0.485273 -Adjusted R square: 0.471903 - -Variable Estimate Standard Error t(Est/SE) --------------------- --------------- --------------- --------------- -Intercept 23.854615 1.173043 20.335661 -PctRural -0.111395 0.012878 -8.649661 -PctPov -0.345778 0.070863 -4.879540 -PctBlack 0.058331 0.029187 1.998499 - -***************************************************************************** - GWR (Geographically weighted regression) bandwidth selection -***************************************************************************** - -Bandwidth search - Limits: 48, 159 - Golden section search begins... - Initial values - pL Bandwidth: 48.000 Criterion: 909.256 - p1 Bandwidth: 90.398 Criterion: 896.463 - p2 Bandwidth: 116.602 Criterion: 898.615 - pU Bandwidth: 159.000 Criterion: 903.072 - iter 1 (p1) Bandwidth: 90.398 Criterion: 896.463 Diff: 26.204 - iter 2 (p2) Bandwidth: 90.398 Criterion: 896.463 Diff: 16.195 - iter 3 (p1) Bandwidth: 90.398 Criterion: 896.463 Diff: 10.009 - iter 4 (p2) Bandwidth: 90.398 Criterion: 896.463 Diff: 6.186 - iter 5 (p1) Bandwidth: 90.398 Criterion: 896.463 Diff: 3.823 - iter 6 (p2) Bandwidth: 90.398 Criterion: 896.463 Diff: 2.363 - iter 7 (p1) Bandwidth: 90.398 Criterion: 896.463 Diff: 1.460 - iter 8 (p2) Bandwidth: 90.398 Criterion: 896.463 Diff: 0.902 -Best bandwidth size 90.000 -Minimum AICc 896.463 - -***************************************************************************** - GWR (Geographically weighted regression) result -***************************************************************************** - Bandwidth and geographic ranges -Bandwidth size: 90.398227 -Coordinate Min Max Range ---------------- --------------- --------------- --------------- -X-coord 635964.300000 1059706.000000 423741.700000 -Y-coord 3401148.000000 3872640.000000 471492.000000 - - Diagnostic information -Residual sum of squares: 2090.125305 -Effective number of parameters (model: trace(S)): 14.925095 -Effective number of parameters (variance: trace(S'S)): 10.193958 -Degree of freedom (model: n - trace(S)): 144.074905 -Degree of freedom (residual: n - 2trace(S) + trace(S'S)): 139.343769 -ML based sigma estimate: 3.625664 -Unbiased sigma estimate: 3.872954 --2 log-likelihood: 860.818394 -Classic AIC: 892.668583 -AICc: 896.462831 -BIC/MDL: 941.541173 -CV: 19.186726 -R square: 0.592415 -Adjusted R square: 0.534505 - -*********************************************************** - << Geographically varying (Local) coefficients >> -*********************************************************** -Estimates of varying coefficients have been saved in the following file. - Listwise output file: C:\Users\IEUser\Desktop\georgia_BS_NN_listwise.csv - -Summary statistics for varying (Local) coefficients -Variable Mean STD --------------------- --------------- --------------- -Intercept 23.067890 4.184766 -PctRural -0.118169 0.038043 -PctPov -0.261744 0.097335 -PctBlack 0.044847 0.059488 - -Variable Min Max Range --------------------- --------------- --------------- --------------- -Intercept 16.844017 29.625733 12.781716 -PctRural -0.190065 -0.070745 0.119320 -PctPov -0.534973 -0.064893 0.470080 -PctBlack -0.073499 0.133808 0.207307 - -Variable Lwr Quartile Median Upr Quartile --------------------- --------------- --------------- --------------- -Intercept 18.853338 22.653152 27.237578 -PctRural -0.153281 -0.103888 -0.082675 -PctPov -0.326117 -0.248679 -0.199793 -PctBlack 0.002808 0.057636 0.093798 - -Variable Interquartile R Robust STD --------------------- --------------- --------------- -Intercept 8.384240 6.215152 -PctRural 0.070606 0.052340 -PctPov 0.126324 0.093643 -PctBlack 0.090990 0.067450 - (Note: Robust STD is given by (interquartile range / 1.349) ) - -***************************************************************************** - GWR ANOVA Table -***************************************************************************** -Source SS DF MS F ------------------ ------------------- ---------- --------------- ---------- -Global Residuals 2639.559 155.000 -GWR Improvement 549.434 15.656 35.094 -GWR Residuals 2090.125 139.344 15.000 2.339611 - -***************************************************************************** -Program terminated at 7/25/2016 2:04:17 AM diff --git a/mgwr/tests/georgia/georgia_GS_F.ctl b/mgwr/tests/georgia/georgia_GS_F.ctl deleted file mode 100755 index b21c0ff..0000000 --- a/mgwr/tests/georgia/georgia_GS_F.ctl +++ /dev/null @@ -1,45 +0,0 @@ - -C:\Users\IEUser\Desktop\georgia\georgia\GData_utm.csv -FORMAT/DELIMITER: 1 -Number_of_fields: 13 -Number_of_areas: 159 -Fields -AreaKey 001 AreaKey -X 012 X -Y 013 Y -Gmetric 0 -Dependent 006 PctBach -Offset -Independent_geo 4 -000 Intercept -005 PctRural -009 PctPov -010 PctBlack -Independent_fix 0 -Unused_fields 6 -002 Latitude -003 Longitud -004 TotPop90 -007 PctEld -008 PctFB -011 ID -MODELTYPE: 0 -STANDARDISATION: 0 -GTEST: 0 -VSL2G: 0 -VSG2L: 0 -KERNELTYPE: 0 -BANDSELECTIONMETHOD: 1 -Goldrangeflag: 0 -Goldenmax: -Goldenmin: -Fixedbandsize: -IntervalMax: -IntervalMin: -IntervalStep: -Criteria: -summary_output: C:\Users\IEUser\Desktop\goergia_GS_F_summary.txt -listwise_output: C:\Users\IEUser\Desktop\goergia_GS_F_listwise.csv -predictflag: 0 -prediction_def: -prediction_output: diff --git a/mgwr/tests/georgia/georgia_GS_F_listwise.csv b/mgwr/tests/georgia/georgia_GS_F_listwise.csv deleted file mode 100755 index c0d326b..0000000 --- a/mgwr/tests/georgia/georgia_GS_F_listwise.csv +++ /dev/null @@ -1,160 +0,0 @@ -Area_num, Area_key, x_coord, y_coord, est_Intercept, se_Intercept, t_Intercept, est_PctRural, se_PctRural, t_PctRural, est_PctPov, se_PctPov, t_PctPov, est_PctBlack, se_PctBlack, t_PctBlack, y, yhat, residual, std_residual, localR2, influence, CooksD - 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65, 13133, 855577.3, 3722330, 27.614066, 1.443284, 19.132797, -0.155722, 0.016846, -9.243944, -0.180914, 0.096739, -1.870132, -0.037934, 0.038129, -0.994891, 8.800000, 9.392443, -0.592443, -0.158002, 0.552804, 0.054404, 0.000088 - 66, 13135, 772634.6, 3764306, 28.317671, 1.495672, 18.933077, -0.170234, 0.018110, -9.399803, -0.226008, 0.112667, -2.005983, 0.002136, 0.038081, 0.056080, 29.600000, 25.109368, 4.490632, 1.255697, 0.577869, 0.139833, 0.015721 - 67, 13137, 818917.1, 3839931, 29.102117, 1.658962, 17.542360, -0.181252, 0.021332, -8.496781, -0.140894, 0.137260, -1.026473, -0.033065, 0.044178, -0.748465, 12.000000, 11.247688, 0.752312, 0.200326, 0.530018, 0.051456, 0.000134 - 68, 13139, 794419.5, 3803344, 28.802410, 1.567821, 18.370980, -0.177649, 0.019567, -9.078889, -0.173887, 0.124973, -1.391401, -0.017117, 0.040512, -0.422534, 15.400000, 12.406697, 2.993303, 0.792643, 0.557016, 0.040857, 0.001641 - 69, 13141, 873518.8, 3689861, 26.191848, 1.490939, 17.567346, -0.135710, 0.016417, -8.266234, -0.228075, 0.088423, -2.579358, -0.020842, 0.038181, -0.545874, 6.800000, 4.095962, 2.704038, 0.803859, 0.564162, 0.238967, 0.012445 - 70, 13143, 665933.8, 3740622, 26.479932, 1.599145, 16.558807, -0.132012, 0.020433, -6.460604, -0.551697, 0.132283, -4.170579, 0.138860, 0.045223, 3.070580, 7.500000, 10.483535, -2.983535, -0.807741, 0.633274, 0.082396, 0.003593 - 71, 13145, 695500.6, 3624790, 23.378014, 1.466275, 15.943809, -0.111261, 0.017870, -6.226062, -0.374268, 0.101218, -3.697653, 0.085800, 0.046237, 1.855660, 13.600000, 9.778825, 3.821175, 1.038411, 0.641321, 0.089263, 0.006482 - 72, 13147, 870749.9, 3810303, 29.333040, 1.622431, 18.079685, -0.182709, 0.021087, -8.664439, -0.100954, 0.129078, -0.782120, -0.064110, 0.044316, -1.446662, 9.100000, 13.107079, -4.007079, -1.061090, 0.509441, 0.040845, 0.002941 - 73, 13149, 675280.4, 3685569, 25.424168, 1.454941, 17.474367, -0.127761, 0.018468, -6.917860, -0.480233, 0.114981, -4.176637, 0.119105, 0.045492, 2.618134, 5.700000, 5.069225, 0.630775, 0.175473, 0.634195, 0.130906, 0.000284 - 74, 13151, 763488.4, 3699716, 26.930567, 1.369624, 19.662739, -0.150840, 0.016190, -9.316803, -0.285118, 0.094155, -3.028173, 0.022232, 0.036205, 0.614070, 10.700000, 13.955140, -3.255140, -0.877954, 0.596477, 0.075443, 0.003858 - 75, 13153, 814118.9, 3590553, 21.982592, 1.496302, 14.691284, -0.100682, 0.015972, -6.303489, -0.294182, 0.081281, -3.619307, 0.049882, 0.036931, 1.350709, 16.000000, 17.847440, -1.847440, -0.510687, 0.595102, 0.119826, 0.002178 - 76, 13155, 855461.8, 3506293, 18.652067, 2.017468, 9.245284, -0.078814, 0.018306, -4.305454, -0.289209, 0.095128, -3.040205, 0.090590, 0.040997, 2.209654, 8.300000, 8.551789, -0.251789, -0.067196, 0.540918, 0.055684, 0.000016 - 77, 13157, 815753.1, 3783949, 28.983295, 1.509199, 19.204429, -0.179740, 0.019053, -9.433847, -0.145667, 0.119305, -1.220965, -0.035969, 0.040082, -0.897384, 9.000000, 12.565084, -3.565084, -0.940643, 0.545602, 0.033889, 0.001904 - 78, 13159, 807249.1, 3695092, 26.936093, 1.345103, 20.025305, -0.149485, 0.015707, -9.517036, -0.233942, 0.089293, -2.619921, -0.007764, 0.035689, -0.217556, 10.800000, 7.646782, 3.153218, 0.842637, 0.578437, 0.058189, 0.002691 - 79, 13161, 915741.9, 3530869, 18.587587, 2.054473, 9.047376, -0.083293, 0.018903, -4.406281, -0.240391, 0.097275, -2.471261, 0.067675, 0.041599, 1.626853, 8.300000, 9.685356, -1.385356, -0.372440, 0.524075, 0.069434, 0.000635 - 80, 13163, 924108.1, 3668080, 24.397253, 1.802536, 13.534962, -0.115770, 0.018593, -6.226577, -0.248282, 0.094249, -2.634317, -0.008504, 0.041647, -0.204187, 6.200000, 4.573482, 1.626518, 0.447613, 0.568228, 0.111925, 0.001549 - 81, 13165, 970465.7, 3640263, 22.345855, 2.068655, 10.802117, -0.105004, 0.020758, -5.058458, -0.214416, 0.108275, -1.980282, -0.001922, 0.044876, -0.042839, 7.700000, 10.656099, -2.956099, -0.805448, 0.554384, 0.094058, 0.004131 - 82, 13167, 908636.7, 3624562, 22.458347, 1.678174, 13.382612, -0.102373, 0.017465, -5.861512, -0.257747, 0.087956, -2.930394, 0.015788, 0.039758, 0.397087, 4.900000, 7.034152, -2.134152, -0.570500, 0.555599, 0.058811, 0.001247 - 83, 13169, 821367.1, 3660143, 25.412835, 1.341701, 18.940764, -0.130589, 0.015256, -8.559730, -0.263712, 0.082290, -3.204677, 0.006213, 0.035484, 0.175103, 12.000000, 12.028576, -0.028576, -0.007668, 0.585494, 0.065861, 0.000000 - 84, 13171, 766461.7, 3663959, 25.616247, 1.327782, 19.292508, -0.135230, 0.015594, -8.672001, -0.299656, 0.087013, -3.443820, 0.031063, 0.036000, 0.862850, 10.000000, 13.188263, -3.188263, -0.838623, 0.606050, 0.027897, 0.001238 - 85, 13173, 873804.3, 3439981, 18.485508, 2.662577, 6.942712, -0.079760, 0.023282, -3.425762, -0.325976, 0.128610, -2.534609, 0.122959, 0.052478, 2.343070, 5.400000, 5.335018, 0.064982, 0.018003, 0.571666, 0.123737, 0.000003 - 86, 13175, 884830.4, 3599291, 21.513434, 1.629498, 13.202490, -0.096680, 0.016747, -5.772866, -0.265558, 0.084313, -3.149651, 0.032999, 0.037891, 0.870910, 12.000000, 12.054656, -0.054656, -0.014424, 0.554368, 0.034337, 0.000000 - 87, 13177, 770455.5, 3520161, 19.713219, 1.888118, 10.440670, -0.080357, 0.018910, -4.249449, -0.324217, 0.095161, -3.407032, 0.092205, 0.046346, 1.989518, 13.700000, 11.116389, 2.583611, 0.712229, 0.592749, 0.114982, 0.004042 - 88, 13179, 1014742, 3537225, 19.271658, 2.889841, 6.668761, -0.098901, 0.025702, -3.847953, -0.186311, 0.144917, -1.285637, 0.051835, 0.059925, 0.864987, 13.400000, 14.842581, -1.442581, -0.418043, 0.599004, 0.199104, 0.002665 - 89, 13181, 919396.5, 3752562, 27.545928, 1.877197, 14.673966, -0.151795, 0.020423, -7.432526, -0.159834, 0.111833, -1.429224, -0.055239, 0.042638, -1.295542, 8.200000, 7.411757, 0.788243, 0.217535, 0.544230, 0.116920, 0.000384 - 90, 13183, 1004544, 3517834, 19.147246, 2.911110, 6.577301, -0.096494, 0.025509, -3.782714, -0.213249, 0.142579, -1.495655, 0.067821, 0.062017, 1.093589, 5.200000, 5.918927, -0.718927, -0.197629, 0.619662, 0.109966, 0.000296 - 91, 13185, 864781.1, 3419313, 18.702818, 2.898650, 6.452252, -0.080632, 0.025021, -3.222632, -0.346748, 0.141637, -2.448148, 0.132853, 0.056694, 2.343351, 16.300000, 12.199780, 4.100220, 1.127872, 0.579007, 0.111142, 0.009756 - 92, 13187, 772600, 3832429, 28.034016, 1.680274, 16.684198, -0.165973, 0.020545, -8.078570, -0.225653, 0.135128, -1.669915, 0.014869, 0.042678, 0.348387, 11.100000, 11.557024, -0.457024, -0.123355, 0.570811, 0.076780, 0.000078 - 93, 13189, 917730.9, 3716368, 26.491635, 1.823878, 14.524892, -0.136822, 0.019204, -7.124735, -0.206799, 0.100526, -2.057180, -0.036307, 0.041310, -0.878878, 10.400000, 11.687393, -1.287393, -0.339950, 0.560658, 0.035441, 0.000260 - 94, 13191, 1030500, 3500535, 19.531073, 3.306463, 5.906939, -0.100700, 0.028465, -3.537675, -0.224337, 0.158667, -1.413885, 0.076261, 0.073323, 1.040078, 8.700000, 7.763560, 0.936440, 0.298694, 0.678354, 0.338933, 0.002805 - 95, 13193, 777055.3, 3584821, 21.948697, 1.501384, 14.618972, -0.100007, 0.016436, -6.084695, -0.307740, 0.083805, -3.672086, 0.060169, 0.039379, 1.527960, 10.100000, 9.935315, 0.164685, 0.044507, 0.614154, 0.079139, 0.000010 - 96, 13195, 848638.8, 3785405, 29.108660, 1.525087, 19.086557, -0.179940, 0.019541, -9.208353, -0.119019, 0.120262, -0.989659, -0.054095, 0.041502, -1.303442, 9.700000, 8.795952, 0.904048, 0.242503, 0.527752, 0.065270, 0.000252 - 97, 13197, 732876.8, 3584393, 21.964645, 1.555356, 14.121942, -0.098795, 0.017498, -5.646017, -0.324893, 0.091508, -3.550442, 0.070566, 0.044300, 1.592908, 4.600000, 5.838986, -1.238986, -0.333421, 0.633268, 0.071284, 0.000523 - 98, 13199, 715359.8, 3660275, 25.031495, 1.381684, 18.116654, -0.128288, 0.016812, -7.630803, -0.375748, 0.097493, -3.854114, 0.074536, 0.041025, 1.816861, 6.700000, 9.382455, -2.682455, -0.714991, 0.626994, 0.053324, 0.001766 - 99, 13201, 716369.8, 3451034, 18.498969, 2.647591, 6.987095, -0.063562, 0.024691, -2.574316, -0.359424, 0.137856, -2.607251, 0.119639, 0.064298, 1.860682, 8.200000, 7.487133, 0.712867, 0.202623, 0.541033, 0.167515, 0.000507 - 100, 13205, 766238.6, 3453930, 18.697196, 2.444988, 7.647153, -0.068668, 0.022730, -3.021070, -0.360146, 0.121524, -2.963568, 0.121464, 0.055233, 2.199129, 7.800000, 10.321223, -2.521223, -0.677451, 0.546752, 0.068453, 0.002068 - 101, 13207, 790338.7, 3660608, 25.531500, 1.319869, 19.343960, -0.133514, 0.015307, -8.722150, -0.278916, 0.083919, -3.323649, 0.018022, 0.035218, 0.511733, 12.900000, 12.228318, 0.671682, 0.178147, 0.597232, 0.043893, 0.000089 - 102, 13209, 920887.4, 3568473, 19.640398, 1.866662, 10.521669, -0.088268, 0.018357, -4.808384, -0.233561, 0.092494, -2.525151, 0.044110, 0.039715, 1.110669, 10.100000, 6.461978, 3.638022, 0.977197, 0.520617, 0.067811, 0.004260 - 103, 13211, 825920.1, 3717990, 27.667554, 1.385998, 19.962182, -0.158520, 0.016396, -9.667943, -0.193737, 0.095679, -2.024863, -0.027128, 0.036967, -0.733837, 11.000000, 12.247136, -1.247136, -0.330924, 0.562550, 0.044766, 0.000315 - 104, 13213, 707834.3, 3854188, 25.179507, 1.989430, 12.656647, -0.118071, 0.024765, -4.767757, -0.434567, 0.159217, -2.729392, 0.126532, 0.052441, 2.412830, 5.500000, 9.793444, -4.293444, -1.152274, 0.605491, 0.066235, 0.005776 - 105, 13215, 700833.7, 3598228, 22.372666, 1.560179, 14.339810, -0.101636, 0.018243, -5.571270, -0.347518, 0.099784, -3.482699, 0.079690, 0.047623, 1.673341, 16.600000, 18.607828, -2.007828, -0.574018, 0.643793, 0.177115, 0.004350 - 106, 13217, 793263.9, 3719734, 27.696364, 1.385437, 19.991071, -0.160723, 0.016476, -9.754942, -0.221214, 0.097472, -2.269504, -0.009041, 0.036440, -0.248116, 9.500000, 12.093887, -2.593887, -0.680759, 0.575874, 0.023543, 0.000685 - 107, 13219, 830735.9, 3750903, 28.515470, 1.443850, 19.749609, -0.171342, 0.017761, -9.647245, -0.153598, 0.107785, -1.425043, -0.040542, 0.038794, -1.045074, 28.400000, 10.691485, 17.708515, 4.805037, 0.547939, 0.086501, 0.134089 - 108, 13221, 863291.8, 3756777, 28.486943, 1.508388, 18.885691, -0.168929, 0.018442, -9.159875, -0.137865, 0.109730, -1.256411, -0.053214, 0.040260, -1.321768, 12.800000, 8.044145, 4.755855, 1.269712, 0.535643, 0.056408, 0.005911 - 109, 13223, 695329.2, 3758093, 27.039824, 1.594510, 16.958082, -0.143242, 0.019665, -7.283955, -0.461479, 0.124511, -3.706325, 0.102088, 0.041745, 2.445530, 7.600000, 9.959265, -2.359265, -0.641397, 0.623576, 0.090009, 0.002496 - 110, 13225, 798061.4, 3609091, 23.007522, 1.409771, 16.320047, -0.109026, 0.015673, -6.956206, -0.296656, 0.081178, -3.654367, 0.042495, 0.036544, 1.162854, 15.200000, 11.224301, 3.975699, 1.052399, 0.604418, 0.040150, 0.002841 - 111, 13227, 733846.7, 3812828, 27.309286, 1.686626, 16.191670, -0.152000, 0.020362, -7.464944, -0.333428, 0.132044, -2.525130, 0.061004, 0.042185, 1.446102, 9.000000, 7.931665, 1.068335, 0.285900, 0.601971, 0.060873, 0.000325 - 112, 13229, 953533.8, 3482044, 18.638725, 2.712076, 6.872493, -0.087985, 0.023539, -3.737747, -0.264189, 0.130720, -2.021036, 0.095320, 0.057866, 1.647250, 6.300000, 7.579729, -1.279729, -0.353824, 0.612330, 0.120170, 0.001049 - 113, 13231, 744180.8, 3665561, 25.531390, 1.347820, 18.942725, -0.134407, 0.016029, -8.385050, -0.329567, 0.091036, -3.620183, 0.048136, 0.037576, 1.281036, 9.300000, 8.639087, 0.660913, 0.177246, 0.614761, 0.064863, 0.000134 - 114, 13233, 668031.4, 3764766, 26.573909, 1.670221, 15.910416, -0.130883, 0.021420, -6.110385, -0.555955, 0.138710, -4.008026, 0.140370, 0.045606, 3.077899, 6.800000, 10.815419, -4.015419, -1.076518, 0.632586, 0.064257, 0.004881 - 115, 13235, 833819.6, 3567447, 20.717000, 1.633885, 12.679598, -0.091711, 0.016455, -5.573389, -0.287903, 0.083191, -3.460729, 0.060053, 0.037378, 1.606647, 10.700000, 10.488630, 0.211370, 0.055826, 0.575861, 0.035842, 0.000007 - 116, 13237, 840169.1, 3695254, 26.792405, 1.380916, 19.401908, -0.145428, 0.015827, -9.188835, -0.219663, 0.088415, -2.484450, -0.019925, 0.036604, -0.544337, 11.700000, 12.865669, -1.165669, -0.306488, 0.567530, 0.027117, 0.000161 - 117, 13239, 686875.4, 3524124, 19.938606, 2.101376, 9.488358, -0.076743, 0.021189, -3.621913, -0.335561, 0.116903, -2.870428, 0.092779, 0.058495, 1.586108, 7.300000, 5.823211, 1.476789, 0.408066, 0.623753, 0.119125, 0.001381 - 118, 13241, 824645.5, 3864805, 28.946875, 1.753802, 16.505215, -0.178627, 0.022537, -7.925863, -0.141280, 0.146443, -0.964748, -0.033037, 0.047105, -0.701358, 11.600000, 9.151176, 2.448824, 0.663099, 0.517122, 0.082733, 0.002432 - 119, 13243, 712437.1, 3519627, 19.834717, 2.045356, 9.697438, -0.077315, 0.020591, -3.754887, -0.334245, 0.109112, -3.063314, 0.093909, 0.054724, 1.716049, 6.000000, 9.161675, -3.161675, -0.892170, 0.613757, 0.155351, 0.008979 - 120, 13245, 954272.3, 3697862, 25.181684, 2.131237, 11.815523, -0.121909, 0.021231, -5.741997, -0.228763, 0.107676, -2.124543, -0.024839, 0.044398, -0.559478, 17.300000, 18.769040, -1.469040, -0.445945, 0.578971, 0.270136, 0.004514 - 121, 13247, 777759, 3729605, 27.840812, 1.415120, 19.673812, -0.163184, 0.016897, -9.657556, -0.236713, 0.101440, -2.333519, 0.001163, 0.036761, 0.031625, 18.100000, 16.722048, 1.377952, 0.368396, 0.581161, 0.059032, 0.000522 - 122, 13249, 752973.1, 3570222, 21.401431, 1.596530, 13.404969, -0.094463, 0.017377, -5.435993, -0.317336, 0.088541, -3.584061, 0.071331, 0.043188, 1.651642, 8.000000, 8.071782, -0.071782, -0.019219, 0.623306, 0.061796, 0.000001 - 123, 13251, 1004028, 3641918, 21.997494, 2.323465, 9.467537, -0.107861, 0.022711, -4.749200, -0.173649, 0.125644, -1.382070, -0.012026, 0.049352, -0.243675, 8.600000, 8.930102, -0.330102, -0.089911, 0.555384, 0.093413, 0.000051 - 124, 13253, 704495.6, 3422002, 18.016084, 3.027796, 5.950231, -0.058428, 0.027344, -2.136750, -0.366760, 0.162354, -2.259018, 0.128272, 0.073049, 1.755977, 7.800000, 7.488072, 0.311928, 0.090810, 0.506163, 0.206436, 0.000132 - 125, 13255, 754916.2, 3685029, 26.366844, 1.355004, 19.458874, -0.143917, 0.016033, -8.976018, -0.307879, 0.092178, -3.340046, 0.034163, 0.036502, 0.935930, 11.100000, 14.843453, -3.743453, -0.983761, 0.604639, 0.026126, 0.001592 - 126, 13257, 842085.9, 3827075, 29.440723, 1.620637, 18.166140, -0.185429, 0.021257, -8.723231, -0.114260, 0.133555, -0.855527, -0.051254, 0.044108, -1.162005, 13.100000, 14.932844, -1.832844, -0.494817, 0.516765, 0.077218, 0.001257 - 127, 13259, 703256.8, 3552857, 20.782084, 1.826887, 11.375683, -0.086125, 0.019515, -4.413305, -0.331453, 0.104173, -3.181761, 0.084002, 0.052007, 1.615207, 8.000000, 7.092777, 0.907223, 0.254883, 0.636837, 0.147911, 0.000692 - 128, 13261, 763457.1, 3551752, 20.685430, 1.690728, 12.234627, -0.088696, 0.017764, -4.992976, -0.316879, 0.089315, -3.547864, 0.078125, 0.043847, 1.781777, 15.900000, 12.435222, 3.464778, 0.924448, 0.612953, 0.055237, 0.003064 - 129, 13263, 734217.9, 3623162, 23.625745, 1.401385, 16.858854, -0.114740, 0.016535, -6.939219, -0.333560, 0.090162, -3.699563, 0.062009, 0.040795, 1.520009, 7.100000, 7.952706, -0.852706, -0.242691, 0.629253, 0.169709, 0.000738 - 130, 13265, 884376.9, 3717493, 27.089792, 1.566017, 17.298530, -0.146418, 0.017474, -8.379044, -0.193156, 0.096119, -2.009559, -0.038055, 0.039240, -0.969798, 5.600000, 3.951246, 1.648754, 0.457485, 0.553403, 0.126435, 0.001858 - 131, 13267, 963427.8, 3560039, 19.217058, 2.200403, 8.733426, -0.091326, 0.020954, -4.358374, -0.201567, 0.110553, -1.823263, 0.042392, 0.045119, 0.939556, 6.500000, 8.798031, -2.298031, -0.606947, 0.527615, 0.035842, 0.000840 - 132, 13269, 759410.8, 3608179, 23.039230, 1.416355, 16.266563, -0.109369, 0.016195, -6.753421, -0.313715, 0.085188, -3.682624, 0.054559, 0.039100, 1.395362, 7.100000, 5.205194, 1.894806, 0.510932, 0.621828, 0.075003, 0.001298 - 133, 13271, 882069.4, 3534470, 18.922035, 1.913377, 9.889339, -0.082409, 0.017695, -4.657153, -0.261421, 0.090604, -2.885327, 0.070699, 0.038845, 1.820027, 8.600000, 8.237958, 0.362042, 0.095925, 0.529706, 0.041946, 0.000025 - 134, 13273, 743031.8, 3522636, 19.875275, 1.928382, 10.306709, -0.079853, 0.019541, -4.086380, -0.329413, 0.099615, -3.306880, 0.092239, 0.049655, 1.857614, 9.200000, 11.797883, -2.597883, -0.716655, 0.606052, 0.116194, 0.004141 - 135, 13275, 795506.2, 3421725, 18.643305, 2.742233, 6.798586, -0.070259, 0.023874, -2.942869, -0.376501, 0.133889, -2.812046, 0.135664, 0.057479, 2.360227, 13.400000, 11.401834, 1.998166, 0.537226, 0.540407, 0.069565, 0.001323 - 136, 13277, 831682.3, 3487715, 18.685509, 2.102202, 8.888541, -0.076433, 0.019228, -3.975025, -0.314747, 0.100573, -3.129535, 0.103134, 0.044193, 2.333723, 14.000000, 10.323671, 3.676329, 0.979917, 0.548679, 0.053355, 0.003319 - 137, 13279, 941734.4, 3567586, 19.466237, 1.985710, 9.803160, -0.089505, 0.019441, -4.604034, -0.217248, 0.099299, -2.187827, 0.041084, 0.041683, 0.985641, 11.400000, 12.017499, -0.617499, -0.171186, 0.518705, 0.124870, 0.000256 - 138, 13281, 797981.7, 3872640, 27.974459, 1.810354, 15.452483, -0.165515, 0.022514, -7.351677, -0.187459, 0.150689, -1.244015, -0.001395, 0.047718, -0.029238, 11.400000, 8.798515, 2.601485, 0.703537, 0.537970, 0.080385, 0.002654 - 139, 13283, 919077.6, 3595170, 20.817967, 1.766099, 11.787543, -0.093618, 0.018007, -5.198952, -0.242249, 0.090264, -2.683772, 0.029578, 0.039579, 0.747331, 6.300000, 10.242200, -3.942200, -1.061068, 0.533919, 0.071614, 0.005327 - 140, 13285, 682616.8, 3660254, 24.628583, 1.429226, 17.232112, -0.122024, 0.018024, -6.769916, -0.434456, 0.108277, -4.012437, 0.105230, 0.045825, 2.296363, 13.600000, 15.337945, -1.737945, -0.462263, 0.636860, 0.049325, 0.000680 - 141, 13287, 819399.6, 3514927, 19.212872, 1.905344, 10.083674, -0.079944, 0.018122, -4.411469, -0.306889, 0.092643, -3.312610, 0.090595, 0.041947, 2.159766, 7.200000, 9.733350, -2.533350, -0.696716, 0.563038, 0.110766, 0.003708 - 142, 13289, 832935, 3623868, 23.494623, 1.415751, 16.595167, -0.112114, 0.015528, -7.219911, -0.282337, 0.080497, -3.507406, 0.026196, 0.036111, 0.725423, 4.800000, 6.145666, -1.345666, -0.359394, 0.587368, 0.057089, 0.000480 - 143, 13291, 777040.1, 3858779, 27.581783, 1.780050, 15.494948, -0.159725, 0.021761, -7.339813, -0.225600, 0.145778, -1.547555, 0.020375, 0.045908, 0.443822, 10.100000, 7.482873, 2.617127, 0.732462, 0.561207, 0.141350, 0.005417 - 144, 13293, 752165.2, 3639192, 24.448551, 1.349780, 18.112984, -0.123045, 0.015880, -7.748661, -0.319512, 0.087064, -3.669837, 0.048127, 0.037840, 1.271852, 9.000000, 13.053828, -4.053828, -1.066415, 0.618836, 0.028113, 0.002018 - 145, 13295, 658870.4, 3842167, 24.393692, 2.128643, 11.459738, -0.096237, 0.028760, -3.346260, -0.599597, 0.177716, -3.373902, 0.187969, 0.060689, 3.097279, 8.400000, 13.108544, -4.708544, -1.342428, 0.613305, 0.172575, 0.023053 - 146, 13297, 800384.3, 3742691, 28.270402, 1.427432, 19.805076, -0.169111, 0.017295, -9.777959, -0.190096, 0.105132, -1.808170, -0.020479, 0.037547, -0.545415, 9.400000, 15.035356, -5.635356, -1.480005, 0.565278, 0.024890, 0.003429 - 147, 13299, 938349.6, 3446675, 18.807464, 2.938334, 6.400723, -0.088284, 0.025722, -3.432238, -0.299348, 0.145918, -2.051490, 0.114781, 0.062671, 1.831479, 10.400000, 10.676743, -0.276743, -0.074001, 0.639735, 0.059378, 0.000021 - 148, 13301, 902471.1, 3699878, 26.143704, 1.676856, 15.590902, -0.133400, 0.017886, -7.458245, -0.222181, 0.093988, -2.363941, -0.027191, 0.040097, -0.678129, 4.200000, 3.922915, 0.277085, 0.076889, 0.562307, 0.126549, 0.000053 - 149, 13303, 894704.3, 3648583, 23.926078, 1.599239, 14.960916, -0.112817, 0.016900, -6.675395, -0.261576, 0.086445, -3.025915, 0.003985, 0.039419, 0.101086, 9.800000, 10.912660, -1.112660, -0.300339, 0.567106, 0.076920, 0.000461 - 150, 13305, 986832.8, 3494323, 19.023537, 2.921649, 6.511234, -0.093408, 0.025174, -3.710469, -0.245696, 0.140586, -1.747657, 0.085550, 0.063490, 1.347440, 9.600000, 9.883568, -0.283568, -0.076397, 0.637893, 0.073385, 0.000028 - 151, 13307, 731576.3, 3544716, 20.531972, 1.800189, 11.405456, -0.085371, 0.018907, -4.515398, -0.326009, 0.097203, -3.353916, 0.083926, 0.048730, 1.722268, 5.500000, 8.872715, -3.372715, -0.931006, 0.623628, 0.117344, 0.007067 - 152, 13309, 898776.3, 3563384, 19.676502, 1.807888, 10.883698, -0.087088, 0.017568, -4.957200, -0.249081, 0.088557, -2.812663, 0.050958, 0.038374, 1.327925, 8.600000, 4.952280, 3.647720, 1.007925, 0.527792, 0.119104, 0.008425 - 153, 13311, 796905.6, 3841086, 28.582087, 1.677912, 17.034317, -0.174207, 0.021028, -8.284629, -0.176172, 0.137634, -1.280004, -0.011304, 0.043669, -0.258850, 13.600000, 8.929962, 4.670038, 1.251453, 0.548425, 0.063411, 0.006503 - 154, 13313, 686891.4, 3855274, 24.474271, 2.100238, 11.653092, -0.104160, 0.027046, -3.851167, -0.506701, 0.169414, -2.990907, 0.159743, 0.057374, 2.784246, 12.000000, 12.207216, -0.207216, -0.055252, 0.608355, 0.053997, 0.000011 - 155, 13315, 838551.5, 3538547, 19.592759, 1.793470, 10.924498, -0.084198, 0.017164, -4.905592, -0.288637, 0.087414, -3.301955, 0.075524, 0.038778, 1.947599, 7.600000, 5.316609, 2.283391, 0.620364, 0.560011, 0.088819, 0.002301 - 156, 13317, 891228.5, 3749769, 27.962051, 1.636165, 17.089996, -0.159172, 0.018915, -8.415137, -0.150957, 0.107657, -1.402208, -0.054321, 0.040961, -1.326172, 10.400000, 12.568266, -2.168266, -0.580414, 0.538262, 0.061387, 0.001351 - 157, 13319, 858796.9, 3637891, 23.916868, 1.456211, 16.424038, -0.114371, 0.015790, -7.243026, -0.271957, 0.081857, -3.322330, 0.013616, 0.037177, 0.366235, 8.800000, 8.890582, -0.090582, -0.025045, 0.576505, 0.120216, 0.000005 - 158, 13321, 801018.1, 3487328, 18.929377, 2.092550, 9.046081, -0.075227, 0.019763, -3.806492, -0.330297, 0.102164, -3.233022, 0.105827, 0.046739, 2.264206, 6.300000, 8.176916, -1.876916, -0.499318, 0.559498, 0.049672, 0.000799 diff --git a/mgwr/tests/georgia/georgia_GS_F_summary.txt b/mgwr/tests/georgia/georgia_GS_F_summary.txt deleted file mode 100755 index 478708f..0000000 --- a/mgwr/tests/georgia/georgia_GS_F_summary.txt +++ /dev/null @@ -1,168 +0,0 @@ -***************************************************************************** -* Semiparametric Geographically Weighted Regression * -* Release 1.0.90 (GWR 4.0.90) * -* 12 May 2015 * -* (Originally coded by T. Nakaya: 1 Nov 2009) * -* * -* Tomoki Nakaya(1), Martin Charlton(2), Chris Brunsdon (2) * -* Paul Lewis (2), Jing Yao (3), A Stewart Fotheringham (4) * -* (c) GWR4 development team * -* (1) Ritsumeikan University, (2) National University of Ireland, Maynooth, * -* (3) University of Glasgow, (4) Arizona State University * -***************************************************************************** - -Program began at 7/25/2016 2:05:10 AM - -***************************************************************************** -Session: -Session control file: C:\Users\IEUser\Desktop\goergia_GS_F.ctl -***************************************************************************** -Data filename: C:\Users\IEUser\Desktop\georgia\georgia\GData_utm.csv -Number of areas/points: 159 - -Model settings--------------------------------- -Model type: Gaussian -Geographic kernel: fixed Gaussian -Method for optimal bandwidth search: Golden section search -Criterion for optimal bandwidth: AICc -Number of varying coefficients: 4 -Number of fixed coefficients: 0 - -Modelling options--------------------------------- -Standardisation of independent variables: OFF -Testing geographical variability of local coefficients: OFF -Local to Global Variable selection: OFF -Global to Local Variable selection: OFF -Prediction at non-regression points: OFF - -Variable settings--------------------------------- -Area key: field1: AreaKey -Easting (x-coord): field12 : X -Northing (y-coord): field13: Y -Cartesian coordinates: Euclidean distance -Dependent variable: field6: PctBach -Offset variable is not specified -Intercept: varying (Local) intercept -Independent variable with varying (Local) coefficient: field5: PctRural -Independent variable with varying (Local) coefficient: field9: PctPov -Independent variable with varying (Local) coefficient: field10: PctBlack -***************************************************************************** - -***************************************************************************** - Global regression result -***************************************************************************** - < Diagnostic information > -Residual sum of squares: 2639.559476 -Number of parameters: 4 - (Note: this num does not include an error variance term for a Gaussian model) -ML based global sigma estimate: 4.074433 -Unbiased global sigma estimate: 4.126671 --2 log-likelihood: 897.927089 -Classic AIC: 907.927089 -AICc: 908.319245 -BIC/MDL: 923.271610 -CV: 18.100197 -R square: 0.485273 -Adjusted R square: 0.471903 - -Variable Estimate Standard Error t(Est/SE) --------------------- --------------- --------------- --------------- -Intercept 23.854615 1.173043 20.335661 -PctRural -0.111395 0.012878 -8.649661 -PctPov -0.345778 0.070863 -4.879540 -PctBlack 0.058331 0.029187 1.998499 - -***************************************************************************** - GWR (Geographically weighted regression) bandwidth selection -***************************************************************************** - -Bandwidth search - Limits: 54486.3131542225, 279451.547243655 - Golden section search begins... - Initial values - pL Bandwidth: 54486.313 Criterion: 914.115 - p1 Bandwidth: 140415.386 Criterion: 900.141 - p2 Bandwidth: 193522.474 Criterion: 903.842 - pU Bandwidth: 279451.547 Criterion: 906.294 - iter 1 (p1) Bandwidth: 140415.386 Criterion: 900.141 Diff: 53107.088 - iter 2 (p1) Bandwidth: 107593.401 Criterion: 896.616 Diff: 32821.985 - iter 3 (p1) Bandwidth: 87308.298 Criterion: 895.290 Diff: 20285.103 - iter 4 (p2) Bandwidth: 87308.298 Criterion: 895.290 Diff: 12536.883 - iter 5 (p1) Bandwidth: 87308.298 Criterion: 895.290 Diff: 7748.220 - iter 6 (p2) Bandwidth: 87308.298 Criterion: 895.290 Diff: 4788.663 -Best bandwidth size 87308.298 -Minimum AICc 895.290 - -***************************************************************************** - GWR (Geographically weighted regression) result -***************************************************************************** - Bandwidth and geographic ranges -Bandwidth size: 87308.298470 -Coordinate Min Max Range ---------------- --------------- --------------- --------------- -X-coord 635964.300000 1059706.000000 423741.700000 -Y-coord 3401148.000000 3872640.000000 471492.000000 - - Diagnostic information -Residual sum of squares: 2030.010213 -Effective number of parameters (model: trace(S)): 16.304601 -Effective number of parameters (variance: trace(S'S)): 10.141574 -Degree of freedom (model: n - trace(S)): 142.695399 -Degree of freedom (residual: n - 2trace(S) + trace(S'S)): 136.532371 -ML based sigma estimate: 3.573144 -Unbiased sigma estimate: 3.855949 --2 log-likelihood: 856.178266 -Classic AIC: 890.787468 -AICc: 895.290158 -BIC/MDL: 943.893632 -CV: 18.212841 -R square: 0.604138 -Adjusted R square: 0.538515 - -*********************************************************** - << Geographically varying (Local) coefficients >> -*********************************************************** -Estimates of varying coefficients have been saved in the following file. - Listwise output file: C:\Users\IEUser\Desktop\goergia_GS_F_listwise.csv - -Summary statistics for varying (Local) coefficients -Variable Mean STD --------------------- --------------- --------------- -Intercept 23.315956 3.742747 -PctRural -0.116469 0.034541 -PctPov -0.290012 0.105098 -PctBlack 0.053228 0.060773 - -Variable Min Max Range --------------------- --------------- --------------- --------------- -Intercept 18.016084 29.440723 11.424639 -PctRural -0.185429 -0.058428 0.127001 -PctPov -0.661246 -0.100954 0.560292 -PctBlack -0.064110 0.222182 0.286293 - -Variable Lwr Quartile Median Upr Quartile --------------------- --------------- --------------- --------------- -Intercept 19.466237 23.494623 26.843339 -PctRural -0.145428 -0.108359 -0.087985 -PctPov -0.337452 -0.285118 -0.224337 -PctBlack 0.003985 0.055632 0.102447 - -Variable Interquartile R Robust STD --------------------- --------------- --------------- -Intercept 7.377102 5.468571 -PctRural 0.057443 0.042582 -PctPov 0.113114 0.083851 -PctBlack 0.098462 0.072989 - (Note: Robust STD is given by (interquartile range / 1.349) ) - -***************************************************************************** - GWR ANOVA Table -***************************************************************************** -Source SS DF MS F ------------------ ------------------- ---------- --------------- ---------- -Global Residuals 2639.559 155.000 -GWR Improvement 609.549 18.468 33.006 -GWR Residuals 2030.010 136.532 14.868 2.219909 - -***************************************************************************** -Program terminated at 7/25/2016 2:05:11 AM diff --git a/mgwr/tests/georgia/georgia_GS_NN.ctl b/mgwr/tests/georgia/georgia_GS_NN.ctl deleted file mode 100755 index dfd46ed..0000000 --- a/mgwr/tests/georgia/georgia_GS_NN.ctl +++ /dev/null @@ -1,45 +0,0 @@ - -C:\Users\IEUser\Desktop\georgia\georgia\GData_utm.csv -FORMAT/DELIMITER: 1 -Number_of_fields: 13 -Number_of_areas: 159 -Fields -AreaKey 001 AreaKey -X 012 X -Y 013 Y -Gmetric 0 -Dependent 006 PctBach -Offset -Independent_geo 4 -000 Intercept -005 PctRural -009 PctPov -010 PctBlack -Independent_fix 0 -Unused_fields 6 -002 Latitude -003 Longitud -004 TotPop90 -007 PctEld -008 PctFB -011 ID -MODELTYPE: 0 -STANDARDISATION: 0 -GTEST: 0 -VSL2G: 0 -VSG2L: 0 -KERNELTYPE: 3 -BANDSELECTIONMETHOD: 1 -Goldrangeflag: 0 -Goldenmax: -Goldenmin: -Fixedbandsize: -IntervalMax: -IntervalMin: -IntervalStep: -Criteria: -summary_output: C:\Users\IEUser\Desktop\georgia_GS_NN_summary.txt -listwise_output: C:\Users\IEUser\Desktop\georgia_GS_NN_listwise.csv -predictflag: 0 -prediction_def: -prediction_output: diff --git a/mgwr/tests/georgia/georgia_GS_NN_listwise.csv b/mgwr/tests/georgia/georgia_GS_NN_listwise.csv deleted file mode 100755 index 3f4e06e..0000000 --- a/mgwr/tests/georgia/georgia_GS_NN_listwise.csv +++ /dev/null @@ -1,160 +0,0 @@ -Area_num, Area_key, x_coord, y_coord, est_Intercept, se_Intercept, t_Intercept, est_PctRural, se_PctRural, t_PctRural, est_PctPov, se_PctPov, t_PctPov, est_PctBlack, se_PctBlack, t_PctBlack, y, yhat, residual, std_residual, localR2, influence, CooksD - 0, 13001, 941396.6, 3521764, 21.626865, 1.457152, 14.841875, -0.099036, 0.015041, -6.584203, -0.301756, 0.078805, -3.829137, 0.058822, 0.035097, 1.675968, 8.200000, 9.355951, -1.155951, -0.296583, 0.548471, 0.025265, 0.000284 - 1, 13003, 895553, 3471916, 20.960601, 1.471999, 14.239551, -0.093944, 0.015137, -6.206105, -0.317883, 0.078902, -4.028843, 0.077650, 0.035724, 2.173583, 6.400000, 5.386899, 1.013101, 0.263500, 0.547804, 0.051486, 0.000469 - 2, 13005, 930946.4, 3502787, 21.340670, 1.474600, 14.472180, -0.097237, 0.015138, -6.423569, -0.305775, 0.079193, -3.861155, 0.065534, 0.035436, 1.849372, 6.600000, 8.982485, -2.382485, -0.626296, 0.548923, 0.071458, 0.003758 - 3, 13007, 745398.6, 3474765, 21.652602, 1.362160, 15.895783, -0.093473, 0.014904, -6.271598, -0.354833, 0.076919, -4.613079, 0.086734, 0.034545, 2.510765, 9.400000, 7.986999, 1.413001, 0.370642, 0.559055, 0.067443, 0.001237 - 4, 13009, 849431.3, 3665553, 25.292516, 1.277116, 19.804392, -0.126365, 0.014113, -8.953548, -0.286840, 0.076082, -3.770127, 0.014001, 0.032322, 0.433170, 13.300000, 15.470543, -2.170543, -0.563221, 0.550567, 0.047028, 0.001949 - 5, 13011, 819317.3, 3807616, 26.978495, 1.333220, 20.235594, -0.148715, 0.015739, -9.449124, -0.260333, 0.091248, -2.853020, 0.007208, 0.034424, 0.209374, 6.400000, 8.201092, -1.801092, -0.467232, 0.524135, 0.046529, 0.001326 - 6, 13013, 803747.1, 3769623, 27.413438, 1.366252, 20.064702, -0.155327, 0.016220, -9.576329, -0.244799, 0.095199, -2.571454, 0.001305, 0.035223, 0.037042, 9.200000, 13.795686, -4.595686, -1.181006, 0.529843, 0.028379, 0.005071 - 7, 13015, 699011.5, 3793408, 25.928870, 1.348245, 19.231566, -0.135314, 0.015806, -8.560655, -0.351185, 0.091376, -3.843275, 0.058437, 0.034661, 1.685933, 9.000000, 12.533795, -3.533795, -0.907169, 0.539704, 0.026342, 0.002772 - 8, 13017, 863020.8, 3520432, 21.263945, 1.402561, 15.160798, -0.094378, 0.014629, -6.451384, -0.317260, 0.076506, -4.146880, 0.070311, 0.034527, 2.036377, 7.600000, 12.051309, -4.451309, -1.143223, 0.541964, 0.027221, 0.004553 - 9, 13019, 859915.8, 3466377, 20.946762, 1.437381, 14.572870, -0.092411, 0.014981, -6.168355, -0.328129, 0.077704, -4.222829, 0.082514, 0.035502, 2.324250, 7.500000, 9.455091, -1.955091, -0.509968, 0.546717, 0.056922, 0.001954 - 10, 13021, 809736.9, 3636468, 24.532429, 1.242482, 19.744688, -0.119568, 0.013828, -8.646600, -0.311192, 0.074254, -4.190933, 0.034437, 0.031432, 1.095605, 17.000000, 18.067824, -1.067824, -0.280841, 0.564208, 0.072364, 0.000766 - 11, 13023, 844270.1, 3595691, 23.262635, 1.262917, 18.419766, -0.107683, 0.013697, -7.861986, -0.315491, 0.073405, -4.297938, 0.044395, 0.031850, 1.393871, 10.300000, 12.246973, -1.946973, -0.498717, 0.556205, 0.022063, 0.000698 - 12, 13025, 979288.9, 3463849, 22.023185, 1.396647, 15.768609, -0.101057, 0.014628, -6.908428, -0.317358, 0.076963, -4.123500, 0.063226, 0.034002, 1.859490, 5.800000, 6.431159, -0.631159, -0.167422, 0.563934, 0.088082, 0.000337 - 13, 13027, 827822, 3421638, 21.329854, 1.366845, 15.605175, -0.093531, 0.014621, -6.396952, -0.343350, 0.076056, -4.514434, 0.085757, 0.034564, 2.481142, 9.100000, 9.857815, -0.757815, -0.194052, 0.555169, 0.021434, 0.000103 - 14, 13029, 1023145, 3554982, 22.981765, 1.347595, 17.053916, -0.107186, 0.014312, -7.489271, -0.305664, 0.075797, -4.032678, 0.042913, 0.032692, 1.312639, 11.800000, 10.945100, 0.854900, 0.221137, 0.559079, 0.041022, 0.000260 - 15, 13031, 994903.4, 3600493, 23.375461, 1.335470, 17.503547, -0.109687, 0.014217, -7.714992, -0.298428, 0.075780, -3.938083, 0.033330, 0.032506, 1.025339, 19.900000, 9.101424, 10.798576, 2.820742, 0.552292, 0.059614, 0.062787 - 16, 13033, 971593.8, 3671394, 24.524585, 1.271622, 19.286061, -0.118149, 0.013720, -8.611196, -0.293751, 0.074107, -3.963890, 0.019467, 0.031047, 0.627025, 9.600000, 8.097758, 1.502242, 0.389514, 0.546350, 0.045594, 0.000902 - 17, 13035, 782448.2, 3684504, 25.845630, 1.264212, 20.444069, -0.134835, 0.014378, -9.377889, -0.301626, 0.078752, -3.830055, 0.027407, 0.032060, 0.854867, 7.200000, 12.215750, -5.015750, -1.291497, 0.560975, 0.032201, 0.006908 - 18, 13037, 724741.2, 3492653, 22.163047, 1.303120, 17.007678, -0.097085, 0.014424, -6.730702, -0.357953, 0.075290, -4.754320, 0.082866, 0.032980, 2.512604, 10.100000, 5.951669, 4.148331, 1.087798, 0.564186, 0.066852, 0.010553 - 19, 13039, 1008480, 3437933, 22.298215, 1.354182, 16.466182, -0.102417, 0.014323, -7.150385, -0.322044, 0.075664, -4.256225, 0.062073, 0.033138, 1.873175, 13.500000, 15.024108, -1.524108, -0.396779, 0.566825, 0.053252, 0.001102 - 20, 13043, 964264.9, 3598842, 23.220616, 1.345121, 17.262846, -0.108483, 0.014277, -7.598441, -0.296664, 0.076121, -3.897282, 0.033632, 0.032948, 1.020737, 9.900000, 11.459604, -1.559604, -0.401240, 0.548581, 0.030562, 0.000632 - 21, 13045, 678778.6, 3713250, 25.157218, 1.269114, 19.822667, -0.126873, 0.014523, -8.736229, -0.363915, 0.080214, -4.536773, 0.067373, 0.032358, 2.082123, 12.000000, 12.267665, -0.267665, -0.068432, 0.559438, 0.018319, 0.000011 - 22, 13047, 670055.9, 3862318, 25.331887, 1.282320, 19.754728, -0.127703, 0.014738, -8.664685, -0.349253, 0.082617, -4.227359, 0.057703, 0.032827, 1.757798, 8.100000, 15.625499, -7.525499, -1.969222, 0.530409, 0.062910, 0.032406 - 23, 13049, 962612.3, 3432769, 21.933757, 1.374678, 15.955558, -0.100015, 0.014470, -6.911946, -0.324232, 0.076267, -4.251280, 0.068344, 0.033790, 2.022611, 6.400000, 7.847483, -1.447483, -0.375138, 0.564887, 0.044685, 0.000819 - 24, 13051, 1059706, 3556747, 23.289156, 1.293318, 18.007290, -0.108878, 0.013869, -7.850502, -0.311044, 0.073959, -4.205622, 0.042239, 0.031514, 1.340328, 18.600000, 18.989849, -0.389849, -0.104014, 0.560384, 0.098616, 0.000147 - 25, 13053, 704959.2, 3577608, 23.135000, 1.280280, 18.070272, -0.105939, 0.014463, -7.324642, -0.357265, 0.075946, -4.704196, 0.075003, 0.032382, 2.316225, 20.200000, 20.288682, -0.088682, -0.023620, 0.575797, 0.095466, 0.000007 - 26, 13055, 653026.6, 3813760, 25.238584, 1.281379, 19.696426, -0.126760, 0.014709, -8.617714, -0.360001, 0.082164, -4.381511, 0.064186, 0.032783, 1.957891, 5.900000, 10.724024, -4.824024, -1.237205, 0.537294, 0.024480, 0.004781 - 27, 13057, 734240.9, 3794110, 26.738871, 1.424971, 18.764502, -0.146237, 0.016977, -8.613888, -0.320312, 0.102328, -3.130254, 0.043669, 0.036422, 1.198981, 18.400000, 16.409754, 1.990246, 0.518110, 0.537072, 0.053174, 0.001877 - 28, 13059, 832508.6, 3762905, 27.241892, 1.337499, 20.367791, -0.151566, 0.015698, -9.655305, -0.241590, 0.089623, -2.695614, -0.005162, 0.034409, -0.150026, 37.500000, 17.916007, 19.583993, 5.637950, 0.528664, 0.225787, 1.153944 - 29, 13061, 695793.9, 3495219, 22.465026, 1.270648, 17.679979, -0.099292, 0.014146, -7.018840, -0.361903, 0.074478, -4.859210, 0.082242, 0.032021, 2.568401, 11.200000, 4.612950, 6.587050, 1.736724, 0.565559, 0.076960, 0.031304 - 30, 13063, 745538.8, 3711726, 26.037250, 1.288900, 20.201146, -0.137682, 0.014805, -9.299565, -0.322383, 0.082647, -3.900711, 0.041183, 0.032699, 1.259476, 14.700000, 23.639943, -8.939943, -2.380434, 0.559406, 0.094981, 0.074028 - 31, 13065, 908046.1, 3428340, 21.390248, 1.409248, 15.178488, -0.096206, 0.014752, -6.521417, -0.328048, 0.077266, -4.245695, 0.077908, 0.034791, 2.239323, 6.700000, 9.218220, -2.518220, -0.653012, 0.559225, 0.045789, 0.002547 - 32, 13067, 724646.8, 3757187, 26.497784, 1.374988, 19.271281, -0.143156, 0.016165, -8.855953, -0.335996, 0.094592, -3.552069, 0.049705, 0.035141, 1.414433, 33.000000, 24.274999, 8.725001, 2.347843, 0.549968, 0.113879, 0.088184 - 33, 13069, 894463.9, 3492465, 20.885014, 1.484667, 14.067134, -0.093591, 0.015185, -6.163280, -0.311700, 0.079148, -3.938196, 0.074614, 0.035770, 2.085908, 11.100000, 9.725475, 1.374525, 0.352331, 0.541801, 0.023428, 0.000371 - 34, 13071, 808691.8, 3455994, 20.974560, 1.432494, 14.641986, -0.090631, 0.015182, -5.969623, -0.341884, 0.077899, -4.388810, 0.088745, 0.035863, 2.474588, 10.000000, 9.940225, 0.059775, 0.015399, 0.547955, 0.033141, 0.000001 - 35, 13073, 942527.9, 3722100, 25.350877, 1.257987, 20.151939, -0.126053, 0.013781, -9.146791, -0.285976, 0.074525, -3.837321, 0.011209, 0.030816, 0.363745, 23.900000, 19.728729, 4.171271, 1.097571, 0.540724, 0.073230, 0.011849 - 36, 13075, 839816.1, 3449007, 20.886343, 1.442878, 14.475479, -0.091298, 0.015128, -6.035140, -0.335224, 0.078026, -4.296340, 0.087194, 0.035844, 2.432583, 6.500000, 10.327458, -3.827458, -0.978516, 0.547300, 0.018286, 0.002220 - 37, 13077, 705457.9, 3694344, 25.248781, 1.266038, 19.943153, -0.128314, 0.014483, -8.859451, -0.352857, 0.079632, -4.431110, 0.060779, 0.032212, 1.886862, 13.300000, 12.834523, 0.465477, 0.120059, 0.565264, 0.035493, 0.000066 - 38, 13079, 783416.5, 3623343, 24.137975, 1.257677, 19.192506, -0.116338, 0.014127, -8.235422, -0.321860, 0.075176, -4.281410, 0.045172, 0.031943, 1.414139, 5.700000, 9.383128, -3.683128, -0.962851, 0.572259, 0.061107, 0.007511 - 39, 13081, 805648.4, 3537103, 21.592900, 1.393683, 15.493407, -0.094784, 0.014912, -6.356108, -0.330252, 0.076861, -4.296744, 0.072826, 0.034764, 2.094870, 10.000000, 10.389179, -0.389179, -0.101096, 0.554365, 0.049100, 0.000066 - 40, 13083, 635964.3, 3854592, 25.083669, 1.262275, 19.871794, -0.124773, 0.014393, -8.668721, -0.356768, 0.079859, -4.467488, 0.062462, 0.032201, 1.939749, 8.000000, 7.856142, 0.143858, 0.037355, 0.533234, 0.048391, 0.000009 - 41, 13085, 764386.1, 3812502, 26.868314, 1.394493, 19.267440, -0.148320, 0.016612, -8.928722, -0.288164, 0.099641, -2.892034, 0.026975, 0.035860, 0.752225, 8.600000, 8.355611, 0.244389, 0.063431, 0.527469, 0.047506, 0.000025 - 42, 13087, 732628.4, 3421800, 22.124986, 1.266331, 17.471722, -0.097094, 0.013967, -6.951684, -0.358400, 0.073768, -4.858455, 0.083571, 0.032205, 2.594992, 11.700000, 11.441353, 0.258647, 0.066134, 0.561516, 0.018561, 0.000010 - 43, 13089, 759231.9, 3735253, 26.737946, 1.338756, 19.972231, -0.146720, 0.015584, -9.414543, -0.298696, 0.089527, -3.336383, 0.028916, 0.034056, 0.849076, 32.700000, 24.635191, 8.064809, 2.215523, 0.550693, 0.149771, 0.107633 - 44, 13091, 860451.4, 3569933, 22.529280, 1.294454, 17.404472, -0.102329, 0.013849, -7.388895, -0.315711, 0.073899, -4.272189, 0.052185, 0.032520, 1.604720, 8.000000, 9.854491, -1.854491, -0.473775, 0.550529, 0.016885, 0.000480 - 45, 13093, 800031.3, 3564188, 22.436651, 1.315523, 17.055303, -0.100934, 0.014358, -7.029859, -0.330303, 0.075012, -4.403343, 0.063222, 0.033126, 1.908512, 9.500000, 7.338501, 2.161499, 0.562255, 0.563390, 0.051705, 0.002146 - 46, 13095, 764116.9, 3494367, 21.601262, 1.373680, 15.725106, -0.093458, 0.014946, -6.253129, -0.349492, 0.076881, -4.545911, 0.084130, 0.034741, 2.421652, 17.000000, 16.358180, 0.641820, 0.172276, 0.558333, 0.109415, 0.000454 - 47, 13097, 707288.7, 3731361, 25.759714, 1.300638, 19.805444, -0.134014, 0.015013, -8.926626, -0.350421, 0.084457, -4.149131, 0.057860, 0.033206, 1.742481, 12.000000, 20.310238, -8.310238, -2.176220, 0.556473, 0.064333, 0.040534 - 48, 13099, 703495.1, 3467152, 22.326801, 1.267734, 17.611578, -0.098238, 0.014066, -6.984022, -0.361275, 0.074165, -4.871227, 0.083087, 0.032042, 2.593064, 9.400000, 9.459113, -0.059113, -0.015399, 0.563587, 0.054474, 0.000002 - 49, 13101, 896654, 3401148, 21.613746, 1.356626, 15.931985, -0.096862, 0.014407, -6.723192, -0.335297, 0.075746, -4.426595, 0.078575, 0.033966, 2.313328, 4.700000, 7.934266, -3.234266, -0.849447, 0.561749, 0.069793, 0.006739 - 50, 13103, 1031899, 3596117, 23.514698, 1.298005, 18.116031, -0.110522, 0.013912, -7.944620, -0.304638, 0.074352, -4.097226, 0.035651, 0.031599, 1.128231, 7.600000, 10.298432, -2.698432, -0.699582, 0.556170, 0.045348, 0.002894 - 51, 13105, 879541.2, 3785425, 26.422653, 1.276360, 20.701565, -0.139464, 0.014560, -9.578633, -0.269325, 0.080294, -3.354242, 0.004482, 0.032171, 0.139322, 8.000000, 11.488889, -3.488889, -0.890795, 0.530168, 0.015720, 0.001578 - 52, 13107, 943066.2, 3616602, 23.502224, 1.341194, 17.523363, -0.110178, 0.014251, -7.731131, -0.293336, 0.076207, -3.849183, 0.027954, 0.032987, 0.847428, 9.100000, 9.800785, -0.700785, -0.180155, 0.544660, 0.029094, 0.000121 - 53, 13109, 981727.8, 3571315, 22.844642, 1.369323, 16.683159, -0.106310, 0.014472, -7.345959, -0.299763, 0.076645, -3.911055, 0.040702, 0.033332, 1.221124, 8.600000, 5.978683, 2.621317, 0.676420, 0.552915, 0.036379, 0.002150 - 54, 13111, 739255.8, 3866604, 25.966029, 1.327670, 19.557594, -0.135737, 0.015532, -8.738966, -0.320902, 0.089690, -3.577924, 0.041984, 0.034204, 1.227444, 7.800000, 6.874114, 0.925886, 0.242884, 0.523927, 0.067566, 0.000532 - 55, 13113, 731468.7, 3700612, 25.713313, 1.281256, 20.068828, -0.133910, 0.014707, -9.105176, -0.335719, 0.081525, -4.118017, 0.049492, 0.032602, 1.518067, 25.800000, 17.876603, 7.923397, 2.084836, 0.564123, 0.073215, 0.042743 - 56, 13115, 662257.4, 3789664, 25.329397, 1.289511, 19.642634, -0.127989, 0.014844, -8.622318, -0.361980, 0.083187, -4.351412, 0.065185, 0.033022, 1.973971, 13.700000, 16.669958, -2.969958, -0.770127, 0.541330, 0.045720, 0.003537 - 57, 13117, 765397.3, 3789005, 27.217890, 1.416777, 19.211132, -0.153408, 0.016912, -9.070889, -0.277472, 0.102162, -2.716013, 0.022295, 0.036240, 0.615205, 15.600000, 10.956786, 4.643214, 1.219540, 0.532535, 0.069867, 0.013907 - 58, 13119, 845701.3, 3813323, 26.747176, 1.304195, 20.508570, -0.144801, 0.015217, -9.515998, -0.264086, 0.086362, -3.057886, 0.006001, 0.033483, 0.179219, 9.500000, 9.822459, -0.322459, -0.082965, 0.524456, 0.030685, 0.000027 - 59, 13121, 733728.4, 3733248, 26.226757, 1.318121, 19.897085, -0.139975, 0.015266, -9.169317, -0.328881, 0.086743, -3.791463, 0.045281, 0.033544, 1.349901, 31.600000, 21.847863, 9.752137, 2.628754, 0.554936, 0.116920, 0.113892 - 60, 13123, 732702.3, 3844809, 26.094035, 1.350269, 19.325060, -0.137486, 0.015884, -8.655651, -0.323300, 0.092643, -3.489757, 0.043905, 0.034782, 1.262277, 8.600000, 6.990108, 1.609892, 0.421208, 0.526284, 0.062653, 0.001476 - 61, 13125, 908386.8, 3685752, 25.238593, 1.287573, 19.601682, -0.124773, 0.014019, -8.899995, -0.284014, 0.075471, -3.763229, 0.009988, 0.031732, 0.314765, 5.300000, 8.116558, -2.816558, -0.730182, 0.542747, 0.045282, 0.003148 - 62, 13127, 1023411, 3471063, 22.574579, 1.332218, 16.945111, -0.104157, 0.014156, -7.357776, -0.319103, 0.074988, -4.255414, 0.056371, 0.032606, 1.728839, 19.900000, 17.338436, 2.561564, 0.672373, 0.565426, 0.068698, 0.004151 - 63, 13129, 695325.1, 3822135, 25.745399, 1.329798, 19.360381, -0.132914, 0.015521, -8.563642, -0.347277, 0.088988, -3.902522, 0.056555, 0.034190, 1.654116, 9.200000, 11.511165, -2.311165, -0.594163, 0.533433, 0.029155, 0.001320 - 64, 13131, 765058.1, 3421817, 21.728070, 1.314857, 16.525044, -0.094657, 0.014364, -6.589926, -0.354386, 0.075070, -4.720736, 0.086080, 0.033504, 2.569239, 7.700000, 11.292764, -3.592764, -0.919093, 0.558723, 0.019521, 0.002094 - 65, 13133, 855577.3, 3722330, 26.378777, 1.284989, 20.528410, -0.138732, 0.014510, -9.561145, -0.267647, 0.079278, -3.376058, 0.002434, 0.032339, 0.075275, 8.800000, 9.280275, -0.480275, -0.123947, 0.539330, 0.036592, 0.000073 - 66, 13135, 772634.6, 3764306, 27.310592, 1.392082, 19.618517, -0.154665, 0.016499, -9.374155, -0.269247, 0.098034, -2.746465, 0.016311, 0.035558, 0.458704, 29.600000, 24.213506, 5.386494, 1.448551, 0.537976, 0.112752, 0.033193 - 67, 13137, 818917.1, 3839931, 26.498005, 1.301944, 20.352648, -0.142127, 0.015202, -9.349437, -0.280887, 0.087067, -3.226089, 0.017159, 0.033517, 0.511965, 12.000000, 10.754510, 1.245490, 0.320560, 0.523532, 0.031364, 0.000414 - 68, 13139, 794419.5, 3803344, 26.942175, 1.347280, 19.997457, -0.148799, 0.015924, -9.344456, -0.270773, 0.093327, -2.901328, 0.014760, 0.034696, 0.425410, 15.400000, 12.129558, 3.270442, 0.840820, 0.527021, 0.029249, 0.002652 - 69, 13141, 873518.8, 3689861, 25.644482, 1.277618, 20.072101, -0.129488, 0.014103, -9.181695, -0.280701, 0.076104, -3.688380, 0.008187, 0.031903, 0.256618, 6.800000, 4.898588, 1.901412, 0.527760, 0.544134, 0.167124, 0.006957 - 70, 13143, 665933.8, 3740622, 25.172592, 1.269066, 19.835527, -0.126616, 0.014505, -8.728963, -0.364681, 0.080401, -4.535780, 0.067431, 0.032336, 2.085337, 7.500000, 11.772941, -4.272941, -1.100987, 0.551759, 0.033527, 0.005235 - 71, 13145, 695500.6, 3624790, 23.994987, 1.249243, 19.207618, -0.114627, 0.014185, -8.080707, -0.358522, 0.075700, -4.736114, 0.070234, 0.031565, 2.225061, 13.600000, 9.892243, 3.707757, 0.962209, 0.574790, 0.047240, 0.005714 - 72, 13147, 870749.9, 3810303, 26.495349, 1.280673, 20.688610, -0.140853, 0.014740, -9.555683, -0.269541, 0.082159, -3.280734, 0.006306, 0.032555, 0.193703, 9.100000, 12.401649, -3.301649, -0.844182, 0.526819, 0.018501, 0.001672 - 73, 13149, 675280.4, 3685569, 24.763509, 1.244702, 19.895126, -0.122486, 0.014128, -8.669918, -0.363174, 0.076972, -4.718290, 0.068613, 0.031476, 2.179838, 5.700000, 6.496331, -0.796331, -0.206343, 0.563366, 0.044328, 0.000246 - 74, 13151, 763488.4, 3699716, 26.080037, 1.283398, 20.321079, -0.138175, 0.014711, -9.392889, -0.308780, 0.081606, -3.783801, 0.032804, 0.032587, 1.006644, 10.700000, 14.031100, -3.331100, -0.868969, 0.560510, 0.057097, 0.005692 - 75, 13153, 814118.9, 3590553, 23.123421, 1.277572, 18.099504, -0.106684, 0.013999, -7.620882, -0.321880, 0.074161, -4.340283, 0.050922, 0.032218, 1.580555, 16.000000, 18.591906, -2.591906, -0.682568, 0.563339, 0.074776, 0.004687 - 76, 13155, 855461.8, 3506293, 21.118265, 1.414128, 14.933768, -0.093225, 0.014753, -6.319137, -0.321952, 0.076812, -4.191414, 0.075272, 0.034868, 2.158776, 8.300000, 8.746538, -0.446538, -0.115359, 0.542969, 0.038585, 0.000066 - 77, 13157, 815753.1, 3783949, 27.239729, 1.347679, 20.212331, -0.152407, 0.015955, -9.552067, -0.249037, 0.092829, -2.682760, 0.001826, 0.034779, 0.052516, 9.000000, 11.858055, -2.858055, -0.733071, 0.526683, 0.024676, 0.001692 - 78, 13159, 807249.1, 3695092, 26.188931, 1.274454, 20.549143, -0.138184, 0.014478, -9.544332, -0.282280, 0.079546, -3.548631, 0.014406, 0.032378, 0.444929, 10.800000, 7.960193, 2.839807, 0.736547, 0.552592, 0.046157, 0.003268 - 79, 13161, 915741.9, 3530869, 21.450218, 1.443341, 14.861503, -0.097352, 0.014903, -6.532369, -0.301487, 0.078153, -3.857671, 0.059953, 0.034948, 1.715511, 8.300000, 10.365510, -2.065510, -0.533703, 0.540836, 0.038929, 0.001436 - 80, 13163, 924108.1, 3668080, 24.759704, 1.287756, 19.227014, -0.120111, 0.013903, -8.639116, -0.289456, 0.074927, -3.863167, 0.015175, 0.031688, 0.478895, 6.200000, 4.537200, 1.662800, 0.435738, 0.544296, 0.065608, 0.001660 - 81, 13165, 970465.7, 3640263, 24.042759, 1.294028, 18.579786, -0.114235, 0.013872, -8.234643, -0.295813, 0.074681, -3.961008, 0.024461, 0.031668, 0.772420, 7.700000, 10.688727, -2.988727, -0.772542, 0.548162, 0.039652, 0.003067 - 82, 13167, 908636.7, 3624562, 23.795515, 1.306035, 18.219660, -0.111990, 0.013972, -8.015044, -0.296469, 0.075083, -3.948553, 0.026534, 0.032540, 0.815427, 4.900000, 6.914133, -2.014133, -0.518737, 0.545170, 0.032652, 0.001131 - 83, 13169, 821367.1, 3660143, 25.223169, 1.252243, 20.142390, -0.126466, 0.013972, -9.051632, -0.296997, 0.075468, -3.935410, 0.022389, 0.031736, 0.705469, 12.000000, 12.231220, -0.231220, -0.060104, 0.558019, 0.050406, 0.000024 - 84, 13171, 766461.7, 3663959, 25.225085, 1.249823, 20.182930, -0.127935, 0.014183, -9.020351, -0.319686, 0.076834, -4.160745, 0.039869, 0.031667, 1.258993, 10.000000, 13.234267, -3.234267, -0.828499, 0.568682, 0.022160, 0.001936 - 85, 13173, 873804.3, 3439981, 21.079641, 1.427563, 14.766176, -0.093616, 0.014926, -6.272150, -0.330426, 0.077665, -4.254514, 0.082885, 0.035346, 2.344957, 5.400000, 5.363050, 0.036950, 0.009609, 0.552619, 0.051254, 0.000001 - 86, 13175, 884830.4, 3599291, 23.223845, 1.287460, 18.038498, -0.107549, 0.013789, -7.799647, -0.305803, 0.074106, -4.126573, 0.038028, 0.032347, 1.175639, 12.000000, 12.532665, -0.532665, -0.136258, 0.547955, 0.019424, 0.000046 - 87, 13177, 770455.5, 3520161, 21.569259, 1.405085, 15.350861, -0.093476, 0.015243, -6.132438, -0.343436, 0.077896, -4.408912, 0.080926, 0.035427, 2.284331, 13.700000, 11.487546, 2.212454, 0.575087, 0.559423, 0.050311, 0.002181 - 88, 13179, 1014742, 3537225, 22.799269, 1.351395, 16.870913, -0.105966, 0.014326, -7.396630, -0.308256, 0.075802, -4.066580, 0.046822, 0.032859, 1.424941, 13.400000, 15.844071, -2.444071, -0.637622, 0.560297, 0.057241, 0.003073 - 89, 13181, 919396.5, 3752562, 25.863459, 1.263435, 20.470751, -0.131902, 0.014052, -9.386941, -0.278288, 0.076188, -3.652642, 0.006679, 0.031204, 0.214042, 8.200000, 7.974770, 0.225230, 0.058709, 0.536129, 0.055634, 0.000025 - 90, 13183, 1004544, 3517834, 22.517410, 1.376897, 16.353742, -0.104302, 0.014513, -7.187056, -0.309259, 0.076520, -4.041563, 0.051352, 0.033412, 1.536925, 5.200000, 5.874617, -0.674617, -0.174980, 0.561239, 0.046245, 0.000185 - 91, 13185, 864781.1, 3419313, 21.212384, 1.400129, 15.150307, -0.093978, 0.014778, -6.359384, -0.335963, 0.076975, -4.364578, 0.084114, 0.034986, 2.404206, 16.300000, 12.734888, 3.565112, 0.915513, 0.555520, 0.026990, 0.002894 - 92, 13187, 772600, 3832429, 26.659548, 1.368132, 19.486094, -0.145249, 0.016237, -8.945698, -0.289384, 0.096300, -3.005040, 0.026374, 0.035325, 0.746611, 11.100000, 10.852624, 0.247376, 0.064307, 0.523557, 0.050494, 0.000027 - 93, 13189, 917730.9, 3716368, 25.538593, 1.265303, 20.183775, -0.128043, 0.013908, -9.206430, -0.282955, 0.075132, -3.766098, 0.009018, 0.031124, 0.289729, 10.400000, 11.316831, -0.916831, -0.234185, 0.540450, 0.016530, 0.000115 - 94, 13191, 1030500, 3500535, 22.725238, 1.334534, 17.028590, -0.105271, 0.014183, -7.422160, -0.314971, 0.075121, -4.192819, 0.051889, 0.032550, 1.594126, 8.700000, 7.423117, 1.276883, 0.332516, 0.563907, 0.053809, 0.000783 - 95, 13193, 777055.3, 3584821, 23.063470, 1.286343, 17.929482, -0.106020, 0.014306, -7.410883, -0.333660, 0.074971, -4.450515, 0.060042, 0.032562, 1.843935, 10.100000, 9.891313, 0.208687, 0.054292, 0.571875, 0.051981, 0.000020 - 96, 13195, 848638.8, 3785405, 26.860762, 1.306998, 20.551494, -0.145941, 0.015193, -9.605777, -0.257499, 0.085585, -3.008704, 0.001250, 0.033404, 0.037417, 9.700000, 8.234382, 1.465618, 0.378922, 0.527489, 0.040066, 0.000746 - 97, 13197, 732876.8, 3584393, 23.228957, 1.273166, 18.245040, -0.107028, 0.014332, -7.467695, -0.348912, 0.075219, -4.638645, 0.068774, 0.032189, 2.136580, 4.600000, 5.528588, -0.928588, -0.240074, 0.575757, 0.040032, 0.000299 - 98, 13199, 715359.8, 3660275, 24.739448, 1.244273, 19.882645, -0.122619, 0.014142, -8.670301, -0.349295, 0.076420, -4.570732, 0.060502, 0.031457, 1.923311, 6.700000, 9.523347, -2.823347, -0.726444, 0.571063, 0.030776, 0.002086 - 99, 13201, 716369.8, 3451034, 22.044812, 1.296768, 16.999810, -0.096146, 0.014332, -6.708391, -0.360223, 0.075102, -4.796476, 0.085512, 0.032906, 2.598692, 8.200000, 6.819109, 1.380891, 0.356257, 0.561827, 0.035964, 0.000589 - 100, 13205, 766238.6, 3453930, 21.627535, 1.340862, 16.129579, -0.093820, 0.014605, -6.423666, -0.352686, 0.075799, -4.652907, 0.086036, 0.034063, 2.525789, 7.800000, 10.354769, -2.554769, -0.659032, 0.557598, 0.035747, 0.002004 - 101, 13207, 790338.7, 3660608, 25.262530, 1.253102, 20.159988, -0.127966, 0.014154, -9.041029, -0.306392, 0.076558, -4.002114, 0.030566, 0.031825, 0.960460, 12.900000, 12.395489, 0.504511, 0.130142, 0.565321, 0.035719, 0.000078 - 102, 13209, 920887.4, 3568473, 22.299833, 1.375191, 16.215808, -0.102158, 0.014452, -7.068860, -0.299037, 0.076546, -3.906616, 0.045841, 0.033869, 1.353478, 10.100000, 6.196557, 3.903443, 1.008548, 0.542189, 0.038825, 0.005114 - 103, 13211, 825920.1, 3717990, 26.716105, 1.302938, 20.504503, -0.144232, 0.014927, -9.662794, -0.259980, 0.082897, -3.136199, 0.001557, 0.033231, 0.046862, 11.000000, 12.341566, -1.341566, -0.346162, 0.541722, 0.036245, 0.000561 - 104, 13213, 707834.3, 3854188, 25.735135, 1.320632, 19.486986, -0.132743, 0.015387, -8.627182, -0.337412, 0.088075, -3.830979, 0.051086, 0.033960, 1.504278, 5.500000, 10.121524, -4.621524, -1.193221, 0.527820, 0.037438, 0.006893 - 105, 13215, 700833.7, 3598228, 23.557579, 1.252386, 18.810151, -0.109972, 0.014154, -7.769717, -0.357574, 0.074959, -4.770273, 0.072000, 0.031515, 2.284639, 16.600000, 19.287204, -2.687204, -0.714243, 0.574677, 0.091742, 0.006414 - 106, 13217, 793263.9, 3719734, 26.736963, 1.307661, 20.446395, -0.145890, 0.015089, -9.668503, -0.274042, 0.084921, -3.227027, 0.012693, 0.033326, 0.380867, 9.500000, 11.986799, -2.486799, -0.635947, 0.547837, 0.018839, 0.000967 - 107, 13219, 830735.9, 3750903, 27.163865, 1.330351, 20.418574, -0.150388, 0.015523, -9.687875, -0.244495, 0.088048, -2.776837, -0.004420, 0.034137, -0.129477, 28.400000, 10.882836, 17.517164, 4.585888, 0.531759, 0.063774, 0.178324 - 108, 13221, 863291.8, 3756777, 26.674468, 1.298442, 20.543445, -0.142654, 0.014857, -9.601669, -0.259291, 0.082037, -3.160666, -0.000916, 0.032800, -0.027935, 12.800000, 8.185910, 4.614090, 1.190375, 0.532067, 0.035939, 0.006575 - 109, 13223, 695329.2, 3758093, 25.865326, 1.333978, 19.389620, -0.134835, 0.015565, -8.662842, -0.358980, 0.089095, -4.029188, 0.062370, 0.034281, 1.819380, 7.600000, 10.317978, -2.717978, -0.707259, 0.550018, 0.052379, 0.003442 - 110, 13225, 798061.4, 3609091, 23.694213, 1.264489, 18.738171, -0.111871, 0.014052, -7.961033, -0.321217, 0.074559, -4.308231, 0.046753, 0.032017, 1.460276, 15.200000, 11.349504, 3.850496, 0.989658, 0.568857, 0.028677, 0.003600 - 111, 13227, 733846.7, 3812828, 26.395182, 1.379322, 19.136340, -0.141586, 0.016316, -8.677928, -0.321461, 0.096520, -3.330526, 0.043465, 0.035416, 1.227274, 9.000000, 8.186218, 0.813782, 0.210928, 0.531891, 0.044901, 0.000260 - 112, 13229, 953533.8, 3482044, 21.510919, 1.478179, 14.552305, -0.098544, 0.015194, -6.485723, -0.309409, 0.079476, -3.893119, 0.066136, 0.035419, 1.867225, 6.300000, 8.361965, -2.061965, -0.539233, 0.557355, 0.061770, 0.002383 - 113, 13231, 744180.8, 3665561, 25.119406, 1.254246, 20.027494, -0.127042, 0.014293, -8.888630, -0.332683, 0.077524, -4.291341, 0.048885, 0.031845, 1.535117, 9.300000, 8.936941, 0.363059, 0.094067, 0.571162, 0.044167, 0.000051 - 114, 13233, 668031.4, 3764766, 25.399011, 1.298321, 19.562960, -0.129032, 0.014994, -8.605520, -0.366066, 0.084254, -4.344805, 0.067397, 0.033317, 2.022874, 6.800000, 11.815313, -5.015313, -1.286169, 0.547072, 0.024336, 0.005136 - 115, 13235, 833819.6, 3567447, 22.397670, 1.311822, 17.073705, -0.101126, 0.014108, -7.167820, -0.320478, 0.074528, -4.300076, 0.056974, 0.032876, 1.732988, 10.700000, 10.745821, -0.045821, -0.011753, 0.554685, 0.024765, 0.000000 - 116, 13237, 840169.1, 3695254, 26.121336, 1.283539, 20.351029, -0.136024, 0.014429, -9.427117, -0.272897, 0.078583, -3.472723, 0.005897, 0.032525, 0.181308, 11.700000, 12.793557, -1.093557, -0.280007, 0.546087, 0.021308, 0.000212 - 117, 13239, 686875.4, 3524124, 22.689339, 1.268292, 17.889679, -0.101223, 0.014185, -7.136171, -0.362640, 0.074697, -4.854789, 0.080922, 0.031874, 2.538844, 7.300000, 4.640322, 2.659678, 0.693738, 0.568156, 0.056880, 0.003613 - 118, 13241, 824645.5, 3864805, 26.232987, 1.281846, 20.465007, -0.138519, 0.014861, -9.321139, -0.289726, 0.084361, -3.434349, 0.021245, 0.032933, 0.645099, 11.600000, 8.448284, 3.151716, 0.817736, 0.523120, 0.046835, 0.004090 - 119, 13243, 712437.1, 3519627, 22.337821, 1.311149, 17.036832, -0.098433, 0.014608, -6.738153, -0.358671, 0.076079, -4.714478, 0.081855, 0.033156, 2.468744, 6.000000, 8.956848, -2.956848, -0.782407, 0.567976, 0.083585, 0.006950 - 120, 13245, 954272.3, 3697862, 24.964272, 1.255194, 19.888776, -0.122132, 0.013648, -8.948835, -0.291246, 0.073844, -3.944050, 0.015560, 0.030716, 0.506562, 17.300000, 19.107372, -1.807372, -0.479345, 0.543851, 0.087781, 0.002752 - 121, 13247, 777759, 3729605, 26.762500, 1.318837, 20.292507, -0.146667, 0.015282, -9.597346, -0.283942, 0.086872, -3.268519, 0.019648, 0.033546, 0.585713, 18.100000, 16.477142, 1.622858, 0.421148, 0.548392, 0.047222, 0.001094 - 122, 13249, 752973.1, 3570222, 22.784234, 1.305353, 17.454467, -0.103224, 0.014576, -7.081815, -0.343440, 0.075832, -4.528980, 0.068889, 0.033107, 2.080840, 8.000000, 7.975851, 0.024149, 0.006236, 0.573734, 0.037663, 0.000000 - 123, 13251, 1004028, 3641918, 24.041928, 1.271378, 18.910132, -0.114264, 0.013687, -8.348297, -0.300113, 0.073751, -4.069253, 0.027323, 0.031039, 0.880291, 8.600000, 9.329259, -0.729259, -0.187457, 0.550947, 0.028909, 0.000130 - 124, 13253, 704495.6, 3422002, 22.266919, 1.254723, 17.746485, -0.097905, 0.013893, -7.046999, -0.361150, 0.073606, -4.906564, 0.083617, 0.031814, 2.628339, 7.800000, 7.700483, 0.099517, 0.025824, 0.562129, 0.047127, 0.000004 - 125, 13255, 754916.2, 3685029, 25.695665, 1.271701, 20.205742, -0.133711, 0.014547, -9.191798, -0.319980, 0.079927, -4.003389, 0.039878, 0.032339, 1.233107, 11.100000, 14.696711, -3.596711, -0.920364, 0.566109, 0.020078, 0.002160 - 126, 13257, 842085.9, 3827075, 26.601030, 1.296181, 20.522624, -0.143006, 0.015096, -9.473103, -0.271413, 0.085704, -3.166873, 0.010275, 0.033286, 0.308681, 13.100000, 12.884492, 0.215508, 0.055554, 0.524079, 0.034416, 0.000014 - 127, 13259, 703256.8, 3552857, 22.788324, 1.289551, 17.671515, -0.102469, 0.014488, -7.072719, -0.358890, 0.075827, -4.733036, 0.078348, 0.032555, 2.406633, 8.000000, 6.244272, 1.755728, 0.463862, 0.572626, 0.080743, 0.002353 - 128, 13261, 763457.1, 3551752, 22.360808, 1.326941, 16.851392, -0.099601, 0.014662, -6.793372, -0.343021, 0.075920, -4.518181, 0.072200, 0.033583, 2.149910, 15.900000, 12.691463, 3.208537, 0.827891, 0.568800, 0.036242, 0.003208 - 129, 13263, 734217.9, 3623162, 24.079998, 1.245494, 19.333693, -0.115582, 0.014099, -8.197990, -0.344525, 0.075026, -4.592052, 0.060565, 0.031456, 1.925388, 7.100000, 7.961499, -0.861499, -0.230071, 0.575672, 0.100320, 0.000735 - 130, 13265, 884376.9, 3717493, 25.998467, 1.283042, 20.263151, -0.133447, 0.014272, -9.350431, -0.273641, 0.077193, -3.544921, 0.003799, 0.031882, 0.119145, 5.600000, 4.157742, 1.442258, 0.381068, 0.539251, 0.080863, 0.001590 - 131, 13267, 963427.8, 3560039, 22.492834, 1.391210, 16.167815, -0.104093, 0.014620, -7.120116, -0.299292, 0.077201, -3.876782, 0.044880, 0.033883, 1.324571, 6.500000, 8.993789, -2.493789, -0.637136, 0.550527, 0.016995, 0.000874 - 132, 13269, 759410.8, 3608179, 23.731964, 1.259273, 18.845770, -0.112210, 0.014179, -7.913623, -0.336046, 0.074941, -4.484150, 0.057034, 0.031902, 1.787786, 7.100000, 5.062050, 2.037950, 0.528589, 0.575649, 0.046212, 0.001685 - 133, 13271, 882069.4, 3534470, 21.462036, 1.397440, 15.358106, -0.096210, 0.014554, -6.610558, -0.309592, 0.076507, -4.046560, 0.063163, 0.034302, 1.841401, 8.600000, 8.201335, 0.398665, 0.102474, 0.540215, 0.028849, 0.000039 - 134, 13273, 743031.8, 3522636, 22.013891, 1.352305, 16.278786, -0.096243, 0.014924, -6.449107, -0.351534, 0.076931, -4.569455, 0.080816, 0.034268, 2.358365, 9.200000, 11.784095, -2.584095, -0.677216, 0.565820, 0.065750, 0.004018 - 135, 13275, 795506.2, 3421725, 21.387320, 1.360198, 15.723683, -0.093014, 0.014671, -6.339839, -0.349073, 0.076130, -4.585250, 0.087696, 0.034531, 2.539660, 13.400000, 11.690223, 1.709777, 0.437721, 0.555274, 0.020998, 0.000512 - 136, 13277, 831682.3, 3487715, 21.046187, 1.419442, 14.827085, -0.091863, 0.014927, -6.154106, -0.332055, 0.077091, -4.307310, 0.081979, 0.035284, 2.323422, 14.000000, 10.935151, 3.064849, 0.789240, 0.545902, 0.032389, 0.002595 - 137, 13279, 941734.4, 3567586, 22.354540, 1.398172, 15.988401, -0.102991, 0.014660, -7.025063, -0.296264, 0.077421, -3.826678, 0.044210, 0.034151, 1.294570, 11.400000, 12.601083, -1.201083, -0.315779, 0.544307, 0.071720, 0.000959 - 138, 13281, 797981.7, 3872640, 26.187925, 1.298469, 20.168311, -0.138287, 0.015123, -9.144145, -0.296807, 0.086622, -3.426455, 0.026732, 0.033450, 0.799170, 11.400000, 8.203890, 3.196110, 0.829913, 0.522031, 0.048347, 0.004356 - 139, 13283, 919077.6, 3595170, 22.911636, 1.352208, 16.943867, -0.105895, 0.014311, -7.399408, -0.296226, 0.076227, -3.886126, 0.035970, 0.033477, 1.074476, 6.300000, 10.430319, -4.130319, -1.070745, 0.542012, 0.045237, 0.006762 - 140, 13285, 682616.8, 3660254, 24.488487, 1.237517, 19.788408, -0.119587, 0.014018, -8.530763, -0.361082, 0.075740, -4.767396, 0.068752, 0.031181, 2.204926, 13.600000, 15.405653, -1.805653, -0.463140, 0.567847, 0.024686, 0.000676 - 141, 13287, 819399.6, 3514927, 21.160631, 1.425250, 14.846959, -0.092183, 0.015024, -6.135672, -0.329558, 0.077357, -4.260214, 0.078149, 0.035293, 2.214312, 7.200000, 9.920877, -2.720877, -0.716491, 0.546459, 0.074669, 0.005157 - 142, 13289, 832935, 3623868, 24.112693, 1.243922, 19.384410, -0.115030, 0.013659, -8.421378, -0.311021, 0.073451, -4.234395, 0.035169, 0.031419, 1.119353, 4.800000, 6.138474, -1.338474, -0.345911, 0.559332, 0.039290, 0.000609 - 143, 13291, 777040.1, 3858779, 26.307561, 1.329114, 19.793300, -0.140192, 0.015610, -8.981203, -0.298658, 0.090727, -3.291817, 0.029471, 0.034310, 0.858967, 10.100000, 6.825862, 3.274138, 0.863972, 0.522070, 0.078501, 0.007916 - 144, 13293, 752165.2, 3639192, 24.523777, 1.248666, 19.639976, -0.120443, 0.014166, -8.502338, -0.333228, 0.075837, -4.393986, 0.051271, 0.031682, 1.618310, 9.000000, 13.184717, -4.184717, -1.071102, 0.574796, 0.020576, 0.003000 - 145, 13295, 658870.4, 3842167, 25.297942, 1.287718, 19.645564, -0.127341, 0.014821, -8.591776, -0.355393, 0.083087, -4.277373, 0.061396, 0.032997, 1.860642, 8.400000, 15.273031, -6.873031, -1.792049, 0.532850, 0.056163, 0.023788 - 146, 13297, 800384.3, 3742691, 27.070055, 1.328626, 20.374471, -0.150235, 0.015493, -9.697143, -0.260522, 0.088612, -2.940020, 0.006636, 0.033974, 0.195309, 9.400000, 14.558662, -5.158662, -1.319656, 0.539275, 0.019489, 0.004309 - 147, 13299, 938349.6, 3446675, 21.588492, 1.416300, 15.242878, -0.098077, 0.014765, -6.642723, -0.321614, 0.077497, -4.149994, 0.071726, 0.034639, 2.070681, 10.400000, 11.342936, -0.942936, -0.241744, 0.561072, 0.023767, 0.000177 - 148, 13301, 902471.1, 3699878, 25.518424, 1.278721, 19.956204, -0.127785, 0.014026, -9.110419, -0.281588, 0.075609, -3.724266, 0.008107, 0.031556, 0.256904, 4.200000, 4.048411, 0.151589, 0.039980, 0.541682, 0.077513, 0.000017 - 149, 13303, 894704.3, 3648583, 24.507604, 1.286531, 19.049374, -0.117883, 0.013895, -8.483970, -0.293333, 0.074825, -3.920244, 0.019240, 0.032089, 0.599586, 9.800000, 11.259452, -1.459452, -0.379238, 0.546271, 0.049712, 0.000937 - 150, 13305, 986832.8, 3494323, 22.159594, 1.404615, 15.776280, -0.102157, 0.014698, -6.950247, -0.311409, 0.077277, -4.029799, 0.057751, 0.034035, 1.696826, 9.600000, 10.561794, -0.961794, -0.247827, 0.561813, 0.033578, 0.000266 - 151, 13307, 731576.3, 3544716, 22.427085, 1.324349, 16.934421, -0.099608, 0.014767, -6.745119, -0.352410, 0.076536, -4.604486, 0.077505, 0.033575, 2.308392, 5.500000, 8.427806, -2.927806, -0.768755, 0.571194, 0.069302, 0.005478 - 152, 13309, 898776.3, 3563384, 22.228511, 1.345674, 16.518493, -0.101159, 0.014194, -7.126785, -0.305392, 0.075420, -4.049212, 0.050079, 0.033425, 1.498229, 8.600000, 4.364565, 4.235435, 1.113910, 0.543424, 0.072324, 0.012042 - 153, 13311, 796905.6, 3841086, 26.598193, 1.333140, 19.951534, -0.143989, 0.015714, -9.163000, -0.282387, 0.091631, -3.081803, 0.020378, 0.034454, 0.591443, 13.600000, 8.722204, 4.877796, 1.262861, 0.522149, 0.042725, 0.008861 - 154, 13313, 686891.4, 3855274, 25.497494, 1.297537, 19.650685, -0.129748, 0.014995, -8.652585, -0.345291, 0.084725, -4.075416, 0.055372, 0.033279, 1.663852, 12.000000, 12.807179, -0.807179, -0.207372, 0.529822, 0.027832, 0.000153 - 155, 13315, 838551.5, 3538547, 21.693832, 1.358017, 15.974644, -0.096277, 0.014404, -6.684182, -0.322872, 0.075426, -4.280632, 0.067143, 0.033810, 1.985899, 7.600000, 4.964453, 2.635547, 0.686789, 0.548817, 0.055079, 0.003422 - 156, 13317, 891228.5, 3749769, 26.260394, 1.284919, 20.437390, -0.136783, 0.014449, -9.466801, -0.268534, 0.078616, -3.415779, 0.001705, 0.031996, 0.053303, 10.400000, 12.117592, -1.717592, -0.442258, 0.534309, 0.032191, 0.000810 - 157, 13319, 858796.9, 3637891, 24.404390, 1.269233, 19.227671, -0.117382, 0.013836, -8.483651, -0.299309, 0.074464, -4.019492, 0.024811, 0.032036, 0.774484, 8.800000, 9.128546, -0.328546, -0.087141, 0.552086, 0.087897, 0.000091 - 158, 13321, 801018.1, 3487328, 20.871637, 1.470317, 14.195329, -0.089579, 0.015502, -5.778412, -0.338248, 0.078975, -4.282987, 0.087129, 0.036500, 2.387132, 6.300000, 8.316193, -2.016193, -0.518987, 0.545081, 0.031607, 0.001094 diff --git a/mgwr/tests/georgia/georgia_GS_NN_summary.txt b/mgwr/tests/georgia/georgia_GS_NN_summary.txt deleted file mode 100755 index 97f0764..0000000 --- a/mgwr/tests/georgia/georgia_GS_NN_summary.txt +++ /dev/null @@ -1,165 +0,0 @@ -***************************************************************************** -* Semiparametric Geographically Weighted Regression * -* Release 1.0.90 (GWR 4.0.90) * -* 12 May 2015 * -* (Originally coded by T. Nakaya: 1 Nov 2009) * -* * -* Tomoki Nakaya(1), Martin Charlton(2), Chris Brunsdon (2) * -* Paul Lewis (2), Jing Yao (3), A Stewart Fotheringham (4) * -* (c) GWR4 development team * -* (1) Ritsumeikan University, (2) National University of Ireland, Maynooth, * -* (3) University of Glasgow, (4) Arizona State University * -***************************************************************************** - -Program began at 7/25/2016 2:04:57 AM - -***************************************************************************** -Session: -Session control file: C:\Users\IEUser\Desktop\georgia_GS_NN.ctl -***************************************************************************** -Data filename: C:\Users\IEUser\Desktop\georgia\georgia\GData_utm.csv -Number of areas/points: 159 - -Model settings--------------------------------- -Model type: Gaussian -Geographic kernel: adaptive Gaussian -Method for optimal bandwidth search: Golden section search -Criterion for optimal bandwidth: AICc -Number of varying coefficients: 4 -Number of fixed coefficients: 0 - -Modelling options--------------------------------- -Standardisation of independent variables: OFF -Testing geographical variability of local coefficients: OFF -Local to Global Variable selection: OFF -Global to Local Variable selection: OFF -Prediction at non-regression points: OFF - -Variable settings--------------------------------- -Area key: field1: AreaKey -Easting (x-coord): field12 : X -Northing (y-coord): field13: Y -Cartesian coordinates: Euclidean distance -Dependent variable: field6: PctBach -Offset variable is not specified -Intercept: varying (Local) intercept -Independent variable with varying (Local) coefficient: field5: PctRural -Independent variable with varying (Local) coefficient: field9: PctPov -Independent variable with varying (Local) coefficient: field10: PctBlack -***************************************************************************** - -***************************************************************************** - Global regression result -***************************************************************************** - < Diagnostic information > -Residual sum of squares: 2639.559476 -Number of parameters: 4 - (Note: this num does not include an error variance term for a Gaussian model) -ML based global sigma estimate: 4.074433 -Unbiased global sigma estimate: 4.126671 --2 log-likelihood: 897.927089 -Classic AIC: 907.927089 -AICc: 908.319245 -BIC/MDL: 923.271610 -CV: 18.100197 -R square: 0.485273 -Adjusted R square: 0.471903 - -Variable Estimate Standard Error t(Est/SE) --------------------- --------------- --------------- --------------- -Intercept 23.854615 1.173043 20.335661 -PctRural -0.111395 0.012878 -8.649661 -PctPov -0.345778 0.070863 -4.879540 -PctBlack 0.058331 0.029187 1.998499 - -***************************************************************************** - GWR (Geographically weighted regression) bandwidth selection -***************************************************************************** - -Bandwidth search - Limits: 48, 159 - Golden section search begins... - Initial values - pL Bandwidth: 48.000 Criterion: 896.274 - p1 Bandwidth: 50.363 Criterion: 896.244 - p2 Bandwidth: 51.823 Criterion: 897.469 - pU Bandwidth: 54.186 Criterion: 897.673 - iter 1 (p1) Bandwidth: 50.363 Criterion: 896.244 Diff: 1.460 - iter 2 (p1) Bandwidth: 49.460 Criterion: 896.184 Diff: 0.902 - iter 3 (p2) Bandwidth: 49.460 Criterion: 896.184 Diff: 0.558 -Best bandwidth size 49.000 -Minimum AICc 896.184 - -***************************************************************************** - GWR (Geographically weighted regression) result -***************************************************************************** - Bandwidth and geographic ranges -Bandwidth size: 49.460274 -Coordinate Min Max Range ---------------- --------------- --------------- --------------- -X-coord 635964.300000 1059706.000000 423741.700000 -Y-coord 3401148.000000 3872640.000000 471492.000000 - - Diagnostic information -Residual sum of squares: 2312.592458 -Effective number of parameters (model: trace(S)): 8.033359 -Effective number of parameters (variance: trace(S'S)): 5.454906 -Degree of freedom (model: n - trace(S)): 150.966641 -Degree of freedom (residual: n - 2trace(S) + trace(S'S)): 148.388187 -ML based sigma estimate: 3.813739 -Unbiased sigma estimate: 3.947752 --2 log-likelihood: 876.900473 -Classic AIC: 894.967192 -AICc: 896.184041 -BIC/MDL: 922.689706 -CV: 17.914091 -R square: 0.549033 -Adjusted R square: 0.516564 - -*********************************************************** - << Geographically varying (Local) coefficients >> -*********************************************************** -Estimates of varying coefficients have been saved in the following file. - Listwise output file: C:\Users\IEUser\Desktop\georgia_GS_NN_listwise.csv - -Summary statistics for varying (Local) coefficients -Variable Mean STD --------------------- --------------- --------------- -Intercept 24.095617 1.993847 -PctRural -0.118018 0.019034 -PctPov -0.315782 0.031402 -PctBlack 0.047231 0.026783 - -Variable Min Max Range --------------------- --------------- --------------- --------------- -Intercept 20.871637 27.413438 6.541801 -PctRural -0.155327 -0.089579 0.065748 -PctPov -0.366066 -0.241590 0.124476 -PctBlack -0.005162 0.088745 0.093907 - -Variable Lwr Quartile Median Upr Quartile --------------------- --------------- --------------- --------------- -Intercept 22.299833 24.112693 25.928870 -PctRural -0.134835 -0.115582 -0.100015 -PctPov -0.343436 -0.317883 -0.293751 -PctBlack 0.024811 0.050079 0.068774 - -Variable Interquartile R Robust STD --------------------- --------------- --------------- -Intercept 3.629037 2.690168 -PctRural 0.034820 0.025812 -PctPov 0.049685 0.036831 -PctBlack 0.043963 0.032589 - (Note: Robust STD is given by (interquartile range / 1.349) ) - -***************************************************************************** - GWR ANOVA Table -***************************************************************************** -Source SS DF MS F ------------------ ------------------- ---------- --------------- ---------- -Global Residuals 2639.559 155.000 -GWR Improvement 326.967 6.612 49.452 -GWR Residuals 2312.592 148.388 15.585 3.173099 - -***************************************************************************** -Program terminated at 7/25/2016 2:04:57 AM diff --git a/mgwr/tests/georgia_mgwr_results.csv b/mgwr/tests/georgia_mgwr_results.csv new file mode 100644 index 0000000..721676d --- /dev/null +++ b/mgwr/tests/georgia_mgwr_results.csv @@ -0,0 +1,160 @@ +predy,resid_response,local_collinearity,X0,X1,X2,X3,X0_bse,X1_bse,X2_bse,X3_bse,X0_tvalues,X1_tvalues,X2_tvalues,X3_tvalues,X0_filter_tvalues,X1_filter_tvalues,X2_filter_tvalues,X3_filter_tvalues 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+-0.7662249720319954,0.3881414773558688,2.5571870563312067,0.08000625651414126,0.5974005352445491,-0.13130916235062468,-0.29702614467203636,0.07891820823787346,0.08836674676529492,0.0731721021075071,0.06493352968653496,1.0137870372447917,6.760467677182517,-1.7945249428217942,-4.574310777589373,0.0,6.760467677182517,0.0,-4.574310777589373 +-0.28596212302497853,-0.5323328219550745,1.9905925654456211,-0.1953464928961504,0.17002613594846555,-0.029781569553927214,-0.3011740461709441,0.07995702872716565,0.08096650066477519,0.07321779006237668,0.06317106677416084,-2.4431434735115514,2.099956581456115,-0.40675318837888036,-4.767594747887569,0.0,0.0,0.0,-4.767594747887569 diff --git a/mgwr/tests/test_gwr.py b/mgwr/tests/test_gwr.py index 78a4bc4..604e339 100644 --- a/mgwr/tests/test_gwr.py +++ b/mgwr/tests/test_gwr.py @@ -3,34 +3,51 @@ """ import os +import libpysal as ps from libpysal import io import numpy as np import unittest -import pickle as pk +import pandas from types import SimpleNamespace from ..gwr import GWR, MGWR, MGWRResults from ..sel_bw import Sel_BW from ..diagnostics import get_AICc, get_AIC, get_BIC, get_CV from spglm.family import Gaussian, Poisson, Binomial + class TestGWRGaussian(unittest.TestCase): def setUp(self): - data_path = os.path.join(os.path.dirname(__file__),'georgia/GData_utm.csv') + data_path = ps.examples.get_path("GData_utm.csv") data = io.open(data_path) self.coords = list(zip(data.by_col('X'), data.by_col('Y'))) - self.y = np.array(data.by_col('PctBach')).reshape((-1,1)) - rural = np.array(data.by_col('PctRural')).reshape((-1,1)) - pov = np.array(data.by_col('PctPov')).reshape((-1,1)) - black = np.array(data.by_col('PctBlack')).reshape((-1,1)) - fb = np.array(data.by_col('PctFB')).reshape((-1,1)) + self.y = np.array(data.by_col('PctBach')).reshape((-1, 1)) + rural = np.array(data.by_col('PctRural')).reshape((-1, 1)) + pov = np.array(data.by_col('PctPov')).reshape((-1, 1)) + black = np.array(data.by_col('PctBlack')).reshape((-1, 1)) + fb = np.array(data.by_col('PctFB')).reshape((-1, 1)) self.X = np.hstack([rural, pov, black]) self.mgwr_X = np.hstack([fb, black, rural]) - self.BS_F = io.open(os.path.join(os.path.dirname(__file__),'georgia/georgia_BS_F_listwise.csv')) - self.BS_NN = io.open(os.path.join(os.path.dirname(__file__),'georgia/georgia_BS_NN_listwise.csv')) - self.GS_F = io.open(os.path.join(os.path.dirname(__file__),'georgia/georgia_GS_F_listwise.csv')) - self.GS_NN = io.open(os.path.join(os.path.dirname(__file__),'georgia/georgia_GS_NN_listwise.csv')) - MGWR_path = os.path.join(os.path.dirname(__file__),'mgwr_example.p') - self.MGWR = pk.load(open(MGWR_path, 'rb')) + self.BS_F = io.open(ps.examples.get_path('georgia_BS_F_listwise.csv')) + self.BS_NN = io.open(ps.examples.get_path('georgia_BS_NN_listwise.csv')) + self.GS_F = io.open(ps.examples.get_path('georgia_GS_F_listwise.csv')) + self.GS_NN = io.open(ps.examples.get_path('georgia_GS_NN_listwise.csv')) + # self.BS_F = io.open(os.path.join(os.path.dirname( + # __file__), 'georgia/georgia_BS_F_listwise.csv')) + # self.BS_NN = io.open(os.path.join(os.path.dirname( + # __file__), 'georgia/georgia_BS_NN_listwise.csv')) + # self.GS_F = io.open(os.path.join(os.path.dirname( + # __file__), 'georgia/georgia_GS_F_listwise.csv')) + # self.GS_NN = io.open(os.path.join(os.path.dirname( + # __file__), 'georgia/georgia_GS_NN_listwise.csv')) + # MGWR_path = os.path.join(os.path.dirname( + # __file__), 'georgia_mgwr_model_frame.csv') + # MGWRp_path = os.path.join(os.path.dirname( + # __file__), 'georgia_mgwr_param_frame.csv') + # self.MGWR_n = pandas.read_csv(MGWR_path) + # self.MGWR_p = pandas.read_csv(MGWRp_path) + MGWR_path = os.path.join(os.path.dirname(__file__), + 'georgia_mgwr_results.csv') + self.MGWR = pandas.read_csv(MGWR_path) def test_BS_F(self): est_Int = self.BS_F.by_col(' est_Intercept') @@ -47,15 +64,15 @@ def test_BS_F(self): t_black = self.BS_F.by_col(' t_PctBlack') yhat = self.BS_F.by_col(' yhat') res = np.array(self.BS_F.by_col(' residual')) - std_res = np.array(self.BS_F.by_col(' std_residual')).reshape((-1,1)) - localR2 = np.array(self.BS_F.by_col(' localR2')).reshape((-1,1)) - inf = np.array(self.BS_F.by_col(' influence')).reshape((-1,1)) - cooksD = np.array(self.BS_F.by_col(' CooksD')).reshape((-1,1)) + std_res = np.array(self.BS_F.by_col(' std_residual')).reshape((-1, 1)) + localR2 = np.array(self.BS_F.by_col(' localR2')).reshape((-1, 1)) + inf = np.array(self.BS_F.by_col(' influence')).reshape((-1, 1)) + cooksD = np.array(self.BS_F.by_col(' CooksD')).reshape((-1, 1)) model = GWR(self.coords, self.y, self.X, bw=209267.689, fixed=True, - sigma2_v1=False) + sigma2_v1=False) rslt = model.fit() - + AICc = get_AICc(rslt) AIC = get_AIC(rslt) BIC = get_BIC(rslt) @@ -64,19 +81,19 @@ def test_BS_F(self): self.assertAlmostEquals(np.floor(AICc), 894.0) self.assertAlmostEquals(np.floor(AIC), 890.0) self.assertAlmostEquals(np.floor(BIC), 944.0) - self.assertAlmostEquals(np.round(CV,2), 18.25) - np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04) - np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-04) - np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-04) - np.testing.assert_allclose(est_rural, rslt.params[:,1], rtol=1e-04) - np.testing.assert_allclose(se_rural, rslt.bse[:,1], rtol=1e-04) - np.testing.assert_allclose(t_rural, rslt.tvalues[:,1], rtol=1e-04) - np.testing.assert_allclose(est_pov, rslt.params[:,2], rtol=1e-04) - np.testing.assert_allclose(se_pov, rslt.bse[:,2], rtol=1e-04) - np.testing.assert_allclose(t_pov, rslt.tvalues[:,2], rtol=1e-04) - np.testing.assert_allclose(est_black, rslt.params[:,3], rtol=1e-02) - np.testing.assert_allclose(se_black, rslt.bse[:,3], rtol=1e-02) - np.testing.assert_allclose(t_black, rslt.tvalues[:,3], rtol=1e-02) + self.assertAlmostEquals(np.round(CV, 2), 18.25) + np.testing.assert_allclose(est_Int, rslt.params[:, 0], rtol=1e-04) + np.testing.assert_allclose(se_Int, rslt.bse[:, 0], rtol=1e-04) + np.testing.assert_allclose(t_Int, rslt.tvalues[:, 0], rtol=1e-04) + np.testing.assert_allclose(est_rural, rslt.params[:, 1], rtol=1e-04) + np.testing.assert_allclose(se_rural, rslt.bse[:, 1], rtol=1e-04) + np.testing.assert_allclose(t_rural, rslt.tvalues[:, 1], rtol=1e-04) + np.testing.assert_allclose(est_pov, rslt.params[:, 2], rtol=1e-04) + np.testing.assert_allclose(se_pov, rslt.bse[:, 2], rtol=1e-04) + np.testing.assert_allclose(t_pov, rslt.tvalues[:, 2], rtol=1e-04) + np.testing.assert_allclose(est_black, rslt.params[:, 3], rtol=1e-02) + np.testing.assert_allclose(se_black, rslt.bse[:, 3], rtol=1e-02) + np.testing.assert_allclose(t_black, rslt.tvalues[:, 3], rtol=1e-02) np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-05) np.testing.assert_allclose(res, rslt.resid_response, rtol=1e-04) np.testing.assert_allclose(std_res, rslt.std_res, rtol=1e-04) @@ -99,24 +116,23 @@ def test_BS_NN(self): t_black = self.BS_NN.by_col(' t_PctBlack') yhat = self.BS_NN.by_col(' yhat') res = np.array(self.BS_NN.by_col(' residual')) - std_res = np.array(self.BS_NN.by_col(' std_residual')).reshape((-1,1)) - localR2 = np.array(self.BS_NN.by_col(' localR2')).reshape((-1,1)) - inf = np.array(self.BS_NN.by_col(' influence')).reshape((-1,1)) - cooksD = np.array(self.BS_NN.by_col(' CooksD')).reshape((-1,1)) - local_corr = os.path.join(os.path.dirname(__file__),'local_corr.csv') + std_res = np.array(self.BS_NN.by_col(' std_residual')).reshape((-1, 1)) + localR2 = np.array(self.BS_NN.by_col(' localR2')).reshape((-1, 1)) + inf = np.array(self.BS_NN.by_col(' influence')).reshape((-1, 1)) + cooksD = np.array(self.BS_NN.by_col(' CooksD')).reshape((-1, 1)) + local_corr = os.path.join(os.path.dirname(__file__), 'local_corr.csv') corr1 = np.array(io.open(local_corr)) - local_vif = os.path.join(os.path.dirname(__file__),'local_vif.csv') + local_vif = os.path.join(os.path.dirname(__file__), 'local_vif.csv') vif1 = np.array(io.open(local_vif)) - local_cn = os.path.join(os.path.dirname(__file__),'local_cn.csv') + local_cn = os.path.join(os.path.dirname(__file__), 'local_cn.csv') cn1 = np.array(io.open(local_cn)) - local_vdp = os.path.join(os.path.dirname(__file__),'local_vdp.csv') + local_vdp = os.path.join(os.path.dirname(__file__), 'local_vdp.csv') vdp1 = np.array(io.open(local_vdp), dtype=np.float64) - spat_var_p_vals = [0. , 0.0 , 0.5, 0.2] + spat_var_p_vals = [0., 0.0, 0.5, 0.2] model = GWR(self.coords, self.y, self.X, bw=90.000, fixed=False, - sigma2_v1=False) + sigma2_v1=False) rslt = model.fit() - adj_alpha = rslt.adj_alpha alpha = 0.01017489 @@ -125,14 +141,13 @@ def test_BS_NN(self): AIC = get_AIC(rslt) BIC = get_BIC(rslt) CV = get_CV(rslt) - + corr2, vif2, cn2, vdp2 = rslt.local_collinearity() R2 = rslt.R2 - - np.testing.assert_allclose(adj_alpha, np.array([ 0.02034978, 0.01017489, - 0.0002035 ]), rtol=1e-04) + np.testing.assert_allclose(adj_alpha, np.array([0.02034978, 0.01017489, + 0.0002035]), rtol=1e-04) self.assertAlmostEquals(critical_t, 2.6011011542649394) self.assertAlmostEquals(np.around(R2, 4), 0.5924) self.assertAlmostEquals(np.floor(AICc), 896.0) @@ -144,18 +159,18 @@ def test_BS_NN(self): np.testing.assert_allclose(cn1, cn2, rtol=1e-04) np.testing.assert_allclose(vdp1, vdp2, rtol=1e-04) - np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04) - np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-04) - np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-04) - np.testing.assert_allclose(est_rural, rslt.params[:,1], rtol=1e-04) - np.testing.assert_allclose(se_rural, rslt.bse[:,1], rtol=1e-04) - np.testing.assert_allclose(t_rural, rslt.tvalues[:,1], rtol=1e-04) - np.testing.assert_allclose(est_pov, rslt.params[:,2], rtol=1e-04) - np.testing.assert_allclose(se_pov, rslt.bse[:,2], rtol=1e-04) - np.testing.assert_allclose(t_pov, rslt.tvalues[:,2], rtol=1e-04) - np.testing.assert_allclose(est_black, rslt.params[:,3], rtol=1e-02) - np.testing.assert_allclose(se_black, rslt.bse[:,3], rtol=1e-02) - np.testing.assert_allclose(t_black, rslt.tvalues[:,3], rtol=1e-02) + np.testing.assert_allclose(est_Int, rslt.params[:, 0], rtol=1e-04) + np.testing.assert_allclose(se_Int, rslt.bse[:, 0], rtol=1e-04) + np.testing.assert_allclose(t_Int, rslt.tvalues[:, 0], rtol=1e-04) + np.testing.assert_allclose(est_rural, rslt.params[:, 1], rtol=1e-04) + np.testing.assert_allclose(se_rural, rslt.bse[:, 1], rtol=1e-04) + np.testing.assert_allclose(t_rural, rslt.tvalues[:, 1], rtol=1e-04) + np.testing.assert_allclose(est_pov, rslt.params[:, 2], rtol=1e-04) + np.testing.assert_allclose(se_pov, rslt.bse[:, 2], rtol=1e-04) + np.testing.assert_allclose(t_pov, rslt.tvalues[:, 2], rtol=1e-04) + np.testing.assert_allclose(est_black, rslt.params[:, 3], rtol=1e-02) + np.testing.assert_allclose(se_black, rslt.bse[:, 3], rtol=1e-02) + np.testing.assert_allclose(t_black, rslt.tvalues[:, 3], rtol=1e-02) np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-05) np.testing.assert_allclose(res, rslt.resid_response, rtol=1e-04) np.testing.assert_allclose(std_res, rslt.std_res, rtol=1e-04) @@ -171,7 +186,6 @@ def test_BS_NN(self): p_vals = result.spatial_variability(sel, 10) np.testing.assert_allclose(spat_var_p_vals, p_vals, rtol=1e-04) - def test_GS_F(self): est_Int = self.GS_F.by_col(' est_Intercept') se_Int = self.GS_F.by_col(' se_Intercept') @@ -187,43 +201,43 @@ def test_GS_F(self): t_black = self.GS_F.by_col(' t_PctBlack') yhat = self.GS_F.by_col(' yhat') res = np.array(self.GS_F.by_col(' residual')) - std_res = np.array(self.GS_F.by_col(' std_residual')).reshape((-1,1)) - localR2 = np.array(self.GS_F.by_col(' localR2')).reshape((-1,1)) - inf = np.array(self.GS_F.by_col(' influence')).reshape((-1,1)) - cooksD = np.array(self.GS_F.by_col(' CooksD')).reshape((-1,1)) - + std_res = np.array(self.GS_F.by_col(' std_residual')).reshape((-1, 1)) + localR2 = np.array(self.GS_F.by_col(' localR2')).reshape((-1, 1)) + inf = np.array(self.GS_F.by_col(' influence')).reshape((-1, 1)) + cooksD = np.array(self.GS_F.by_col(' CooksD')).reshape((-1, 1)) + model = GWR(self.coords, self.y, self.X, bw=87308.298, - kernel='gaussian', fixed=True, sigma2_v1=False) + kernel='gaussian', fixed=True, sigma2_v1=False) rslt = model.fit() - + AICc = get_AICc(rslt) AIC = get_AIC(rslt) BIC = get_BIC(rslt) CV = get_CV(rslt) - + self.assertAlmostEquals(np.floor(AICc), 895.0) self.assertAlmostEquals(np.floor(AIC), 890.0) self.assertAlmostEquals(np.floor(BIC), 943.0) self.assertAlmostEquals(np.around(CV, 2), 18.21) - np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04) - np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-04) - np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-04) - np.testing.assert_allclose(est_rural, rslt.params[:,1], rtol=1e-04) - np.testing.assert_allclose(se_rural, rslt.bse[:,1], rtol=1e-04) - np.testing.assert_allclose(t_rural, rslt.tvalues[:,1], rtol=1e-04) - np.testing.assert_allclose(est_pov, rslt.params[:,2], rtol=1e-04) - np.testing.assert_allclose(se_pov, rslt.bse[:,2], rtol=1e-04) - np.testing.assert_allclose(t_pov, rslt.tvalues[:,2], rtol=1e-04) - np.testing.assert_allclose(est_black, rslt.params[:,3], rtol=1e-02) - np.testing.assert_allclose(se_black, rslt.bse[:,3], rtol=1e-02) - np.testing.assert_allclose(t_black, rslt.tvalues[:,3], rtol=1e-02) + np.testing.assert_allclose(est_Int, rslt.params[:, 0], rtol=1e-04) + np.testing.assert_allclose(se_Int, rslt.bse[:, 0], rtol=1e-04) + np.testing.assert_allclose(t_Int, rslt.tvalues[:, 0], rtol=1e-04) + np.testing.assert_allclose(est_rural, rslt.params[:, 1], rtol=1e-04) + np.testing.assert_allclose(se_rural, rslt.bse[:, 1], rtol=1e-04) + np.testing.assert_allclose(t_rural, rslt.tvalues[:, 1], rtol=1e-04) + np.testing.assert_allclose(est_pov, rslt.params[:, 2], rtol=1e-04) + np.testing.assert_allclose(se_pov, rslt.bse[:, 2], rtol=1e-04) + np.testing.assert_allclose(t_pov, rslt.tvalues[:, 2], rtol=1e-04) + np.testing.assert_allclose(est_black, rslt.params[:, 3], rtol=1e-02) + np.testing.assert_allclose(se_black, rslt.bse[:, 3], rtol=1e-02) + np.testing.assert_allclose(t_black, rslt.tvalues[:, 3], rtol=1e-02) np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-05) np.testing.assert_allclose(res, rslt.resid_response, rtol=1e-04) np.testing.assert_allclose(std_res, rslt.std_res, rtol=1e-04) np.testing.assert_allclose(localR2, rslt.localR2, rtol=1e-05) np.testing.assert_allclose(inf, rslt.influ, rtol=1e-04) np.testing.assert_allclose(cooksD, rslt.cooksD, rtol=1e-00) - + def test_GS_NN(self): est_Int = self.GS_NN.by_col(' est_Intercept') se_Int = self.GS_NN.by_col(' se_Intercept') @@ -239,151 +253,171 @@ def test_GS_NN(self): t_black = self.GS_NN.by_col(' t_PctBlack') yhat = self.GS_NN.by_col(' yhat') res = np.array(self.GS_NN.by_col(' residual')) - std_res = np.array(self.GS_NN.by_col(' std_residual')).reshape((-1,1)) - localR2 = np.array(self.GS_NN.by_col(' localR2')).reshape((-1,1)) - inf = np.array(self.GS_NN.by_col(' influence')).reshape((-1,1)) - cooksD = np.array(self.GS_NN.by_col(' CooksD')).reshape((-1,1)) + std_res = np.array(self.GS_NN.by_col(' std_residual')).reshape((-1, 1)) + localR2 = np.array(self.GS_NN.by_col(' localR2')).reshape((-1, 1)) + inf = np.array(self.GS_NN.by_col(' influence')).reshape((-1, 1)) + cooksD = np.array(self.GS_NN.by_col(' CooksD')).reshape((-1, 1)) model = GWR(self.coords, self.y, self.X, bw=49.000, - kernel='gaussian', fixed=False, sigma2_v1=False) + kernel='gaussian', fixed=False, sigma2_v1=False) rslt = model.fit() - + AICc = get_AICc(rslt) AIC = get_AIC(rslt) BIC = get_BIC(rslt) CV = get_CV(rslt) - + self.assertAlmostEquals(np.floor(AICc), 896.0) self.assertAlmostEquals(np.floor(AIC), 894.0) self.assertAlmostEquals(np.floor(BIC), 922.0) self.assertAlmostEquals(np.around(CV, 2), 17.91) - np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04) - np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-04) - np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-04) - np.testing.assert_allclose(est_rural, rslt.params[:,1], rtol=1e-04) - np.testing.assert_allclose(se_rural, rslt.bse[:,1], rtol=1e-04) - np.testing.assert_allclose(t_rural, rslt.tvalues[:,1], rtol=1e-04) - np.testing.assert_allclose(est_pov, rslt.params[:,2], rtol=1e-04) - np.testing.assert_allclose(se_pov, rslt.bse[:,2], rtol=1e-04) - np.testing.assert_allclose(t_pov, rslt.tvalues[:,2], rtol=1e-04) - np.testing.assert_allclose(est_black, rslt.params[:,3], rtol=1e-02) - np.testing.assert_allclose(se_black, rslt.bse[:,3], rtol=1e-02) - np.testing.assert_allclose(t_black, rslt.tvalues[:,3], rtol=1e-02) + np.testing.assert_allclose(est_Int, rslt.params[:, 0], rtol=1e-04) + np.testing.assert_allclose(se_Int, rslt.bse[:, 0], rtol=1e-04) + np.testing.assert_allclose(t_Int, rslt.tvalues[:, 0], rtol=1e-04) + np.testing.assert_allclose(est_rural, rslt.params[:, 1], rtol=1e-04) + np.testing.assert_allclose(se_rural, rslt.bse[:, 1], rtol=1e-04) + np.testing.assert_allclose(t_rural, rslt.tvalues[:, 1], rtol=1e-04) + np.testing.assert_allclose(est_pov, rslt.params[:, 2], rtol=1e-04) + np.testing.assert_allclose(se_pov, rslt.bse[:, 2], rtol=1e-04) + np.testing.assert_allclose(t_pov, rslt.tvalues[:, 2], rtol=1e-04) + np.testing.assert_allclose(est_black, rslt.params[:, 3], rtol=1e-02) + np.testing.assert_allclose(se_black, rslt.bse[:, 3], rtol=1e-02) + np.testing.assert_allclose(t_black, rslt.tvalues[:, 3], rtol=1e-02) np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-05) np.testing.assert_allclose(res, rslt.resid_response, rtol=1e-04) np.testing.assert_allclose(std_res, rslt.std_res, rtol=1e-04) np.testing.assert_allclose(localR2, rslt.localR2, rtol=1e-05) np.testing.assert_allclose(inf, rslt.influ, rtol=1e-04) np.testing.assert_allclose(cooksD, rslt.cooksD, rtol=1e-00) - + def test_MGWR(self): std_y = (self.y - self.y.mean()) / self.y.std() - std_X = (self.mgwr_X - self.mgwr_X.mean(axis=0)) / self.mgwr_X.std(axis=0) + std_X = (self.mgwr_X - self.mgwr_X.mean(axis=0)) / \ + self.mgwr_X.std(axis=0) selector = Sel_BW(self.coords, std_y, std_X, multi=True, - constant=True) + constant=True) selector.search(multi_bw_min=[2], multi_bw_max=[159]) model = MGWR(self.coords, std_y, std_X, selector=selector, - constant=True) + constant=True) rslt = model.fit() - np.testing.assert_allclose(rslt.predy, self.MGWR.predy, atol=1e-07) - np.testing.assert_allclose(rslt.params, self.MGWR.params, atol=1e-07) - np.testing.assert_allclose(rslt.bse, self.MGWR.bse, atol=1e-07) - np.testing.assert_allclose(rslt.tvalues, self.MGWR.tvalues, atol=1e-07) - np.testing.assert_allclose(rslt.resid_response, self.MGWR.resid_response, - atol=1e-04, rtol=1e-04) - np.testing.assert_almost_equal(rslt.resid_ss, self.MGWR.resid_ss) - np.testing.assert_almost_equal(rslt.aicc, self.MGWR.aicc) - np.testing.assert_almost_equal(rslt.ENP, self.MGWR.ENP) - np.testing.assert_allclose(rslt.ENP_j, self.MGWR.ENP_j) - np.testing.assert_allclose(rslt.adj_alpha_j, self.MGWR.adj_alpha_j) + varnames = ['X0', 'X1', 'X2', 'X3'] + + # def suffixed(x): + # """ Quick anonymous function to suffix strings""" + # return ['_'.join(x) for x in varnames] + + np.testing.assert_allclose(rslt.predy.flatten(), self.MGWR.predy, + atol=1e-07) + np.testing.assert_allclose(rslt.params, + self.MGWR[varnames].values, atol=1e-07) + np.testing.assert_allclose(rslt.bse, + self.MGWR[[s + "_bse" for s in varnames]].values, atol=1e-07) + np.testing.assert_allclose(rslt.tvalues, + self.MGWR[[s + "_tvalues" for s in varnames]].values, atol=1e-07) + np.testing.assert_allclose(rslt.resid_response, + self.MGWR.resid_response, + atol=1e-04, rtol=1e-04) + np.testing.assert_almost_equal(rslt.resid_ss, 50.899379467870425) + np.testing.assert_almost_equal(rslt.aicc, 297.12013812258783) + np.testing.assert_almost_equal(rslt.ENP, 11.36825087269831) + np.testing.assert_allclose(rslt.ENP_j, [3.844671080264143, 3.513770805151652, + 2.2580525278898254, 1.7517564593926895]) + np.testing.assert_allclose(rslt.adj_alpha_j, np.array([[0.02601003, 0.01300501, 0.0002601 ], + [0.02845945, 0.01422973, 0.00028459], + [0.04428595, 0.02214297, 0.00044286], + [0.05708556, + 0.02854278, + 0.00057086]]), atol=1e-07) np.testing.assert_allclose(rslt.critical_tval(), - self.MGWR.critical_tval) - np.testing.assert_allclose(rslt.filter_tvals(), - self.MGWR.filter_tvals) - np.testing.assert_allclose(rslt.local_collinearity()[0], - self.MGWR.local_collinearity) - + np.array([2.51210749, 2.47888792, + 2.31069113, 2.21000184]), atol=1e-07) + np.testing.assert_allclose(rslt.filter_tvals(), + self.MGWR[[s + "_filter_tvalues" for s in + varnames]].values, atol=1e-07) + np.testing.assert_allclose(rslt.local_collinearity()[0].flatten(), + self.MGWR.local_collinearity, atol=1e-07) + def test_Prediction(self): - coords =np.array(self.coords) + coords = np.array(self.coords) index = np.arange(len(self.y)) test = index[-10:] X_test = self.X[test] coords_test = list(coords[test]) - model = GWR(self.coords, self.y, self.X, 93, family=Gaussian(), - fixed=False, kernel='bisquare', sigma2_v1=False) + fixed=False, kernel='bisquare', sigma2_v1=False) results = model.predict(coords_test, X_test) - + params = np.array([22.77198, -0.10254, -0.215093, -0.01405, - 19.10531, -0.094177, -0.232529, 0.071913, - 19.743421, -0.080447, -0.30893, 0.083206, - 17.505759, -0.078919, -0.187955, 0.051719, - 27.747402, -0.165335, -0.208553, 0.004067, - 26.210627, -0.138398, -0.360514, 0.072199, - 18.034833, -0.077047, -0.260556, 0.084319, - 28.452802, -0.163408, -0.14097, -0.063076, - 22.353095, -0.103046, -0.226654, 0.002992, - 18.220508, -0.074034, -0.309812, 0.108636]).reshape((10,4)) + 19.10531, -0.094177, -0.232529, 0.071913, + 19.743421, -0.080447, -0.30893, 0.083206, + 17.505759, -0.078919, -0.187955, 0.051719, + 27.747402, -0.165335, -0.208553, 0.004067, + 26.210627, -0.138398, -0.360514, 0.072199, + 18.034833, -0.077047, -0.260556, 0.084319, + 28.452802, -0.163408, -0.14097, -0.063076, + 22.353095, -0.103046, -0.226654, 0.002992, + 18.220508, -0.074034, -0.309812, 0.108636]).reshape((10, 4)) np.testing.assert_allclose(params, results.params, rtol=1e-03) bse = np.array([2.080166, 0.021462, 0.102954, 0.049627, - 2.536355, 0.022111, 0.123857, 0.051917, - 1.967813, 0.019716, 0.102562, 0.054918, - 2.463219, 0.021745, 0.110297, 0.044189, - 1.556056, 0.019513, 0.12764, 0.040315, - 1.664108, 0.020114, 0.131208, 0.041613, - 2.5835, 0.021481, 0.113158, 0.047243, - 1.709483, 0.019752, 0.116944, 0.043636, - 1.958233, 0.020947, 0.09974, 0.049821, - 2.276849, 0.020122, 0.107867, 0.047842]).reshape((10,4)) + 2.536355, 0.022111, 0.123857, 0.051917, + 1.967813, 0.019716, 0.102562, 0.054918, + 2.463219, 0.021745, 0.110297, 0.044189, + 1.556056, 0.019513, 0.12764, 0.040315, + 1.664108, 0.020114, 0.131208, 0.041613, + 2.5835, 0.021481, 0.113158, 0.047243, + 1.709483, 0.019752, 0.116944, 0.043636, + 1.958233, 0.020947, 0.09974, 0.049821, + 2.276849, 0.020122, 0.107867, 0.047842]).reshape((10, 4)) np.testing.assert_allclose(bse, results.bse, rtol=1e-03) tvalues = np.array([10.947193, -4.777659, -2.089223, -0.283103, - 7.532584, -4.259179, -1.877395, 1.385161, - 10.033179, -4.080362, -3.012133, 1.515096, - 7.106862, -3.629311, -1.704079, 1.17042, - 17.831878, -8.473156, -1.633924, 0.100891, - 15.750552, -6.880725, -2.74765, 1.734978, - 6.980774, -3.586757, -2.302575, 1.784818, - 16.644095, -8.273001, -1.205451, -1.445501, - 11.414933, -4.919384, -2.272458, 0.060064, - 8.00251, -3.679274, -2.872176, 2.270738]).reshape((10,4)) + 7.532584, -4.259179, -1.877395, 1.385161, + 10.033179, -4.080362, -3.012133, 1.515096, + 7.106862, -3.629311, -1.704079, 1.17042, + 17.831878, -8.473156, -1.633924, 0.100891, + 15.750552, -6.880725, -2.74765, 1.734978, + 6.980774, -3.586757, -2.302575, 1.784818, + 16.644095, -8.273001, -1.205451, -1.445501, + 11.414933, -4.919384, -2.272458, 0.060064, + 8.00251, -3.679274, -2.872176, 2.270738]).reshape((10, 4)) np.testing.assert_allclose(tvalues, results.tvalues, rtol=1e-03) - localR2 = np.array([[ 0.53068693], - [ 0.59582647], - [ 0.59700925], - [ 0.45769954], - [ 0.54634509], - [ 0.5494828 ], - [ 0.55159604], - [ 0.55634237], - [ 0.53903842], - [ 0.55884954]]) + localR2 = np.array([[0.53068693], + [0.59582647], + [0.59700925], + [0.45769954], + [0.54634509], + [0.5494828], + [0.55159604], + [0.55634237], + [0.53903842], + [0.55884954]]) np.testing.assert_allclose(localR2, results.localR2, rtol=1e-05) - predictions = np.array([[ 10.51695514], - [ 9.93321992], - [ 8.92473026], - [ 5.47350219], - [ 8.61756585], - [ 12.8141851 ], - [ 5.55619405], - [ 12.63004172], - [ 8.70638418], - [ 8.17582599]]) - np.testing.assert_allclose(predictions, results.predictions, rtol=1e-05) - - + predictions = np.array([[10.51695514], + [9.93321992], + [8.92473026], + [5.47350219], + [8.61756585], + [12.8141851], + [5.55619405], + [12.63004172], + [8.70638418], + [8.17582599]]) + np.testing.assert_allclose( + predictions, results.predictions, rtol=1e-05) def test_BS_NN_longlat(self): - GA_longlat = os.path.join(os.path.dirname(__file__),'ga_bs_nn_longlat_listwise.csv') + GA_longlat = os.path.join(os.path.dirname( + __file__), 'ga_bs_nn_longlat_listwise.csv') self.BS_NN_longlat = io.open(GA_longlat) - - coords_longlat = list(zip(self.BS_NN_longlat.by_col(' x_coord'), self.BS_NN_longlat.by_col(' y_coord'))) + + coords_longlat = list(zip(self.BS_NN_longlat.by_col( + ' x_coord'), self.BS_NN_longlat.by_col(' y_coord'))) est_Int = self.BS_NN_longlat.by_col(' est_Intercept') se_Int = self.BS_NN_longlat.by_col(' se_Intercept') t_Int = self.BS_NN_longlat.by_col(' t_Intercept') @@ -398,39 +432,43 @@ def test_BS_NN_longlat(self): t_black = self.BS_NN_longlat.by_col(' t_PctBlack') yhat = self.BS_NN_longlat.by_col(' yhat') res = np.array(self.BS_NN_longlat.by_col(' residual')) - std_res = np.array(self.BS_NN_longlat.by_col(' std_residual')).reshape((-1,1)) - localR2 = np.array(self.BS_NN_longlat.by_col(' localR2')).reshape((-1,1)) - inf = np.array(self.BS_NN_longlat.by_col(' influence')).reshape((-1,1)) - cooksD = np.array(self.BS_NN_longlat.by_col(' CooksD')).reshape((-1,1)) - + std_res = np.array(self.BS_NN_longlat.by_col( + ' std_residual')).reshape((-1, 1)) + localR2 = np.array(self.BS_NN_longlat.by_col( + ' localR2')).reshape((-1, 1)) + inf = np.array(self.BS_NN_longlat.by_col( + ' influence')).reshape((-1, 1)) + cooksD = np.array(self.BS_NN_longlat.by_col( + ' CooksD')).reshape((-1, 1)) + model = GWR(coords_longlat, self.y, self.X, bw=90.000, fixed=False, - spherical=True, sigma2_v1=False) + spherical=True, sigma2_v1=False) rslt = model.fit() - + AICc = get_AICc(rslt) AIC = get_AIC(rslt) BIC = get_BIC(rslt) CV = get_CV(rslt) - + R2 = rslt.R2 - + self.assertAlmostEquals(np.around(R2, 4), 0.5921) self.assertAlmostEquals(np.floor(AICc), 896.0) self.assertAlmostEquals(np.floor(AIC), 892.0) self.assertAlmostEquals(np.floor(BIC), 941.0) self.assertAlmostEquals(np.around(CV, 2), 19.11) - np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04) - np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-04) - np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-04) - np.testing.assert_allclose(est_rural, rslt.params[:,1], rtol=1e-04) - np.testing.assert_allclose(se_rural, rslt.bse[:,1], rtol=1e-04) - np.testing.assert_allclose(t_rural, rslt.tvalues[:,1], rtol=1e-04) - np.testing.assert_allclose(est_pov, rslt.params[:,2], rtol=1e-04) - np.testing.assert_allclose(se_pov, rslt.bse[:,2], rtol=1e-04) - np.testing.assert_allclose(t_pov, rslt.tvalues[:,2], rtol=1e-04) - np.testing.assert_allclose(est_black, rslt.params[:,3], rtol=1e-02) - np.testing.assert_allclose(se_black, rslt.bse[:,3], rtol=1e-02) - np.testing.assert_allclose(t_black, rslt.tvalues[:,3], rtol=1e-02) + np.testing.assert_allclose(est_Int, rslt.params[:, 0], rtol=1e-04) + np.testing.assert_allclose(se_Int, rslt.bse[:, 0], rtol=1e-04) + np.testing.assert_allclose(t_Int, rslt.tvalues[:, 0], rtol=1e-04) + np.testing.assert_allclose(est_rural, rslt.params[:, 1], rtol=1e-04) + np.testing.assert_allclose(se_rural, rslt.bse[:, 1], rtol=1e-04) + np.testing.assert_allclose(t_rural, rslt.tvalues[:, 1], rtol=1e-04) + np.testing.assert_allclose(est_pov, rslt.params[:, 2], rtol=1e-04) + np.testing.assert_allclose(se_pov, rslt.bse[:, 2], rtol=1e-04) + np.testing.assert_allclose(t_pov, rslt.tvalues[:, 2], rtol=1e-04) + np.testing.assert_allclose(est_black, rslt.params[:, 3], rtol=1e-02) + np.testing.assert_allclose(se_black, rslt.bse[:, 3], rtol=1e-02) + np.testing.assert_allclose(t_black, rslt.tvalues[:, 3], rtol=1e-02) np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-05) np.testing.assert_allclose(res, rslt.resid_response, rtol=1e-04) np.testing.assert_allclose(std_res, rslt.std_res, rtol=1e-04) @@ -441,21 +479,28 @@ def test_BS_NN_longlat(self): class TestGWRPoisson(unittest.TestCase): def setUp(self): - data_path = os.path.join(os.path.dirname(__file__),'tokyo/Tokyomortality.csv') + data_path = os.path.join(os.path.dirname( + __file__), 'tokyo/Tokyomortality.csv') data = io.open(data_path, mode='Ur') - self.coords = list(zip(data.by_col('X_CENTROID'), data.by_col('Y_CENTROID'))) - self.y = np.array(data.by_col('db2564')).reshape((-1,1)) - self.off = np.array(data.by_col('eb2564')).reshape((-1,1)) - OCC = np.array(data.by_col('OCC_TEC')).reshape((-1,1)) - OWN = np.array(data.by_col('OWNH')).reshape((-1,1)) - POP = np.array(data.by_col('POP65')).reshape((-1,1)) - UNEMP = np.array(data.by_col('UNEMP')).reshape((-1,1)) - self.X = np.hstack([OCC,OWN,POP,UNEMP]) - self.BS_F = io.open(os.path.join(os.path.dirname(__file__),'tokyo/tokyo_BS_F_listwise.csv')) - self.BS_NN = io.open(os.path.join(os.path.dirname(__file__),'tokyo/tokyo_BS_NN_listwise.csv')) - self.GS_F = io.open(os.path.join(os.path.dirname(__file__),'tokyo/tokyo_GS_F_listwise.csv')) - self.GS_NN = io.open(os.path.join(os.path.dirname(__file__),'tokyo/tokyo_GS_NN_listwise.csv')) - self.BS_NN_OFF = io.open(os.path.join(os.path.dirname(__file__),'tokyo/tokyo_BS_NN_OFF_listwise.csv')) + self.coords = list( + zip(data.by_col('X_CENTROID'), data.by_col('Y_CENTROID'))) + self.y = np.array(data.by_col('db2564')).reshape((-1, 1)) + self.off = np.array(data.by_col('eb2564')).reshape((-1, 1)) + OCC = np.array(data.by_col('OCC_TEC')).reshape((-1, 1)) + OWN = np.array(data.by_col('OWNH')).reshape((-1, 1)) + POP = np.array(data.by_col('POP65')).reshape((-1, 1)) + UNEMP = np.array(data.by_col('UNEMP')).reshape((-1, 1)) + self.X = np.hstack([OCC, OWN, POP, UNEMP]) + self.BS_F = io.open(os.path.join(os.path.dirname( + __file__), 'tokyo/tokyo_BS_F_listwise.csv')) + self.BS_NN = io.open(os.path.join(os.path.dirname( + __file__), 'tokyo/tokyo_BS_NN_listwise.csv')) + self.GS_F = io.open(os.path.join(os.path.dirname( + __file__), 'tokyo/tokyo_GS_F_listwise.csv')) + self.GS_NN = io.open(os.path.join(os.path.dirname( + __file__), 'tokyo/tokyo_GS_NN_listwise.csv')) + self.BS_NN_OFF = io.open(os.path.join(os.path.dirname( + __file__), 'tokyo/tokyo_BS_NN_OFF_listwise.csv')) def test_BS_F(self): est_Int = self.BS_F.by_col(' est_Intercept') @@ -474,38 +519,37 @@ def test_BS_F(self): se_UNEMP = self.BS_F.by_col(' se_UNEMP') t_UNEMP = self.BS_F.by_col(' t_UNEMP') yhat = self.BS_F.by_col(' yhat') - pdev = np.array(self.BS_F.by_col(' localpdev')).reshape((-1,1)) - - model = GWR(self.coords, self.y, self.X, bw=26029.625, family=Poisson(), - kernel='bisquare', fixed=True, sigma2_v1=False) + pdev = np.array(self.BS_F.by_col(' localpdev')).reshape((-1, 1)) + + model = GWR(self.coords, self.y, self.X, bw=26029.625, family=Poisson(), + kernel='bisquare', fixed=True, sigma2_v1=False) rslt = model.fit() AICc = get_AICc(rslt) AIC = get_AIC(rslt) BIC = get_BIC(rslt) - + self.assertAlmostEquals(np.floor(AICc), 13294.0) self.assertAlmostEquals(np.floor(AIC), 13247.0) self.assertAlmostEquals(np.floor(BIC), 13485.0) - np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-05) - np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-03) - np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-03) - np.testing.assert_allclose(est_OCC, rslt.params[:,1], rtol=1e-04) - np.testing.assert_allclose(se_OCC, rslt.bse[:,1], rtol=1e-02) - np.testing.assert_allclose(t_OCC, rslt.tvalues[:,1], rtol=1e-02) - np.testing.assert_allclose(est_OWN, rslt.params[:,2], rtol=1e-04) - np.testing.assert_allclose(se_OWN, rslt.bse[:,2], rtol=1e-03) - np.testing.assert_allclose(t_OWN, rslt.tvalues[:,2], rtol=1e-03) - np.testing.assert_allclose(est_POP, rslt.params[:,3], rtol=1e-04) - np.testing.assert_allclose(se_POP, rslt.bse[:,3], rtol=1e-02) - np.testing.assert_allclose(t_POP, rslt.tvalues[:,3], rtol=1e-02) - np.testing.assert_allclose(est_UNEMP, rslt.params[:,4], rtol=1e-04) - np.testing.assert_allclose(se_UNEMP, rslt.bse[:,4], rtol=1e-02) - np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:,4], rtol=1e-02) + np.testing.assert_allclose(est_Int, rslt.params[:, 0], rtol=1e-05) + np.testing.assert_allclose(se_Int, rslt.bse[:, 0], rtol=1e-03) + np.testing.assert_allclose(t_Int, rslt.tvalues[:, 0], rtol=1e-03) + np.testing.assert_allclose(est_OCC, rslt.params[:, 1], rtol=1e-04) + np.testing.assert_allclose(se_OCC, rslt.bse[:, 1], rtol=1e-02) + np.testing.assert_allclose(t_OCC, rslt.tvalues[:, 1], rtol=1e-02) + np.testing.assert_allclose(est_OWN, rslt.params[:, 2], rtol=1e-04) + np.testing.assert_allclose(se_OWN, rslt.bse[:, 2], rtol=1e-03) + np.testing.assert_allclose(t_OWN, rslt.tvalues[:, 2], rtol=1e-03) + np.testing.assert_allclose(est_POP, rslt.params[:, 3], rtol=1e-04) + np.testing.assert_allclose(se_POP, rslt.bse[:, 3], rtol=1e-02) + np.testing.assert_allclose(t_POP, rslt.tvalues[:, 3], rtol=1e-02) + np.testing.assert_allclose(est_UNEMP, rslt.params[:, 4], rtol=1e-04) + np.testing.assert_allclose(se_UNEMP, rslt.bse[:, 4], rtol=1e-02) + np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:, 4], rtol=1e-02) np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-05) np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05) - def test_BS_NN(self): est_Int = self.BS_NN.by_col(' est_Intercept') se_Int = self.BS_NN.by_col(' se_Intercept') @@ -523,37 +567,37 @@ def test_BS_NN(self): se_UNEMP = self.BS_NN.by_col(' se_UNEMP') t_UNEMP = self.BS_NN.by_col(' t_UNEMP') yhat = self.BS_NN.by_col(' yhat') - pdev = np.array(self.BS_NN.by_col(' localpdev')).reshape((-1,1)) + pdev = np.array(self.BS_NN.by_col(' localpdev')).reshape((-1, 1)) - model = GWR(self.coords, self.y, self.X, bw=50, family=Poisson(), - kernel='bisquare', fixed=False, sigma2_v1=False) + model = GWR(self.coords, self.y, self.X, bw=50, family=Poisson(), + kernel='bisquare', fixed=False, sigma2_v1=False) rslt = model.fit() - + AICc = get_AICc(rslt) AIC = get_AIC(rslt) BIC = get_BIC(rslt) - + self.assertAlmostEquals(np.floor(AICc), 13285) self.assertAlmostEquals(np.floor(AIC), 13259.0) self.assertAlmostEquals(np.floor(BIC), 13442.0) - np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04) - np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-02) - np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-02) - np.testing.assert_allclose(est_OCC, rslt.params[:,1], rtol=1e-03) - np.testing.assert_allclose(se_OCC, rslt.bse[:,1], rtol=1e-02) - np.testing.assert_allclose(t_OCC, rslt.tvalues[:,1], rtol=1e-02) - np.testing.assert_allclose(est_OWN, rslt.params[:,2], rtol=1e-04) - np.testing.assert_allclose(se_OWN, rslt.bse[:,2], rtol=1e-02) - np.testing.assert_allclose(t_OWN, rslt.tvalues[:,2], rtol=1e-02) - np.testing.assert_allclose(est_POP, rslt.params[:,3], rtol=1e-03) - np.testing.assert_allclose(se_POP, rslt.bse[:,3], rtol=1e-02) - np.testing.assert_allclose(t_POP, rslt.tvalues[:,3], rtol=1e-02) - np.testing.assert_allclose(est_UNEMP, rslt.params[:,4], rtol=1e-04) - np.testing.assert_allclose(se_UNEMP, rslt.bse[:,4], rtol=1e-02) - np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:,4], rtol=1e-02) + np.testing.assert_allclose(est_Int, rslt.params[:, 0], rtol=1e-04) + np.testing.assert_allclose(se_Int, rslt.bse[:, 0], rtol=1e-02) + np.testing.assert_allclose(t_Int, rslt.tvalues[:, 0], rtol=1e-02) + np.testing.assert_allclose(est_OCC, rslt.params[:, 1], rtol=1e-03) + np.testing.assert_allclose(se_OCC, rslt.bse[:, 1], rtol=1e-02) + np.testing.assert_allclose(t_OCC, rslt.tvalues[:, 1], rtol=1e-02) + np.testing.assert_allclose(est_OWN, rslt.params[:, 2], rtol=1e-04) + np.testing.assert_allclose(se_OWN, rslt.bse[:, 2], rtol=1e-02) + np.testing.assert_allclose(t_OWN, rslt.tvalues[:, 2], rtol=1e-02) + np.testing.assert_allclose(est_POP, rslt.params[:, 3], rtol=1e-03) + np.testing.assert_allclose(se_POP, rslt.bse[:, 3], rtol=1e-02) + np.testing.assert_allclose(t_POP, rslt.tvalues[:, 3], rtol=1e-02) + np.testing.assert_allclose(est_UNEMP, rslt.params[:, 4], rtol=1e-04) + np.testing.assert_allclose(se_UNEMP, rslt.bse[:, 4], rtol=1e-02) + np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:, 4], rtol=1e-02) np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-04) np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05) - + def test_BS_NN_Offset(self): est_Int = self.BS_NN_OFF.by_col(' est_Intercept') se_Int = self.BS_NN_OFF.by_col(' se_Intercept') @@ -571,45 +615,49 @@ def test_BS_NN_Offset(self): se_UNEMP = self.BS_NN_OFF.by_col(' se_UNEMP') t_UNEMP = self.BS_NN_OFF.by_col(' t_UNEMP') yhat = self.BS_NN_OFF.by_col(' yhat') - pdev = np.array(self.BS_NN_OFF.by_col(' localpdev')).reshape((-1,1)) + pdev = np.array(self.BS_NN_OFF.by_col(' localpdev')).reshape((-1, 1)) - model = GWR(self.coords, self.y, self.X, bw=100, offset=self.off, family=Poisson(), - kernel='bisquare', fixed=False, sigma2_v1=False) + model = GWR(self.coords, self.y, self.X, bw=100, offset=self.off, family=Poisson(), + kernel='bisquare', fixed=False, sigma2_v1=False) rslt = model.fit() - + AICc = get_AICc(rslt) AIC = get_AIC(rslt) BIC = get_BIC(rslt) - + self.assertAlmostEquals(np.floor(AICc), 367.0) self.assertAlmostEquals(np.floor(AIC), 361.0) self.assertAlmostEquals(np.floor(BIC), 451.0) - np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-02, - atol=1e-02) - np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-02, atol=1e-02) - np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-01, - atol=1e-02) - np.testing.assert_allclose(est_OCC, rslt.params[:,1], rtol=1e-03, - atol=1e-02) - np.testing.assert_allclose(se_OCC, rslt.bse[:,1], rtol=1e-02, atol=1e-02) - np.testing.assert_allclose(t_OCC, rslt.tvalues[:,1], rtol=1e-01, - atol=1e-02) - np.testing.assert_allclose(est_OWN, rslt.params[:,2], rtol=1e-04, - atol=1e-02) - np.testing.assert_allclose(se_OWN, rslt.bse[:,2], rtol=1e-02, atol=1e-02) - np.testing.assert_allclose(t_OWN, rslt.tvalues[:,2], rtol=1e-01, - atol=1e-02) - np.testing.assert_allclose(est_POP, rslt.params[:,3], rtol=1e-03, - atol=1e-02) - np.testing.assert_allclose(se_POP, rslt.bse[:,3], rtol=1e-02, atol=1e-02) - np.testing.assert_allclose(t_POP, rslt.tvalues[:,3], rtol=1e-01, - atol=1e-02) - np.testing.assert_allclose(est_UNEMP, rslt.params[:,4], rtol=1e-04, - atol=1e-02) - np.testing.assert_allclose(se_UNEMP, rslt.bse[:,4], rtol=1e-02, - atol=1e-02) - np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:,4], rtol=1e-01, - atol=1e-02) + np.testing.assert_allclose(est_Int, rslt.params[:, 0], rtol=1e-02, + atol=1e-02) + np.testing.assert_allclose( + se_Int, rslt.bse[:, 0], rtol=1e-02, atol=1e-02) + np.testing.assert_allclose(t_Int, rslt.tvalues[:, 0], rtol=1e-01, + atol=1e-02) + np.testing.assert_allclose(est_OCC, rslt.params[:, 1], rtol=1e-03, + atol=1e-02) + np.testing.assert_allclose( + se_OCC, rslt.bse[:, 1], rtol=1e-02, atol=1e-02) + np.testing.assert_allclose(t_OCC, rslt.tvalues[:, 1], rtol=1e-01, + atol=1e-02) + np.testing.assert_allclose(est_OWN, rslt.params[:, 2], rtol=1e-04, + atol=1e-02) + np.testing.assert_allclose( + se_OWN, rslt.bse[:, 2], rtol=1e-02, atol=1e-02) + np.testing.assert_allclose(t_OWN, rslt.tvalues[:, 2], rtol=1e-01, + atol=1e-02) + np.testing.assert_allclose(est_POP, rslt.params[:, 3], rtol=1e-03, + atol=1e-02) + np.testing.assert_allclose( + se_POP, rslt.bse[:, 3], rtol=1e-02, atol=1e-02) + np.testing.assert_allclose(t_POP, rslt.tvalues[:, 3], rtol=1e-01, + atol=1e-02) + np.testing.assert_allclose(est_UNEMP, rslt.params[:, 4], rtol=1e-04, + atol=1e-02) + np.testing.assert_allclose(se_UNEMP, rslt.bse[:, 4], rtol=1e-02, + atol=1e-02) + np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:, 4], rtol=1e-01, + atol=1e-02) np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-03, atol=1e-02) np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-04, atol=1e-02) @@ -630,34 +678,34 @@ def test_GS_F(self): se_UNEMP = self.GS_F.by_col(' se_UNEMP') t_UNEMP = self.GS_F.by_col(' t_UNEMP') yhat = self.GS_F.by_col(' yhat') - pdev = np.array(self.GS_F.by_col(' localpdev')).reshape((-1,1)) - - model = GWR(self.coords, self.y, self.X, bw=8764.474, family=Poisson(), - kernel='gaussian', fixed=True, sigma2_v1=False) + pdev = np.array(self.GS_F.by_col(' localpdev')).reshape((-1, 1)) + + model = GWR(self.coords, self.y, self.X, bw=8764.474, family=Poisson(), + kernel='gaussian', fixed=True, sigma2_v1=False) rslt = model.fit() - + AICc = get_AICc(rslt) AIC = get_AIC(rslt) BIC = get_BIC(rslt) - + self.assertAlmostEquals(np.floor(AICc), 11283.0) self.assertAlmostEquals(np.floor(AIC), 11211.0) self.assertAlmostEquals(np.floor(BIC), 11497.0) - np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-03) - np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-02) - np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-02) - np.testing.assert_allclose(est_OCC, rslt.params[:,1], rtol=1e-03) - np.testing.assert_allclose(se_OCC, rslt.bse[:,1], rtol=1e-02) - np.testing.assert_allclose(t_OCC, rslt.tvalues[:,1], rtol=1e-02) - np.testing.assert_allclose(est_OWN, rslt.params[:,2], rtol=1e-03) - np.testing.assert_allclose(se_OWN, rslt.bse[:,2], rtol=1e-02) - np.testing.assert_allclose(t_OWN, rslt.tvalues[:,2], rtol=1e-02) - np.testing.assert_allclose(est_POP, rslt.params[:,3], rtol=1e-02) - np.testing.assert_allclose(se_POP, rslt.bse[:,3], rtol=1e-02) - np.testing.assert_allclose(t_POP, rslt.tvalues[:,3], rtol=1e-02) - np.testing.assert_allclose(est_UNEMP, rslt.params[:,4], rtol=1e-02) - np.testing.assert_allclose(se_UNEMP, rslt.bse[:,4], rtol=1e-02) - np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:,4], rtol=1e-02) + np.testing.assert_allclose(est_Int, rslt.params[:, 0], rtol=1e-03) + np.testing.assert_allclose(se_Int, rslt.bse[:, 0], rtol=1e-02) + np.testing.assert_allclose(t_Int, rslt.tvalues[:, 0], rtol=1e-02) + np.testing.assert_allclose(est_OCC, rslt.params[:, 1], rtol=1e-03) + np.testing.assert_allclose(se_OCC, rslt.bse[:, 1], rtol=1e-02) + np.testing.assert_allclose(t_OCC, rslt.tvalues[:, 1], rtol=1e-02) + np.testing.assert_allclose(est_OWN, rslt.params[:, 2], rtol=1e-03) + np.testing.assert_allclose(se_OWN, rslt.bse[:, 2], rtol=1e-02) + np.testing.assert_allclose(t_OWN, rslt.tvalues[:, 2], rtol=1e-02) + np.testing.assert_allclose(est_POP, rslt.params[:, 3], rtol=1e-02) + np.testing.assert_allclose(se_POP, rslt.bse[:, 3], rtol=1e-02) + np.testing.assert_allclose(t_POP, rslt.tvalues[:, 3], rtol=1e-02) + np.testing.assert_allclose(est_UNEMP, rslt.params[:, 4], rtol=1e-02) + np.testing.assert_allclose(se_UNEMP, rslt.bse[:, 4], rtol=1e-02) + np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:, 4], rtol=1e-02) np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-04) np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05) @@ -678,54 +726,60 @@ def test_GS_NN(self): se_UNEMP = self.GS_NN.by_col(' se_UNEMP') t_UNEMP = self.GS_NN.by_col(' t_UNEMP') yhat = self.GS_NN.by_col(' yhat') - pdev = np.array(self.GS_NN.by_col(' localpdev')).reshape((-1,1)) - - model = GWR(self.coords, self.y, self.X, bw=50, family=Poisson(), - kernel='gaussian', fixed=False, sigma2_v1=False) + pdev = np.array(self.GS_NN.by_col(' localpdev')).reshape((-1, 1)) + + model = GWR(self.coords, self.y, self.X, bw=50, family=Poisson(), + kernel='gaussian', fixed=False, sigma2_v1=False) rslt = model.fit() - + AICc = get_AICc(rslt) AIC = get_AIC(rslt) BIC = get_BIC(rslt) - + self.assertAlmostEquals(np.floor(AICc), 21070.0) self.assertAlmostEquals(np.floor(AIC), 21069.0) self.assertAlmostEquals(np.floor(BIC), 21111.0) - np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04) - np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-02) - np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-02) - np.testing.assert_allclose(est_OCC, rslt.params[:,1], rtol=1e-03) - np.testing.assert_allclose(se_OCC, rslt.bse[:,1], rtol=1e-02) - np.testing.assert_allclose(t_OCC, rslt.tvalues[:,1], rtol=1e-02) - np.testing.assert_allclose(est_OWN, rslt.params[:,2], rtol=1e-04) - np.testing.assert_allclose(se_OWN, rslt.bse[:,2], rtol=1e-02) - np.testing.assert_allclose(t_OWN, rslt.tvalues[:,2], rtol=1e-02) - np.testing.assert_allclose(est_POP, rslt.params[:,3], rtol=1e-02) - np.testing.assert_allclose(se_POP, rslt.bse[:,3], rtol=1e-02) - np.testing.assert_allclose(t_POP, rslt.tvalues[:,3], rtol=1e-02) - np.testing.assert_allclose(est_UNEMP, rslt.params[:,4], rtol=1e-02) - np.testing.assert_allclose(se_UNEMP, rslt.bse[:,4], rtol=1e-02) - np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:,4], rtol=1e-02) + np.testing.assert_allclose(est_Int, rslt.params[:, 0], rtol=1e-04) + np.testing.assert_allclose(se_Int, rslt.bse[:, 0], rtol=1e-02) + np.testing.assert_allclose(t_Int, rslt.tvalues[:, 0], rtol=1e-02) + np.testing.assert_allclose(est_OCC, rslt.params[:, 1], rtol=1e-03) + np.testing.assert_allclose(se_OCC, rslt.bse[:, 1], rtol=1e-02) + np.testing.assert_allclose(t_OCC, rslt.tvalues[:, 1], rtol=1e-02) + np.testing.assert_allclose(est_OWN, rslt.params[:, 2], rtol=1e-04) + np.testing.assert_allclose(se_OWN, rslt.bse[:, 2], rtol=1e-02) + np.testing.assert_allclose(t_OWN, rslt.tvalues[:, 2], rtol=1e-02) + np.testing.assert_allclose(est_POP, rslt.params[:, 3], rtol=1e-02) + np.testing.assert_allclose(se_POP, rslt.bse[:, 3], rtol=1e-02) + np.testing.assert_allclose(t_POP, rslt.tvalues[:, 3], rtol=1e-02) + np.testing.assert_allclose(est_UNEMP, rslt.params[:, 4], rtol=1e-02) + np.testing.assert_allclose(se_UNEMP, rslt.bse[:, 4], rtol=1e-02) + np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:, 4], rtol=1e-02) np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-04) np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05) + class TestGWRBinomial(unittest.TestCase): def setUp(self): - data_path = os.path.join(os.path.dirname(__file__),'clearwater/landslides.csv') + data_path = os.path.join(os.path.dirname( + __file__), 'clearwater/landslides.csv') data = io.open(data_path) self.coords = list(zip(data.by_col('X'), data.by_col('Y'))) - self.y = np.array(data.by_col('Landslid')).reshape((-1,1)) - ELEV = np.array(data.by_col('Elev')).reshape((-1,1)) - SLOPE = np.array(data.by_col('Slope')).reshape((-1,1)) - SIN = np.array(data.by_col('SinAspct')).reshape((-1,1)) - COS = np.array(data.by_col('CosAspct')).reshape((-1,1)) - SOUTH = np.array(data.by_col('AbsSouth')).reshape((-1,1)) - DIST = np.array(data.by_col('DistStrm')).reshape((-1,1)) + self.y = np.array(data.by_col('Landslid')).reshape((-1, 1)) + ELEV = np.array(data.by_col('Elev')).reshape((-1, 1)) + SLOPE = np.array(data.by_col('Slope')).reshape((-1, 1)) + SIN = np.array(data.by_col('SinAspct')).reshape((-1, 1)) + COS = np.array(data.by_col('CosAspct')).reshape((-1, 1)) + SOUTH = np.array(data.by_col('AbsSouth')).reshape((-1, 1)) + DIST = np.array(data.by_col('DistStrm')).reshape((-1, 1)) self.X = np.hstack([ELEV, SLOPE, SIN, COS, SOUTH, DIST]) - self.BS_F = io.open(os.path.join(os.path.dirname(__file__),'clearwater/clearwater_BS_F_listwise.csv')) - self.BS_NN = io.open(os.path.join(os.path.dirname(__file__),'clearwater/clearwater_BS_NN_listwise.csv')) - self.GS_F = io.open(os.path.join(os.path.dirname(__file__),'clearwater/clearwater_GS_F_listwise.csv')) - self.GS_NN = io.open(os.path.join(os.path.dirname(__file__),'clearwater/clearwater_GS_NN_listwise.csv')) + self.BS_F = io.open(os.path.join(os.path.dirname( + __file__), 'clearwater/clearwater_BS_F_listwise.csv')) + self.BS_NN = io.open(os.path.join(os.path.dirname( + __file__), 'clearwater/clearwater_BS_NN_listwise.csv')) + self.GS_F = io.open(os.path.join(os.path.dirname( + __file__), 'clearwater/clearwater_GS_F_listwise.csv')) + self.GS_NN = io.open(os.path.join(os.path.dirname( + __file__), 'clearwater/clearwater_GS_NN_listwise.csv')) def test_BS_F(self): est_Int = self.BS_F.by_col(' est_Intercept') @@ -748,48 +802,48 @@ def test_BS_F(self): t_south = self.BS_F.by_col(' t_AbsSouth') est_strm = self.BS_F.by_col(' est_DistStrm') se_strm = self.BS_F.by_col(' se_DistStrm') - t_strm = self.BS_F.by_col(' t_DistStrm') + t_strm = self.BS_F.by_col(' t_DistStrm') yhat = self.BS_F.by_col(' yhat') - pdev = np.array(self.BS_F.by_col(' localpdev')).reshape((-1,1)) + pdev = np.array(self.BS_F.by_col(' localpdev')).reshape((-1, 1)) - model = GWR(self.coords, self.y, self.X, bw=19642.170, family=Binomial(), - kernel='bisquare', fixed=True, sigma2_v1=False) + model = GWR(self.coords, self.y, self.X, bw=19642.170, family=Binomial(), + kernel='bisquare', fixed=True, sigma2_v1=False) rslt = model.fit() AICc = get_AICc(rslt) AIC = get_AIC(rslt) BIC = get_BIC(rslt) - + self.assertAlmostEquals(np.floor(AICc), 275.0) self.assertAlmostEquals(np.floor(AIC), 271.0) self.assertAlmostEquals(np.floor(BIC), 349.0) - np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-00) - np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-00) - np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-00) - np.testing.assert_allclose(est_elev, rslt.params[:,1], rtol=1e-00) - np.testing.assert_allclose(se_elev, rslt.bse[:,1], rtol=1e-00) - np.testing.assert_allclose(t_elev, rslt.tvalues[:,1], rtol=1e-00) - np.testing.assert_allclose(est_slope, rslt.params[:,2], rtol=1e-00) - np.testing.assert_allclose(se_slope, rslt.bse[:,2], rtol=1e-00) - np.testing.assert_allclose(t_slope, rslt.tvalues[:,2], rtol=1e-00) - np.testing.assert_allclose(est_sin, rslt.params[:,3], rtol=1e01) - np.testing.assert_allclose(se_sin, rslt.bse[:,3], rtol=1e01) - np.testing.assert_allclose(t_sin, rslt.tvalues[:,3], rtol=1e01) - np.testing.assert_allclose(est_cos, rslt.params[:,4], rtol=1e01) - np.testing.assert_allclose(se_cos, rslt.bse[:,4], rtol=1e01) - np.testing.assert_allclose(t_cos, rslt.tvalues[:,4], rtol=1e01) - np.testing.assert_allclose(est_south, rslt.params[:,5], rtol=1e01) - np.testing.assert_allclose(se_south, rslt.bse[:,5], rtol=1e01) - np.testing.assert_allclose(t_south, rslt.tvalues[:,5], rtol=1e01) - np.testing.assert_allclose(est_strm, rslt.params[:,6], rtol=1e02) - np.testing.assert_allclose(se_strm, rslt.bse[:,6], rtol=1e01) - np.testing.assert_allclose(t_strm, rslt.tvalues[:,6], rtol=1e02) + np.testing.assert_allclose(est_Int, rslt.params[:, 0], rtol=1e-00) + np.testing.assert_allclose(se_Int, rslt.bse[:, 0], rtol=1e-00) + np.testing.assert_allclose(t_Int, rslt.tvalues[:, 0], rtol=1e-00) + np.testing.assert_allclose(est_elev, rslt.params[:, 1], rtol=1e-00) + np.testing.assert_allclose(se_elev, rslt.bse[:, 1], rtol=1e-00) + np.testing.assert_allclose(t_elev, rslt.tvalues[:, 1], rtol=1e-00) + np.testing.assert_allclose(est_slope, rslt.params[:, 2], rtol=1e-00) + np.testing.assert_allclose(se_slope, rslt.bse[:, 2], rtol=1e-00) + np.testing.assert_allclose(t_slope, rslt.tvalues[:, 2], rtol=1e-00) + np.testing.assert_allclose(est_sin, rslt.params[:, 3], rtol=1e01) + np.testing.assert_allclose(se_sin, rslt.bse[:, 3], rtol=1e01) + np.testing.assert_allclose(t_sin, rslt.tvalues[:, 3], rtol=1e01) + np.testing.assert_allclose(est_cos, rslt.params[:, 4], rtol=1e01) + np.testing.assert_allclose(se_cos, rslt.bse[:, 4], rtol=1e01) + np.testing.assert_allclose(t_cos, rslt.tvalues[:, 4], rtol=1e01) + np.testing.assert_allclose(est_south, rslt.params[:, 5], rtol=1e01) + np.testing.assert_allclose(se_south, rslt.bse[:, 5], rtol=1e01) + np.testing.assert_allclose(t_south, rslt.tvalues[:, 5], rtol=1e01) + np.testing.assert_allclose(est_strm, rslt.params[:, 6], rtol=1e02) + np.testing.assert_allclose(se_strm, rslt.bse[:, 6], rtol=1e01) + np.testing.assert_allclose(t_strm, rslt.tvalues[:, 6], rtol=1e02) np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-01) - #This test fails - likely due to compound rounding errors - #Has been tested using statsmodels.family calculations and - #code from Jing's python version, which both yield the same + # This test fails - likely due to compound rounding errors + # Has been tested using statsmodels.family calculations and + # code from Jing's python version, which both yield the same #np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05) - + def test_BS_NN(self): est_Int = self.BS_NN.by_col(' est_Intercept') se_Int = self.BS_NN.by_col(' se_Intercept') @@ -811,46 +865,46 @@ def test_BS_NN(self): t_south = self.BS_NN.by_col(' t_AbsSouth') est_strm = self.BS_NN.by_col(' est_DistStrm') se_strm = self.BS_NN.by_col(' se_DistStrm') - t_strm = self.BS_NN.by_col(' t_DistStrm') + t_strm = self.BS_NN.by_col(' t_DistStrm') yhat = self.BS_NN.by_col(' yhat') pdev = self.BS_NN.by_col(' localpdev') - - model = GWR(self.coords, self.y, self.X, bw=158, family=Binomial(), - kernel='bisquare', fixed=False, sigma2_v1=False) + + model = GWR(self.coords, self.y, self.X, bw=158, family=Binomial(), + kernel='bisquare', fixed=False, sigma2_v1=False) rslt = model.fit() AICc = get_AICc(rslt) AIC = get_AIC(rslt) BIC = get_BIC(rslt) - + self.assertAlmostEquals(np.floor(AICc), 277.0) self.assertAlmostEquals(np.floor(AIC), 271.0) self.assertAlmostEquals(np.floor(BIC), 358.0) - np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-00) - np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-00) - np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-00) - np.testing.assert_allclose(est_elev, rslt.params[:,1], rtol=1e-00) - np.testing.assert_allclose(se_elev, rslt.bse[:,1], rtol=1e-00) - np.testing.assert_allclose(t_elev, rslt.tvalues[:,1], rtol=1e-00) - np.testing.assert_allclose(est_slope, rslt.params[:,2], rtol=1e-00) - np.testing.assert_allclose(se_slope, rslt.bse[:,2], rtol=1e-00) - np.testing.assert_allclose(t_slope, rslt.tvalues[:,2], rtol=1e-00) - np.testing.assert_allclose(est_sin, rslt.params[:,3], rtol=1e01) - np.testing.assert_allclose(se_sin, rslt.bse[:,3], rtol=1e01) - np.testing.assert_allclose(t_sin, rslt.tvalues[:,3], rtol=1e01) - np.testing.assert_allclose(est_cos, rslt.params[:,4], rtol=1e01) - np.testing.assert_allclose(se_cos, rslt.bse[:,4], rtol=1e01) - np.testing.assert_allclose(t_cos, rslt.tvalues[:,4], rtol=1e01) - np.testing.assert_allclose(est_south, rslt.params[:,5], rtol=1e01) - np.testing.assert_allclose(se_south, rslt.bse[:,5], rtol=1e01) - np.testing.assert_allclose(t_south, rslt.tvalues[:,5], rtol=1e01) - np.testing.assert_allclose(est_strm, rslt.params[:,6], rtol=1e03) - np.testing.assert_allclose(se_strm, rslt.bse[:,6], rtol=1e01) - np.testing.assert_allclose(t_strm, rslt.tvalues[:,6], rtol=1e03) + np.testing.assert_allclose(est_Int, rslt.params[:, 0], rtol=1e-00) + np.testing.assert_allclose(se_Int, rslt.bse[:, 0], rtol=1e-00) + np.testing.assert_allclose(t_Int, rslt.tvalues[:, 0], rtol=1e-00) + np.testing.assert_allclose(est_elev, rslt.params[:, 1], rtol=1e-00) + np.testing.assert_allclose(se_elev, rslt.bse[:, 1], rtol=1e-00) + np.testing.assert_allclose(t_elev, rslt.tvalues[:, 1], rtol=1e-00) + np.testing.assert_allclose(est_slope, rslt.params[:, 2], rtol=1e-00) + np.testing.assert_allclose(se_slope, rslt.bse[:, 2], rtol=1e-00) + np.testing.assert_allclose(t_slope, rslt.tvalues[:, 2], rtol=1e-00) + np.testing.assert_allclose(est_sin, rslt.params[:, 3], rtol=1e01) + np.testing.assert_allclose(se_sin, rslt.bse[:, 3], rtol=1e01) + np.testing.assert_allclose(t_sin, rslt.tvalues[:, 3], rtol=1e01) + np.testing.assert_allclose(est_cos, rslt.params[:, 4], rtol=1e01) + np.testing.assert_allclose(se_cos, rslt.bse[:, 4], rtol=1e01) + np.testing.assert_allclose(t_cos, rslt.tvalues[:, 4], rtol=1e01) + np.testing.assert_allclose(est_south, rslt.params[:, 5], rtol=1e01) + np.testing.assert_allclose(se_south, rslt.bse[:, 5], rtol=1e01) + np.testing.assert_allclose(t_south, rslt.tvalues[:, 5], rtol=1e01) + np.testing.assert_allclose(est_strm, rslt.params[:, 6], rtol=1e03) + np.testing.assert_allclose(se_strm, rslt.bse[:, 6], rtol=1e01) + np.testing.assert_allclose(t_strm, rslt.tvalues[:, 6], rtol=1e03) np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-01) - #This test fails - likely due to compound rounding errors - #Has been tested using statsmodels.family calculations and - #code from Jing's python version, which both yield the same + # This test fails - likely due to compound rounding errors + # Has been tested using statsmodels.family calculations and + # code from Jing's python version, which both yield the same #np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05) def test_GS_F(self): @@ -874,46 +928,46 @@ def test_GS_F(self): t_south = self.GS_F.by_col(' t_AbsSouth') est_strm = self.GS_F.by_col(' est_DistStrm') se_strm = self.GS_F.by_col(' se_DistStrm') - t_strm = self.GS_F.by_col(' t_DistStrm') + t_strm = self.GS_F.by_col(' t_DistStrm') yhat = self.GS_F.by_col(' yhat') pdev = self.GS_F.by_col(' localpdev') - model = GWR(self.coords, self.y, self.X, bw=8929.061, family=Binomial(), - kernel='gaussian', fixed=True, sigma2_v1=False) + model = GWR(self.coords, self.y, self.X, bw=8929.061, family=Binomial(), + kernel='gaussian', fixed=True, sigma2_v1=False) rslt = model.fit() - + AICc = get_AICc(rslt) AIC = get_AIC(rslt) BIC = get_BIC(rslt) - + self.assertAlmostEquals(np.floor(AICc), 276.0) self.assertAlmostEquals(np.floor(AIC), 272.0) self.assertAlmostEquals(np.floor(BIC), 341.0) - np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-00) - np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-00) - np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-00) - np.testing.assert_allclose(est_elev, rslt.params[:,1], rtol=1e-00) - np.testing.assert_allclose(se_elev, rslt.bse[:,1], rtol=1e-00) - np.testing.assert_allclose(t_elev, rslt.tvalues[:,1], rtol=1e-00) - np.testing.assert_allclose(est_slope, rslt.params[:,2], rtol=1e-00) - np.testing.assert_allclose(se_slope, rslt.bse[:,2], rtol=1e-00) - np.testing.assert_allclose(t_slope, rslt.tvalues[:,2], rtol=1e-00) - np.testing.assert_allclose(est_sin, rslt.params[:,3], rtol=1e01) - np.testing.assert_allclose(se_sin, rslt.bse[:,3], rtol=1e01) - np.testing.assert_allclose(t_sin, rslt.tvalues[:,3], rtol=1e01) - np.testing.assert_allclose(est_cos, rslt.params[:,4], rtol=1e01) - np.testing.assert_allclose(se_cos, rslt.bse[:,4], rtol=1e01) - np.testing.assert_allclose(t_cos, rslt.tvalues[:,4], rtol=1e01) - np.testing.assert_allclose(est_south, rslt.params[:,5], rtol=1e01) - np.testing.assert_allclose(se_south, rslt.bse[:,5], rtol=1e01) - np.testing.assert_allclose(t_south, rslt.tvalues[:,5], rtol=1e01) - np.testing.assert_allclose(est_strm, rslt.params[:,6], rtol=1e02) - np.testing.assert_allclose(se_strm, rslt.bse[:,6], rtol=1e01) - np.testing.assert_allclose(t_strm, rslt.tvalues[:,6], rtol=1e02) + np.testing.assert_allclose(est_Int, rslt.params[:, 0], rtol=1e-00) + np.testing.assert_allclose(se_Int, rslt.bse[:, 0], rtol=1e-00) + np.testing.assert_allclose(t_Int, rslt.tvalues[:, 0], rtol=1e-00) + np.testing.assert_allclose(est_elev, rslt.params[:, 1], rtol=1e-00) + np.testing.assert_allclose(se_elev, rslt.bse[:, 1], rtol=1e-00) + np.testing.assert_allclose(t_elev, rslt.tvalues[:, 1], rtol=1e-00) + np.testing.assert_allclose(est_slope, rslt.params[:, 2], rtol=1e-00) + np.testing.assert_allclose(se_slope, rslt.bse[:, 2], rtol=1e-00) + np.testing.assert_allclose(t_slope, rslt.tvalues[:, 2], rtol=1e-00) + np.testing.assert_allclose(est_sin, rslt.params[:, 3], rtol=1e01) + np.testing.assert_allclose(se_sin, rslt.bse[:, 3], rtol=1e01) + np.testing.assert_allclose(t_sin, rslt.tvalues[:, 3], rtol=1e01) + np.testing.assert_allclose(est_cos, rslt.params[:, 4], rtol=1e01) + np.testing.assert_allclose(se_cos, rslt.bse[:, 4], rtol=1e01) + np.testing.assert_allclose(t_cos, rslt.tvalues[:, 4], rtol=1e01) + np.testing.assert_allclose(est_south, rslt.params[:, 5], rtol=1e01) + np.testing.assert_allclose(se_south, rslt.bse[:, 5], rtol=1e01) + np.testing.assert_allclose(t_south, rslt.tvalues[:, 5], rtol=1e01) + np.testing.assert_allclose(est_strm, rslt.params[:, 6], rtol=1e02) + np.testing.assert_allclose(se_strm, rslt.bse[:, 6], rtol=1e01) + np.testing.assert_allclose(t_strm, rslt.tvalues[:, 6], rtol=1e02) np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-01) - #This test fails - likely due to compound rounding errors - #Has been tested using statsmodels.family calculations and - #code from Jing's python version, which both yield the same + # This test fails - likely due to compound rounding errors + # Has been tested using statsmodels.family calculations and + # code from Jing's python version, which both yield the same #np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05) def test_GS_NN(self): @@ -937,47 +991,48 @@ def test_GS_NN(self): t_south = self.GS_NN.by_col(' t_AbsSouth') est_strm = self.GS_NN.by_col(' est_DistStrm') se_strm = self.GS_NN.by_col(' se_DistStrm') - t_strm = self.GS_NN.by_col(' t_DistStrm') + t_strm = self.GS_NN.by_col(' t_DistStrm') yhat = self.GS_NN.by_col(' yhat') pdev = self.GS_NN.by_col(' localpdev') - - model = GWR(self.coords, self.y, self.X, bw=64, family=Binomial(), - kernel='gaussian', fixed=False, sigma2_v1=False) + + model = GWR(self.coords, self.y, self.X, bw=64, family=Binomial(), + kernel='gaussian', fixed=False, sigma2_v1=False) rslt = model.fit() AICc = get_AICc(rslt) AIC = get_AIC(rslt) BIC = get_BIC(rslt) - + self.assertAlmostEquals(np.floor(AICc), 276.0) self.assertAlmostEquals(np.floor(AIC), 273.0) self.assertAlmostEquals(np.floor(BIC), 331.0) - np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-00) - np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-00) - np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-00) - np.testing.assert_allclose(est_elev, rslt.params[:,1], rtol=1e-00) - np.testing.assert_allclose(se_elev, rslt.bse[:,1], rtol=1e-00) - np.testing.assert_allclose(t_elev, rslt.tvalues[:,1], rtol=1e-00) - np.testing.assert_allclose(est_slope, rslt.params[:,2], rtol=1e-00) - np.testing.assert_allclose(se_slope, rslt.bse[:,2], rtol=1e-00) - np.testing.assert_allclose(t_slope, rslt.tvalues[:,2], rtol=1e-00) - np.testing.assert_allclose(est_sin, rslt.params[:,3], rtol=1e01) - np.testing.assert_allclose(se_sin, rslt.bse[:,3], rtol=1e01) - np.testing.assert_allclose(t_sin, rslt.tvalues[:,3], rtol=1e01) - np.testing.assert_allclose(est_cos, rslt.params[:,4], rtol=1e01) - np.testing.assert_allclose(se_cos, rslt.bse[:,4], rtol=1e01) - np.testing.assert_allclose(t_cos, rslt.tvalues[:,4], rtol=1e01) - np.testing.assert_allclose(est_south, rslt.params[:,5], rtol=1e01) - np.testing.assert_allclose(se_south, rslt.bse[:,5], rtol=1e01) - np.testing.assert_allclose(t_south, rslt.tvalues[:,5], rtol=1e01) - np.testing.assert_allclose(est_strm, rslt.params[:,6], rtol=1e02) - np.testing.assert_allclose(se_strm, rslt.bse[:,6], rtol=1e01) - np.testing.assert_allclose(t_strm, rslt.tvalues[:,6], rtol=1e02) + np.testing.assert_allclose(est_Int, rslt.params[:, 0], rtol=1e-00) + np.testing.assert_allclose(se_Int, rslt.bse[:, 0], rtol=1e-00) + np.testing.assert_allclose(t_Int, rslt.tvalues[:, 0], rtol=1e-00) + np.testing.assert_allclose(est_elev, rslt.params[:, 1], rtol=1e-00) + np.testing.assert_allclose(se_elev, rslt.bse[:, 1], rtol=1e-00) + np.testing.assert_allclose(t_elev, rslt.tvalues[:, 1], rtol=1e-00) + np.testing.assert_allclose(est_slope, rslt.params[:, 2], rtol=1e-00) + np.testing.assert_allclose(se_slope, rslt.bse[:, 2], rtol=1e-00) + np.testing.assert_allclose(t_slope, rslt.tvalues[:, 2], rtol=1e-00) + np.testing.assert_allclose(est_sin, rslt.params[:, 3], rtol=1e01) + np.testing.assert_allclose(se_sin, rslt.bse[:, 3], rtol=1e01) + np.testing.assert_allclose(t_sin, rslt.tvalues[:, 3], rtol=1e01) + np.testing.assert_allclose(est_cos, rslt.params[:, 4], rtol=1e01) + np.testing.assert_allclose(se_cos, rslt.bse[:, 4], rtol=1e01) + np.testing.assert_allclose(t_cos, rslt.tvalues[:, 4], rtol=1e01) + np.testing.assert_allclose(est_south, rslt.params[:, 5], rtol=1e01) + np.testing.assert_allclose(se_south, rslt.bse[:, 5], rtol=1e01) + np.testing.assert_allclose(t_south, rslt.tvalues[:, 5], rtol=1e01) + np.testing.assert_allclose(est_strm, rslt.params[:, 6], rtol=1e02) + np.testing.assert_allclose(se_strm, rslt.bse[:, 6], rtol=1e01) + np.testing.assert_allclose(t_strm, rslt.tvalues[:, 6], rtol=1e02) np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-00) - #This test fails - likely due to compound rounding errors - #Has been tested using statsmodels.family calculations and - #code from Jing's python version, which both yield the same + # This test fails - likely due to compound rounding errors + # Has been tested using statsmodels.family calculations and + # code from Jing's python version, which both yield the same #np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05) + if __name__ == '__main__': - unittest.main() + unittest.main() diff --git a/mgwr/tests/test_sel_bw.py b/mgwr/tests/test_sel_bw.py index 6f83896..eb855bd 100644 --- a/mgwr/tests/test_sel_bw.py +++ b/mgwr/tests/test_sel_bw.py @@ -5,6 +5,7 @@ import os import numpy as np from libpysal import io +import libpysal as ps import unittest from spglm.family import Gaussian, Poisson, Binomial from ..sel_bw import Sel_BW @@ -12,8 +13,7 @@ class TestSelBW(unittest.TestCase): def setUp(self): - data_path = os.path.join(os.path.dirname(__file__),'georgia/GData_utm.csv') - #data = libpysal.open(data_path) + data_path = ps.examples.get_path("GData_utm.csv") data = io.open(data_path) self.coords = list(zip(data.by_col('X'), data.by_col('Y'))) self.y = np.array(data.by_col('PctBach')).reshape((-1,1)) diff --git a/notebooks/GWR_MGWR_example.ipynb b/notebooks/GWR_MGWR_example.ipynb index 455190a..8b0b3b4 100644 --- a/notebooks/GWR_MGWR_example.ipynb +++ b/notebooks/GWR_MGWR_example.ipynb @@ -2,28 +2,24 @@ "cells": [ { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, + "execution_count": 1, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", - "import scipy as sp\n", + "# import scipy as sp\n", "from mgwr.sel_bw import Sel_BW\n", - "from mgwr.gwr import GWR, MGWR\n", + "# from mgwr.gwr import GWR, MGWR\n", "import pandas as pd\n", "import libpysal as ps\n", - "import pickle as pk\n", - "import os" + "# import pickle as pk\n", + "# import os" ] }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, + "execution_count": 2, + "metadata": {}, "outputs": [], "source": [ "data = ps.io.open(ps.examples.get_path('GData_utm.csv'))\n", @@ -41,17 +37,25 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "bw: 117.0\n", - "aicc: 299.0508086830288\n", - "ENP: 11.804769716730096\n", - "sigma2: 0.34774354749782793\n" + "bw: 117.0\n" + ] + }, + { + "ename": "NameError", + "evalue": "name 'GWR' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mbw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msearch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'bw:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mgwr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGWR\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcoords\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0mgwr_results\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgwr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'aicc:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgwr_results\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maicc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'GWR' is not defined" ] } ], @@ -75,44 +79,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 4%|▍ | 9/200 [00:01<00:37, 5.10it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "bw(intercept): 92.0\n", - "bw(foreign): 101.0\n", - "bw(african_amer): 136.0\n", - "bw(rural): 158.0\n", - "aicc: 297.12013812258783\n", - "sigma2: 0.34477258292171475\n", - "ENP(model): 11.36825087269831\n", - "adj_alpha(model): 0.017592855949398047\n", - "critical_t(model): 2.399257840857394\n", - "ENP(intercept): 3.844671080264143\n", - "adj_alpha(intercept): 0.013005013681577365\n", - "critical_t(intercept): 2.512107491068591\n", - "ENP(foreign): 3.513770805151652\n", - "adj_alpha(foreign): 0.014229727199820033\n", - "critical_t(foreign): 2.4788879239423856\n", - "ENP(african_amer): 2.2580525278898254\n", - "adj_alpha(african_amer): 0.022142974701622884\n", - "critical_t(african_amer): 2.3106911297007184\n", - "ENP(rural): 1.7517564593926893\n", - "adj_alpha(rural): 0.028542780437261432\n", - "critical_t(rural): 2.210001836555586\n" - ] - } - ], + "outputs": [], "source": [ "X = (X - X.mean(axis=0)) / X.std(axis=0)\n", "\n", @@ -152,9 +121,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [] } @@ -176,7 +143,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.4" + "version": "3.6.6" } }, "nbformat": 4, diff --git a/requirements_tests.txt b/requirements_tests.txt index 358622d..9dd9b40 100644 --- a/requirements_tests.txt +++ b/requirements_tests.txt @@ -2,4 +2,5 @@ nose nose-progressive nose-exclude coverage -coveralls \ No newline at end of file +coveralls +pandas \ No newline at end of file