受験番号と合格率の関係
受験番号と合格率の関係
rs_fan_jp
2016年4月3日
2016年4月3日20:20念のため追記。言うまでもなく受験番号から合格への直接的因果関係はないからね。統計上有意ですが、単なる見かけ上の関係です。間違っても受験番号１ゲットなどという不毛なことを目指さないように。追記終了
すでに、旬は終わっているが、かつての上司のお子さんの合否が気になり 大学のサイトをみたものの、当たり前だが合格した人の受験番号しか書かれてなくて 合否は分からず。 その時に合格した受験番号だけダウンロードしておいた。
これを使って遊んでみる。
受験番号が若いほど合格率が高いのではないか？と気になったのである。 受付開始と同時に願書を出す人と、、ぎりぎりまで悩んだ人とでは 多分学力が違いそうという仮説である。
まずは元データの再現
hist.goukaku <- function(x, bw = 10){
check.ok <- function(y){any(x == y)}
goukaku <- sapply(range(x)[1]:range(x)[2], check.ok)
hist(x, breaks = seq(range(x)[1], (2 + range(x)[2] %/% bw) * bw, by= bw))
}
###
sougouningen_bunkei <- c(1, 2, 6, 8, 12, 13, 14, 15, 16, 21, 25, 29, 30, 32, 33, 35,
37, 44, 49, 55, 56, 61, 63, 65, 69, 70, 72, 74, 75, 76, 83, 89,
90, 92, 94, 106, 107, 116, 118, 119, 125, 126, 131, 141, 146,
147, 148, 149, 150, 154, 156, 159, 163, 165, 169, 174, 178, 182,
185, 187, 195, 207, 209, 211, 217)
sougouningen_rikei <- c(3007, 3011, 3012, 3013, 3015, 3016, 3020, 3021, 3024, 3025,
3032, 3034, 3037, 3039, 3041, 3042, 3043, 3044, 3049, 3052, 3057,
3061, 3063, 3073, 3076, 3078, 3083, 3091, 3092, 3093, 3095, 3097,
3101, 3103, 3104, 3106, 3109, 3110, 3112, 3113, 3128, 3131, 3132,
3139, 3148, 3151, 3155, 3158, 3159, 3160, 3164, 3169, 3186, 3187
)
igaku <- c(1, 3, 4, 5, 7, 8, 9, 15, 16, 24, 25, 27, 33, 40, 47, 48, 54,
58, 59, 61, 62, 67, 69, 71, 74, 75, 78, 79, 83, 84, 85, 86, 88,
89, 90, 94, 97, 99, 100, 102, 103, 104, 105, 111, 113, 114, 115,
116, 118, 125, 131, 132, 133, 134, 141, 143, 145, 146, 147, 151,
152, 153, 155, 156, 158, 162, 165, 168, 169, 170, 171, 172, 179,
185, 187, 188, 190, 192, 195, 197, 198, 201, 202, 203, 206, 207,
209, 210, 211, 214, 219, 223, 224, 225, 226, 227, 230, 233, 235,
237, 242, 245, 248, 256, 258, 270, 272, 276, 278, 294, 304, 314
)
bungaku <- c(1, 6, 8, 10, 12, 13, 14, 16, 18, 22, 23, 25, 26, 29, 31, 33,
34, 35, 37, 41, 42, 43, 45, 51, 52, 54, 55, 56, 57, 58, 61, 62,
63, 65, 66, 69, 72, 75, 77, 85, 86, 91, 94, 98, 101, 103, 104,
107, 110, 112, 114, 126, 132, 135, 136, 137, 138, 139, 141, 148,
155, 156, 159, 161, 165, 167, 168, 169, 170, 171, 172, 179, 180,
181, 185, 186, 194, 195, 196, 197, 200, 204, 205, 206, 208, 209,
211, 214, 215, 216, 222, 223, 226, 228, 229, 230, 234, 237, 238,
239, 246, 251, 253, 254, 255, 256, 258, 261, 264, 265, 269, 273,
281, 286, 290, 300, 302, 303, 305, 306, 307, 308, 310, 311, 315,
317, 322, 323, 329, 331, 332, 336, 337, 338, 339, 340, 345, 352,
353, 357, 359, 361, 363, 364, 366, 367, 372, 375, 376, 378, 382,
383, 393, 397, 399, 405, 406, 407, 411, 423, 425, 426, 428, 432,
434, 436, 438, 446, 448, 449, 450, 454, 456, 457, 460, 461, 463,
464, 470, 477, 481, 484, 487, 491, 493, 494, 495, 502, 505, 507,
508, 512, 513, 515, 516, 519, 522, 524, 528, 532, 535, 537, 538,
540, 551, 557, 562, 563, 568, 578, 580, 595, 598, 612, 613, 614
)
kyoiku_bunkei <- c(1, 2, 3, 4, 8, 11, 14, 15, 18, 19, 20, 21, 24, 25, 26, 28,
35, 36, 37, 38, 39, 41, 42, 44, 48, 50, 54, 60, 63, 64, 68, 69,
72, 80, 82, 83, 86, 95, 96, 99, 101, 104, 110, 114, 124, 128,
136)
kyoiku_rikei <- c(3001, 3003, 3007, 3008, 3015, 3016, 3018, 3022, 3028, 3038)
hougaku <- c(1, 3, 4, 5, 9, 11, 12, 18, 20, 23, 24, 27, 29, 36, 37, 38,
39, 40, 41, 43, 44, 46, 48, 50, 51, 52, 53, 55, 57, 59, 68, 70,
71, 76, 78, 81, 82, 84, 85, 86, 87, 88, 89, 90, 93, 94, 95, 96,
97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 112, 116,
117, 123, 124, 125, 128, 133, 134, 136, 137, 139, 141, 145, 146,
149, 150, 152, 153, 155, 156, 161, 162, 163, 167, 168, 172, 175,
177, 180, 182, 183, 184, 185, 189, 191, 192, 193, 195, 196, 197,
202, 206, 210, 212, 213, 216, 219, 222, 224, 225, 228, 229, 232,
234, 238, 240, 244, 246, 248, 249, 250, 251, 253, 254, 255, 256,
257, 258, 259, 260, 262, 263, 264, 265, 266, 267, 269, 270, 272,
275, 277, 279, 283, 284, 289, 290, 295, 296, 297, 300, 302, 303,
304, 305, 306, 311, 312, 315, 316, 318, 319, 321, 323, 324, 325,
326, 332, 334, 336, 338, 339, 342, 343, 345, 350, 351, 353, 355,
356, 359, 360, 362, 363, 364, 365, 367, 368, 370, 371, 372, 373,
374, 376, 379, 382, 383, 386, 387, 389, 390, 392, 393, 399, 400,
401, 403, 404, 407, 410, 411, 412, 421, 424, 428, 433, 435, 440,
441, 442, 444, 446, 448, 450, 451, 452, 457, 462, 463, 464, 467,
479, 480, 484, 486, 487, 488, 507, 517, 519, 528, 531, 537, 538,
540, 541, 543, 548, 550, 562, 563, 577, 580, 582, 595, 598, 599,
600, 602, 603, 605, 610, 625, 627, 629, 632, 636, 638, 639, 641,
645, 649, 652, 654, 655, 656, 663, 666, 673, 674, 682, 686, 693,
694, 695, 697, 698, 705, 709, 712, 718, 721, 722, 726, 728, 729,
737, 742, 746, 748, 752, 753, 755, 760, 761, 764, 766, 776, 787,
789, 801, 804, 814)
keizai_bunkei <- c(2, 3, 6, 7, 9, 11, 17, 19, 20, 22, 24, 28, 30, 31, 36, 37,
40, 41, 44, 45, 46, 47, 48, 50, 51, 52, 58, 59, 60, 62, 65, 66,
67, 68, 69, 70, 72, 73, 74, 75, 77, 79, 80, 81, 82, 86, 89, 90,
91, 92, 93, 94, 96, 97, 102, 103, 104, 105, 106, 107, 108, 109,
110, 111, 112, 113, 114, 116, 118, 123, 124, 125, 128, 130, 131,
132, 134, 135, 137, 142, 144, 145, 148, 149, 160, 163, 165, 168,
169, 170, 171, 181, 183, 187, 188, 191, 193, 194, 197, 198, 200,
201, 203, 204, 206, 215, 218, 219, 223, 224, 225, 229, 232, 236,
239, 240, 244, 245, 246, 247, 252, 255, 256, 259, 261, 264, 273,
274, 278, 279, 280, 281, 282, 284, 286, 289, 290, 296, 299, 303,
305, 309, 310, 312, 319, 320, 321, 322, 324, 325, 326, 327, 332,
336, 338, 340, 346, 349, 357, 360, 363, 370, 374, 378, 380, 384,
388, 391, 393, 394, 400, 401, 402, 405, 406, 410, 414, 415, 416,
419, 421, 429, 433, 436, 442, 447, 450, 452, 462, 468)
keizai_rikei <- c(4002, 4003, 4005, 4006, 4024, 4031, 4033, 4040, 4042, 4045,
4046, 4051, 4054, 4055, 4058, 4059, 4071, 4072, 4081, 4084, 4097,
4102, 4110, 4112, 4131)
rigaku <- c(1, 3, 9, 10, 11, 12, 14, 15, 17, 22, 24, 26, 32, 35, 36, 37,
39, 41, 43, 44, 45, 46, 50, 51, 55, 56, 58, 59, 61, 62, 63, 67,
68, 72, 75, 76, 77, 82, 83, 85, 86, 93, 97, 101, 102, 103, 104,
105, 110, 111, 113, 117, 119, 120, 123, 124, 126, 127, 128, 129,
130, 132, 133, 134, 135, 139, 140, 145, 147, 148, 150, 151, 153,
154, 156, 157, 160, 162, 164, 165, 171, 172, 174, 178, 181, 182,
183, 184, 185, 187, 189, 190, 192, 193, 196, 197, 199, 200, 202,
203, 204, 206, 207, 218, 219, 222, 229, 230, 233, 234, 236, 238,
239, 240, 241, 242, 254, 256, 258, 263, 264, 265, 267, 268, 271,
277, 281, 283, 286, 287, 289, 290, 292, 298, 299, 304, 307, 310,
312, 314, 316, 317, 318, 321, 322, 324, 326, 327, 328, 332, 334,
336, 340, 341, 342, 343, 345, 351, 352, 354, 357, 358, 360, 361,
363, 365, 366, 372, 375, 380, 381, 384, 385, 386, 388, 389, 391,
392, 393, 394, 395, 400, 402, 408, 409, 413, 414, 415, 420, 423,
429, 432, 436, 440, 449, 451, 453, 454, 458, 463, 469, 473, 474,
475, 476, 478, 481, 482, 484, 485, 493, 500, 509, 517, 522, 524,
526, 533, 536, 540, 541, 542, 551, 555, 557, 559, 564, 565, 569,
573, 574, 579, 580, 582, 590, 592, 596, 604, 605, 607, 608, 610,
613, 616, 618, 620, 624, 627, 633, 634, 635, 636, 637, 644, 651,
653, 655, 656, 657, 661, 669, 670, 672, 673, 676, 679, 680, 683,
684, 687, 689, 690, 692, 693, 696, 698, 700, 701, 706, 707, 711,
721, 722, 728, 732, 734, 735, 739, 741, 742, 745, 754, 758, 760,
766, 769, 772, 774, 781, 788, 791, 798, 799, 804, 809, 810, 822,
824, 827, 829)
kango <- c(2001, 2002, 2003, 2010, 2013, 2015, 2016, 2017, 2018, 2020,
2021, 2023, 2025, 2027, 2031, 2033, 2034, 2035, 2036, 2037, 2038,
2039, 2040, 2042, 2043, 2044, 2046, 2047, 2048, 2049, 2050, 2052,
2053, 2055, 2056, 2057, 2061, 2062, 2063, 2064, 2065, 2066, 2067,
2071, 2075, 2078, 2079, 2080, 2088, 2091, 2094, 2095, 2096, 2097,
2098, 2100, 2101, 2102, 2105, 2106, 2107, 2110, 2113, 2114, 2115,
2117, 2118, 2120, 2123, 2124, 2125, 2129, 2131)
kensagijutu <- c(4001, 4002, 4005, 4006, 4008, 4012, 4013, 4014, 4016, 4017,
4019, 4023, 4025, 4027, 4029, 4031, 4034, 4036, 4037, 4041, 4044,
4048, 4049, 4050, 4053, 4054, 4055, 4057, 4058, 4066, 4068, 4070,
4071, 4072, 4076, 4077, 4081, 4084, 4085)
rigakuryouhou <- c(5002, 5003, 5005, 5006, 5007, 5008, 5009, 5012, 5014, 5015,
5019, 5020, 5025, 5029, 5032, 5033, 5034, 5036)
sagyouryouhou <- c(6001, 6002, 6003, 6004, 6005, 6006, 6007, 6009, 6010, 6011,
6012, 6015, 6018, 6019, 6021, 6022, 6024, 6025)
yakka <- c(1, 3, 7, 8, 9, 10, 11, 12, 15, 17, 19, 20, 25, 26, 27, 29,
31, 32, 34, 37, 39, 46, 49, 50, 51, 52, 55, 56, 57, 59, 60, 62,
64, 67, 69, 72, 73, 78, 83, 89, 91, 92, 93, 97, 102, 103, 107,
110, 113, 116, 117, 119, 121)
yakugaku <- c(3001, 3003, 3004, 3005, 3007, 3008, 3010, 3013, 3014, 3016,
3018, 3020, 3021, 3023, 3025, 3027, 3028, 3031, 3032, 3036, 3040,
3043, 3044, 3047, 3048, 3050, 3054, 3057, 3058, 3059, 3067)
kougaku <- c(5, 6, 10, 16, 21, 22, 23, 26, 28, 30, 32, 36, 44, 45, 46, 48,
50, 51, 52, 53, 54, 55, 56, 57, 59, 60, 61, 62, 63, 66, 71, 73,
80, 85, 86, 87, 88, 89, 94, 98, 99, 101, 106, 108, 109, 112,
113, 114, 115, 116, 118, 119, 120, 122, 124, 129, 130, 132, 133,
137, 138, 142, 143, 147, 148, 156, 157, 160, 161, 164, 167, 171,
172, 179, 185, 191, 192, 193, 196, 198, 200, 203, 207, 209, 211,
212, 213, 221, 227, 231, 232, 234, 239, 243, 247, 249, 250, 253,
254, 255, 257, 259, 262, 264, 265, 270, 274, 275, 277, 278, 282,
285, 292, 293, 298, 303, 304, 305, 306, 311, 313, 316, 317, 318,
319, 320, 321, 323, 329, 331, 332, 333, 341, 344, 345, 346, 347,
348, 349, 350, 353, 354, 357, 358, 359, 360, 361, 362, 363, 368,
369, 372, 373, 374, 377, 381, 383, 385, 387, 388, 390, 391, 397,
400, 401, 403, 404, 406, 407, 409, 412, 414, 418, 421, 423, 425,
426, 430, 432, 433, 436, 437, 438, 439, 450, 453, 454, 458, 459,
462, 463, 465, 466, 467, 471, 478, 485, 487, 490, 491, 496, 497,
499, 500, 501, 503, 504, 510, 511, 514, 515, 517, 518, 521, 527,
530, 532, 533, 536, 542, 545, 549, 550, 555, 558, 560, 562, 563,
565, 566, 568, 571, 575, 579, 582, 585, 586, 587, 588, 589, 593,
594, 607, 613, 616, 622, 624, 628, 629, 630, 631, 635, 636, 637,
638, 643, 645, 647, 651, 652, 654, 655, 657, 659, 661, 662, 665,
669, 671, 672, 673, 678, 680, 681, 696, 698, 700, 704, 707, 708,
711, 712, 714, 718, 719, 720, 722, 723, 728, 729, 731, 732, 734,
737, 738, 740, 741, 742, 743, 745, 747, 748, 750, 753, 756, 760,
761, 762, 766, 767, 769, 770, 773, 774, 777, 778, 781, 787, 791,
795, 797, 801, 802, 813, 815, 817, 818, 822, 825, 828, 830, 834,
838, 840, 841, 842, 843, 845, 846, 848, 852, 854, 855, 857, 858,
859, 866, 867, 872, 873, 874, 875, 876, 877, 878, 881, 884, 885,
888, 889, 890, 892, 895, 897, 899, 901, 902, 908, 909, 910, 913,
914, 915, 916, 917, 918, 919, 922, 923, 927, 929, 932, 936, 938,
939, 945, 946, 949, 950, 952, 955, 958, 968, 970, 971, 973, 976,
979, 984, 985, 988, 989, 992, 993, 994, 998, 999, 1001, 1002,
1003, 1007, 1013, 1014, 1016, 1017, 1018, 1020, 1021, 1024, 1026,
1028, 1029, 1034, 1035, 1040, 1041, 1043, 1045, 1048, 1051, 1052,
1053, 1058, 1062, 1063, 1076, 1078, 1079, 1083, 1084, 1088, 1090,
1091, 1092, 1101, 1102, 1106, 1110, 1112, 1116, 1117, 1118, 1122,
1123, 1124, 1126, 1127, 1128, 1129, 1130, 1131, 1133, 1136, 1138,
1139, 1140, 1142, 1144, 1145, 1149, 1151, 1152, 1156, 1166, 1168,
1169, 1170, 1174, 1176, 1185, 1189, 1191, 1194, 1195, 1197, 1199,
1203, 1206, 1208, 1214, 1220, 1221, 1223, 1224, 1225, 1226, 1230,
1231, 1234, 1235, 1240, 1252, 1253, 1255, 1256, 1257, 1258, 1261,
1262, 1265, 1266, 1267, 1268, 1273, 1274, 1275, 1277, 1278, 1279,
1283, 1293, 1294, 1296, 1302, 1303, 1306, 1309, 1311, 1312, 1313,
1314, 1317, 1318, 1319, 1320, 1321, 1324, 1326, 1329, 1330, 1331,
1332, 1336, 1338, 1339, 1340, 1341, 1342, 1347, 1349, 1351, 1352,
1353, 1354, 1355, 1356, 1360, 1362, 1366, 1367, 1370, 1373, 1374,
1375, 1378, 1385, 1386, 1387, 1390, 1391, 1393, 1399, 1400, 1403,
1404, 1408, 1409, 1411, 1417, 1418, 1421, 1425, 1428, 1431, 1432,
1433, 1437, 1440, 1441, 1444, 1452, 1456, 1465, 1466, 1467, 1468,
1478, 1479, 1482, 1484, 1485, 1489, 1496, 1497, 1499, 1500, 1505,
1512, 1513, 1515, 1516, 1520, 1522, 1525, 1527, 1531, 1536, 1539,
1544, 1546, 1551, 1558, 1560, 1562, 1565, 1566, 1568, 1569, 1577,
1579, 1580, 1583, 1588, 1600, 1606, 1609, 1616, 1617, 1618, 1622,
1624, 1628, 1634, 1639, 1644, 1645, 1646, 1652, 1654, 1655, 1660,
1669, 1671, 1676, 1677, 1685, 1686, 1689, 1690, 1691, 1692, 1693,
1704, 1706, 1710, 1714, 1715, 1720, 1721, 1724, 1725, 1727, 1733,
1735, 1737, 1738, 1741, 1745, 1746, 1750, 1751, 1753, 1754, 1755,
1756, 1761, 1763, 1764, 1766, 1767, 1769, 1772, 1774, 1783, 1796,
1797, 1798, 1804, 1805, 1806, 1807, 1811, 1813, 1814, 1820, 1822,
1827, 1828, 1830, 1831, 1838, 1840, 1841, 1842, 1846, 1851, 1855,
1856, 1857, 1859, 1860, 1864, 1865, 1875, 1876, 1877, 1882, 1883,
1886, 1888, 1889, 1896, 1897, 1903, 1915, 1916, 1917, 1920, 1921,
1922, 1928, 1931, 1932, 1935, 1946, 1952, 1962, 1963, 1964, 1965,
1967, 1969, 1972, 1973, 1974, 1975, 1977, 1981, 1983, 1985, 1987,
1990, 1991, 1992, 1994, 1996, 1997, 1999, 2017, 2023, 2026, 2027,
2028, 2036, 2038, 2046, 2048, 2050, 2055, 2058, 2059, 2061, 2063,
2068, 2069, 2073, 2074, 2075, 2076, 2077, 2083, 2084, 2086, 2090,
2092, 2093, 2100, 2103, 2104, 2105, 2107, 2109, 2110, 2112, 2115,
2119, 2120, 2124, 2127, 2129, 2131, 2132, 2134, 2141, 2144, 2148,
2154, 2156, 2165, 2170, 2171, 2172, 2177, 2186, 2190, 2192, 2193,
2197, 2198, 2200, 2204, 2205, 2208, 2210, 2221, 2225, 2231, 2232,
2237, 2241, 2242, 2243, 2251, 2261, 2265, 2266, 2267, 2269, 2274,
2287, 2291, 2292, 2295, 2298, 2300, 2304, 2305, 2312, 2316, 2319,
2320, 2323, 2324, 2326, 2329, 2330, 2333, 2334, 2336, 2340, 2357,
2358, 2359, 2360, 2362, 2364, 2365, 2367, 2372, 2375, 2381, 2383,
2384, 2386, 2390, 2394, 2397, 2406, 2414, 2415, 2420, 2421, 2429,
2438, 2439, 2441, 2443, 2446, 2449, 2452, 2455, 2459, 2466, 2474,
2475, 2486, 2487, 2489, 2492, 2493, 2495, 2515, 2516, 2522, 2524,
2529, 2537, 2540, 2545, 2553, 2558, 2559, 2571, 2581, 2585, 2591,
2596, 2597, 2607, 2613, 2616, 2621, 2623, 2630, 2643, 2647, 2651,
2652, 2667, 2691, 2705, 2716, 2723, 2725, 2727)
kougaku_gakka <- structure(c(3L, 3L, 4L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 1L, 2L, 1L,
3L, 6L, 3L, 3L, 3L, 3L, 3L, 3L, 6L, 3L, 1L, 3L, 4L, 1L, 3L, 1L,
4L, 5L, 6L, 1L, 2L, 2L, 2L, 2L, 3L, 1L, 2L, 2L, 2L, 1L, 3L, 1L,
3L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 3L, 3L, 1L, 6L, 3L, 1L, 4L,
6L, 6L, 6L, 5L, 4L, 4L, 6L, 3L, 3L, 3L, 6L, 6L, 6L, 5L, 6L, 6L,
6L, 6L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 6L, 6L, 6L, 5L, 5L,
4L, 1L, 2L, 2L, 4L, 4L, 4L, 6L, 4L, 6L, 3L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 6L, 3L, 3L, 2L, 2L, 1L, 1L, 2L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 4L, 4L, 4L, 1L, 6L, 6L, 5L, 3L, 3L, 3L, 1L, 4L,
4L, 5L, 5L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 5L, 4L, 6L, 6L, 3L,
6L, 3L, 1L, 3L, 3L, 6L, 3L, 3L, 3L, 3L, 3L, 3L, 5L, 3L, 3L, 5L,
1L, 4L, 3L, 3L, 5L, 5L, 4L, 3L, 3L, 1L, 4L, 6L, 3L, 1L, 3L, 3L,
5L, 5L, 5L, 5L, 5L, 6L, 2L, 6L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L,
3L, 1L, 4L, 4L, 4L, 6L, 6L, 6L, 1L, 5L, 5L, 1L, 1L, 2L, 6L, 6L,
1L, 6L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 6L, 3L, 4L, 6L, 6L, 6L, 6L,
6L, 5L, 1L, 5L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 6L, 3L, 6L, 1L, 3L, 3L, 3L, 3L, 6L, 6L, 5L, 6L,
5L, 1L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 3L, 6L,
3L, 3L, 3L, 3L, 6L, 1L, 4L, 3L, 3L, 6L, 6L, 3L, 3L, 6L, 1L, 3L,
3L, 4L, 4L, 4L, 4L, 6L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 1L, 3L, 3L,
5L, 1L, 6L, 6L, 6L, 6L, 6L, 1L, 3L, 3L, 1L, 1L, 6L, 3L, 1L, 3L,
3L, 3L, 1L, 4L, 4L, 4L, 4L, 6L, 6L, 6L, 6L, 6L, 6L, 1L, 3L, 3L,
3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 6L, 3L, 3L, 3L, 4L, 4L, 6L,
6L, 6L, 6L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 5L, 4L, 4L, 4L, 4L, 6L,
6L, 6L, 5L, 5L, 1L, 6L, 3L, 6L, 3L, 3L, 6L, 6L, 6L, 6L, 3L, 4L,
3L, 6L, 6L, 1L, 3L, 3L, 6L, 1L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 3L, 6L, 3L, 4L, 4L, 6L, 6L, 6L, 4L, 4L, 4L, 4L,
5L, 5L, 5L, 6L, 6L, 6L, 2L, 2L, 2L, 4L, 4L, 4L, 6L, 6L, 6L, 3L,
3L, 1L, 3L, 1L, 3L, 1L, 4L, 3L, 3L, 6L, 3L, 6L, 3L, 6L, 3L, 6L,
6L, 6L, 1L, 6L, 3L, 3L, 3L, 6L, 6L, 6L, 6L, 1L, 3L, 4L, 3L, 6L,
2L, 2L, 5L, 3L, 3L, 3L, 1L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 3L,
3L, 3L, 3L, 3L, 3L, 6L, 6L, 1L, 6L, 1L, 1L, 3L, 2L, 1L, 2L, 4L,
6L, 6L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 6L, 5L, 5L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 3L, 6L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 6L, 1L, 4L,
5L, 6L, 6L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
3L, 3L, 6L, 3L, 3L, 3L, 3L, 6L, 6L, 5L, 1L, 4L, 4L, 4L, 4L, 4L,
3L, 3L, 3L, 4L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 2L, 2L, 2L, 2L, 6L,
2L, 1L, 1L, 6L, 6L, 6L, 6L, 6L, 5L, 3L, 3L, 6L, 4L, 5L, 5L, 6L,
6L, 6L, 6L, 6L, 1L, 3L, 3L, 1L, 4L, 4L, 4L, 5L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 3L, 3L, 4L, 6L, 1L, 1L, 1L, 1L, 2L, 4L, 5L, 5L, 1L,
1L, 6L, 6L, 6L, 6L, 6L, 3L, 3L, 1L, 1L, 6L, 1L, 6L, 6L, 5L, 5L,
5L, 6L, 1L, 3L, 1L, 1L, 1L, 5L, 5L, 1L, 5L, 3L, 4L, 3L, 6L, 1L,
6L, 4L, 6L, 3L, 1L, 1L, 1L, 5L, 5L, 5L, 4L, 4L, 3L, 3L, 3L, 3L,
3L, 3L, 1L, 3L, 6L, 3L, 3L, 6L, 1L, 6L, 6L, 6L, 5L, 5L, 5L, 4L,
4L, 4L, 4L, 6L, 1L, 6L, 6L, 3L, 3L, 3L, 6L, 6L, 6L, 3L, 6L, 2L,
2L, 3L, 1L, 3L, 3L, 3L, 4L, 4L, 4L, 2L, 2L, 2L, 1L, 2L, 1L, 3L,
3L, 1L, 2L, 6L, 4L, 6L, 3L, 3L, 4L, 3L, 3L, 2L, 4L, 5L, 5L, 4L,
4L, 6L, 4L, 3L, 6L, 3L, 4L, 6L, 4L, 5L, 5L, 4L, 3L, 3L, 6L, 3L,
1L, 3L, 2L, 1L, 6L, 6L, 6L, 6L, 5L, 4L, 4L, 4L, 4L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 3L, 3L, 4L,
4L, 4L, 4L, 3L, 6L, 1L, 3L, 1L, 6L, 6L, 6L, 3L, 3L, 3L, 3L, 6L,
2L, 2L, 2L, 2L, 2L, 6L, 5L, 5L, 4L, 6L, 4L, 6L, 4L, 3L, 6L, 6L,
6L, 1L, 3L, 1L, 1L, 6L, 6L, 5L, 5L, 2L, 1L, 1L, 1L, 4L, 5L, 5L,
5L, 5L, 6L, 6L, 6L, 3L, 3L, 1L, 4L, 1L, 4L, 4L, 5L, 3L, 3L, 3L,
5L, 4L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 4L, 1L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 3L, 4L, 3L, 6L, 6L, 6L, 1L, 3L, 6L, 3L, 3L, 3L, 4L, 2L,
6L, 5L, 5L, 3L, 6L, 4L, 1L, 1L, 6L, 4L, 1L, 1L, 6L, 6L, 6L, 1L,
5L, 1L, 1L, 5L, 5L, 4L, 6L, 6L, 1L, 1L, 1L, 3L, 6L, 4L, 5L, 3L,
4L, 6L, 4L, 5L, 6L, 6L, 3L, 6L, 3L, 1L, 1L, 1L, 3L, 1L, 3L, 4L,
1L, 6L, 1L, 6L), .Label = c("A", "B", "C", "D", "E", "F"),
class = "factor")
nougaku <- c(1, 3, 9, 11, 13, 14, 15, 16, 18, 20, 21, 22, 23, 24, 25, 28,
30, 35, 36, 38, 43, 52, 54, 55, 56, 60, 61, 63, 67, 69, 70, 71,
72, 76, 77, 78, 82, 83, 93, 95, 96, 97, 98, 100, 103, 106, 107,
109, 116, 119, 120, 123, 124, 128, 129, 130, 134, 142, 143, 145,
147, 149, 150, 151, 152, 154, 155, 158, 159, 163, 164, 166, 168,
172, 173, 175, 177, 178, 179, 182, 185, 186, 188, 189, 190, 194,
197, 198, 202, 205, 206, 207, 211, 212, 218, 219, 224, 225, 228,
231, 233, 236, 239, 244, 248, 249, 251, 252, 253, 256, 260, 261,
262, 266, 268, 271, 272, 274, 286, 291, 292, 296, 311, 316, 318,
319, 320, 325, 326, 329, 333, 334, 336, 337, 339, 347, 348, 350,
352, 353, 359, 366, 369, 371, 374, 376, 377, 378, 379, 381, 385,
386, 387, 391, 394, 395, 399, 400, 406, 407, 427, 428, 429, 432,
434, 435, 438, 441, 443, 447, 451, 452, 457, 460, 461, 463, 466,
468, 475, 478, 480, 482, 485, 486, 492, 495, 498, 501, 504, 505,
506, 510, 512, 521, 523, 525, 527, 531, 532, 533, 536, 538, 539,
542, 545, 546, 552, 553, 554, 558, 567, 569, 583, 584, 587, 589,
590, 591, 593, 595, 597, 598, 602, 603, 605, 609, 612, 613, 614,
618, 621, 624, 625, 626, 627, 634, 640, 642, 647, 649, 651, 652,
653, 654, 655, 660, 662, 663, 664, 665, 668, 669, 671, 672, 673,
676, 677, 680, 681, 683, 685, 686, 688, 692, 693, 695, 700, 701,
706, 707, 708, 713, 717, 720, 725, 727, 728, 731, 735, 736, 738,
745, 746, 748, 752, 754, 759, 760, 761, 765, 766, 770, 772, 773,
783, 785, 786, 791, 793, 805, 810, 819, 824, 826, 831, 833, 835,
842, 844, 845)
nougaku_gakka <- c(1, 3, 9, 11, 13, 14, 15, 16, 18, 20, 21, 22, 23, 24, 25, 28,
30, 35, 36, 38, 43, 52, 54, 55, 56, 60, 61, 63, 67, 69, 70, 71,
72, 76, 77, 78, 82, 83, 93, 95, 96, 97, 98, 100, 103, 106, 107,
109, 116, 119, 120, 123, 124, 128, 129, 130, 134, 142, 143, 145,
147, 149, 150, 151, 152, 154, 155, 158, 159, 163, 164, 166, 168,
172, 173, 175, 177, 178, 179, 182, 185, 186, 188, 189, 190, 194,
197, 198, 202, 205, 206, 207, 211, 212, 218, 219, 224, 225, 228,
231, 233, 236, 239, 244, 248, 249, 251, 252, 253, 256, 260, 261,
262, 266, 268, 271, 272, 274, 286, 291, 292, 296, 311, 316, 318,
319, 320, 325, 326, 329, 333, 334, 336, 337, 339, 347, 348, 350,
352, 353, 359, 366, 369, 371, 374, 376, 377, 378, 379, 381, 385,
386, 387, 391, 394, 395, 399, 400, 406, 407, 427, 428, 429, 432,
434, 435, 438, 441, 443, 447, 451, 452, 457, 460, 461, 463, 466,
468, 475, 478, 480, 482, 485, 486, 492, 495, 498, 501, 504, 505,
506, 510, 512, 521, 523, 525, 527, 531, 532, 533, 536, 538, 539,
542, 545, 546, 552, 553, 554, 558, 567, 569, 583, 584, 587, 589,
590, 591, 593, 595, 597, 598, 602, 603, 605, 609, 612, 613, 614,
618, 621, 624, 625, 626, 627, 634, 640, 642, 647, 649, 651, 652,
653, 654, 655, 660, 662, 663, 664, 665, 668, 669, 671, 672, 673,
676, 677, 680, 681, 683, 685, 686, 688, 692, 693, 695, 700, 701,
706, 707, 708, 713, 717, 720, 725, 727, 728, 731, 735, 736, 738,
745, 746, 748, 752, 754, 759, 760, 761, 765, 766, 770, 772, 773,
783, 785, 786, 791, 793, 805, 810, 819, 824, 826, 831, 833, 835,
842, 844, 845)
nougaku_gakka <- structure(c(5L, 1L, 3L, 5L, 1L, 2L, 2L, 3L, 3L, 2L, 6L, 2L, 4L,
5L, 1L, 4L, 1L, 5L, 3L, 3L, 4L, 5L, 6L, 1L, 5L, 3L, 1L, 5L, 6L,
1L, 1L, 6L, 1L, 1L, 2L, 2L, 1L, 5L, 5L, 3L, 6L, 6L, 1L, 4L, 2L,
3L, 4L, 5L, 5L, 5L, 6L, 1L, 2L, 2L, 4L, 6L, 2L, 5L, 1L, 5L, 2L,
6L, 1L, 1L, 1L, 2L, 6L, 5L, 4L, 1L, 1L, 2L, 1L, 1L, 1L, 4L, 4L,
5L, 1L, 2L, 4L, 6L, 2L, 1L, 2L, 6L, 1L, 4L, 2L, 3L, 2L, 3L, 5L,
6L, 5L, 2L, 5L, 3L, 4L, 6L, 3L, 1L, 1L, 1L, 3L, 5L, 2L, 1L, 2L,
5L, 2L, 1L, 1L, 4L, 1L, 1L, 1L, 5L, 1L, 4L, 1L, 5L, 4L, 1L, 5L,
6L, 1L, 1L, 1L, 5L, 3L, 2L, 2L, 1L, 1L, 3L, 1L, 5L, 2L, 1L, 3L,
6L, 5L, 3L, 1L, 4L, 1L, 6L, 4L, 1L, 5L, 4L, 4L, 4L, 1L, 4L, 1L,
1L, 5L, 6L, 5L, 1L, 1L, 1L, 5L, 2L, 3L, 2L, 3L, 6L, 3L, 5L, 1L,
3L, 3L, 6L, 5L, 1L, 2L, 5L, 6L, 5L, 2L, 5L, 1L, 2L, 6L, 1L, 3L,
3L, 2L, 4L, 2L, 3L, 1L, 1L, 1L, 3L, 5L, 1L, 6L, 3L, 4L, 2L, 6L,
3L, 1L, 1L, 1L, 5L, 2L, 5L, 6L, 1L, 1L, 2L, 4L, 1L, 2L, 5L, 2L,
5L, 1L, 1L, 1L, 1L, 5L, 5L, 3L, 1L, 5L, 1L, 3L, 1L, 5L, 1L, 4L,
1L, 3L, 4L, 2L, 2L, 5L, 1L, 1L, 5L, 3L, 4L, 3L, 5L, 5L, 1L, 1L,
1L, 3L, 5L, 6L, 6L, 6L, 1L, 3L, 5L, 2L, 6L, 1L, 6L, 3L, 1L, 2L,
1L, 1L, 1L, 3L, 5L, 1L, 2L, 1L, 4L, 1L, 1L, 3L, 5L, 2L, 6L, 2L,
2L, 5L, 2L, 6L, 1L, 4L, 6L, 4L, 1L, 2L, 2L, 6L, 5L, 5L, 5L, 3L,
1L, 2L, 4L, 3L, 5L, 5L, 6L, 1L, 1L),
.Label = c("A", "B", "C", "D", "E", "F"), class = "factor")これをデータフレーム化
name1 <- c("bungaku", "hougaku", "igaku", "kango", "keizai_bunkei",
"keizai_rikei", "kensagijutu", "kougaku", "kyoiku_bunkei", "kyoiku_rikei",
"nougaku", "rigaku", "rigakuryouhou", "sagyouryouhou", "sougouningen_bunkei",
"sougouningen_rikei", "yakka", "yakugaku")
hist.goukaku2 <- function(x, bw = 10){
eval(parse(text = paste0("z <- ", x, sep="", collapse = "")))
check.ok <- function(y){any(z == y)}
goukaku <- sapply(range(z)[1]:range(z)[2], check.ok)
hist(z, breaks = seq(range(z)[1], (2 + range(z)[2] %/% bw) * bw, by= bw))
}
data1 <- data.frame() #data frame化
for(i in name1){
eval(parse(text = paste0("tmp_data <-", i)))
data_one <- tmp_data - (tmp_data[1] %/% 10) *10 #はじめの10人が連続不合格した学科はないと仮定
#最後の受験番号は不明。最後と最後から二番目合格者の間の人数/2（切り上げ）を追加
temp_length <- length(data_one)
temp_delta <- data_one[temp_length] - data_one[temp_length - 1]
ceiling(temp_delta / 2)
num_one <- seq_len(max(data_one) + ceiling(temp_delta / 2))
goukaku <- sapply(num_one, function(x)any(x == data_one))
data_rate <- num_one / max(num_one)
tmp <- data.frame(gakka = i, num_one = num_one, num_rate = data_rate, goukaku = goukaku)
data1 <- rbind(data1, tmp)
}まずは普通のロジスティック回帰
res_1 <- glm(goukaku ~ num_rate, data = data1, family = binomial(logit))
summary(res_1)##
## Call:
## glm(formula = goukaku ~ num_rate, family = binomial(logit), data = data1)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.1564 -0.9665 -0.8186 1.3172 1.6730
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.04933 0.04627 -1.066 0.286
## num_rate -1.06729 0.08327 -12.817 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 10247 on 7833 degrees of freedom
## Residual deviance: 10079 on 7832 degrees of freedom
## AIC: 10083
##
## Number of Fisher Scoring iterations: 4res_2 <- glm(goukaku ~ num_one, data = data1, family = binomial(logit))
summary(res_2)##
## Call:
## glm(formula = goukaku ~ num_one, family = binomial(logit), data = data1)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.0145 -0.9829 -0.8923 1.3673 1.6837
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.959e-01 3.188e-02 -12.42 < 2e-16 ***
## num_one -2.728e-04 3.462e-05 -7.88 3.28e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 10247 on 7833 degrees of freedom
## Residual deviance: 10182 on 7832 degrees of freedom
## AIC: 10186
##
## Number of Fisher Scoring iterations: 4res_3 <- glm(goukaku ~ num_rate + num_one, data = data1, family = binomial(logit))
summary(res_3)##
## Call:
## glm(formula = goukaku ~ num_rate + num_one, family = binomial(logit),
## data = data1)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.1565 -0.9678 -0.8189 1.3159 1.7075
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.908e-02 4.627e-02 -1.061 0.289
## num_rate -9.965e-01 9.868e-02 -10.098 <2e-16 ***
## num_one -5.413e-05 4.092e-05 -1.323 0.186
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 10247 on 7833 degrees of freedom
## Residual deviance: 10077 on 7831 degrees of freedom
## AIC: 10083
##
## Number of Fisher Scoring iterations: 4res_4 <- glm(goukaku ~ num_rate + gakka, data = data1, family = binomial(logit))
summary(res_4)##
## Call:
## glm(formula = goukaku ~ num_rate + gakka, family = binomial(logit),
## data = data1)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.6469 -0.9622 -0.8043 1.3038 2.0736
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.0866914 0.0941919 -0.920 0.357380
## num_rate -1.0810523 0.0837775 -12.904 < 2e-16 ***
## gakkahougaku 0.1308349 0.1122017 1.166 0.243586
## gakkaigaku 0.0002669 0.1461376 0.002 0.998543
## gakkakango 0.8493735 0.1966870 4.318 1.57e-05 ***
## gakkakeizai_bunkei 0.2276737 0.1277555 1.782 0.074732 .
## gakkakeizai_rikei -0.9348748 0.2379133 -3.929 8.51e-05 ***
## gakkakensagijutu 0.4424097 0.2352474 1.881 0.060024 .
## gakkakougaku -0.0223312 0.0945930 -0.236 0.813373
## gakkakyoiku_bunkei -0.0672868 0.2000343 -0.336 0.736587
## gakkakyoiku_rikei -0.5800056 0.3739524 -1.551 0.120898
## gakkanougaku 0.0673537 0.1118000 0.602 0.546876
## gakkarigaku 0.0983553 0.1120666 0.878 0.380134
## gakkarigakuryouhou 0.5864504 0.3436677 1.706 0.087925 .
## gakkasagyouryouhou 1.4775357 0.4377294 3.375 0.000737 ***
## gakkasougouningen_bunkei -0.2591714 0.1719577 -1.507 0.131764
## gakkasougouningen_rikei -0.2993656 0.1838186 -1.629 0.103399
## gakkayakka 0.3614791 0.2035938 1.775 0.075817 .
## gakkayakugaku 0.3737973 0.2567470 1.456 0.145421
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 10246.7 on 7833 degrees of freedom
## Residual deviance: 9990.3 on 7815 degrees of freedom
## AIC: 10028
##
## Number of Fisher Scoring iterations: 4とりあえず、受験番号そのものよりも、各学科での“個人の受験番号/各学科最大の受験番号” num_rateの方がより良い予測因子のようだ。 係数は負であり、各学科での受験番号が大きいほど、合格率は低いようだ。
次に混合モデル。学科ごとにランダムな合格率があると仮定。 本当は正しい合格者数があるはずだが、調べるのが面倒なのでこれで行く。
library(lme4)## Loading required package: Matrixlibrary(lmerTest)## Warning: package 'lmerTest' was built under R version 3.2.4## Warning: replacing previous import by 'ggplot2::unit' when loading 'Hmisc'## Warning: replacing previous import by 'ggplot2::arrow' when loading 'Hmisc'## Warning: replacing previous import by 'scales::alpha' when loading 'Hmisc'##
## Attaching package: 'lmerTest'
##
## 以下のオブジェクトは 'package:lme4' からマスクされています:
##
## lmer
##
## 以下のオブジェクトは 'package:stats' からマスクされています:
##
## stepres_mix1 <- glmer(goukaku ~ num_rate + (1 + num_rate|gakka), data = data1, family = binomial(logit))
summary(res_mix1)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: goukaku ~ num_rate + (1 + num_rate | gakka)
## Data: data1
##
## AIC BIC logLik deviance df.resid
## 10057.5 10092.3 -5023.7 10047.5 7829
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2820 -0.7676 -0.6236 1.1626 2.5547
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## gakka (Intercept) 0.12325 0.3511
## num_rate 0.07179 0.2679 -0.01
## Number of obs: 7834, groups: gakka, 18
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.02136 0.10618 0.201 0.841
## num_rate -1.10296 0.12397 -8.897 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## num_rate -0.411同じく、各学科での受験番号が大きいほど、合格率は低いようだ。
“個人の受験番号/各学科最大の受験番号”ごとの合格者のヒストグラム。 大まかに合格率と比例。
data1_goukaku <- subset(data1, goukaku == TRUE)
hist(data1_goukaku$num_rate, breaks = seq(0, 1, by = 0.05))
なんだか、“個人の受験番号/各学科最大の受験番号”が0.5くらいまでは 合格率は良好。その後下がり、最後の駆け込み１０％くらいは激しく 悪い感じ。
線形のモデルは相性が悪いと考えて、最後にgbmでの解析を追加する
library(gbm)## Loading required package: survival
## Loading required package: lattice
## Loading required package: splines
## Loading required package: parallel
## Loaded gbm 2.1.1res_gbm <- gbm(goukaku ~ num_rate + gakka, data = data1, distribution = "bernoulli",
n.trees = 10000,
interaction.depth = 3,
n.minobsinnode = 10,
shrinkage = 0.001,
bag.fraction = 0.5,
train.fraction = 1.0,
cv.folds=5,
keep.data = TRUE,
verbose = "CV",
class.stratify.cv=NULL,
n.cores = 4)
best.iter <- gbm.perf(res_gbm,method="cv")
print(best.iter)## [1] 4212summary(res_gbm, n.trees=best.iter) 
## var rel.inf
## num_rate num_rate 59.60418
## gakka gakka 40.39582print(pretty.gbm.tree(res_gbm,res_gbm$n.trees))## SplitVar SplitCodePred LeftNode RightNode MissingNode ErrorReduction
## 0 1 1.257000e+04 1 8 9 0.6199666
## 1 0 1.581571e-02 2 3 7 0.5989936
## 2 -1 6.670651e-04 -1 -1 -1 0.0000000
## 3 0 6.823725e-02 4 5 6 0.6161752
## 4 -1 -5.788456e-04 -1 -1 -1 0.0000000
## 5 -1 -9.316886e-05 -1 -1 -1 0.0000000
## 6 -1 -1.167603e-04 -1 -1 -1 0.0000000
## 7 -1 -1.021398e-04 -1 -1 -1 0.0000000
## 8 -1 2.800834e-05 -1 -1 -1 0.0000000
## 9 -1 -4.055205e-06 -1 -1 -1 0.0000000
## Weight Prediction
## 0 3917 -4.055205e-06
## 1 965 -1.021398e-04
## 2 18 6.670651e-04
## 3 947 -1.167603e-04
## 4 46 -5.788456e-04
## 5 901 -9.316886e-05
## 6 947 -1.167603e-04
## 7 965 -1.021398e-04
## 8 2952 2.800834e-05
## 9 3917 -4.055205e-06plot(res_gbm,1:2,best.iter, type = "response")
クロスバリデーションありのgbmではこんな感じになった。 やはり受験番号が大きいほど合格率は悪い感じ。
最後に受験番号ごとの合格率（最小値～＋９９まで）
bangoubetu <- function(x){
temp <- subset(data1, data1$num_one == x)
sum(temp$goukaku) / nrow(temp)
}
barplot(sapply(1:100, bangoubetu), names.arg = 1:100)
abline(h = sum(data1$goukaku) / nrow(data1))
受験番号1とか3001とかその学科最小の受験番号の合格率は ７割超えであった。 有意だが、100個の検定を繰り返したことを調整すると、有意 ではなくなる程度のもの。グラフを見る限り、たまたまの印象。
