我有时间序列数据（以xts形式），如下所示：
EUR.CLOSE
2014-08-28 1.3182
2014-08-29 1.3132
2014-09-01 1.3128
2014-09-02 1.3133
2014-09-03 1.3150
2014-09-04 1.2944
2014-09-05 1.2951
2014-09-08 1.2895
2014-09-09 1.2937
2014-09-10 1.2917
2014-09-11 1.2925
2014-09-12 1.2963
2014-09-15 1.2940
2014-09-16 1.2960
2014-09-17 1.2865
2014-09-18 1.2923
2014-09-19 1.2829
2014-09-22 1.2849
2014-09-23 1.2847
2014-09-24 1.2780
2014-09-25 1.2751
2014-09-26 1.2684
2014-09-29 1.2685
2014-09-30 1.2631
2014-10-01 1.2623
2014-10-02 1.2669
2014-10-03 1.2516
2014-10-06 1.2655
我有基于某些信号计算的行间隔。在这种情况下，我们假设计算的行间隔为rowintv <- c(5,10,17,22)。我希望根据行间隔的周期计算最大收盘价。例如，我想要data[1,], data[5,]和data[6,], data[10,]以及data[11,], data[17,]之间的最大值。
附件是一个示例输入：
structure(c(1.3182, 1.3132, 1.3128, 1.3133, 1.315, 1.2944, 1.2951,
1.2895, 1.2937, 1.2917, 1.2925, 1.2963, 1.294, 1.296, 1.2865,
1.2923, 1.2829, 1.2849, 1.2847, 1.278, 1.2751, 1.2684, 1.2685,
1.2631, 1.2623, 1.2669, 1.2516, 1.2655, 1.2669, 1.2734, 1.2691,
1.2628, 1.2752, 1.2658, 1.2838, 1.2809, 1.2761, 1.28, 1.2716,
1.2649, 1.2646, 1.2671, 1.2698, 1.2734, 1.2632, 1.2613, 1.2525,
1.2482, 1.2546, 1.2486, 1.2375, 1.2455, 1.2421, 1.2475, 1.2438,
1.2477, 1.2525, 1.245, 1.2536, 1.2554, 1.2539, 1.2391, 1.2442,
1.2474, 1.2506, 1.2467, 1.2452, 1.247, 1.2383, 1.2311, 1.2379,
1.2283, 1.2317, 1.2374, 1.2448, 1.2411, 1.2462, 1.2437, 1.2511,
1.2342, 1.2286, 1.2229, 1.223, 1.2172, 1.2196, 1.2225, 1.2183,
1.2152, 1.2156, 1.2098, 1.2104, 1.2002, 1.1933, 1.189, 1.1839,
1.1793, 1.1842, 1.1834, 1.1773, 1.1789, 1.1633, 1.1567, 1.1606,
1.155, 1.161, 1.1366, 1.1204, 1.1238, 1.1381, 1.1287, 1.132,
1.1291, 1.1341, 1.1481, 1.1345, 1.1477, 1.1316, 1.1325, 1.1321,
1.1336, 1.1403, 1.1394, 1.1355, 1.1411, 1.1397, 1.1368, 1.1381,
1.1335, 1.134, 1.1361, 1.1198, 1.1196, 1.1184, 1.1176, 1.1078,
1.103, 1.0844, 1.0852, 1.0698, 1.0547, 1.0635, 1.0496, 1.0568,
1.0597, 1.0864, 1.066, 1.0821, 1.0946, 1.0924, 1.097, 1.0884,
1.0889, 1.0833, 1.0731, 1.0763, 1.088, 1.0969, 1.0922, 1.0814,
1.0781, 1.0659, 1.0604, 1.0567, 1.0655, 1.0684, 1.0761, 1.0806,
1.0738, 1.0736, 1.0725, 1.0824, 1.0873, 1.0891, 1.0981, 1.1128,
1.1224, 1.1199, 1.1146, 1.1185, 1.1347, 1.1267, 1.1199, 1.1155,
1.1213, 1.1354, 1.141, 1.1451, 1.1315, 1.115, 1.1094, 1.1112,
1.1013, 1.0978, 1.0873, 1.0904, 1.0949, 1.0986, 1.0927, 1.1151,
1.1275, 1.1238, 1.1114, 1.1291, 1.1283, 1.1324, 1.1258, 1.1266,
1.1283, 1.1248, 1.1337, 1.1359, 1.1352, 1.1341, 1.1167, 1.1205,
1.1205, 1.1167, 1.1236, 1.1147, 1.1053, 1.1084, 1.1114, 1.1056,
1.1011, 1.1077, 1.1036, 1.1162, 1.1002, 1.1009, 1.095, 1.0875,
1.083, 1.0825, 1.0935, 1.0929, 1.0984, 1.0984, 1.1088, 1.106,
1.0984, 1.0932, 1.0984, 1.095, 1.0881, 1.0906, 1.0925, 1.0967,
1.1019, 1.1042, 1.1159, 1.115, 1.1109, 1.1078, 1.1024, 1.112,
1.1242, 1.1386, 1.1619, 1.1517, 1.1314, 1.1246, 1.1185, 1.1211,
1.1315, 1.1227, 1.1123, 1.1149, 1.117, 1.1203, 1.1207, 1.128,
1.1338, 1.1317, 1.1269, 1.129, 1.1435, 1.1298, 1.119, 1.112,
1.1186, 1.123, 1.1195, 1.1244, 1.1249, 1.1177, 1.1195, 1.1216,
1.1188, 1.1272, 1.1237, 1.1276, 1.1358, 1.1358, 1.1379, 1.1474,
1.1386, 1.1348, 1.1327, 1.1346, 1.1339, 1.1109, 1.1018, 1.1058,
1.1051, 1.0923, 1.0977, 1.1006, 1.1016, 1.0964, 1.0866, 1.0884,
1.0741, 1.0752, 1.0724, 1.0743, 1.0814, 1.0773, 1.0686, 1.0642,
1.066, 1.0734, 1.0646, 1.0636, 1.0643, 1.0624, 1.061, 1.0593,
1.0565, 1.0633, 1.0615, 1.094, 1.0881, 1.0837, 1.0892, 1.1025,
1.0941, 1.0986, 1.0992, 1.0931, 1.0912, 1.0826, 1.0868, 1.0915,
1.0957, 1.0912, 1.0963, 1.096, 1.0968, 1.092, 1.0933, 1.0862,
1.0856, 1.0831, 1.0748, 1.0781, 1.0932, 1.0922, 1.0859, 1.0858,
1.0877, 1.0865, 1.0916, 1.0892, 1.0908, 1.089, 1.0874, 1.0796,
1.0849, 1.087, 1.0893, 1.094, 1.0831, 1.0888, 1.0919, 1.1105,
1.1209, 1.1158, 1.1193, 1.1293, 1.1292, 1.1323, 1.1256, 1.1156,
1.1144, 1.1128, 1.1107, 1.113, 1.103, 1.102, 1.1013, 1.1018,
1.0934, 1.0873, 1.0868, 1.0868, 1.0957, 1.1005, 1.1014, 1.1011,
1.0999, 1.1177, 1.1156, 1.1103, 1.1109, 1.1224, 1.1318, 1.127,
1.1241, 1.1217, 1.1181, 1.1174, 1.1167, 1.1196, 1.1291, 1.1338,
1.138, 1.1391, 1.1391, 1.1384, 1.1399, 1.1378, 1.1399, 1.1408,
1.1386, 1.1274, 1.1268, 1.1284, 1.1313, 1.1358, 1.1297, 1.1288,
1.1222, 1.1268, 1.1297, 1.1322, 1.1352, 1.1451, 1.1534, 1.1496,
1.1487, 1.1405, 1.1404, 1.1383, 1.1372, 1.1426, 1.1377, 1.1309,
1.1321, 1.1313, 1.1216, 1.1203, 1.1224, 1.122, 1.1141, 1.1155,
1.1194, 1.1115, 1.1138, 1.1132, 1.1188, 1.1151, 1.1367, 1.1355,
1.1358, 1.1395, 1.1316, 1.1251, 1.1292, 1.1207, 1.126, 1.1225,
1.1277, 1.1314, 1.1242, 1.1296, 1.1385, 1.1117, 1.1025, 1.1065,
1.1125, 1.1106, 1.1136, 1.1154, 1.1076, 1.11, 1.1063, 1.1051,
1.1058, 1.1061, 1.109, 1.112, 1.1035, 1.1075, 1.1021, 1.1015,
1.1026, 1.0977, 1.0995, 1.0986, 1.1058, 1.1077, 1.1174, 1.1163,
1.1224, 1.1149, 1.113, 1.1086, 1.1088, 1.1117, 1.1176, 1.1137,
1.1162, 1.1184, 1.1279, 1.1289, 1.1354, 1.1325, 1.132, 1.1305,
1.1264, 1.1285, 1.1198, 1.1189, 1.1143, 1.1158, 1.1197, 1.1156,
1.1147, 1.1255, 1.1239, 1.126, 1.1233, 1.1235, 1.122, 1.125,
1.1244, 1.1155, 1.1175, 1.1151, 1.1189, 1.1208, 1.1226, 1.1254,
1.1215, 1.1217, 1.1222, 1.1235, 1.1211, 1.1204, 1.1205, 1.1151,
1.1201, 1.1139, 1.1054, 1.1007, 1.1056, 1.0972, 1.1, 1.0981,
1.0974, 1.0929, 1.0884, 1.0882, 1.0889, 1.0908, 1.0897, 1.0985,
1.0981, 1.1055, 1.1098, 1.1105, 1.1141, 1.1041, 1.1026, 1.091,
1.0893, 1.0855, 1.0737, 1.0722, 1.0691, 1.0626, 1.0588, 1.063,
1.0627, 1.0553, 1.0554, 1.0589, 1.0614, 1.065, 1.0589, 1.0661,
1.0664, 1.0764, 1.0718, 1.0753, 1.0615, 1.0561, 1.0635, 1.0626,
1.0536, 1.0414, 1.0451, 1.0402, 1.0388, 1.0424, 1.0437, 1.0456,
1.0455, 1.0457, 1.0413, 1.049, 1.0517, 1.0455, 1.0405, 1.0489,
1.0607, 1.0532, 1.0574, 1.0554, 1.0582, 1.0613, 1.0643, 1.0601,
1.0713, 1.063, 1.0664, 1.0703, 1.0765, 1.0731, 1.0748, 1.0682,
1.0699, 1.0695, 1.0798, 1.0769, 1.0759, 1.0783, 1.075, 1.0683,
1.0698, 1.0655, 1.0643, 1.0598, 1.0578, 1.0601, 1.0674, 1.0616,
1.0614, 1.0536, 1.0558, 1.0582), index = structure(c(1409202000,
1409288400, 1409547600, 1409634000, 1409720400, 1409806800, 1409893200,
1410152400, 1410238800, 1410325200, 1410411600, 1410498000, 1410757200,
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1487743200, 1487829600), tzone = "", tclass = c("POSIXct", "POSIXt"
)), .indexCLASS = c("POSIXct", "POSIXt"), tclass = c("POSIXct",
"POSIXt"), .indexTZ = "", tzone = "", class = c("xts", "zoo"), .Dim = c(651L,
1L), .Dimnames = list(NULL, "EUR.CLOSE"))
答案 0 :(得分：1)
使用findInterval将数据分成rowintv
返回间隔以确认其正常运行
library(zoo) # may need to change seq_along if using different data type
lapply(seq_along(rowintv), function(x) data[findInterval(seq_along(data), rowintv, left.open = TRUE) == (x - 1)])
[[1]]
1409202000 1409288400 1409547600 1409634000 1409720400
1.3182 1.3132 1.3128 1.3133 1.3150
[[2]]
1409806800 1409893200 1410152400 1410238800 1410325200
1.2944 1.2951 1.2895 1.2937 1.2917
[[3]]
1410411600 1410498000 1410757200 1410843600 1410930000 1411016400 1411102800
1.2925 1.2963 1.2940 1.2960 1.2865 1.2923 1.2829
[[4]]
1411362000 1411448400 1411534800 1411621200 1411707600
1.2849 1.2847 1.2780 1.2751 1.2684
获取最大值
maxintv <- sapply(seq_along(rowintv), function(x) max(data[findInterval(seq_along(data), rowintv, left.open = TRUE) == (x - 1)]))
[1] 1.3182 1.2951 1.2963 1.2849
<强>更新
生成与原始数据长度相等且仅包含最大值
的数组rep(maxintv, c(rowintv[1], diff(rowintv)))
[1] 1.3182 1.3182 1.3182 1.3182 1.3182 1.2951 1.2951 1.2951 1.2951 1.2951 1.2963 1.2963 1.2963 1.2963 1.2963 1.2963
[17] 1.2963 1.2849 1.2849 1.2849 1.2849 1.2849