2D (kde2d) contour plots with conditions - r
I have 4 variables x1,x2 y1,y2 (365 values for each variable). I want to plot the 2d kernel density with specific contour levels. I need to overlay the density plots (x1 vs y1) and (x2 vs y2).
x1 <- c(772.522, 1806.75, 2388.73, 2619.04, 2695.6, 2747.14, 2772.58,
2773.86, 2812.93, 3338.98, 3299.18, 3269.85, 3179.74, 3185.36,
3274.99, 3391.08, 3541.91, 3563.56, 3551.63, 3626.92, 3602.07,
3535.31, 3482.09, 3567.54, 3502.1, 3440.78, 3437.95, 3722.05,
3702.45, 3636.89, 3565.1, 3485.51, 3398.42, 3311, 3231.97, 3129.51,
3055.22, 2968.45, 3435.38, 3605.31, 3468.35, 3845.2, 3858.71,
4388.68, 5056.55, 5601.96, 5968.48, 6033.75, 5938.22, 5807.13,
5671.36, 5612.84, 5475.63, 5329.19, 5179.73, 5239.82, 5264.78,
5553.4, 5478.35, 5352.85, 5227.08, 5213.33, 5160.05, 5399.89,
5554.96, 5592.91, 5541.88, 5517.83, 5614.53, 5522.72, 5410.01,
5289.24, 5154.86, 5014.21, 4868.89, 4732.69, 4608.15, 4457.99,
4299.06, 4142.57, 3991.74, 3841.69, 3695.19, 3552.06, 3436.21,
3308.64, 3178.24, 3056.39, 2938.67, 2824.59, 2714.82, 2610.78,
2515.79, 2424.83, 2346.9, 2274.12, 2202.44, 2132.47, 2068.17,
1986.15, 1905.02, 1828.44, 1754.68, 1685.86, 1621.62, 1560.92,
1504.27, 1450.55, 1400.78, 1352.88, 1304.74, 1257.36, 1219.04,
1213.48, 1202.94, 1423.37, 1542.41, 1494.66, 1482.53, 1599.09,
1544, 1482.98, 1446.54, 1395.88, 1346.6, 1295.43, 1248.17, 1206.5,
1161.74, 1142.75, 1304.01, 1261.43, 1221.17, 1339.85, 1382.48,
1333.32, 1298.32, 1269.32, 1259.52, 1236.89, 1268.37, 1327.74,
1459.69, 1451.84, 1418.96, 1390, 1609.57, 1638.19, 1610.33, 1624.47,
1575.08, 1526.3, 1487.86, 1474.29, 1497.28, 1457.82, 1444.52,
1448.25, 1458.49, 1496.27, 1534.7, 1593.66, 1636.95, 1632.44,
1660.17, 1738.57, 1765.32, 1784.72, 2015.57, 2050.61, 2051.55,
2045.69, 2044.79, 2050.25, 2038.62, 2016.73, 1996.23, 1986.9,
1963.9, 1929.55, 1886.8, 1834.12, 1780.86, 1732.32, 1680.39,
1624.13, 1568.53, 1519.12, 1474.84, 1428.67, 1380.09, 1334.61,
1290.76, 1247.5, 1212.04, 1183.16, 1152.2, 1171.52, 1130.61,
1092.57, 1091.14, 1054.6, 1020.15, 988.19, 1027.7, 1014.29, 979.729,
947.145, 915.957, 1002.37, 1161.34, 1130.55, 1168.49, 1126.99,
1086.23, 1048.46, 1011.48, 976.161, 942.963, 968.045, 1072.01,
1075.4, 1059.16, 1043.81, 1176.16, 1140.94, 1101.78, 1078.93,
1043.95, 1004.95, 968.521, 934.568, 904.955, 878.469, 849.94,
821.994, 795.893, 770.1, 745.538, 722.857, 701.089, 680.118,
660.585, 667.87, 666.708, 646.888, 626.794, 607.768, 591.769,
635.32, 738.938, 717.112, 732.378, 891.413, 1165.41, 1137.85,
1345.26, 1373.03, 1341.85, 1381.03, 1332.81, 1279.92, 1261.64,
1448.94, 1417.41, 1399.06, 1365.79, 1312.99, 1262.5, 1215.59,
1173.54, 1130.01, 1322.27, 1411.67, 1357, 1304.07, 1252.96, 1204.73,
1159.53, 1116, 1081.3, 1042.57, 1003.76, 967.089, 932.187, 897.657,
864.375, 832.293, 801.206, 771.326, 742.13, 716.694, 690.45,
664.076, 639.827, 617.01, 593.567, 570.818, 551.133, 593.432,
833.715, 871.919, 845.388, 865.802, 937.158, 972.532, 1030.36,
1006.08, 974.112, 937.399, 902.049, 872.061, 886.442, 892.396,
859.156, 825.958, 793.783, 762.704, 758.36, 999.93, 967.713,
961.368, 1012.97, 998.855, 1197.95, 1163.77, 1122.32, 1213.45,
1302.05, 1281.74, 1254.06, 1204.14, 1155.98, 1109.55, 1064.83,
1021.78, 980.367, 940.548, 925.483, 1144.38, 1125.92, 1109.17,
1222.15, 1503.71, 2656.42, 2550.13, 2446.94, 2358.74, 2263.33,
2171.81, 2248.6, 2316.71, 2675.05, 3015.03, 3716.48, 4441.43,
4742.74, 5476.79, 5313.57, 5106.1, 5178.79, 5160.45, 5020.48,
4825.68, 4730.04)
y1 <- c(0.127958331257105, 0.010291666626775, 0.0578749990284753, 0.830833333233992,
-0.0829583332330609, -0.217708332619319, 0.172125002286824, 0.232208333676681,
0.235375001948948, 0.0380416669261952, 0.0393333347359051, -0.0440416663574676,
0.162666665079693, -0.0932500026344011, -0.0905833330471069,
-0.305250000208616, 1.0349166871359, 0.334833333579202, -0.0301250003588696,
-0.175166667904705, -0.0697083329238618, 0.824125001827876, -0.532083340920508,
0.233000000123866, 0.0752083340097063, 0.409375000745058, 0.114333332865499,
0.359583331989901, -0.189749999437481, -0.164124998962507, -0.250208334065974,
0.694499998974303, -0.00312500035700699, 0.210833334363997, -0.0586666659607242,
0.305125000498568, 0.188458332403873, -0.101833333649362, 0.09737500102104,
0.273249999930461, -0.0283333340194076, 0.320541665268441, -0.0570416667421038,
-0.16370833478868, 0.0965000004313576, 0.156541665977178, 0.000791666388977319,
-0.17350000096485, 0.204625002418955, -0.175041667728995, -0.776166667540868,
0.0604166665192073, -0.0879583329757831, 0.357666667240361, 0.425541667888562,
-0.0276250006475796, 0.116624999713774, 0.044666666809159, -0.0109583338732288,
0.398333337565418, 0.201500000820185, -0.273708331709107, -0.126250000049671,
0.223624998082717, -0.0117499992872278, -0.0997916681614394,
0.121583334170282, 0.0962499987799674, -0.17191666799287, 0.002666666599301,
-0.340916665426145, 0.132625000396123, 0.32058333295087, 0.254250001162291,
0.372083335435794, -0.0369166672850649, 0.662124995142221, -0.0916666652386387,
0.0278750000870787, 0.0751666669190551, 0.620958338181178, 0.751416672021151,
-0.130499999620952, 0.170041667142262, 0.691666666107873, -0.0391250009512684,
0.294833332921068, -0.0795000011567026, 0.115291667714094, 0.0676250006072223,
0.318208330931763, -0.311458331843217, 0.45366666217645, 0.232166665392773,
0.117749998811632, 0.207750001301368, 0.92275000611941, -0.272541665161649,
0.103125000217309, 0.220291670741669, -0.191500000655651, 1.05833334475756,
0.671833337595065, -0.0487916663405485, -0.0473333336703945,
-0.169916665491958, -0.100500000247848, 0.0271666669577826, -0.10191666687994,
0.0568750000869234, 0.14375000144355, 0.108666666705782, 0.388583331524084,
-0.147958333914479, -0.103041666346447, -0.491375003010035, 0.0465833337899918,
0.286458336282521, 0.00633333355654031, 0.0260416660748888, -0.112708333239425,
-0.548541671286027, 0.0103333332614663, 0.148666666975866, -0.157583331689239,
0.325874996837229, -0.143708332757039, 0.0945833313356464, 0.0853333330742316,
0.313833336035411, -0.352624999048809, -0.136625000392087, -0.29474999755621,
-0.549458327392737, -0.0799166670185514, -0.0107916667620884,
-0.169333333459993, 0.321541666053236, 0.07195833309864, 0.146708333787198,
-0.246458334848285, 0.368250001221895, -0.159666667692363, -0.00275000064478566,
-0.0460416663748523, -0.138958334340714, -0.0874166679180538,
-0.0167500003784274, 0.091583332628943, 0.00845833330337579,
-0.0542083333760578, 0.112666667555459, -0.138541666480402, 0.259916665653388,
0.0581666673533618, -0.134541667697097, 0.525916664550702, 0.0101249999473415,
-0.127000000327826, -0.0889166663400829, -0.190124999731779,
-0.108375000612189, -0.107916666815678, 0.0988750007624428, 0.0848750000974784,
0.0244583335976737, -0.0702916663188565, -0.0600416688297022,
0.0206666665617377, 0.329208332424362, -0.0249166667636018, -0.167916666561117,
0.11137499815474, 0.00529166660271585, -0.412708333383004, 0.155208332464099,
0.322999999547998, -0.153541666455567, -0.0445416663618137, 0.0242500004387693,
-0.115666666689018, 0.0627916665980592, 0.10774999926798, -0.242875003643955,
-0.1862083322679, 0.0298750002645344, -0.059916666985373, -0.0553333335216545,
0.124124999691655, 0.215458335238509, -0.0642499998599912, -0.0367083334034154,
0.203250000505553, -0.0517083338151375, -0.0830416663084179,
-0.033833333698567, 0.272166667544904, 0.294208334758878, -0.234416666751107,
0.0510000000552585, -0.0260000005364418, 0.00383333330197881,
0.214041665196419, 0.212249997537583, -0.0273749998110967, 0.0852083338735004,
-0.133291667327285, -0.15349999970446, -0.0748333332982535, -0.0968749993480742,
0.0880833331029862, 0.190416667843238, -0.00887500051370201,
-0.0115416667006987, 0.149958331448336, -0.274749999245008, -0.0932916667855655,
0.109999999869615, -0.135416666356226, 0.0456666671185909, 0.135458334514018,
-0.073291666728134, 0.0852083340287209, 0.0665000005683396, 0.104958332454165,
-0.0821666670963168, -0.168583333181838, 0.178333333072563, 0.0781666664018606,
-0.175666667210559, -0.0343750003861108, 0.0142083335570836,
-0.0451250005474625, -0.154000000096858, -0.0315833336208016,
-0.0986250000860309, 0.201541664127338, -0.000624999937523777,
-0.0668333338884016, -0.0365833334314326, 0.0162083323860619,
-0.161374998899798, -0.0683333337462197, 0.0342499999824213,
-0.0376666667483126, -0.13674999990811, 0.0712083332861463, -0.0789166667188207,
0.0838333335850621, -0.107625000178814, -0.15395833303531, 0.151750000969817,
0.0107083340020229, 0.0111666666537834, 0.0764583332881254, 0.12216666713357,
-0.135750001917283, -0.139166665884356, -0.0763333337381482,
0.0223750005534384, 0.239708331103126, -0.121791667304933, 0.183583331371968,
-0.173791667446494, -0.00875000042530398, -0.107416666268061,
-0.00929166671994608, 0.0561666658128767, 0.082166666785876,
-0.0237500001627874, -0.048374999819013, 0.17375000162671, -0.15087499966224,
0.187791665395101, -0.0918750003135453, 0.309750000635783, -0.231125000243386,
-0.14383333416481, -0.0552083337291454, -0.121250000433065, 0.202124998904765,
-0.193333331495523, -0.0752083341746281, -0.153416666667908,
-0.0242500006376455, 0.0107499997441967, 0.0742916671248774,
-0.0477500005896824, -0.00087499994939814, -0.120625000757476,
0.22333333392938, 0.0522916664000756, -0.0239999999369805, 0.413791667670012,
0.00141666718991473, 0.162708333072563, 0.0484583335734593, 0.0710833334984879,
0.078208333812654, -0.0702916664692263, -0.108500000399848, -0.180708333849907,
0.123083333640049, 0.0157916666357778, 0.0192083331833904, -0.205250000581145,
0.0680416667601094, 0.0161666665517259, -0.11483333290865, -0.173625001683831,
-0.0131666665741553, -0.130791667072723, 0.209041668102145, -0.0475416670863827,
-0.101625000592321, -0.0217083335834711, 0.0751250004007791,
-0.0733333341777325, 0.0290416674300407, -0.136833332479, -0.0747916662755112,
-0.0304166664670144, 0.0384583333798219, -0.0781250001552204,
0.0489166672729577, 0.000500000169267878, -0.14054166796753,
0.0298750003178914, 0.00916666674796337, 0.0164583334699273,
0.0552083333604969, 0.0388333338196389, 0.359333331075807, 0.205291667332252,
-0.026708333355297, -0.0674583336221986, 0.0282916666183155,
-0.0927500004569689, -0.0379166668280959, -0.0953750004215787,
0.0110416668661249, -0.120208332935969, 0.0384999999660067, -0.0578333336549501,
0.0397500003067156, 0.0279166665568482, -0.0609166669504096,
0.104874998796731, -0.156874999403954, -0.0550833336698512, 0.195958332469066,
0.055291667037333, 0.0537499998608837, 0.145833333333333, 0.0199999999992239,
0.0791666666045785, -0.0392083331826143, 0.306416667997837, -0.00125000059294204,
0.124166667150954, -0.0162083334774555, 0.141874998798206, -0.0859166665468365,
-0.185750000178814, 0.0495833333213037)
x2 <- c(307.991, 460.697, 579.639, 1297.73, 2091.27, 3334.57, 3675.05,
3772.43, 3675.89, 3604.88, 3584.83, 3669.77, 3649.38, 3546.33,
3425.51, 3306.32, 3194.85, 3080.73, 2973.95, 2871.36, 2759.01,
2653.29, 2548.64, 2470.45, 2399.17, 2443.32, 2642.11, 2708.22,
2811.78, 2907.94, 3031.58, 3127.1, 3160.46, 3210.85, 3181.56,
3243.83, 3712.01, 3913.5, 3927.51, 3958.53, 3920.48, 3864.41,
3796.78, 3722.65, 3691.73, 3644.18, 3543.42, 3438.32, 3330.14,
3220.07, 3109.24, 3004.57, 2895.68, 2787.51, 2681.53, 2578.11,
2477.52, 2379.95, 2813.87, 2788.22, 2728.48, 2756.85, 2786.68,
2694.65, 2608.24, 2597.77, 2545.36, 2491.73, 2412.97, 2336.46,
2271.19, 2188.86, 2108.19, 2040.78, 1986.68, 1936.91, 1878.75,
1806.1, 1738.22, 1677.78, 1629.61, 1576.72, 1522.31, 1468.47,
1415.22, 1360.73, 1310.14, 1263.29, 1220.5, 1186.29, 1176.45,
1146.52, 1296.16, 1402.02, 1400.11, 1564.91, 1585.36, 1550.73,
1527.26, 1554.59, 1681.56, 1809.45, 1922.11, 1888.08, 1883.8,
1838.53, 1792.08, 1752.16, 1755.79, 1801.08, 1750.14, 1704.65,
1660.78, 1738.31, 1814.29, 1946.35, 1915.42, 1874.03, 1837.08,
1797.03, 1745.39, 1692.97, 1638.4, 1582.78, 1528, 1482.9, 1446,
1392.06, 1368.92, 1336.07, 1295.59, 1252.26, 1219.42, 1217.08,
1189.72, 1160.78, 1136.55, 1102.22, 1069.61, 1046.33, 1042.26,
1049.2, 1077.69, 1137.23, 1279.42, 1384.82, 1535.59, 1751.06,
1776.16, 1795.9, 1942.66, 2397.41, 3508.54, 3446.5, 3360.68,
3272.21, 3181.58, 3183.02, 3075.52, 2966.5, 2869.19, 2861.11,
2968.42, 3074.72, 2981.29, 2918.92, 2917.28, 2839.04, 2769.58,
2867.63, 3091.58, 2993.72, 2907.2, 2821.7, 2742.23, 3034.28,
3000.26, 2992.62, 2916.74, 3065.56, 3032.59, 3069.44, 3078.66,
3155.65, 3345.97, 3270.34, 3191.47, 3111.74, 3031.16, 2946.79,
2871.31, 2786.59, 2712.88, 2626.39, 2538.42, 2452.23, 2536.5,
2446.21, 2359.14, 2427.6, 2337.26, 2268.88, 2239.2, 2159.32,
2079.14, 2017.22, 2101.43, 2035.56, 1974.59, 1963.55, 2463.37,
2592.44, 2496.95, 2406.56, 2399.59, 2719.11, 2627.14, 2532.03,
2441.72, 2355.8, 2273.24, 2212.13, 2131.78, 2054.68, 2021.56,
1944.85, 1871.6, 1822.82, 1763.29, 1694.74, 1629.67, 1569.39,
1511.37, 1454.11, 1400.78, 1350.58, 1320.89, 1524.41, 1844.56,
1984.72, 3024.6, 2953.2, 2836.92, 2725.89, 2620.15, 2518.29,
2421.03, 2328, 2237.87, 2152.21, 2071.36, 1994.57, 1923.34, 1965.91,
1906.98, 1910.02, 1870.62, 1815.72, 1748.49, 1702.61, 1739.4,
1785.07, 1873.86, 2378.29, 2494.53, 2612.01, 2858.16, 2788.6,
2696.15, 2610.24, 2520.25, 2431.5, 2343.59, 2259.04, 2176.11,
2096.57, 2019.08, 1944.45, 1872.69, 1803.19, 1737.54, 1673.17,
1609.63, 1587.49, 1669.8, 1657.65, 1657.05, 1594.35, 1532.6,
1475, 1416.77, 1360.61, 1306.33, 1253.97, 1203.53, 1155.17, 1114.4,
1075.4, 1034.59, 993.862, 957.333, 918.364, 880.908, 845.121,
814.763, 781.644, 749.727, 719.079, 689.992, 666.463, 658.674,
639.19, 617.655, 595.126, 573.268, 551.763, 530.933, 514.663,
493.969, 473.986, 454.894, 436.422, 418.687, 402.434, 404.804,
422.748, 411.777, 527.699, 511.651, 490.849, 536.02, 555.457,
532.754, 510.963, 490.056, 469.998, 450.755, 432.295, 414.587,
397.6, 381.307, 365.679, 350.69, 336.315, 328.3, 664.914, 1045.6,
1086.51, 1042.35, 999.99, 959.336, 922.295, 889.513, 854.952,
820.273, 802.777, 839.017, 809.869, 776.747, 744.953, 733.373,
1046.14, 1004.25, 963.686, 924.941)
y2<-c(-0.0143333336454816, -0.130041667725891, 0.205333333889333,
0.0751666662593683, -0.567708330228925, 0.00870833483835061,
0.108500000167017, -0.152333330673476, 0.0720833349041641, 0.0236249993322417,
-0.00183332874439657, 0.633374993999799, 0.0230833344782392,
0.17537499712004, 0.126000000241523, 0.0728333333196739, 0.24050000286176,
0.470958332220713, 0.00229166596060774, -0.110000000180056, 0.159374999910748,
0.165541665841981, 0.204583332020169, -0.173458332836162, -0.0836250004940666,
-0.207041666842997, 0.191458333438883, -0.231000000378117, -0.450666667272647,
0.000625000917352736, 0.0672916673744718, -0.0514583328040317,
0.447916670391957, -0.0139166663090388, -0.143041666325492, 0.0312916650048768,
-0.245958331235064, -0.329958332081636, 0.304333332712607, -0.0889166676594565,
-0.361833333348234, 0.0753333327205231, 0.695874998966853, 0.41166666833063,
-0.18824999841551, 0.0396249986952171, 1.06683334087332, 0.0413749999182376,
0.0123749998650358, 0.229791667860506, 0.549791666368643, -0.164916665758938,
-0.135374999294678, 0.273583333939314, -0.0588750006087745, 0.277958332871397,
-0.313208335389694, 0.689124989633759, 0.094624999522542, -0.269583333283663,
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0.0766666668932885, -0.161374999520679)
I have a function(contlevels) that uses the MASS package and calculates the density(kde2d) of the two time series and also gives the specific contour level densities. The function calculates the density and returns the cumulative densities for the specific contour levels.
#####################################################
contlevels <- function(x,y,xmin,xmax,ymin,ymax,clev){
#####################################################
dd <- kde2d(x,y,n=c(60,60),lims=c(xmin,xmax,ymin,ymax))
xx <- dd$x
yy <- dd$y
zz <- dd$z
zsort <- sort(zz,decreasing=T)
p <- zz/sum(zz)
ps <- sort(p, decreasing=T)
n <- length(zz)
pscum <- array(0,dim=n)
pscum[1]<-ps[1]
pscum
for (i in 2:n){
pscum[i]<-pscum[i-1]+ps[i]
}
nlev <- length(clev)
cumlev <- array(0,dim=nlev)
for (ilev in 1:nlev){
for (i in 1:(n-1)){
if(pscum[i] >= clev[ilev]){
zsect <- (clev[ilev] - pscum[i])/(pscum[i+1]-pscum[i])
cumlev[ilev] <- zsort[i] + zsect*(zsort[i+1]-zsort[i])
break
}
}
}
contlevels <- list(xx=xx,yy=yy,zz=zz,cumlev=cumlev)
}
##########################################################################
Followings are the plotting initials
xmin=0
xmax=10000
ymin=-1
ymax=1
clev <- c(0.5,0.7,0.8) ## these are the contour levels I need to plot.
Segregating the variables from the function
cl1<-contlevels(x1,y1,xmin,xmax,ymin,ymax,clev)
xx <- cl1$x
yy <- cl1$y
zz <- cl1$z
cumlev1 <- cl1$cumlev
cl2<-contlevels(x2,y2,xmin,xmax,ymin,ymax,clev)
xxx <- cl2$x
yyy <- cl2$y
zzz <- cl2$z
cumlev2 <- cl2$cumlev
Plotting the distribution
plot(x1,y1,pch=20,cex=1,xlim=c(xmin,xmax),ylim=c(ymin,ymax),xlab="X",ylab="Y")
for (ilev in length(clev):2){
.filled.contour(xx,yy,zz,levels=c(cumlev1[ilev],cumlev1[ilev-1]),col="red")
.filled.contour(xxx,yyy,zzz,levels=c(cumlev2[ilev],cumlev2[ilev-1]),col="white")
}
Contour plots
contour(xx,yy,zz,add=T,col="black",lwd=2,levels=cumlev1,labels=clev)
contour(xxx,yyy,zzz,add=T,col="grey",lwd=1,levels=cumlev2,drawlabels=F)
Running this code will result in the above graph. where the 2nd distribution(white color i.e. x2 vs y2) is overlayed over the 1st distribution (red color, x1,y1). The red color will only pop up if the 2nd distribution is less that 1st. However, I also need the other way around. If the 2nd distribution is greater than the 1st distribution, I want it to be colored blue.
Could anyone of you help me with this?
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I have a vector that I want to transform into a probability density function. The mean is 1. How do I plot this? The vector: x <- c(0.7601401, 0.8607037, 0.8748152, 0.885415, 0.8904619, 0.899021, 0.9034128, 0.9050411, 0.9093876, 0.9141021, 0.9172803, 0.9209636, 0.9238607, 0.9268591, 0.9293789, 0.9313833, 0.9335163, 0.9360798, 0.9406245, 0.9427261, 0.9441703, 0.9473808, 0.9502454, 0.9518683, 0.9540568, 0.955987, 0.9580035, 0.9617511, 0.9635325, 0.964507, 0.9674928, 0.9692979, 0.9705296, 0.9732977, 0.9754498, 0.977204, 0.9793093, 0.9821249, 0.9841156, 0.9864521, 0.9873941, 0.9883275, 0.9904071, 0.9920552, 0.9946789, 0.9967097, 0.997695, 0.9992215, 1.001643, 1.0038606, 1.006269, 1.0077312, 1.0091087, 1.0100767, 1.0113615, 1.0124576, 1.0154025, 1.017386, 1.0189122, 1.021932, 1.0238598, 1.0258631, 1.0273012, 1.0294901, 1.031085, 1.0336801, 1.0371085, 1.0387533, 1.0406862, 1.0436292, 1.0453442, 1.0471563, 1.0514885, 1.0531803, 1.055339, 1.059578, 1.0643068, 1.0668389, 1.0694237, 1.073174, 1.0759322, 1.0786821, 1.0846407, 1.0904819, 1.0968733, 1.1039872, 1.1081845, 1.1144191, 1.124116, 1.1378536, 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1.1653051, 0.8287487, 0.8632563, 0.8783957, 0.8872595, 0.8921496, 0.8991388, 0.9038431, 0.9063199, 0.9106785, 0.9144, 0.9189027, 0.9223262, 0.924352, 0.9275484, 0.9296723, 0.932303, 0.9340644, 0.9375086, 0.9410767, 0.9431117, 0.9455282, 0.9476748, 0.9506839, 0.9524355, 0.9542676, 0.9570338, 0.9591047, 0.9620121, 0.9638592, 0.9660401, 0.9678991, 0.9695856, 0.9710773, 0.9740787, 0.9760428, 0.9773099, 0.9800677, 0.9830478, 0.9845491, 0.9868047, 0.9876641, 0.9885819, 0.990765, 0.9929082, 0.9953852, 0.9972524, 0.9980094, 0.999655, 1.0019781, 1.0041123, 1.0065022, 1.0080436, 1.0093745, 1.0102597, 1.011591, 1.0133388, 1.0160004, 1.0177403, 1.0197461, 1.0223301, 1.0243601, 1.0264419, 1.0277154, 1.0300746, 1.0315714, 1.0348406, 1.0377535, 1.0396123, 1.0416248, 1.0438679, 1.0463796, 1.0473053, 1.0518621, 1.0535013, 1.0566508, 1.0602571, 1.0649945, 1.0675837, 1.0696383, 1.0737915, 1.0768286, 1.0807683, 1.0866947, 1.0922428, 1.0993173, 1.1053873, 1.1097462, 1.1160662, 1.1245894, 1.1439087, 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You can get a nonparametric estimate by using the ?density function to compute the kernel density estimate, with default Gaussian kernel. x <- density(vector) plot(x)
Note that you can also generate an empirical cdf with the base ecdf function. That allows you to calculate F(x) for any x. E.g. x <- rnorm(1000) cdf <- ecdf(x) plot(cdf) f <- cdf(0.5) f [1] 0.692
R: The 61928th question about the "singular gradient matrix at initial parameter estimates" error message
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479140.812500000 -105.001426697 479140.875000000 -106.180221558 479140.937500000 -106.504646301 479141.000000000 -104.772674561 479141.062500000 -104.167114258 479141.125000000 -102.925132751 479141.187500000 -102.731872559 479141.250000000 -104.101806641 479141.312500000 -104.532470703 479141.375000000 -103.677726746 479141.437500000 -103.467483521 479141.500000000 -104.314605713 479141.562500000 -106.088348389 479141.625000000 -105.849678040 479141.687500000 -104.784294128 479141.750000000 -104.685859680 479141.812500000 -102.816184998 479141.875000000 -103.009178162 479141.937500000 -105.581695557 479142.000000000 -104.964607239 479142.062500000 -103.978279114 479142.125000000 -104.709609985 479142.187500000 -105.373786926 479142.250000000 -105.477348328 479142.312500000 -107.076698303 479142.375000000 -108.599830627 479142.437500000 -107.518699646 To which I want to fit the function While the formula is kind of a beast, it has physical meaning, so I would like to not change it. I have the following code: index_min <- which(mydf[,2] == min(mydf[,2]))[1] n0start <- -119 n1start <- 16 df0start <- 120 df1start <- 1 f0start <- mydf[index_min,1] f1start <- mydf[index_min,1] plot(x=mydf[,1],y=mydf[,2]) eq = function(f,n0, n1, f0, f1, df0, df1){ n0+n1*4*(f-f1)^2/(4*(f-f1)^2+(4*((f-f0)/df0)*(f-f1)-df1)^2)} lines(mydf[,1], eq(mydf[,1],n0start, n1start, f0start, f1start, df0start, df1start), col="red" ) res <- try(nlsLM( y ~ n0+n1*4*(f-f1)^2/(4*(f-f1)^2+(4*((f-f0)/df0)*(f-f1)-df1)^2), start=c(n0=n0start, n1=n1start,f0=f0start,df0=df0start,f1=f1start,df1=df1start) , data = mydf)) coef(res) As you can see, the starting values look rather decent, but I get the "singular gradient matrix at initial parameter estimates" error. I have looked through all the other posts, however, I don't see why my formula is overdetermined or why the starting values should be bad.
Okay, I figured out the mistake. nlsLM requires data to be a data-frame and not just a bare matrix. The error message is simply misleading.
Calculate moments based on a probability distribution
I have a probability density distribution that I calculated dividing the ending probabilities by the difference in returns: deltaR <- c(NA) for (i in 2:204) { deltaR[i - 1] = (R[i + 1] - R[i - 1]) / 2 } for (i in 1:204) { probability_density[i] = End_Probabilities[i + 1] / deltaR[i] } Now I should be able to calculate the moments (theoretically) by integrating the probability density function from -Inf to Inf and multiplying it with x**j. How do I implement this last step in R? I had a look at the package moments and this requires another type of input, than a density function. Here is my calculated probability density: c(1.060127e-01, 7.808639e-02, 5.351772e-02, 3.271984e-02, 1.653464e-02, 6.228544e-03, 1.406439e-03, 9.808728e-05, 2.659169e-09, 1.526135e-05, 2.540583e-04, 9.029513e-04, 1.695233e-03, 2.376100e-03, 2.854467e-03, 3.152196e-03, 3.332074e-03, 3.391327e-03, 3.314459e-03, 3.103407e-03, 2.782234e-03, 2.421864e-03, 2.095342e-03, 1.866990e-03, 1.799232e-03, 1.995382e-03, 2.643171e-03, 4.140963e-03, 7.222687e-03, 1.290895e-02, 2.261961e-02, 3.847810e-02, 6.267389e-02, 9.368153e-02, 1.257818e-01, 1.508366e-01, 1.633405e-01, 1.614039e-01, 1.503522e-01, 1.369496e-01, 1.256595e-01, 1.203709e-01, 1.266565e-01, 1.518232e-01, 2.052942e-01, 3.027602e-01, 4.690844e-01, 7.386214e-01, 1.145504e+00, 1.716563e+00, 2.443353e+00, 3.283689e+00, 4.184745e+00, 5.080073e+00, 5.893442e+00, 6.546659e+00, 6.980054e+00, 7.150451e+00, 7.039760e+00, 6.653884e+00, 6.033344e+00, 5.240736e+00, 4.364490e+00, 3.488565e+00, 2.677145e+00, 1.975665e+00, 1.411421e+00, 9.921453e-01, 7.066775e-01, 5.266718e-01, 4.271196e-01, 3.924719e-01, 4.133705e-01, 4.695568e-01, 5.348714e-01, 5.759546e-01, 5.631329e-01, 5.025344e-01, 4.130699e-01, 3.141471e-01, 2.215556e-01, 1.447194e-01, 8.790208e-02, 5.098624e-02, 3.014762e-02, 1.910260e-02, 1.344742e-02, 1.065112e-02, 9.321387e-03, 8.835730e-03, 8.816743e-03, 8.942431e-03, 8.948472e-03, 8.794572e-03, 8.501278e-03, 8.093566e-03, 7.602140e-03, 7.072520e-03, 6.550003e-03, 6.074361e-03, 5.673101e-03, 5.343198e-03, 5.075543e-03, 4.861257e-03, 4.688752e-03, 4.544127e-03, 4.404867e-03, 4.250920e-03, 4.077948e-03, 3.889586e-03, 3.704711e-03, 3.541290e-03, 3.403317e-03, 3.289065e-03, 3.187416e-03, 3.087250e-03, 2.984974e-03, 2.880152e-03, 2.777254e-03, 2.680905e-03, 2.592347e-03, 2.511498e-03, 2.436366e-03, 2.364864e-03, 2.296123e-03, 2.229745e-03, 2.165964e-03, 2.105085e-03, 2.047108e-03, 1.991933e-03, 1.939349e-03, 1.889138e-03, 1.841124e-03, 1.795147e-03, 1.751063e-03, 1.708734e-03, 1.668043e-03, 1.628947e-03, 1.591417e-03, 1.555428e-03, 1.520928e-03, 1.487760e-03, 1.455745e-03, 1.424711e-03, 1.394546e-03, 1.365347e-03, 1.337263e-03, 1.310441e-03, 1.284943e-03, 1.260516e-03, 1.236840e-03, 1.213614e-03, 1.190628e-03, 1.167988e-03, 1.145885e-03, 1.124552e-03, 1.104209e-03, 1.084995e-03, 1.067065e-03, 1.050736e-03, 1.035943e-03, 1.020968e-03, 1.003863e-03, 9.833325e-04, 9.592756e-04, 9.355316e-04, 9.172070e-04, 9.110385e-04, 9.202415e-04, 9.318257e-04, 9.283292e-04, 8.961524e-04, 8.376603e-04, 7.670913e-04, 7.256675e-04, 7.485672e-04, 8.356126e-04, 9.654478e-04, 1.042324e-03, 9.663562e-04, 7.592500e-04, 5.156526e-04, 3.910638e-04, 4.753052e-04, 8.290892e-04, 1.517836e-03, 2.148325e-03, 2.052454e-03, 1.269326e-03, 4.111317e-04, 8.685183e-05, 1.546110e-04, 9.204908e-04, 3.439569e-03, 6.646307e-03, 7.001711e-03, 4.076310e-03, 8.055459e-04, 6.350488e-10, 2.312147e-06, 1.584547e-03, 1.315706e-02, 4.601747e-02, NA) and this is my return: c(-0.935414347, -0.908840790, -0.882955147, -0.857722698, -0.833111287, -0.809091076, -0.785634328, -0.762715212, -0.740309633, -0.718395083, -0.696950500, -0.675956149, -0.655393513, -0.635245193, -0.615494824, -0.596126988, -0.577127150, -0.558481585, -0.540177322, -0.522202092, -0.504544274, -0.487192852, -0.470137374, -0.453367914, -0.436875037, -0.420649768, -0.404683560, -0.388968270, -0.373496135, -0.358259744, -0.343252021, -0.328466203, -0.313895825, -0.299534697, -0.285376894, -0.271416740, -0.257648791, -0.244067828, -0.230668838, -0.217447010, -0.204397720, -0.191516523, -0.178799144, -0.166241467, -0.153839531, -0.141589522, -0.129487761, -0.117530703, -0.105714928, -0.094037138, -0.082494145, -0.071082874, -0.059800353, -0.048643708, -0.037610161, -0.026697027, -0.015901704, -0.005221677, 0.005345492, 0.015802162, 0.026150621, 0.036393085, 0.046531703, 0.056568562, 0.066505681, 0.076345025, 0.086088499, 0.095737954, 0.105295185, 0.114761940, 0.124139915, 0.133430760, 0.142636079, 0.151757433, 0.160796339, 0.169754274, 0.178632677, 0.187432947, 0.196156448, 0.204804506, 0.213378417, 0.221879440, 0.230308804, 0.238667708, 0.246957319, 0.255178777, 0.263333194, 0.271421653, 0.279445214, 0.287404910, 0.295301748, 0.303136715, 0.310910773, 0.318624860, 0.326279895, 0.333876775, 0.341416378, 0.348899561, 0.356327161, 0.363699998, 0.371018874, 0.378284573, 0.385497862, 0.392659493, 0.399770198, 0.406830699, 0.413841698, 0.420803885, 0.427717934, 0.434584508, 0.441404253, 0.448177805, 0.454905784, 0.461588799, 0.468227448, 0.474822316, 0.481373976, 0.487882992, 0.494349914, 0.500775283, 0.507159631, 0.513503477, 0.519807332, 0.526071697, 0.532297065, 0.538483916, 0.544632726, 0.550743959, 0.556818072, 0.562855512, 0.568856721, 0.574822129, 0.580752162, 0.586647238, 0.592507765, 0.598334146, 0.604126777, 0.609886046, 0.615612336, 0.621306023, 0.626967475, 0.632597055, 0.638195120, 0.643762022, 0.649298104, 0.654803707, 0.660279164, 0.665724804, 0.671140949, 0.676527917, 0.681886022, 0.687215570, 0.692516865, 0.697790204, 0.703035881, 0.708254184, 0.713445397, 0.718609802, 0.723747672, 0.728859279, 0.733944890, 0.739004769, 0.744039175, 0.749048362, 0.754032582, 0.758992083, 0.763927108, 0.768837899, 0.773724691, 0.778587719, 0.783427212, 0.788243398, 0.793036498, 0.797806735, 0.802554324, 0.807279480, 0.811982414, 0.816663334, 0.821322445, 0.825959950, 0.830576047, 0.835170933, 0.839744804, 0.844297849, 0.848830257, 0.853342216, 0.857833908, 0.862305516, 0.866757217, 0.871189188, 0.875601603, 0.879994635, 0.884368452, 0.888723222, 0.893059110, 0.897376280, 0.901674891, 0.905955104, 0.910217074, 0.914460957, 0.918686906, 0.922895071, 0.927085602, 0.931258645, 0.935414347) The return is longer than the density, since I divided by the difference above.
Bootstrap p value contradicts p value for likelihood ratio test
I have the same problem as the one posted by #soapsuds here. I did not want to ask a duplicate question but when I tried to edit the original question to provide the reproducible example that was missing in the original post my edits got rejected. Since the reproducible example has a lot of elements, I could not write it as a comment to the original question either, so I provide my code and my reproducible data here, as a separate question. I am trying to compare two models using the likelihood ratio test. From bootstrapping I get a set of 1000 p-values. Here are the numbers I get: chi2 <- c(41.83803376, 69.23970174, 42.5479637, 50.90208302, 39.18366824, 78.88589665, 28.88469406, 34.99980796, 85.80860848, 66.01750186, 29.06286, 46.43221576, 46.50523792, 59.87362884, 46.17274808, 77.97429928, 48.04404216, 12.88592623, 43.1883816, 33.24251471, 53.27310465, 56.92595147, 47.99838583, 46.0718587, 49.0760042, 29.70866297, 66.80696553, 66.61091741, 37.82375112, 50.19760846, 30.99961864, 27.17687828, 37.46944206, 66.36226432, 48.30737714, 43.64410333, 23.78480451, 42.52842793, 60.49309556, 46.29154, 26.96744296, 32.21561396, 48.20316788, 38.73153704, 67.80328765, 55.00664931, 36.74645735, 23.3647159, 56.35290442, 38.11055268, 58.3316501, 36.00500638, 41.36949956, 49.09067881, 64.42712507, 23.97787069, 54.5394799, 87.02114296, 26.01402166, 50.47426712, 38.58006084, 48.47626864, 22.28809699, 58.87590487, 17.59264288, 33.32650413, 67.77868338, 60.95427815, 37.19931376, 36.23280256, 53.54379697, 70.06479334, 41.3482703, 34.54099647, 55.99585144, 30.60500406, 32.02745276, 37.92670127, 44.23450124, 40.38607671, 44.02263294, 40.89874789, 62.74174279, 50.95137406, 47.12851204, 26.03848394, 36.6202765, 61.06296311, 50.17094183, 35.93242228, 41.8913277, 35.19089913, 38.88574534, 66.075866, 26.34296242, 49.99887059, 42.97123036, 34.89006324, 66.5460019, 67.61855859, 48.52166614, 41.41324193, 46.76294302, 14.87650733, 24.11661382, 62.28747719, 43.94865019, 44.20328393, 41.17756328, 43.74055584, 49.46236395, 38.59558107, 42.85073398, 49.81046036, 36.60331917, 39.85328124, 59.31376822, 61.36038822, 52.56707689, 29.19196892, 46.473958, 39.12904163, 38.75057931, 36.32493909, 49.61088785, 33.42904297, 34.73661836, 33.97736002, 37.44094284, 57.73605417, 43.14773064, 42.78707831, 26.84112684, 48.47832871, 45.94043053, 71.13563773, 46.28614795, 42.33386157, 59.31216832, 46.72946806, 47.76027545, 52.45174304, 49.99459367, 59.00971014, 24.03299408, 17.09453132, 37.44112252, 46.6352525, 60.42442286, 39.35194465, 46.57121135, 56.28622077, 59.20354176, 57.72511864, 41.97053375, 27.97077407, 29.70497125, 46.63976021, 40.24305901, 24.84335714, 36.08600444, 61.619572, 69.31377401, 86.91496878, 44.47955842, 44.1230351, 46.12514671, 43.97381958, 71.99269072, 47.01277643, 50.08167664, 27.01076954, 31.32586466, 40.96782215, 19.07024825, 53.00009679, 43.15397869, 42.49652848, 53.47325607, 43.45891027, 42.57719313, 39.40459925, 42.15077856, 52.23784844, 33.07947933, 45.02462309, 59.187763, 51.9198527, 48.3179841, 76.10501177, 34.95091433, 40.75545034, 31.27034043, 39.83209227, 47.87278051, 46.25057806, 62.84591205, 41.24656655, 68.14749236, 53.11576938, 39.20515676, 61.96116013, 35.64665684, 72.52689101, 54.64239536, 34.14169048, 34.32282338, 49.60786171, 50.32976034, 43.83560386, 57.49367366, 81.65759842, 61.59398941, 37.77960776, 30.74484476, 34.72859511, 32.46631033, 37.41725027, 34.04569722, 54.11932007, 34.62264522, 28.36753913, 30.95379445, 84.06354755, 29.32445434, 56.7720931, 33.23951864, 48.61860157, 39.3563214, 32.44713462, 61.25078174, 32.49661836, 40.38508488, 26.73565294, 58.16191656, 61.12461262, 23.701462, 22.14004554, 57.80213129, 57.15936762, 31.51238062, 44.60223083, 30.60135802, 46.96637333, 42.79517081, 56.85541543, 48.79421654, 29.72862307, 41.61735121, 43.37983393, 41.16802781, 61.69637392, 37.29991153, 39.0936012, 57.39158494, 57.55033901, 50.72878897, 34.82491685, 42.66486539, 34.54565803, 55.04161695, 44.56687339, 53.46745359, 57.22210412, 34.8578696, 28.81098073, 51.4033337, 51.9568532, 60.98717632, 62.98817996, 44.1335128, 33.38418814, 59.71059054, 45.82016411, 29.47178401, 30.64995791, 28.52106318, 53.98066153, 64.22209517, 58.29438562, 39.18280924, 38.1302144, 41.90062316, 28.68650929, 69.42769639, 33.79539164, 26.08549507, 55.29167497, 97.25975259, 63.07957724, 56.59002373, 51.40088678, 71.33491023, 46.24955174, 33.90101761, 38.0669817, 52.50993176, 51.84637529, 39.93642798, 61.9268346, 30.25561485, 49.57396856, 44.70170977, 57.00286149, 40.39009586, 63.23642634, 59.23643766, 55.80521902, 68.58421775, 24.04456631, 51.64338572, 61.14103174, 59.29371792, 46.51493959, 43.48297587, 39.99164284, 44.62589755, 58.89385062, 60.96824416, 54.02310453, 43.54420281, 44.24628098, 47.0991445, 58.9015349, 60.54157696, 34.86277089, 33.79969585, 34.57183642, 47.21383117, 55.3529805, 36.49813553, 44.94388291, 29.43134497, 43.41469037, 43.033338, 63.37329389, 38.22029171, 43.2894392, 23.42769168, 55.18117532, 19.39227876, 28.29656641, 28.56075122, 39.57260362, 65.48606054, 31.05339648, 24.87488959, 61.6027878, 59.56983406, 37.53918879, 28.67095839, 36.51499868, 44.43350204, 53.35842664, 48.30182354, 31.03494822, 45.68689659, 46.11113306, 53.89204524, 29.75548276, 35.60906482, 53.35195594, 56.28657675, 44.77245145, 60.20671942, 41.62253735, 40.34528594, 38.48551456, 27.39317425, 51.05414332, 38.41986986, 75.05074423, 34.16773046, 52.18497954, 49.63059496, 28.7365636, 10.59466471, 38.1033901, 52.20531405, 47.031987, 47.45955635, 44.64312012, 50.32229588, 62.40798968, 37.7455721, 31.97746406, 51.17250147, 45.91231295, 66.58450378, 32.68956686, 34.35845347, 70.34703042, 41.47493453, 53.67684859, 35.66735299, 19.76630329, 35.69026569, 76.57475236, 62.11269107, 37.06632602, 57.91686258, 33.95869501, 55.18034702, 66.09725866, 46.80608564, 46.75623531, 55.49605214, 45.7813294, 22.37612777, 62.40414132, 50.51745906, 46.86535062, 54.4172637, 35.44713601, 45.40918234, 43.83215257, 57.14754799, 24.20941074, 44.8145542, 50.79673435, 42.14561269, 32.73720673, 28.51047028, 32.14753623, 28.43006627, 39.50188334, 58.51806717, 37.96898151, 73.14656287, 48.23605238, 75.31273481, 29.57608972, 43.62952257, 30.47534709, 43.24927262, 43.61475563, 53.48883918, 53.85263136, 41.91477406, 56.16405384, 46.21202327, 55.52602904, 49.88481191, 46.31478116, 72.29722834, 40.48187205, 35.31368051, 40.57713079, 34.15725967, 65.85738596, 32.16093944, 32.07117679, 46.44579516, 53.3243447, 69.35531671, 21.70205174, 44.30678622, 40.13349937, 51.7431728, 43.03690121, 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p.values <- c(9.92E-11, 8.72E-17, 6.90E-11, 9.71E-13, 3.86E-10, 6.58E-19, 7.68E-08, 3.30E-09, 1.98E-20, 4.47E-16, 7.01E-08, 9.48E-12, 9.14E-12, 1.01E-14, 1.08E-11, 1.04E-18, 4.17E-12, 0.000331062, 4.97E-11, 8.14E-09, 2.90E-13, 4.53E-14, 4.27E-12, 1.14E-11, 2.46E-12, 5.02E-08, 2.99E-16, 3.31E-16, 7.74E-10, 1.39E-12, 2.58E-08, 1.86E-07, 9.29E-10, 3.75E-16, 3.64E-12, 3.94E-11, 1.08E-06, 6.97E-11, 7.38E-15, 1.02E-11, 2.07E-07, 1.38E-08, 3.84E-12, 4.86E-10, 1.81E-16, 1.20E-13, 1.35E-09, 1.34E-06, 6.06E-14, 6.68E-10, 2.21E-14, 1.97E-09, 1.26E-10, 2.44E-12, 1.00E-15, 9.74E-07, 1.52E-13, 1.07E-20, 3.39E-07, 1.21E-12, 5.26E-10, 3.34E-12, 2.35E-06, 1.68E-14, 2.74E-05, 7.79E-09, 1.83E-16, 5.84E-15, 1.07E-09, 1.75E-09, 2.53E-13, 5.74E-17, 1.27E-10, 4.17E-09, 7.26E-14, 3.16E-08, 1.52E-08, 7.35E-10, 2.91E-11, 2.08E-10, 3.25E-11, 1.60E-10, 2.36E-15, 9.47E-13, 6.65E-12, 3.35E-07, 1.44E-09, 5.53E-15, 1.41E-12, 2.04E-09, 9.65E-11, 2.99E-09, 4.49E-10, 4.34E-16, 2.86E-07, 1.54E-12, 5.56E-11, 3.49E-09, 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8.01E-15, 2.21E-09, 1.53E-10, 1.78E-09, 1.74E-11, 4.68E-12, 6.14E-06, 4.41E-05, 6.03E-10, 5.19E-10, 4.04E-17, 1.77E-08, 1.27E-11, 6.70E-12, 2.10E-08, 5.62E-11, 3.55E-10, 4.38E-06, 2.04E-08, 2.19E-13, 9.57E-18, 1.92E-08, 4.19E-10, 7.40E-12, 6.76E-15, 1.04E-14, 6.06E-06, 1.24E-13, 3.13E-16, 2.00E-12, 5.43E-07, 8.30E-15, 2.87E-06, 1.55E-15, 4.93E-10, 2.37E-14, 4.01E-07, 4.47E-15, 9.27E-11, 1.82E-06, 3.27E-12, 1.31E-12, 7.58E-11, 4.56E-11, 1.29E-10, 3.02E-09, 3.38E-12, 3.25E-08, 1.05E-13, 3.13E-17, 4.00E-09, 3.46E-11, 1.14E-11, 2.95E-08, 4.28E-12, 5.43E-09, 7.24E-10, 1.83E-11, 1.74E-10, 1.67E-11, 3.90E-12, 1.57E-15, 5.34E-05, 1.79E-13, 1.17E-11, 1.57E-11, 2.50E-13, 2.04E-13, 8.64E-06, 8.86E-11, 1.54E-11, 9.88E-10, 1.84E-11, 1.88E-12, 4.34E-08, 2.86E-09, 2.71E-17, 4.30E-15, 8.18E-14, 8.15E-10, 2.65E-15, 3.91E-12, 6.54E-16, 3.33E-12, 7.13E-09, 1.46E-08, 8.58E-05, 9.33E-11, 4.17E-08, 7.69E-11, 3.00E-13, 3.71E-12, 9.57E-09, 6.79E-09, 3.21E-11, 1.35E-14, 2.78E-12, 1.76E-15, 1.96E-09, 2.64E-11, 1.50E-06, 2.42E-13, 7.32E-11, 1.10E-07, 3.16E-11, 7.49E-14, 2.77E-08, 5.22E-09, 1.30E-14, 2.90E-14, 8.03E-16, 5.06E-14, 4.82E-11, 2.54E-10, 3.15E-11, 2.87E-05, 1.43E-17, 1.15E-10, 1.64E-15, 1.01E-12, 1.80E-14, 2.86E-09, 7.06E-09, 1.11E-11, 4.49E-14, 2.77E-16, 3.83E-10, 2.79E-06, 6.56E-13, 9.11E-12, 3.47E-08, 9.28E-12, 1.09E-10, 7.56E-11, 1.41E-16, 4.02E-08, 4.46E-10, 1.63E-10, 7.78E-13, 6.37E-13, 1.01E-12, 1.84E-08, 4.94E-14, 4.80E-12, 5.02E-09, 4.26E-13, 3.48E-12, 1.84E-05, 1.16E-08, 8.79E-15, 1.70E-09, 4.19E-10, 3.87E-09, 4.21E-12, 2.14E-08, 7.27E-11, 2.39E-17, 4.83E-09, 5.56E-10, 1.96E-11, 1.70E-15, 1.22E-08, 9.21E-16, 2.21E-13, 1.31E-10, 7.76E-08, 3.56E-09, 1.15E-09, 1.68E-08, 1.41E-09, 1.44E-07, 1.53E-06, 1.11E-10, 7.85E-06, 1.01E-14, 2.75E-10, 6.02E-12, 6.23E-10, 1.50E-10, 2.94E-15, 3.35E-12, 1.41E-06, 4.51E-08, 8.45E-19, 9.79E-11, 6.99E-09, 3.06E-10, 5.04E-18, 6.22E-14, 2.18E-10, 1.48E-14, 6.29E-12, 7.83E-13, 1.57E-11, 9.13E-14, 1.70E-14, 6.05E-10, 1.12E-11, 7.19E-10, 1.51E-10, 1.97E-15, 2.33E-07, 2.13E-08, 3.69E-10, 6.24E-16, 9.17E-15, 1.54E-11, 4.87E-08, 7.55E-13, 1.11E-11, 6.58E-15, 1.95E-11, 1.15E-09, 2.18E-08, 2.86E-14, 4.55E-08, 4.74E-10, 8.38E-14, 2.73E-16, 2.91E-12, 9.83E-09, 6.88E-14, 8.34E-08, 1.52E-10, 1.08E-13, 5.30E-10, 2.99E-06, 5.92E-13, 1.10E-10, 1.82E-17, 1.66E-12, 1.03E-10, 8.50E-14, 1.08E-11, 4.14E-13, 1.59E-06, 7.31E-13, 1.26E-11, 4.45E-12, 3.18E-13, 6.87E-16, 4.25E-12, 7.07E-11, 1.15E-12, 2.65E-06, 4.25E-07, 3.52E-14, 1.14E-09, 6.45E-17, 2.84E-13, 1.83E-09, 2.74E-09, 4.07E-17, 4.28E-08, 9.33E-11, 1.01E-15, 3.99E-12, 6.10E-08, 5.63E-12, 2.22E-09, 2.76E-05, 6.48E-11, 1.31E-07, 7.70E-10, 5.97E-14, 9.53E-09, 1.03E-05, 3.31E-12, 1.41E-15, 3.54E-12, 5.61E-10, 1.39E-15, 4.44E-11, 4.93E-15, 7.08E-12, 1.69E-08, 1.64E-10, 1.02E-08, 3.65E-11, 4.41E-12, 2.71E-07, 1.88E-06, 1.04E-10, 4.83E-08, 2.91E-09, 1.10E-09, 1.51E-15, 2.86E-09, 2.68E-08, 2.94E-09, 1.96E-11, 5.83E-12, 6.11E-15, 3.02E-13, 8.63E-10, 8.35E-16, 3.28E-10, 5.16E-11, 3.89E-09, 1.08E-13, 3.88E-14, 4.09E-08, 4.23E-08, 2.43E-13, 1.83E-10, 1.37E-12, 3.12E-10, 9.16E-14, 2.93E-15, 3.06E-12, 1.22E-14, 7.30E-13, 1.38E-09, 1.36E-11, 2.78E-10, 7.10E-13, 2.60E-10, 2.43E-07, 2.08E-05, 1.13E-10, 1.04E-09, 1.06E-14, 8.29E-11, 3.00E-14, 4.71E-08, 8.34E-07, 2.48E-11, 3.47E-12, 5.13E-09, 9.76E-16, 2.19E-13, 1.33E-11, 9.32E-11, 6.36E-14, 7.25E-11, 1.36E-05, 2.18E-15, 7.90E-11, 9.41E-15, 5.95E-14, 2.50E-13, 3.47E-17, 1.42E-14, 1.85E-17, 2.44E-10, 5.97E-18, 9.87E-12, 3.05E-12, 1.38E-09, 1.30E-13, 3.17E-14, 1.99E-15, 4.34E-07, 1.04E-05, 1.88E-15, 1.34E-13, 8.23E-08, 5.02E-12, 1.90E-09, 3.24E-12, 8.89E-11, 0.000142133, 3.00E-07, 3.60E-14, 5.95E-07, 4.59E-12, 2.48E-09, 3.98E-11, 5.59E-13, 4.13E-14, 1.77E-11, 4.88E-11, 3.83E-12, 1.11E-09, 3.21E-10, 1.68E-11, 7.09E-07, 1.12E-08, 1.88E-08, 8.16E-14, 2.87E-14, 5.17E-09, 5.11E-13, 1.43E-12, 4.19E-09, 4.03E-17, 6.34E-12, 2.63E-09, 1.55E-13, 4.85E-12, 4.49E-06, 7.34E-11, 2.82E-14, 1.82E-12, 1.93E-16, 3.10E-08, 1.64E-08, 1.32E-11, 6.31E-11, 6.48E-14, 2.55E-06, 1.60E-08, 1.58E-08, 8.22E-15, 9.19E-10, 3.12E-08, 1.77E-06, 5.43E-08, 8.03E-14, 3.05E-10, 1.71E-08, 3.57E-15, 1.11E-05, 1.18E-09, 4.99E-15, 6.74E-12, 3.83E-09, 7.73E-09, 2.22E-12, 2.60E-10, 2.83E-12, 6.35E-08, 1.56E-12, 1.10E-16, 1.87E-12, 1.45E-05, 3.44E-13, 8.25E-07, 1.91E-16, 1.79E-13, 2.33E-09, 2.55E-14, 4.51E-17, 8.90E-14, 8.76E-11, 4.16E-15, 2.58E-11, 2.37E-10, 5.16E-15, 3.03E-09, 1.34E-13, 9.61E-12, 9.16E-11, 2.82E-13, 1.28E-05, 5.30E-08, 5.67E-10, 5.09E-15, 1.51E-08, 2.89E-08, 6.98E-10, 6.88E-18, 3.43E-08, 6.38E-11, 7.24E-08, 1.74E-12, 2.76E-08, 1.88E-14, 2.47E-15, 1.21E-06, 8.16E-11, 1.28E-13, 6.72E-10, 9.04E-07, 9.98E-09, 1.57E-19, 7.33E-11, 8.03E-10, 1.50E-08, 4.12E-11, 8.33E-16, 5.56E-15, 2.94E-13, 1.70E-09, 4.45E-15, 2.35E-13, 4.66E-16, 1.06E-12, 6.40E-13, 8.26E-16, 2.27E-14, 8.47E-09, 6.10E-11, 1.12E-13, 3.94E-10, 1.92E-09, 4.25E-08, 8.84E-07, 4.39E-12, 4.19E-13, 5.32E-14, 4.02E-15, 1.51E-06, 1.19E-12, 4.72E-12, 7.05E-12, 2.06E-09, 3.04E-13, 3.42E-12, 2.12E-16, 9.74E-10, 8.71E-06, 4.55E-12, 2.69E-12) While p-values range from 6.08038E-23 to 0.001134145, the bootstrapped p-value I get is 0.4995005 and I don't understand why. I am using the following function to find the bootstrapped p-value: (1+sum(logit.boot$t[,2] > logit.boot$t0[2]))/(1+logit.boot$R) where logit.boot$t[,2] takes on values from the p.values vector, logit.boot$t0[2] equals 2.664684e-11 and logit.boot$R = 1000. EDIT Here is the code I used for bootstrapping: logit.bootstrap <- function(data, indices){ d <- data[indices, ] Mf1 <- glm(Y ~ A + B + C, data = d, family = "binomial") data.setM1 <- na.omit(d[, all.vars(formula(Mf1))]) M1.io <- glm(Y ~ A + B, data = data.setM1, family = "binomial") my.test <- lrtest(Mf1, M1.io) return(c(my.test$"Chisq"[2], my.test$"Pr(>Chisq)"[2])) } logit.boot <- boot(data=my.data, statistic=logit.bootstrap, R=1000) # 10'000 samples
In the result of the boot function, t0 should the p value on the original data, and t is some p values which are generated from random resampling/permutation on the original data. And in your case, you shouldn't use (1+sum(logit.boot$t[,2] > logit.boot$t0[2]))/(1+logit.boot$R) to get information from your bootstrapped p values, you may use quantile(logit.boot$t[,2], c(0.025,0.975)) or something like this to obtain a bootstrapped 95% confidence interval on your p value. This is not very meaningful, since the meaning of p value is already a probability (confidence level), why do you bother to obtain a confidence interval for p value? And the validness of the bootstrap method relies on the correctness of your parametric model. So if you want to use non-parametric approach toward this problem, I think you need to find some other approaches instead of this one.
Why colors did not appear in Key of heatmap.2()
I have a data that looks like this: SC_LT34F_BM SC_LTSL_BM SC_STSL_BM SC_LTSL_FL SC_STSL_FL SC_MPP34F_BM SC_ST34F_BM SC_CMP_BM SC_MEP_BM SC_GMP_BM SC_CDP_BM SC_MDP_BM MLP_BM MLP_FL proB_CLP_BM proB_FrA_BM proB_FrBC_BM preB_FrC_BM preB_FrD_BM B_FrE_BM proB_CLP_FL proB_FrA_FL proB_FrBC_FL preB_FrD_FL B_FrE_FL B_T1_Sp B_T2_Sp B_T3_Sp B_Fo_Sp B_GC_Sp B_MZ_Sp B1a_Sp B_FrF_BM B_Fo_MLN B_Fo_LN B_Fo_PC B1b_PC B1a_PC DC_8-_Th DC_8+_Th DC_4+_Sp DC_8+_Sp DC_8-4-11b-_Sp DC_8-4-11b+_Sp DC_pDC_8-_Sp DC_pDC_8+_Sp DC_4+_SLN DC_8+_SLN DC_8-4-11b-_SLN DC_8-4-11b+_SLN DC_pDC_8+_SLN DC_IIhilang-103-11blo_SLN DC_IIhilang-103-11b+_SLN DC_IIhilang+103+11blo_SLN DC_IIhilang+103-11b+_SLN DC_4+_MLN DC_8+_MLN DC_8-4-11b-_MLN DC_8-4-11b+_MLN DC_pDC_8+_MLN DC_LC_Sk DC_103-11b+_Lv DC_103+11b-_Lv DC_103+11b-_LuLN DC_103-11b+_LuLN DC_103-11b+24+_Lu DC_103+11b-_Lu DC_103-11b+_PolyIC_Lu DC_103+11b-_PolyIC_Lu DC_103-11b+F4/80lo_Kd DC_103+11b-_SI DC_103+11b+_SI DC_103+11b-_Salm3_SI DC_103+11b+_Salm3_SI MF_BM MF_RP_Sp MF_Lu MF_103-11b+24-_Lu MF_II+480lo_PC MF_103-11b+_SI MF_11cloSer_SI MF_103-11b+_Salm3_SI MF_11cloSer_Salm3_SI MF_II-480hi_PC MF_Microglia_CNS MF_Thio5_II+480int_PC MF_Thio5_II-480int_PC MF_Thio5_II-480hi_PC MF_Thio5_II+480lo_PC Mo_6C+II-_BM Mo_6C-II-_BM Mo_6C+II-_Bl Mo_6C+II+_Bl Mo_6C-II-_Bl Mo_6C-II+_Bl Mo_6C-IIint_Bl Mo_6C+II-_LN GN_BM GN_Bl GN_Arth_BM GN_Arth_SynF GN_UrAc_PC GN_Thio_PC NK_Sp NK_49CI-_Sp NK_49CI+_Sp NK_49H-_Sp NK_49H+_Sp NK_MCMV1_Sp NK_MCMV7_Sp NK_H+_MCMV1_Sp NK_H+_MCMV7_Sp NK_b2m-_Sp NK_DAP10-_Sp NK_DAP12-_Sp preT_ETP_Th preT_ETP-2A_Th preT_DN2_Th preT_DN2A_Th preT_DN2B_Th preT_DN2-3_Th preT_DN3A_Th preT_DN3B_Th preT_DN3-4_Th T_DN4_Th T_ISP_Th T_DP_Th T_DPbl_Th T_DPsm_Th T_DP69+_Th T_4+8int_Th T_4SP69+_Th T_4SP24int_Th T_4SP24-_Th T_4int8+_Th T_8SP69+_Th T_8SP24int_Th T_8SP24-_Th T_4Nve_Sp T_4Mem_Sp T_4Mem44h62l_Sp T_4Nve_LN T_4Mem_LN T_4Mem44h62l_LN T_4Nve_PP T_4Nve_MLN T_4_LN_BDC T_4_PLN_BDC T_4_Pa_BDC T_4FP3-_Sp T_4FP3+25+_Sp T_4FP3+25+_AA T_4FP3+25+_LN T_8Nve_Sp T_8Mem_Sp T_8Nve_LN T_8Mem_LN T_8Nve_PP T_8Nve_MLN T_8Nve_Sp_OT1 T_8Eff_Sp_OT1_d5_VSVOva T_8Eff_Sp_OT1_d6_VSVOva T_8Eff_Sp_OT1_d8_VSVOva T_8Eff_Sp_OT1_d15_VSVOva T_8Mem_Sp_OT1_d45_VSVOva T_8Mem_Sp_OT1_d106_VSVOva T_8Eff_Sp_OT1_12hr_LisOva T_8Eff_Sp_OT1_24hr_LisOva T_8Eff_Sp_OT1_48hr_LisOva T_8Eff_Sp_OT1_d6_LisOva T_8Eff_Sp_OT1_d8_LisOva T_8Eff_Sp_OT1_d10_LisOva T_8Eff_Sp_OT1_d15_LisOva T_8Mem_Sp_OT1_d45_LisOva T_8Mem_Sp_OT1_d100_LisOva NKT_44-NK1_1-_Th NKT_44+NK1_1-_Th NKT_44+NK1_1+_Th NKT_4+_Sp NKT_4-_Sp NKT_4+_Lv NKT_4-_Lv Tgd_Th Tgd_vg1+vd6-24ahi_Th Tgd_vg1+vd6+24ahi_Th Tgd_vg2+24ahi_Th Tgd_vg2+24ahi_e17_Th Tgd_vg3+24ahi_e17_Th Tgd_vg5+24ahi_Th Tgd_vg1+vd6-24alo_Th Tgd_vg1+vd6+24alo_Th Tgd_vg2+24alo_Th Tgd_vg3+24alo_e17_Th Tgd_Sp Tgd_vg2-_Sp Tgd_vg2-_act_Sp Tgd_vg2+_Sp Tgd_vg2+_act_Sp Tgd_vg2-_Sp_TCRbko Tgd_vg2+_Sp_TCRbko Tgd_vg5-_IEL Tgd_vg5+_IEL Tgd_vg5-_act_IEL Tgd_vg5+_act_IEL Ep_MEChi_Th Fi_MTS15+_Th Fi_Sk FRC_MLN FRC_SLN LEC_MLN LEC_SLN BEC_MLN BEC_SLN St_31-38-44-_SLN 1415806_at Plat 27.9185 36.5107 33.0332 30.6177 29.9747 28.8708 30.3841 37.5277 30.5361 32.6895 29.4836 27.9885 29.4244 26.5173 35.0402 31.544 30.9292 29.8665 35.6304 33.0442 26.7101 28.2309 30.9805 28.6152 31.8907 32.0462 34.8866 33.0858 35.7239 35.2472 34.3717 29.8923 39.6809 41.3769 42.2323 39.081 33.5901 35.0953 30.3213 27.7287 34.1493 37.4285 32.0074 39.7632 33.5368 30.3562 45.2669 40.6258 195.136 103.185 39.0732 80.0762 153.337 365.59 78.3391 39.9067 44.3187 56.1457 33.6093 41.5659 366.436 40.771 32.906 150.567 55.6916 105.192 44.2745 185.212 76.1094 28.6436 36.086 68.2284 39.585 119.956 26.9137 38.7293 33.0461 60.3476 28.3998 34.0431 32.9896 65.296 59.0182 28.654 40.783 33.7108 29.0525 29.3948 31.408 31.5986 35.9317 31.184 29.0688 34.2658 33.5081 32.2015 35.6911 41.8463 44.3161 38.3131 51.4425 51.4854 42.5922 40.787 39.072 34.6637 33.343 33.0619 36.7676 37.9347 31.0312 35.6631 35.7623 37.7508 33.1229 33.6179 41.1347 32.8821 35.5274 34.5783 29.3629 37.1282 32.6213 29.7352 31.6801 30.03 37.6091 37.3695 35.7894 39.4483 42.3723 41.7823 31.979 33.1111 34.9302 36.7907 34.9848 32.0475 41.4716 37.722 35.7637 43.4169 40.778 34.4366 47.119 41.7399 46.3535 30.093 31.4636 41.9103 46.1681 37.7144 34.474 42.1673 47.1553 37.3054 49.77 40.0073 33.6125 34.3092 38.0424 42.9508 39.9314 55.4645 36.0474 50.1869 38.7767 32.3656 31.0418 27.0207 30.6182 33.8824 42.901 32.0133 39.7088 37.3634 33.07 33.4334 43.9524 35.59 37.7714 42.626 33.3944 33.1647 31.626 39.8802 28.3281 40.7664 34.782 34.9716 31.0598 43.7914 32.7444 35.9125 38.8265 38.2612 32.2323 40.2928 36.7945 34.991 37.4216 40.4704 39.3888 30.0384 105.793 2526.44 640.249 242.364 131.67 1064.84 1056.81 208.29 157.891 271.912 1415899_at Junb 104.359 116.588 117.664 113.224 66.2672 86.464 81.6396 76.6304 100.614 116.538 118.284 129.816 101.239 89.2805 99.7887 125.883 112.003 99.5811 118.3 178.751 87.5629 70.5608 120.18 101.577 137.816 123.722 125.728 168.945 138.178 153.402 104.895 175.298 137.421 113.447 117.66 129.752 143.541 146.186 428.249 412.473 385.435 339.74 473.701 507.498 220.07 194.76 667.376 488.267 354.873 635.98 193.976 507.981 667.498 442.459 449.715 639.196 574.944 542.865 687.359 150.271 889.725 1169.58 459.569 374.314 461.532 1206.02 675.481 1130.99 849.501 280.516 979.627 1324.66 671.702 1053.9 152.166 350.176 337.302 826.052 513.71 1469.49 1517.93 1238.98 1257.99 330.983 1478.1 238.873 212.152 208.743 405.299 236.767 278.17 341.064 345.308 281.135 393.439 302.634 682.04 325.536 960.248 321.11 1291.57 883.89 885.596 219.172 175.986 216.132 278.688 295.721 275.892 215.793 299.334 331.465 185.401 220.586 204.986 456.516 378.278 226.349 454.313 425.804 232.92 146.899 110.746 112.351 125.992 103.248 150.85 94.4725 135.077 229.705 165.416 223.929 232.708 195.205 142.457 182.395 144.899 202.909 193.86 205.682 570.849 231.047 194.275 441.833 382.466 210.373 192.348 221.835 248.175 209.08 276.048 1647.57 249.185 263.404 191.867 211.169 157.855 283.887 155.999 156.376 168.494 222.407 175.15 175.776 180.29 216.886 284.49 206.288 178.744 188.286 163.138 180.242 205.278 286.698 151.971 255.359 293.267 662.865 772.192 520.293 706.199 559.661 146.378 177.677 180.272 144.715 107.154 135.916 149.646 251.437 214.44 328.079 189.697 166.164 252.097 254.642 277.984 260.566 294.951 244.528 709.615 765.502 148.709 310.161 404.302 1408.72 1158.08 1199.58 1694.65 812.419 1004.32 950.417 1083.41 1061.59 ..(more)... With the following code: library(gplots); library(RColorBrewer); dat <- read.table("http://pastebin.com/raw.php?i=wM7WxEvY",sep="\t",na.strings="NA",header=TRUE) dat <- dat[complete.cases(dat),] dat.log <- log2(dat); # Clustering and distance function hclustfunc <- function(x) hclust(x, method="ward") distfunc <- function(x) dist(x,method="maximum") nofval <- length(unique(as.vector(as.matrix(dat.log)))); hmcols <- rev(redblue(nofval)); pdf(file="temp.pdf",width=50,height=40); heatmap.2(as.matrix(dat.log),Colv=FALSE,lhei = c(0.25,4),density.info="none",scale="none",margin=c(10,10),col=hmcols,symkey=F,trace="none",dendrogram="row",keysize=0.3,hclust=hclustfunc,distfun=distfunc); dev.off(); It produces this image: Note that at the top left the colors of KEY did not appear at all. What's the problem with my code above? How can I correct it?
The colours are being parsed at very high resolution. You have assigned a colour gradient to every value in your matrix, total of 17410. Try decreasing the colour gradient to 128 or 256: col=bluered(256). Alternatively, increase the key size keysize=1 to display the higher colour gradient.