R : Copyright 2005, The R Foundation for Statistical Computing Version 2.1.1 (2005-06-20), ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for a HTML browser interface to help. Type 'q()' to quit R. > ### *
> ### > attach(NULL, name = "CheckExEnv") > assign(".CheckExEnv", as.environment(2), pos = length(search())) # base > ## add some hooks to label plot pages for base and grid graphics > setHook("plot.new", ".newplot.hook") > setHook("persp", ".newplot.hook") > setHook("grid.newpage", ".gridplot.hook") > > assign("cleanEx", + function(env = .GlobalEnv) { + rm(list = ls(envir = env, all.names = TRUE), envir = env) + RNGkind("default", "default") + set.seed(1) + options(warn = 1) + delayedAssign("T", stop("T used instead of TRUE"), + assign.env = .CheckExEnv) + delayedAssign("F", stop("F used instead of FALSE"), + assign.env = .CheckExEnv) + sch <- search() + newitems <- sch[! sch %in% .oldSearch] + for(item in rev(newitems)) + eval(substitute(detach(item), list(item=item))) + missitems <- .oldSearch[! .oldSearch %in% sch] + if(length(missitems)) + warning("items ", paste(missitems, collapse=", "), + " have been removed from the search path") + }, + env = .CheckExEnv) > assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) # for now > assign("ptime", proc.time(), env = .CheckExEnv) > grDevices::postscript("simpleboot-Examples.ps") > assign("par.postscript", graphics::par(no.readonly = TRUE), env = .CheckExEnv) > options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly")) > options(warn = 1) > library('simpleboot') Loading required package: boot Simple Bootstrap Routines (version 1.1-1) > > assign(".oldSearch", search(), env = .CheckExEnv) > assign(".oldNS", loadedNamespaces(), env = .CheckExEnv) > cleanEx(); ..nameEx <- "hist.simpleboot" > > ### * hist.simpleboot > > flush(stderr()); flush(stdout()) > > ### Name: hist.simpleboot > ### Title: Histograms for bootstrap sampling distributions. > ### Aliases: hist.simpleboot > ### Keywords: hplot > > ### ** Examples > > x <- rnorm(100) > > ## Bootstrap the 75th percentile > b <- one.boot(x, quantile, R = 1000, probs = 0.75) > hist(b) > > > > cleanEx(); ..nameEx <- "lm.boot" > > ### * lm.boot > > flush(stderr()); flush(stdout()) > > ### Name: lm.boot > ### Title: Linear model bootstrap. > ### Aliases: lm.boot print.lm.simpleboot > ### Keywords: regression > > ### ** Examples > > data(airquality) > attach(airquality) > set.seed(30) > lmodel <- lm(Ozone ~ Wind) > lboot <- lm.boot(lmodel, R = 1000) > summary(lboot) BOOTSTRAP OF LINEAR MODEL (method = rows) Original Model Fit ------------------ Call: lm(formula = Ozone ~ Wind) Coefficients: (Intercept) Wind 96.873 -5.551 Bootstrap SD's: (Intercept) Wind 9.1083683 0.8142714 > > ## With weighting > w <- runif(nrow(model.frame(lmodel))) > lbootw <- lm.boot(lmodel, R = 1000, weights = w) > summary(lbootw) BOOTSTRAP OF LINEAR MODEL (method = rows) Original Model Fit ------------------ Call: lm(formula = Ozone ~ Wind) Coefficients: (Intercept) Wind 96.873 -5.551 Bootstrap SD's: (Intercept) Wind 9.3130444 0.7848792 > > ## Resample residuals > lboot2 <- lm.boot(lmodel, R = 1000, rows = FALSE) > summary(lboot2) BOOTSTRAP OF LINEAR MODEL (method = residuals) Original Model Fit ------------------ Call: lm(formula = Ozone ~ Wind) Coefficients: (Intercept) Wind 96.873 -5.551 Bootstrap SD's: (Intercept) Wind 7.3278953 0.6832981 > > > > cleanEx(); ..nameEx <- "lm.boot.methods" > > ### * lm.boot.methods > > flush(stderr()); flush(stdout()) > > ### Name: lm.simpleboot.methods > ### Title: Methods for linear model bootstrap. > ### Aliases: summary.lm.simpleboot print.summary.lm.simpleboot > ### fitted.lm.simpleboot > ### Keywords: regression > > ### ** Examples > > data(airquality) > attach(airquality) > lmodel <- lm(Ozone ~ Wind + Solar.R) > lboot <- lm.boot(lmodel, R = 300) > summary(lboot) BOOTSTRAP OF LINEAR MODEL (method = rows) Original Model Fit ------------------ Call: lm(formula = Ozone ~ Wind + Solar.R) Coefficients: (Intercept) Wind Solar.R 77.2460 -5.4018 0.1004 Bootstrap SD's: (Intercept) Wind Solar.R 9.42027504 0.80318839 0.02355627 > > > > cleanEx(); ..nameEx <- "loess.boot" > > ### * loess.boot > > flush(stderr()); flush(stdout()) > > ### Name: loess.boot > ### Title: 2-D Loess bootstrap. > ### Aliases: loess.boot print.loess.simpleboot > ### Keywords: loess > > ### ** Examples > > set.seed(1234) > > x <- runif(100) > > ## Simple sine function simulation > y <- sin(2*pi*x) + .2 * rnorm(100) > plot(x, y) ## Sine function with noise > lo <- loess(y ~ x, span = .4) > > ## Bootstrap with resampling of rows > lo.b <- loess.boot(lo, R = 500) > > ## Plot original fit with +/- 2 std. errors > plot(lo.b) > > ## Plot all loess bootstrap fits > plot(lo.b, all.lines = TRUE) > > ## Bootstrap with resampling residuals > lo.b2 <- loess.boot(lo, R = 500, rows = FALSE) > plot(lo.b2) > > > > > cleanEx(); ..nameEx <- "one.boot" > > ### * one.boot > > flush(stderr()); flush(stdout()) > > ### Name: one.boot > ### Title: One sample bootstrap of a univariate statistic. > ### Aliases: one.boot > ### Keywords: univar > > ### ** Examples > > set.seed(20) > x <- rgamma(100, 1) > b.mean <- one.boot(x, mean, 1000) > print(b.mean) $t0 [1] 1.141418 $t [,1] [1,] 0.9784279 [2,] 1.1800902 [3,] 1.0892037 [4,] 1.1684861 [5,] 1.1209477 [6,] 1.1661197 [7,] 1.1332985 [8,] 1.1241989 [9,] 1.1147433 [10,] 1.1240602 [11,] 1.1157520 [12,] 1.0924946 [13,] 1.2043369 [14,] 1.1899671 [15,] 1.1871174 [16,] 1.0477735 [17,] 1.3160014 [18,] 1.0851749 [19,] 1.1520093 [20,] 1.0013207 [21,] 1.2040679 [22,] 1.0421607 [23,] 1.1670592 [24,] 1.1499647 [25,] 1.0983463 [26,] 1.2993921 [27,] 1.1187180 [28,] 1.2296830 [29,] 1.1246766 [30,] 1.1612589 [31,] 1.1069832 [32,] 1.1417609 [33,] 1.1135483 [34,] 1.1869309 [35,] 1.0601059 [36,] 1.1516926 [37,] 1.0486101 [38,] 1.0737764 [39,] 1.0966666 [40,] 1.2330783 [41,] 1.1966111 [42,] 1.2278696 [43,] 1.3143714 [44,] 1.0605376 [45,] 1.1221273 [46,] 1.0702061 [47,] 1.1752681 [48,] 1.1732525 [49,] 1.0671901 [50,] 1.4152576 [51,] 1.0221632 [52,] 1.2505612 [53,] 0.9145191 [54,] 1.2256878 [55,] 1.1582107 [56,] 1.1670272 [57,] 1.1014307 [58,] 1.1847569 [59,] 1.2864520 [60,] 1.1594335 [61,] 1.0797084 [62,] 1.1088907 [63,] 1.0990951 [64,] 1.0218457 [65,] 1.0989625 [66,] 1.2781160 [67,] 1.0736468 [68,] 1.1970469 [69,] 1.2468622 [70,] 1.0999153 [71,] 1.1508990 [72,] 1.1374444 [73,] 1.1420410 [74,] 1.0432088 [75,] 1.1540727 [76,] 1.1345521 [77,] 1.3088205 [78,] 1.1366447 [79,] 1.3993596 [80,] 1.2927139 [81,] 1.0781051 [82,] 1.1709032 [83,] 1.0541043 [84,] 1.1592667 [85,] 1.0521911 [86,] 1.1748675 [87,] 1.0329693 [88,] 1.0749504 [89,] 1.1095175 [90,] 1.2961921 [91,] 1.2493127 [92,] 1.2061936 [93,] 1.4291846 [94,] 1.0519195 [95,] 1.1618136 [96,] 1.0777648 [97,] 1.1597737 [98,] 1.1720161 [99,] 1.0680152 [100,] 1.0618790 [101,] 1.1179338 [102,] 1.2802478 [103,] 1.2808731 [104,] 1.2025209 [105,] 1.2366969 [106,] 1.1244115 [107,] 1.0053408 [108,] 1.0764719 [109,] 1.1178024 [110,] 1.1623303 [111,] 1.1772747 [112,] 1.0612161 [113,] 1.0654069 [114,] 1.0261076 [115,] 1.1243918 [116,] 1.0480842 [117,] 1.1934090 [118,] 1.0942938 [119,] 1.1281564 [120,] 1.0430778 [121,] 1.3048276 [122,] 1.1391698 [123,] 1.1642036 [124,] 1.0709439 [125,] 1.2371243 [126,] 1.3184469 [127,] 1.0111032 [128,] 1.1171933 [129,] 0.9541412 [130,] 1.1304565 [131,] 0.9839437 [132,] 1.0811129 [133,] 1.0086810 [134,] 0.9617258 [135,] 1.1959944 [136,] 1.3251680 [137,] 1.0824857 [138,] 1.2010248 [139,] 1.2986297 [140,] 1.1244406 [141,] 1.2117292 [142,] 1.1057689 [143,] 1.2251805 [144,] 1.1725037 [145,] 1.1976114 [146,] 1.3140456 [147,] 1.0608239 [148,] 1.2085002 [149,] 1.1630845 [150,] 1.1560096 [151,] 1.0351978 [152,] 1.1495865 [153,] 0.9862562 [154,] 1.1427556 [155,] 1.1966394 [156,] 1.3012814 [157,] 1.3910869 [158,] 1.0697178 [159,] 1.3614396 [160,] 1.2004166 [161,] 1.2332945 [162,] 1.0240447 [163,] 1.2922180 [164,] 1.1979820 [165,] 1.2068446 [166,] 1.2696091 [167,] 1.1250644 [168,] 1.1217749 [169,] 1.1736674 [170,] 1.0209422 [171,] 1.0627246 [172,] 1.1507330 [173,] 1.2618342 [174,] 1.1128562 [175,] 1.3135591 [176,] 1.3473971 [177,] 1.0416523 [178,] 1.0788051 [179,] 1.2262795 [180,] 1.3256314 [181,] 1.1916116 [182,] 1.2270381 [183,] 1.0998042 [184,] 1.2608254 [185,] 1.3793397 [186,] 1.1665249 [187,] 1.0868733 [188,] 1.0467739 [189,] 1.1595707 [190,] 1.0967285 [191,] 1.1202725 [192,] 1.2122462 [193,] 1.2420953 [194,] 1.0584840 [195,] 1.0628530 [196,] 0.9732249 [197,] 1.2017107 [198,] 1.0765746 [199,] 1.0965047 [200,] 1.2840071 [201,] 1.2080006 [202,] 1.0322539 [203,] 1.2192237 [204,] 1.1558355 [205,] 1.2725574 [206,] 1.2909050 [207,] 1.2306969 [208,] 1.0451843 [209,] 1.2593138 [210,] 1.2065571 [211,] 1.4376845 [212,] 1.1227230 [213,] 1.0894834 [214,] 1.2391429 [215,] 1.0968128 [216,] 1.3566735 [217,] 1.0077668 [218,] 0.9791181 [219,] 1.3582147 [220,] 1.2317711 [221,] 1.0285805 [222,] 1.0990590 [223,] 1.0138324 [224,] 0.9886781 [225,] 1.1191647 [226,] 1.1440364 [227,] 1.0056615 [228,] 1.0754479 [229,] 1.2087857 [230,] 1.1746964 [231,] 1.2985416 [232,] 1.1067664 [233,] 1.1220982 [234,] 1.0470911 [235,] 1.1183771 [236,] 1.3494055 [237,] 1.1174770 [238,] 1.2171536 [239,] 1.1049717 [240,] 1.1858582 [241,] 1.0470720 [242,] 1.1991374 [243,] 1.1337334 [244,] 1.1437532 [245,] 1.3260155 [246,] 1.0943543 [247,] 1.1843191 [248,] 1.3072838 [249,] 0.9449271 [250,] 1.0074067 [251,] 0.9818107 [252,] 1.2799205 [253,] 1.1983545 [254,] 1.0836194 [255,] 1.0283196 [256,] 1.1734568 [257,] 1.0878782 [258,] 1.1834101 [259,] 1.0914262 [260,] 1.0827359 [261,] 1.0628044 [262,] 1.1260818 [263,] 1.0670984 [264,] 1.0386835 [265,] 1.1267631 [266,] 1.0936372 [267,] 1.1887157 [268,] 1.0255515 [269,] 1.0555687 [270,] 1.2706821 [271,] 1.3078271 [272,] 1.1638223 [273,] 1.1482895 [274,] 1.1464722 [275,] 1.2245561 [276,] 1.1323016 [277,] 1.1420573 [278,] 1.0668070 [279,] 1.2662991 [280,] 1.1427011 [281,] 1.0891121 [282,] 0.9103760 [283,] 1.0550874 [284,] 1.1865577 [285,] 1.2550453 [286,] 1.1816084 [287,] 1.1203232 [288,] 1.1273558 [289,] 1.3672005 [290,] 1.1475698 [291,] 0.9100577 [292,] 1.1229247 [293,] 1.1552455 [294,] 1.1598856 [295,] 1.1701427 [296,] 1.1978979 [297,] 1.3345869 [298,] 1.2002690 [299,] 1.1380735 [300,] 1.1406907 [301,] 1.0046728 [302,] 1.2893200 [303,] 1.1716081 [304,] 1.0739932 [305,] 1.1830883 [306,] 1.0193126 [307,] 1.0607010 [308,] 1.2904846 [309,] 1.1050497 [310,] 1.0735001 [311,] 1.0433152 [312,] 1.1003468 [313,] 1.0834427 [314,] 1.0706003 [315,] 1.0077664 [316,] 1.2663867 [317,] 1.1029667 [318,] 1.2800661 [319,] 1.1219728 [320,] 1.1611333 [321,] 1.1902168 [322,] 1.1349100 [323,] 1.2017777 [324,] 1.1110386 [325,] 1.3684690 [326,] 1.1780368 [327,] 1.1634923 [328,] 1.1186942 [329,] 1.0940464 [330,] 1.0421289 [331,] 1.2309447 [332,] 1.0789238 [333,] 1.1716327 [334,] 1.1402648 [335,] 1.1197344 [336,] 1.3464358 [337,] 1.0735669 [338,] 1.1958594 [339,] 1.1706591 [340,] 1.1626399 [341,] 1.3593212 [342,] 1.0678131 [343,] 1.2097761 [344,] 1.0686127 [345,] 1.2083212 [346,] 1.1284066 [347,] 1.2414697 [348,] 1.1664423 [349,] 1.2054710 [350,] 1.2338146 [351,] 1.1184968 [352,] 0.9837114 [353,] 1.0441602 [354,] 1.2039463 [355,] 1.2050684 [356,] 1.2565118 [357,] 1.1423505 [358,] 1.1111619 [359,] 1.2832002 [360,] 1.0525404 [361,] 1.1694787 [362,] 1.1505215 [363,] 0.9921803 [364,] 1.1537353 [365,] 1.1313145 [366,] 1.0788740 [367,] 1.1109198 [368,] 0.9998380 [369,] 1.0003989 [370,] 1.2825934 [371,] 1.0496933 [372,] 1.1301277 [373,] 1.1618967 [374,] 1.1206381 [375,] 1.2067913 [376,] 1.2091914 [377,] 1.2168508 [378,] 1.2218005 [379,] 1.0679662 [380,] 1.2710343 [381,] 0.9881773 [382,] 1.1431541 [383,] 1.0269688 [384,] 1.1519686 [385,] 1.0516258 [386,] 1.0180393 [387,] 1.2222001 [388,] 1.0463129 [389,] 1.1013762 [390,] 1.0340023 [391,] 0.9777998 [392,] 1.1801463 [393,] 1.1177597 [394,] 1.1011104 [395,] 1.1386527 [396,] 1.1986145 [397,] 1.2128048 [398,] 1.0477222 [399,] 1.1762617 [400,] 1.2017539 [401,] 1.0395316 [402,] 0.9296708 [403,] 1.2482646 [404,] 1.1028602 [405,] 1.2939964 [406,] 1.1680967 [407,] 1.1241038 [408,] 1.0011356 [409,] 1.1894586 [410,] 1.0889224 [411,] 0.9425040 [412,] 1.1232603 [413,] 1.0230436 [414,] 1.2286044 [415,] 1.2361111 [416,] 0.9151183 [417,] 0.9171738 [418,] 1.1099519 [419,] 1.1345938 [420,] 1.1795561 [421,] 1.2147537 [422,] 1.1300538 [423,] 1.0223582 [424,] 0.9408050 [425,] 1.0495044 [426,] 1.0169672 [427,] 1.3148142 [428,] 1.2881829 [429,] 1.1347809 [430,] 1.1305932 [431,] 1.0452842 [432,] 1.1624657 [433,] 1.0117492 [434,] 1.2418191 [435,] 1.2327391 [436,] 0.9058946 [437,] 1.3019918 [438,] 1.0005200 [439,] 1.0016719 [440,] 1.2664886 [441,] 1.1903944 [442,] 1.1216070 [443,] 1.0745904 [444,] 1.2590646 [445,] 1.1262757 [446,] 1.0572097 [447,] 1.1987627 [448,] 1.0316119 [449,] 1.2563678 [450,] 1.1429547 [451,] 0.8648208 [452,] 1.2581005 [453,] 1.1022494 [454,] 0.9995128 [455,] 1.2493226 [456,] 1.0548862 [457,] 1.1175379 [458,] 1.2768413 [459,] 1.0428579 [460,] 1.1193538 [461,] 1.0822225 [462,] 1.0861013 [463,] 1.1404752 [464,] 1.2620882 [465,] 1.0217838 [466,] 1.0326131 [467,] 1.1103765 [468,] 1.3576429 [469,] 1.2862435 [470,] 1.1977381 [471,] 1.2622557 [472,] 1.2166170 [473,] 1.2454355 [474,] 1.0251139 [475,] 0.9446146 [476,] 1.1758828 [477,] 1.1734116 [478,] 1.1208663 [479,] 1.1488024 [480,] 1.0545411 [481,] 0.8444471 [482,] 1.1437529 [483,] 1.0277907 [484,] 0.9724629 [485,] 1.3818114 [486,] 1.0234631 [487,] 1.1693431 [488,] 1.0356255 [489,] 1.0083244 [490,] 1.2163925 [491,] 0.9671025 [492,] 1.0596323 [493,] 1.0800790 [494,] 1.0908707 [495,] 1.0907891 [496,] 1.0886149 [497,] 1.2290063 [498,] 1.1802788 [499,] 1.1680306 [500,] 1.1093243 [501,] 1.1519563 [502,] 1.2858524 [503,] 1.3042979 [504,] 1.0758445 [505,] 1.2103387 [506,] 1.1310146 [507,] 1.1516098 [508,] 1.2690271 [509,] 1.1870859 [510,] 1.1773837 [511,] 0.9482839 [512,] 1.1200051 [513,] 1.1347966 [514,] 1.0623375 [515,] 1.2592990 [516,] 1.1111573 [517,] 1.1472835 [518,] 1.0236568 [519,] 0.9980416 [520,] 1.1535111 [521,] 1.1627796 [522,] 1.0760370 [523,] 1.1201291 [524,] 1.0817931 [525,] 1.0965550 [526,] 1.1774917 [527,] 1.0934749 [528,] 1.2630691 [529,] 1.0999558 [530,] 0.9987555 [531,] 1.1682313 [532,] 1.1996691 [533,] 1.3182436 [534,] 1.0096900 [535,] 1.1032133 [536,] 1.1399161 [537,] 1.2679829 [538,] 0.9923865 [539,] 1.1857262 [540,] 0.9913925 [541,] 1.0997958 [542,] 1.2183739 [543,] 1.1962336 [544,] 1.0789758 [545,] 1.0270435 [546,] 1.2255423 [547,] 1.2676238 [548,] 1.3406606 [549,] 1.2039752 [550,] 1.1088933 [551,] 1.0544971 [552,] 1.1835557 [553,] 1.1472915 [554,] 1.1220613 [555,] 1.0675637 [556,] 1.1497092 [557,] 1.1680574 [558,] 1.1503416 [559,] 1.0082143 [560,] 1.0425698 [561,] 0.9750063 [562,] 1.2048916 [563,] 1.0639712 [564,] 1.2901638 [565,] 0.9541750 [566,] 1.0436328 [567,] 0.9864607 [568,] 1.1180584 [569,] 0.9543951 [570,] 1.1289236 [571,] 1.0537693 [572,] 1.1724778 [573,] 1.0046943 [574,] 1.1187079 [575,] 1.2736479 [576,] 0.9393261 [577,] 1.1040877 [578,] 1.2309817 [579,] 1.0360920 [580,] 1.0561421 [581,] 0.9146108 [582,] 1.1842328 [583,] 1.1398257 [584,] 1.0151372 [585,] 1.0542290 [586,] 1.3192641 [587,] 1.1753889 [588,] 1.1911069 [589,] 1.2339416 [590,] 1.1070909 [591,] 1.1777338 [592,] 1.2689001 [593,] 1.1058432 [594,] 1.1012323 [595,] 1.1331350 [596,] 1.0961607 [597,] 1.0496907 [598,] 1.1207970 [599,] 1.2606513 [600,] 1.0816154 [601,] 1.1041953 [602,] 1.0921474 [603,] 1.0417536 [604,] 1.3194169 [605,] 1.1608030 [606,] 1.0963147 [607,] 1.2685925 [608,] 1.2178025 [609,] 0.8991467 [610,] 0.9786454 [611,] 1.0630239 [612,] 1.0815095 [613,] 1.3139742 [614,] 0.9841903 [615,] 1.1911303 [616,] 1.3359939 [617,] 1.2290268 [618,] 1.1360174 [619,] 1.1560225 [620,] 1.0349504 [621,] 1.2391514 [622,] 1.2526569 [623,] 1.1968548 [624,] 0.9424582 [625,] 1.0706882 [626,] 1.2651287 [627,] 1.2191116 [628,] 1.2612999 [629,] 1.1729011 [630,] 1.1547978 [631,] 1.1926476 [632,] 1.1034251 [633,] 1.2018634 [634,] 1.1435987 [635,] 1.1293386 [636,] 1.2311404 [637,] 1.1123292 [638,] 1.0571593 [639,] 1.3119145 [640,] 1.2287280 [641,] 1.0916262 [642,] 1.0595331 [643,] 1.1695265 [644,] 1.0804341 [645,] 1.0219885 [646,] 1.0919587 [647,] 1.1195610 [648,] 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[565] 841352064 922999262 -791699797 -1662492243 1649702426 -2063701800 [571] -1996860095 -655958541 -1534800876 -1822207582 1054394039 1859358849 [577] -1447909242 256556228 1425103349 1626248287 -1999255880 -1338095722 [583] 1947147683 554516837 -185849726 1013137904 801347481 -1627795061 [589] -228938628 1985611434 588832991 -1691793815 -1519561602 -632832916 [595] -1174676291 -1500449401 630706544 -692911090 -2119648965 -352844259 [601] 690674378 2060441064 927775441 1817233091 375254468 -1013355918 [607] 1590981447 -2000991471 -331516266 -644412428 1983214981 654878927 [613] -733380152 1336197222 1072846483 608110069 -364865582 -1221401568 [619] -825592311 1460105019 1481302668 -613779686 -1802069617 -574949671 [625] 884087150 -363788371 $statistic function (x, idx) { fval <- func(x[idx], ...) if (student) { rs.x <- x[idx] b <- one.boot(rs.x, FUN, R = M, student = FALSE, M = NULL, weights = NULL, ...) fval <- c(fval, var(b$t)) } fval } $sim [1] "ordinary" $call boot(data = data, statistic = boot.func, R = R, weights = weights) $stype [1] "i" $strata [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 $weights [1] 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 [16] 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 [31] 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 [46] 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 [61] 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 [76] 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 [91] 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 $student [1] FALSE attr(,"class") [1] "simpleboot" > boot.ci(b.mean) ## No studentized interval here Warning in boot.ci(b.mean) : bootstrap variances needed for studentized intervals BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 1000 bootstrap replicates CALL : boot.ci(boot.out = b.mean) Intervals : Level Normal Basic 95% ( 0.940, 1.349 ) ( 0.925, 1.340 ) Level Percentile BCa 95% ( 0.943, 1.358 ) ( 0.967, 1.389 ) Calculations and Intervals on Original Scale > hist(b.mean) > > ## This next line could take some time on a slow computer > b.median <- one.boot(x, median, R = 500, student = TRUE, M = 50) > boot.ci(b.median) BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 500 bootstrap replicates CALL : boot.ci(boot.out = b.median) Intervals : Level Normal Basic Studentized 95% ( 0.5960, 1.0390 ) ( 0.5734, 1.0022 ) ( 0.5784, 1.0622 ) Level Percentile BCa 95% ( 0.6672, 1.0960 ) ( 0.6885, 1.1131 ) Calculations and Intervals on Original Scale Some BCa intervals may be unstable > hist(b.median) > > ## Bootstrap with weights > set.seed(10) > w <- runif(100) > bw <- one.boot(x, median, 1000, weights = w) > print(bw) $t0 [1] 0.834687 $t [,1] [1,] 1.1457988 [2,] 1.2182191 [3,] 0.7859004 [4,] 0.7610846 [5,] 1.1184479 [6,] 1.1024426 [7,] 1.0735659 [8,] 0.8442811 [9,] 1.0396397 [10,] 1.4955890 [11,] 1.2182191 [12,] 1.4204361 [13,] 0.6421631 [14,] 1.0396397 [15,] 1.3179902 [16,] 1.3873805 [17,] 1.4208701 [18,] 0.8167523 [19,] 1.0477564 [20,] 0.8346870 [21,] 1.0396397 [22,] 0.8004895 [23,] 0.8001079 [24,] 0.8497615 [25,] 0.8442811 [26,] 1.0396397 [27,] 1.4032420 [28,] 1.0552479 [29,] 1.0552479 [30,] 1.0558732 [31,] 1.0871605 [32,] 1.0866493 [33,] 0.9052467 [34,] 0.9974456 [35,] 0.9974456 [36,] 0.8497615 [37,] 1.1024426 [38,] 1.1024426 [39,] 1.0722110 [40,] 1.1024426 [41,] 1.1457988 [42,] 0.8497615 [43,] 1.3543249 [44,] 0.6484268 [45,] 1.0552479 [46,] 1.1377961 [47,] 0.7925672 [48,] 1.0396397 [49,] 1.3520190 [50,] 0.9974456 [51,] 1.0396397 [52,] 0.8167523 [53,] 1.1104453 [54,] 1.0558732 [55,] 1.4613284 [56,] 1.3179902 [57,] 1.0396397 [58,] 1.1184479 [59,] 1.3179902 [60,] 0.8442811 [61,] 0.9974456 [62,] 1.0866493 [63,] 0.9974456 [64,] 0.8645944 [65,] 1.1184479 [66,] 1.1731497 [67,] 1.0710411 [68,] 1.0552479 [69,] 0.7626184 [70,] 0.9082045 [71,] 0.8401674 [72,] 1.0396397 [73,] 0.8700748 [74,] 1.0396397 [75,] 1.0735659 [76,] 1.0708561 [77,] 1.3361575 [78,] 1.4613284 [79,] 1.1024426 [80,] 1.2455699 [81,] 1.0477564 [82,] 1.0477564 [83,] 0.8442811 [84,] 1.0880043 [85,] 1.0558732 [86,] 1.3873805 [87,] 0.8730325 [88,] 0.8497615 [89,] 1.0710411 [90,] 1.1957780 [91,] 1.0710411 [92,] 1.1104453 [93,] 1.0710411 [94,] 1.1024426 [95,] 0.7764107 [96,] 1.0866493 [97,] 0.8442811 [98,] 0.8581996 [99,] 0.8442811 [100,] 0.8346870 [101,] 1.0477564 [102,] 0.7626184 [103,] 0.8080301 [104,] 1.3179902 [105,] 0.8442811 [106,] 1.0866493 [107,] 0.8497615 [108,] 1.0708561 [109,] 0.9622737 [110,] 1.3692131 [111,] 1.3179902 [112,] 0.8167523 [113,] 1.0710411 [114,] 0.8080301 [115,] 1.0396397 [116,] 1.3520190 [117,] 0.9552516 [118,] 1.0396397 [119,] 1.1104453 [120,] 1.0396397 [121,] 1.4955890 [122,] 1.3860479 [123,] 1.0477564 [124,] 1.1104453 [125,] 1.1104453 [126,] 0.9503985 [127,] 0.9200797 [128,] 1.1024426 [129,] 0.7266533 [130,] 1.4032420 [131,] 1.4032420 [132,] 0.7925672 [133,] 0.8263464 [134,] 0.8346870 [135,] 1.3361575 [136,] 0.9974456 [137,] 1.5734797 [138,] 1.1457988 [139,] 1.3353866 [140,] 1.1184479 [141,] 1.0880043 [142,] 1.3860479 [143,] 1.0396397 [144,] 1.4889573 [145,] 1.0880043 [146,] 0.9974456 [147,] 1.3179902 [148,] 1.0633646 [149,] 1.3179902 [150,] 1.5949014 [151,] 1.1104453 [152,] 1.0130538 [153,] 0.8497615 [154,] 0.8581996 [155,] 1.4204361 [156,] 0.9419604 [157,] 0.8346870 [158,] 0.8167523 [159,] 1.0735659 [160,] 1.0722110 [161,] 1.1104453 [162,] 0.8000743 [163,] 1.1024426 [164,] 0.8442811 [165,] 1.0708561 [166,] 1.0708561 [167,] 0.9474408 [168,] 0.8346870 [169,] 0.8442811 [170,] 1.0722110 [171,] 1.0722110 [172,] 1.0396397 [173,] 1.0722110 [174,] 1.4546967 [175,] 1.5022208 [176,] 1.0791579 [177,] 1.4955890 [178,] 0.9082045 [179,] 0.8442811 [180,] 0.9974456 [181,] 0.8442811 [182,] 1.1024426 [183,] 1.2455699 [184,] 0.8442811 [185,] 0.8346870 [186,] 1.1024426 [187,] 0.8497615 [188,] 0.8497615 [189,] 1.3520190 [190,] 1.6011622 [191,] 1.0558732 [192,] 1.1024426 [193,] 1.0552479 [194,] 0.8346870 [195,] 1.3179902 [196,] 1.0866493 [197,] 1.2455699 [198,] 0.8442811 [199,] 1.0477564 [200,] 1.1233578 [201,] 1.3179902 [202,] 1.0710411 [203,] 1.1457988 [204,] 0.9622737 [205,] 0.8497615 [206,] 1.0647195 [207,] 1.0722110 [208,] 0.8167523 [209,] 1.1024426 [210,] 1.1184479 [211,] 1.5271393 [212,] 1.1184479 [213,] 1.4613284 [214,] 1.0710411 [215,] 0.8167523 [216,] 1.0396397 [217,] 0.8346870 [218,] 1.3179902 [219,] 1.1024426 [220,] 1.0055624 [221,] 1.0552479 [222,] 1.0396397 [223,] 1.1957780 [224,] 1.0396397 [225,] 0.8080301 [226,] 1.0552479 [227,] 0.8442811 [228,] 0.8263464 [229,] 1.2182191 [230,] 1.3179902 [231,] 1.5022208 [232,] 1.0633646 [233,] 0.8497615 [234,] 1.4546967 [235,] 1.0396397 [236,] 0.7626184 [237,] 1.1457988 [238,] 1.0552479 [239,] 1.2182191 [240,] 0.9323663 [241,] 1.1024426 [242,] 1.0558732 [243,] 0.8163707 [244,] 1.0396397 [245,] 0.8442811 [246,] 1.0710411 [247,] 1.1024426 [248,] 1.6570832 [249,] 1.1024426 [250,] 0.9503985 [251,] 1.3179902 [252,] 1.3692131 [253,] 0.8442811 [254,] 0.8552419 [255,] 1.0866493 [256,] 1.3179902 [257,] 1.3179902 [258,] 0.8346870 [259,] 1.2455699 [260,] 1.0633646 [261,] 1.0710411 [262,] 1.1457988 [263,] 1.0722110 [264,] 1.0880043 [265,] 0.8497615 [266,] 1.4204361 [267,] 1.1024426 [268,] 0.8700748 [269,] 1.3361575 [270,] 1.4032420 [271,] 1.3860479 [272,] 1.1377961 [273,] 0.8700748 [274,] 1.1184479 [275,] 0.8167523 [276,] 0.9630490 [277,] 1.4204361 [278,] 0.8552419 [279,] 1.3361575 [280,] 0.9200797 [281,] 1.3520190 [282,] 1.2102164 [283,] 0.7610846 [284,] 0.9474408 [285,] 1.3179902 [286,] 0.9575686 [287,] 1.3179902 [288,] 0.7626184 [289,] 1.0633646 [290,] 0.8497615 [291,] 0.8442811 [292,] 0.8442811 [293,] 1.2182191 [294,] 0.7764107 [295,] 1.1104453 [296,] 0.9419604 [297,] 1.4862470 [298,] 1.3179902 [299,] 1.0558732 [300,] 0.9503985 [301,] 0.8442811 [302,] 0.9622737 [303,] 1.5271393 [304,] 1.1457988 [305,] 0.8346870 [306,] 1.0396397 [307,] 1.1944231 [308,] 1.0477564 [309,] 1.1104453 [310,] 0.9419604 [311,] 0.8497615 [312,] 1.2637373 [313,] 0.8346870 [314,] 0.8001079 [315,] 0.8552419 [316,] 1.0866493 [317,] 1.6570832 [318,] 0.8442811 [319,] 0.8442811 [320,] 1.0708561 [321,] 0.8346870 [322,] 0.9622737 [323,] 1.0866493 [324,] 1.0396397 [325,] 1.0396397 [326,] 1.1457988 [327,] 1.2455699 [328,] 1.0477564 [329,] 0.8442811 [330,] 0.8442811 [331,] 0.8497615 [332,] 0.9974456 [333,] 1.3873805 [334,] 1.0880043 [335,] 0.9575686 [336,] 1.4690529 [337,] 0.8442811 [338,] 1.4032420 [339,] 0.8263464 [340,] 1.0866493 [341,] 0.9974456 [342,] 0.9200797 [343,] 1.0477564 [344,] 0.8497615 [345,] 0.8346870 [346,] 1.1024426 [347,] 0.8167523 [348,] 0.8442811 [349,] 1.0710411 [350,] 1.0708561 [351,] 1.0791579 [352,] 1.0722110 [353,] 0.9974456 [354,] 0.9052467 [355,] 1.0396397 [356,] 1.2455699 [357,] 0.6922220 [358,] 0.8084117 [359,] 1.0396397 [360,] 1.3873805 [361,] 0.9622737 [362,] 0.8088301 [363,] 0.8167523 [364,] 1.0477564 [365,] 1.0396397 [366,] 1.5734797 [367,] 1.0633646 [368,] 0.9974456 [369,] 1.0722110 [370,] 1.1024426 [371,] 1.0710411 [372,] 0.8730325 [373,] 1.4955890 [374,] 1.5022208 [375,] 1.0558732 [376,] 0.8163707 [377,] 0.8497615 [378,] 0.9082045 [379,] 0.8497615 [380,] 0.7610846 [381,] 0.9552516 [382,] 0.9552516 [383,] 0.9503985 [384,] 0.8442811 [385,] 0.8552419 [386,] 1.0396397 [387,] 1.0396397 [388,] 0.8442811 [389,] 1.0396397 [390,] 0.9052467 [391,] 0.8497615 [392,] 1.2455699 [393,] 1.4204361 [394,] 0.8581996 [395,] 1.0558732 [396,] 0.8497615 [397,] 0.9974456 [398,] 1.0791579 [399,] 0.8700748 [400,] 1.4204361 [401,] 1.0710411 [402,] 1.0735659 [403,] 1.1184479 [404,] 1.0055624 [405,] 0.8581996 [406,] 1.1024426 [407,] 1.0396397 [408,] 1.0552479 [409,] 1.4889573 [410,] 1.1457988 [411,] 0.8346870 [412,] 0.9974456 [413,] 1.2455699 [414,] 1.3873805 [415,] 1.1184479 [416,] 1.0960069 [417,] 1.1104453 [418,] 0.8581996 [419,] 0.9974456 [420,] 0.9622737 [421,] 1.0866493 [422,] 1.5520579 [423,] 0.8004895 [424,] 1.0552479 [425,] 1.1024426 [426,] 1.0477564 [427,] 1.0790438 [428,] 1.1184479 [429,] 0.8442811 [430,] 1.3179902 [431,] 1.0396397 [432,] 1.3179902 [433,] 1.0396397 [434,] 0.9052467 [435,] 1.1024426 [436,] 0.8346870 [437,] 1.0633646 [438,] 1.3361575 [439,] 1.1024426 [440,] 1.0396397 [441,] 0.8442811 [442,] 0.8084117 [443,] 1.0396397 [444,] 1.3873805 [445,] 0.8084117 [446,] 1.1184479 [447,] 0.9974456 [448,] 1.3179902 [449,] 1.0880043 [450,] 0.7783597 [451,] 1.3179902 [452,] 1.3520190 [453,] 1.0396397 [454,] 1.5271393 [455,] 1.1104453 [456,] 1.0552479 [457,] 0.9474408 [458,] 1.0722110 [459,] 0.7925672 [460,] 0.8581996 [461,] 1.5271393 [462,] 0.9974456 [463,] 0.9622737 [464,] 1.3520190 [465,] 1.0396397 [466,] 1.3361575 [467,] 1.1184479 [468,] 1.0396397 [469,] 0.9200797 [470,] 1.0946520 [471,] 0.9778819 [472,] 1.2182191 [473,] 1.3520190 [474,] 1.2455699 [475,] 1.1184479 [476,] 0.8442811 [477,] 0.6421631 [478,] 1.0880043 [479,] 1.3860479 [480,] 1.3694154 [481,] 1.0396397 [482,] 1.0558732 [483,] 0.8497615 [484,] 0.8084117 [485,] 1.1024426 [486,] 1.1024426 [487,] 1.0396397 [488,] 0.8167523 [489,] 1.3179902 [490,] 1.0396397 [491,] 1.3179902 [492,] 1.0396397 [493,] 1.4204361 [494,] 0.8552419 [495,] 1.3860479 [496,] 0.8497615 [497,] 0.8442811 [498,] 1.3179902 [499,] 1.0396397 [500,] 0.8552419 [501,] 0.7641522 [502,] 1.0880043 [503,] 1.0477564 [504,] 1.0710411 [505,] 1.0710411 [506,] 1.2182191 [507,] 1.0566028 [508,] 1.0880043 [509,] 1.1104453 [510,] 1.0396397 [511,] 1.0633646 [512,] 0.8497615 [513,] 0.9552516 [514,] 0.8250929 [515,] 0.8497615 [516,] 1.1184479 [517,] 1.2455699 [518,] 1.0552479 [519,] 0.8581996 [520,] 0.9052467 [521,] 1.0946520 [522,] 0.8645944 [523,] 1.0710411 [524,] 0.8084117 [525,] 1.4204361 [526,] 0.8080301 [527,] 0.8167523 [528,] 1.3179902 [529,] 1.4032420 [530,] 1.0880043 [531,] 1.1104453 [532,] 1.0130538 [533,] 0.8080301 [534,] 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769172391 [571] 2087724752 472447086 1160212251 1633874045 199040618 1550958344 [577] 904093617 -1106798557 2089445668 1205460434 1801018919 2031052913 [583] 1878343350 -1013935532 -2140000539 644090607 -1070340568 1236406918 [589] 554970291 804205077 410014834 646516928 1583121705 -11347045 [595] -617608084 -465063814 1077983663 -1122733895 -313023794 -352223204 [601] -780307571 -1214009865 -909491968 -670445730 1998460971 1084086061 [607] -1899209830 -1361807016 1272528065 1966287731 -1021327212 -2104201950 [613] -1432548297 24036609 2022926342 -1503486396 -1030592651 -64292129 [619] -2114566600 1302189590 -1088845277 -782752283 462388226 843488880 [625] 1605749785 -381429566 $statistic function (x, idx) { fval <- func(x[idx], ...) if (student) { rs.x <- x[idx] b <- one.boot(rs.x, FUN, R = M, student = FALSE, M = NULL, weights = NULL, ...) fval <- c(fval, var(b$t)) } fval } $sim [1] "ordinary" $call boot(data = data, statistic = boot.func, R = R, weights = weights) $stype [1] "i" $strata [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 $weights [,1] [,2] [,3] [,4] [,5] [,6] [1,] 0.01139568 0.006888643 0.009586428 0.01556396 0.001911771 0.005062294 [,7] [,8] [,9] [,10] [,11] [,12] [1,] 0.006164722 0.006114749 0.01382876 0.009648492 0.01463326 0.01274884 [,13] [,14] [,15] [,16] [,17] [,18] [1,] 0.002548902 0.01338181 0.008040194 0.009629133 0.001165515 0.005932244 [,19] [,20] [,21] [,22] [,23] [,24] [1,] 0.008955048 0.01877582 0.01941775 0.01381805 0.01740549 0.007984475 [,25] [,26] [,27] [,28] [,29] [,30] [1,] 0.009113567 0.01586812 0.01882418 0.005380096 0.01730807 0.007991865 [,31] [,32] [,33] [,34] [,35] [,36] [1,] 0.01202710 0.002090341 0.003813014 0.02020619 0.009490542 0.01679103 [,37] [,38] [,39] [,40] [,41] [,42] [1,] 0.01847308 0.02143723 0.01539200 0.01123905 0.00618613 0.005140154 [,43] 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[,99] [,100] [1,] 0.005591602 0.01213947 0.01670329 0.0006739314 $student [1] FALSE attr(,"class") [1] "simpleboot" > > ## Studentized > bw.stud <- one.boot(x, median, R = 500, student = TRUE, M = 50, + weights = w) > boot.ci(bw.stud, type = "stud") BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 500 bootstrap replicates CALL : boot.ci(boot.out = bw.stud, type = "stud") Intervals : Level Studentized 95% ( 0.5088, 0.8836 ) Calculations and Intervals on Original Scale > > > > cleanEx(); ..nameEx <- "pairs.boot" > > ### * pairs.boot > > flush(stderr()); flush(stdout()) > > ### Name: pairs.boot > ### Title: Two sample bootstrap. > ### Aliases: pairs.boot > ### Keywords: univar > > ### ** Examples > > set.seed(1) > x <- rnorm(100) > y <- 2 * x + rnorm(100) > boot.cor <- pairs.boot(x, y, FUN = cor, R = 1000) > boot.ci(boot.cor) Warning in boot.ci(boot.cor) : bootstrap variances needed for studentized intervals BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 1000 bootstrap replicates CALL : boot.ci(boot.out = boot.cor) Intervals : Level Normal Basic 95% ( 0.8367, 0.9326 ) ( 0.8449, 0.9404 ) Level Percentile BCa 95% ( 0.8242, 0.9196 ) ( 0.8268, 0.9211 ) Calculations and Intervals on Original Scale > > ## With weighting > set.seed(20) > w <- (100:1)^2 > bw <- pairs.boot(x, y, FUN = cor, R = 5000, weights = w) > boot.ci(bw, type = c("norm", "basic", "perc")) BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 5000 bootstrap replicates CALL : boot.ci(boot.out = bw, type = c("norm", "basic", "perc")) Intervals : Level Normal Basic Percentile 95% ( 0.8232, 0.9172 ) ( 0.8302, 0.9237 ) ( 0.8408, 0.9344 ) Calculations and Intervals on Original Scale > > > > cleanEx(); ..nameEx <- "perc" > > ### * perc > > flush(stderr()); flush(stdout()) > > ### Name: perc > ### Title: Extract percentiles from a bootstrap sampling distribution. > ### Aliases: perc perc.lm > ### Keywords: univar > > ### ** Examples > > x <- rnorm(100) > b <- one.boot(x, median, R = 1000) > perc(b, c(.90, .95, .99)) 90% 95% 99% 0.3331867 0.3557203 0.3940159 > > > > cleanEx(); ..nameEx <- "plot.lm.simpleboot" > > ### * plot.lm.simpleboot > > flush(stderr()); flush(stdout()) > > ### Name: plot.lm.simpleboot > ### Title: Plot method for linear model bootstraps. > ### Aliases: plot.lm.simpleboot > ### Keywords: regression > > ### ** Examples > > ## None right now > > > > cleanEx(); ..nameEx <- "plot.loess.simpleboot" > > ### * plot.loess.simpleboot > > flush(stderr()); flush(stdout()) > > ### Name: plot.loess.simpleboot > ### Title: Plot method for loess bootstraps. > ### Aliases: plot.loess.simpleboot > ### Keywords: loess > > ### ** Examples > > ## See the help page for `loess.boot' for an example. > > > > cleanEx(); ..nameEx <- "samples" > > ### * samples > > flush(stderr()); flush(stdout()) > > ### Name: samples > ### Title: Extract sampling distributions from bootstrapped linear/loess > ### models. > ### Aliases: samples > ### Keywords: utilities > > ### ** Examples > > data(airquality) > attach(airquality) > lmodel <- lm(Ozone ~ Solar.R + Wind) > lboot <- lm.boot(lmodel, R = 500) > > ## Get sampling distributions for coefficients > s <- samples(lboot, "coef") > > ## Histogram for the intercept > hist(s[1,]) > > > > > cleanEx(); ..nameEx <- "two.boot" > > ### * two.boot > > flush(stderr()); flush(stdout()) > > ### Name: two.boot > ### Title: Two sample bootstrap of differences between univariate > ### statistics. > ### Aliases: two.boot > ### Keywords: univar > > ### ** Examples > > set.seed(50) > x <- rnorm(100, 1) ## Mean 1 normals > y <- rnorm(100, 0) ## Mean 0 normals > b <- two.boot(x, y, median, R = 1000) > boot.ci(b) ## No studentized confidence intervals Warning in boot.ci(b) : bootstrap variances needed for studentized intervals BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 1000 bootstrap replicates CALL : boot.ci(boot.out = b) Intervals : Level Normal Basic 95% ( 0.737, 1.426 ) ( 0.717, 1.463 ) Level Percentile BCa 95% ( 0.677, 1.422 ) ( 0.717, 1.483 ) Calculations and Intervals on Original Scale > hist(b) ## Histogram of the bootstrap replicates > > b <- two.boot(x, y, quantile, R = 1000, probs = .75) > > ## With weighting > > ## Here all members of the first group has equal weighting > ## but members of the the second have unequal weighting > w <- list(rep(1, 100), 100:1) > bw <- two.boot(x, y, median, R = 1000, weights = w) > boot.ci(b) Warning in boot.ci(b) : bootstrap variances needed for studentized intervals BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 1000 bootstrap replicates CALL : boot.ci(boot.out = b) Intervals : Level Normal Basic 95% ( 0.6157, 1.3159 ) ( 0.6225, 1.2806 ) Level Percentile BCa 95% ( 0.6361, 1.2943 ) ( 0.6333, 1.2930 ) Calculations and Intervals on Original Scale > > ## Studentized > bstud <- two.boot(x, y, median, R = 500, student = TRUE, M = 50) > boot.ci(bstud, type = "stud") BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 500 bootstrap replicates CALL : boot.ci(boot.out = bstud, type = "stud") Intervals : Level Studentized 95% ( 0.778, 1.462 ) Calculations and Intervals on Original Scale > > ## Studentized with weights > bwstud <- two.boot(x, y, median, R = 500, student = TRUE, M = 50, + weights = w) > boot.ci(bstud, type = "stud") BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 500 bootstrap replicates CALL : boot.ci(boot.out = bstud, type = "stud") Intervals : Level Studentized 95% ( 0.778, 1.462 ) Calculations and Intervals on Original Scale > > > > ### *