one.boot: One sample bootstrap of a univariate statistic.
In simpleboot: Simple Bootstrap Routines
Description
Usage
Arguments
Value
Author(s)
Examples
Description
one.boot is used for bootstrapping a univariate statistic for
one sample problems. Examples include the mean, median,
etc.
Usage
1
Arguments
data
The data. This should be a vector of numbers.
FUN
The statistic to be bootstrapped. This can be either a
quoted string containing the name of a function or simply the
function name.
R
The number of bootstrap replicates to use.
student
Should we do a studentized bootstrap? This requires a
double bootstrap so it might take longer.
M
If student is set to TRUE, then M is the
number of internal bootstrap replications to do.
weights
Resampling weights; a vector of length equal to the
number of observations.
...
Other (named) arguments that should be passed to FUN.
Value
An object of class "simpleboot", which is almost identical to the
regular "boot" object. For example, the boot.ci
function can be used on this object.
Author(s)
Roger D. Peng
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
library(boot)
set.seed(20)
x <- rgamma(100, 1)
b.mean <- one.boot(x, mean, 500)
print(b.mean)
boot.ci(b.mean) ## No studentized interval here
hist(b.mean)
## Bootstrap with weights
set.seed(10)
w <- runif(100)
bw <- one.boot(x, median, 100, weights = w)
print(bw)
## Studentized
bw.stud <- one.boot(x, median, R = 100, student = TRUE, M = 50,
weights = w)
boot.ci(bw.stud, type = "stud")
Example output
Simple Bootstrap Routines (1.1-7)
$t0
[1] 1.141418
$t
[,1]
[1,] 1.0219516
[2,] 1.2196019
[3,] 1.1821639
[4,] 1.2338789
[5,] 1.1830193
[6,] 1.0560445
[7,] 1.0591619
[8,] 1.2529843
[9,] 1.2346992
[10,] 1.1147462
[11,] 1.0599891
[12,] 1.1564735
[13,] 1.0929717
[14,] 1.0904788
[15,] 1.0693141
[16,] 1.0964003
[17,] 1.1508746
[18,] 1.0235503
[19,] 1.0784670
[20,] 1.1427606
[21,] 1.1044024
[22,] 0.9973107
[23,] 1.0994921
[24,] 1.1149875
[25,] 1.0891179
[26,] 1.1554459
[27,] 1.1350064
[28,] 1.1657122
[29,] 0.9502535
[30,] 0.9629885
[31,] 1.2165819
[32,] 1.1844438
[33,] 1.1749877
[34,] 1.1328742
[35,] 1.1087894
[36,] 1.2029912
[37,] 1.0975033
[38,] 1.1066056
[39,] 1.0771606
[40,] 1.1864058
[41,] 1.1599496
[42,] 1.2604339
[43,] 1.2979220
[44,] 0.9684300
[45,] 0.9566766
[46,] 1.1752132
[47,] 1.1549297
[48,] 1.1749862
[49,] 1.0430862
[50,] 1.1837348
[51,] 0.8871475
[52,] 1.1350670
[53,] 0.9105496
[54,] 1.1004107
[55,] 1.1357671
[56,] 1.1515115
[57,] 1.1018604
[58,] 1.0044363
[59,] 1.0369992
[60,] 1.0858716
[61,] 1.0332090
[62,] 1.1925114
[63,] 1.0454457
[64,] 1.1534857
[65,] 1.1208712
[66,] 1.1406828
[67,] 0.9540632
[68,] 1.1807294
[69,] 1.0378775
[70,] 1.1430674
[71,] 1.1882890
[72,] 1.0788544
[73,] 1.0173413
[74,] 1.0876894
[75,] 1.0944733
[76,] 1.1023795
[77,] 1.0923641
[78,] 1.2302176
[79,] 1.1549165
[80,] 1.1323395
[81,] 0.9594158
[82,] 1.0646302
[83,] 1.2100950
[84,] 1.3172579
[85,] 1.0539528
[86,] 1.1784292
[87,] 1.0454329
[88,] 1.1618101
[89,] 1.1461579
[90,] 1.1124846
[91,] 1.2783179
[92,] 1.3308517
[93,] 1.3072195
[94,] 1.0648809
[95,] 1.2042161
[96,] 1.1365066
[97,] 1.2310378
[98,] 1.0954906
[99,] 1.1537080
[100,] 1.0290199
[101,] 1.1582958
[102,] 1.2334902
[103,] 1.2498898
[104,] 1.1110916
[105,] 1.1459877
[106,] 1.1630460
[107,] 1.2351136
[108,] 1.1022337
[109,] 0.9780342
[110,] 1.0612678
[111,] 1.1597928
[112,] 1.0620767
[113,] 1.1818118
[114,] 1.0486785
[115,] 1.1168716
[116,] 1.2272822
[117,] 1.0970429
[118,] 1.0583942
[119,] 1.0922989
[120,] 1.0908989
[121,] 1.2875274
[122,] 1.0118986
[123,] 1.2360866
[124,] 1.0665311
[125,] 1.1816442
[126,] 1.2130479
[127,] 1.0517596
[128,] 1.2376329
[129,] 1.1997994
[130,] 1.1861908
[131,] 1.0540896
[132,] 0.9866427
[133,] 1.1745762
[134,] 1.0161659
[135,] 1.1447022
[136,] 1.2377750
[137,] 1.0086735
[138,] 1.2040854
[139,] 1.2940602
[140,] 1.1743100
[141,] 1.2517427
[142,] 1.0486897
[143,] 1.1927318
[144,] 1.1285868
[145,] 1.1452846
[146,] 1.2199602
[147,] 1.0240343
[148,] 1.3027213
[149,] 1.1826222
[150,] 1.2727795
[151,] 1.1732931
[152,] 1.0192386
[153,] 0.9428618
[154,] 1.1075708
[155,] 1.2908623
[156,] 1.1779530
[157,] 1.3381477
[158,] 1.1247151
[159,] 1.2786261
[160,] 0.9652429
[161,] 1.1841272
[162,] 1.1524632
[163,] 1.2416679
[164,] 1.1082957
[165,] 1.1392683
[166,] 1.3306291
[167,] 1.1584514
[168,] 1.0256946
[169,] 1.1138315
[170,] 1.0177383
[171,] 1.2401879
[172,] 1.1898319
[173,] 1.3749130
[174,] 1.1350515
[175,] 1.3388232
[176,] 1.2500780
[177,] 1.3412292
[178,] 1.2125515
[179,] 1.1815461
[180,] 1.3253936
[181,] 1.2314906
[182,] 1.1598213
[183,] 1.1223625
[184,] 1.2325135
[185,] 1.2409039
[186,] 1.1904821
[187,] 1.1521904
[188,] 0.9959229
[189,] 1.1907599
[190,] 1.1398663
[191,] 1.0992113
[192,] 1.2625212
[193,] 1.2960263
[194,] 1.2230995
[195,] 1.1599114
[196,] 0.8814247
[197,] 1.1600584
[198,] 1.0387332
[199,] 1.0014843
[200,] 1.2044329
[201,] 1.1109786
[202,] 0.9631266
[203,] 0.9243843
[204,] 1.2919869
[205,] 0.9515291
[206,] 1.2181320
[207,] 1.1160335
[208,] 1.0852239
[209,] 1.2054177
[210,] 1.1269074
[211,] 1.4703001
[212,] 1.1577931
[213,] 0.9681569
[214,] 1.4253971
[215,] 0.9563384
[216,] 1.3512714
[217,] 1.1843107
[218,] 1.1137115
[219,] 1.1226371
[220,] 1.1257098
[221,] 1.1931442
[222,] 1.1663128
[223,] 1.2212343
[224,] 1.0363272
[225,] 1.1039092
[226,] 1.1948889
[227,] 0.9897214
[228,] 1.1518323
[229,] 1.3750174
[230,] 1.2072474
[231,] 1.2405840
[232,] 1.0806256
[233,] 1.1499386
[234,] 1.0931435
[235,] 1.1332376
[236,] 1.2273520
[237,] 1.0480677
[238,] 1.2660945
[239,] 1.2410832
[240,] 1.2658881
[241,] 1.1311166
[242,] 1.3178531
[243,] 1.1448205
[244,] 1.1256489
[245,] 1.1407650
[246,] 1.0438767
[247,] 1.1069316
[248,] 1.1386095
[249,] 1.0152740
[250,] 1.0216005
[251,] 1.1111656
[252,] 1.3285302
[253,] 1.1967816
[254,] 1.0170785
[255,] 0.9160759
[256,] 1.4435306
[257,] 1.1067014
[258,] 1.1601081
[259,] 1.0785882
[260,] 1.1737186
[261,] 1.0998500
[262,] 1.1921395
[263,] 0.9341092
[264,] 1.0845798
[265,] 1.1879519
[266,] 1.2004181
[267,] 1.1707971
[268,] 0.9582416
[269,] 1.0193227
[270,] 1.1257769
[271,] 1.1830017
[272,] 1.1542156
[273,] 1.0921255
[274,] 1.2140357
[275,] 1.1291363
[276,] 1.0226527
[277,] 1.3564711
[278,] 1.0543403
[279,] 1.1670479
[280,] 1.1372472
[281,] 1.2775079
[282,] 0.9814272
[283,] 1.1107539
[284,] 1.0676285
[285,] 1.0814195
[286,] 1.1394936
[287,] 0.9960645
[288,] 1.1333409
[289,] 1.2879462
[290,] 1.2295620
[291,] 1.0453684
[292,] 1.0162422
[293,] 1.1006044
[294,] 1.1311635
[295,] 1.1789526
[296,] 1.1487440
[297,] 1.2344014
[298,] 1.1275723
[299,] 1.1109295
[300,] 1.1412574
[301,] 1.0660551
[302,] 1.2561396
[303,] 1.1161273
[304,] 1.3003692
[305,] 1.1555687
[306,] 1.0673619
[307,] 1.1153419
[308,] 1.2701549
[309,] 1.1082970
[310,] 1.1280595
[311,] 1.2025060
[312,] 0.9861541
[313,] 1.0093709
[314,] 1.2713430
[315,] 0.9800962
[316,] 1.1214336
[317,] 1.2496352
[318,] 1.1991890
[319,] 1.1665606
[320,] 1.0797960
[321,] 1.1882395
[322,] 1.0574934
[323,] 1.0650006
[324,] 1.0263107
[325,] 1.3237456
[326,] 1.0487850
[327,] 1.1618059
[328,] 1.1186326
[329,] 1.1044700
[330,] 1.2098880
[331,] 1.1887319
[332,] 1.0876685
[333,] 1.2244676
[334,] 1.1262187
[335,] 1.3187054
[336,] 1.2783481
[337,] 1.0636821
[338,] 1.0702041
[339,] 0.9836167
[340,] 1.2017886
[341,] 1.2947470
[342,] 1.1359625
[343,] 1.1933908
[344,] 0.9516974
[345,] 1.2034354
[346,] 1.1066587
[347,] 1.2642359
[348,] 1.2049810
[349,] 1.2170557
[350,] 1.1362857
[351,] 1.2093391
[352,] 1.1143736
[353,] 0.9994299
[354,] 1.1997917
[355,] 1.2564765
[356,] 1.0355687
[357,] 0.9865839
[358,] 1.0046446
[359,] 1.2007707
[360,] 1.1082438
[361,] 1.1057556
[362,] 0.9978254
[363,] 1.1227038
[364,] 1.1747249
[365,] 1.2227679
[366,] 1.1316945
[367,] 1.3791049
[368,] 1.1111571
[369,] 1.0096134
[370,] 1.1542012
[371,] 1.1178881
[372,] 1.0544766
[373,] 1.1463136
[374,] 1.2265085
[375,] 1.1234785
[376,] 1.1707285
[377,] 0.9963210
[378,] 1.2877894
[379,] 0.9688100
[380,] 1.2770239
[381,] 1.0229010
[382,] 1.1800517
[383,] 1.0483758
[384,] 1.1273695
[385,] 1.1096304
[386,] 1.0550706
[387,] 1.3111883
[388,] 1.0407517
[389,] 1.1297430
[390,] 1.0266622
[391,] 1.0821925
[392,] 1.2830618
[393,] 1.1271598
[394,] 0.9652525
[395,] 1.1657509
[396,] 1.2087072
[397,] 0.9312888
[398,] 1.0313231
[399,] 1.1975512
[400,] 1.1090394
[401,] 1.0377311
[402,] 1.0330151
[403,] 1.1002084
[404,] 1.1627962
[405,] 1.1684280
[406,] 1.1462411
[407,] 1.1528141
[408,] 0.9215105
[409,] 1.0524952
[410,] 1.1401680
[411,] 1.0790763
[412,] 1.1710066
[413,] 1.0066826
[414,] 1.1480604
[415,] 1.0532913
[416,] 1.1375827
[417,] 1.0537580
[418,] 1.0411006
[419,] 1.0925236
[420,] 0.9902160
[421,] 1.1075848
[422,] 1.0588966
[423,] 1.0738688
[424,] 1.0926526
[425,] 0.9318529
[426,] 1.2804156
[427,] 1.1409126
[428,] 1.1616623
[429,] 1.0814007
[430,] 1.1179050
[431,] 1.0270392
[432,] 1.1448280
[433,] 0.9297235
[434,] 1.1435239
[435,] 1.1011026
[436,] 0.9741027
[437,] 1.1325980
[438,] 1.0036246
[439,] 1.1818911
[440,] 1.1278391
[441,] 1.1043831
[442,] 1.0528631
[443,] 1.2459736
[444,] 1.2203617
[445,] 1.2701358
[446,] 1.1939465
[447,] 1.1356561
[448,] 1.0416680
[449,] 1.1476477
[450,] 1.1261690
[451,] 1.0613960
[452,] 1.0541807
[453,] 1.0631082
[454,] 1.0179112
[455,] 1.0835580
[456,] 0.9492498
[457,] 1.0521260
[458,] 1.2904965
[459,] 1.1203708
[460,] 1.0403799
[461,] 1.0809312
[462,] 1.1961582
[463,] 1.0638742
[464,] 1.3402565
[465,] 1.1460701
[466,] 1.2328666
[467,] 1.0438423
[468,] 1.2137275
[469,] 1.1028465
[470,] 1.2314836
[471,] 1.2542518
[472,] 1.1759681
[473,] 1.2966959
[474,] 1.1194930
[475,] 1.0640547
[476,] 1.0648786
[477,] 1.1208084
[478,] 1.2988805
[479,] 0.9986156
[480,] 1.1177867
[481,] 1.0324511
[482,] 1.0508431
[483,] 1.1292981
[484,] 1.0276901
[485,] 1.2011979
[486,] 1.2075388
[487,] 1.2257225
[488,] 1.0303713
[489,] 1.0872661
[490,] 1.2282104
[491,] 1.0599433
[492,] 1.2793819
[493,] 1.0837638
[494,] 1.0915101
[495,] 0.9578676
[496,] 1.2596087
[497,] 1.0641717
[498,] 1.1408272
[499,] 1.1315033
[500,] 1.0922203
$R
[1] 500
$data
[1] 1.66010192 0.17151571 3.01949976 2.56908550 0.74297025 1.35432487
[7] 1.86074189 0.23852757 0.06796258 0.44118248 0.14600106 0.40308108
[13] 0.39204272 0.34348229 0.47279134 0.80841170 1.83230319 0.27808396
[19] 0.63589943 0.31720071 0.84428115 0.08872372 2.22137847 0.40671793
[25] 0.03364937 0.19200140 1.31799017 1.48895728 0.26672287 2.33535005
[31] 4.28638054 0.86115734 1.07356591 2.08785408 0.08190640 0.25305609
[37] 1.73178015 1.66335916 0.02967895 0.03700080 1.07085607 0.14206934
[43] 1.80520095 1.65708325 2.95591031 0.01743079 0.64220459 0.15071405
[49] 0.44344341 1.72285886 2.03721143 3.28091085 5.06389603 1.75504778
[55] 0.41849274 0.16756711 0.85524188 1.60116223 0.20206898 1.11844795
[61] 1.35278299 0.58043853 0.42524822 1.55205789 2.30757416 2.04565254
[67] 3.44015575 1.38604791 0.79173681 1.05587315 0.46642192 0.79256723
[73] 1.10244260 3.13847418 0.73065445 0.02588435 1.67620686 2.18362909
[79] 1.97829218 0.34102672 0.69222196 0.82509289 0.80764854 1.59490145
[85] 1.17314966 0.67864863 1.03963966 0.95525157 0.45496006 0.88490774
[91] 0.27390549 0.64842677 0.15044882 1.50222075 0.76108461 1.87995125
[97] 0.76415221 1.42043610 4.02319597 0.07382617
$seed
[1] 403 342 1919896738 -1966359555 -536885001 -1117352418
[7] -437141288 -1094376389 -923496999 1557216104 -861866346 -703746031
[13] 1573343683 -1201562158 551048292 525173671 -1718014723 375130356
[19] -819077158 -1847692507 1338565183 1019873734 -1303404976 -73018765
[25] 1736304273 -2924864 1859250334 -754269079 31387803 137294250
[31] 1904724428 -842914673 -1528742715 1455598716 1028202706 -1288961971
[37] -214574841 479268078 -1905760824 1289013963 -634277079 866358136
[43] -133183322 -1496067391 577676115 400240642 1375624500 1082267319
[49] -1491359059 1694444484 1478849514 -1415824299 618979823 1613261302
[55] -300486880 6922467 -2033871487 1112781808 227605134 -1050391047
[61] -1628295285 -1289589830 1112691388 -104232449 -836249771 -637816724
[67] -1201937214 1095749405 -712391273 1672541438 -1263034696 -237797221
[73] 1640738553 1977999432 -1096748170 1157326513 224224995 296211890
[79] -2100937212 -2093435577 -81072099 331208340 -699572678 -909627259
[85] -1345735329 844340710 -91769744 1044457363 -170201039 -904806688
[91] 1849784190 -821989879 -1379498437 -227993590 755320812 19590255
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[109] -764649933 1780929442 -1023334764 -572463849 1302160141 361484836
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[121] -833414303 -926137904 973019438 -1800838951 -403375381 -746215590
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[133] -465239497 1956024542 197647384 -531741829 -1696173287 593809320
[139] 171495254 1987065809 1227547523 505267730 -421240412 792155495
[145] 1837947581 1077729844 -521248358 -1774436891 -273344129 -16928506
[151] -1544959344 2120293171 -859348783 1826239104 2010096478 1661243561
[157] -1299713189 -1300037270 1186620428 -776289201 -1244891899 -1799998020
[163] 1266942098 549417869 1139016647 -1994999634 -908265208 1716143883
[169] 1220723177 1422247608 -1105481370 1407988225 -1296192365 -1664084158
[175] 450488564 -1591155465 -2062287635 -1955254396 1821811114 1891391253
[181] -1899280977 -1527190090 1372197856 -1918092509 1935916097 590040880
[187] 595221838 1842110393 1977735627 982391418 -899018884 314073023
[193] 1114622101 -4671828 -1052171134 324764765 -565589161 -585329090
[199] -1775429000 -662460069 -1374041927 -1446893304 1701338038 931928305
[205] 1707593507 -2100569742 -836924476 1947395975 -510562979 -1795833004
[211] -788047494 928961733 -781089249 -71167578 2026439472 -2027736621
[217] 561100785 320114976 -552439874 -814555191 -221253765 1187501386
[223] 288268716 571227311 1040590437 1050451484 -622538638 -82524435
[229] -1746867417 602030572 1462605396 -243903342 971399968 -1792482868
[235] 577508832 -1106082110 -1438922712 -1784793428 674881724 1046437362
[241] 620260656 1963541668 -515827016 -944924326 -599701104 -1732688004
[247] 246573332 1777442626 -517509376 -599088196 2129589072 100654882
[253] 506635592 1479388684 1412012124 281137362 -67121664 1517392212
[259] 462831080 -815006726 -1095443248 1878022444 160573172 -2039087726
[265] -1890295712 -1187810036 -1856628000 -1870578334 -28450136 -1918227892
[271] 2057172348 83302706 -14570384 1713261380 -1349582376 -727706502
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$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
}
<environment: 0x3525570>
$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
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[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
$weights
[1] 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
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$student
[1] FALSE
attr(,"class")
[1] "simpleboot"
attr(,"boot_type")
[1] "boot"
BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
Based on 500 bootstrap replicates
CALL :
boot.ci(boot.out = b.mean)
Intervals :
Level Normal Basic
95% ( 0.956, 1.343 ) ( 0.949, 1.333 )
Level Percentile BCa
95% ( 0.950, 1.334 ) ( 0.969, 1.376 )
Calculations and Intervals on Original Scale
Some BCa intervals may be unstable
Warning message:
In boot.ci(b.mean) : bootstrap variances needed for studentized intervals
$t0
[1] 0.834687
$t
[,1]
[1,] 0.9974456
[2,] 0.9974456
[3,] 0.9622737
[4,] 0.9622737
[5,] 0.9474408
[6,] 0.8263464
[7,] 0.8401674
[8,] 1.3179902
[9,] 0.8552419
[10,] 0.8497615
[11,] 0.7946225
[12,] 1.1731497
[13,] 0.8442811
[14,] 0.9703904
[15,] 1.0633646
[16,] 0.8084117
[17,] 1.0633646
[18,] 1.0396397
[19,] 1.2102164
[20,] 1.1184479
[21,] 0.8084117
[22,] 1.3361575
[23,] 0.8497615
[24,] 0.9974456
[25,] 1.4613284
[26,] 0.9974456
[27,] 0.9052467
[28,] 1.3179902
[29,] 0.9474408
[30,] 1.0396397
[31,] 1.0396397
[32,] 0.9474408
[33,] 1.1184479
[34,] 1.0552479
[35,] 0.7610846
[36,] 0.9703904
[37,] 1.5271393
[38,] 1.3179902
[39,] 1.0866493
[40,] 0.9052467
[41,] 1.3179902
[42,] 1.0880043
[43,] 1.3179902
[44,] 1.1104453
[45,] 0.7925672
[46,] 0.8442811
[47,] 1.1377961
[48,] 1.1024426
[49,] 1.0880043
[50,] 1.0396397
[51,] 1.0866493
[52,] 1.0866493
[53,] 1.0396397
[54,] 1.1104453
[55,] 1.1731497
[56,] 1.0396397
[57,] 0.9052467
[58,] 1.0633646
[59,] 1.0396397
[60,] 0.8167523
[61,] 1.5076688
[62,] 1.0722110
[63,] 0.8552419
[64,] 0.8431251
[65,] 1.0396397
[66,] 1.5520579
[67,] 1.0396397
[68,] 1.0960069
[69,] 1.0396397
[70,] 1.0552479
[71,] 0.8700748
[72,] 0.8552419
[73,] 1.2182191
[74,] 1.2455699
[75,] 1.3179902
[76,] 1.0552479
[77,] 1.1024426
[78,] 0.8552419
[79,] 0.9474408
[80,] 1.4613284
[81,] 1.0477564
[82,] 0.9474408
[83,] 1.0477564
[84,] 0.8997664
[85,] 1.3361575
[86,] 1.3179902
[87,] 1.3179902
[88,] 1.0477564
[89,] 0.9974456
[90,] 1.0552479
[91,] 1.0477564
[92,] 0.8849077
[93,] 1.2182191
[94,] 1.0791579
[95,] 0.7626184
[96,] 1.1184479
[97,] 1.3701864
[98,] 1.1024426
[99,] 0.8552419
[100,] 1.2182191
$R
[1] 100
$data
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[97] 0.76415221 1.42043610 4.02319597 0.07382617
$seed
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$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
}
<bytecode: 0x3ec3f38>
<environment: 0x34d9cc8>
$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,] 0.0120271 0.002090341 0.003813014 0.02020619 0.009490542 0.01679103
[,37] [,38] [,39] [,40] [,41] [,42]
[1,] 0.01847308 0.02143723 0.015392 0.01123905 0.00618613 0.005140154
[,43] [,44] [,45] [,46] [,47] [,48]
[1,] 0.0003241207 0.01636927 0.005611193 0.003619452 0.0003827014 0.01091563
[,49] [,50] [,51] [,52] [,53] [,54]
[1,] 0.002310676 0.01799914 0.007956617 0.02102807 0.005521055 0.01062463
[,55] [,56] [,57] [,58] [,59] [,60]
[1,] 0.004301597 0.01309655 0.0103177 0.01049647 0.008978443 0.01134644
[,61] [,62] [,63] [,64] [,65] [,66]
[1,] 0.0007160647 0.002570428 0.01053018 0.008914539 0.01871934 0.01709138
[,67] [,68] [,69] [,70] [,71] [,72]
[1,] 0.01287501 0.01004902 0.001881817 0.004920867 0.001696969 0.01200082
[,73] [,74] [,75] [,76] [,77] [,78]
[1,] 0.01440199 0.01180574 0.0008820835 0.01225756 0.008370585 0.02158653
[,79] [,80] [,81] [,82] [,83] [,84]
[1,] 0.005778736 0.004669661 0.01934278 0.01042816 0.005004601 0.01400212
[,85] [,86] [,87] [,88] [,89] [,90]
[1,] 0.004573013 0.0004417765 0.01791934 0.00615997 0.003741292 0.003820843
[,91] [,92] [,93] [,94] [,95] [,96]
[1,] 0.01096965 0.01597505 0.0132922 0.01161302 0.008569643 0.01874537
[,97] [,98] [,99] [,100]
[1,] 0.005591602 0.01213947 0.01670329 0.0006739314
$student
[1] FALSE
attr(,"class")
[1] "simpleboot"
attr(,"boot_type")
[1] "boot"
BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
Based on 100 bootstrap replicates
CALL :
boot.ci(boot.out = bw.stud, type = "stud")
Intervals :
Level Studentized
95% ( 0.3289, 0.8903 )
Calculations and Intervals on Original Scale
Some studentized intervals may be unstable
simpleboot documentation built on May 2, 2019, 4:16 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Value Author(s) Examples
Description
one.boot is used for bootstrapping a univariate statistic for
one sample problems. Examples include the mean, median,
etc.
Usage
1 |
Arguments
data |
The data. This should be a vector of numbers. |
FUN |
The statistic to be bootstrapped. This can be either a quoted string containing the name of a function or simply the function name. |
R |
The number of bootstrap replicates to use. |
student |
Should we do a studentized bootstrap? This requires a double bootstrap so it might take longer. |
M |
If |
weights |
Resampling weights; a vector of length equal to the number of observations. |
... |
Other (named) arguments that should be passed to |
Value
An object of class "simpleboot", which is almost identical to the
regular "boot" object. For example, the boot.ci
function can be used on this object.
Author(s)
Roger D. Peng
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | library(boot)
set.seed(20)
x <- rgamma(100, 1)
b.mean <- one.boot(x, mean, 500)
print(b.mean)
boot.ci(b.mean) ## No studentized interval here
hist(b.mean)
## Bootstrap with weights
set.seed(10)
w <- runif(100)
bw <- one.boot(x, median, 100, weights = w)
print(bw)
## Studentized
bw.stud <- one.boot(x, median, R = 100, student = TRUE, M = 50,
weights = w)
boot.ci(bw.stud, type = "stud")
|
Example output
Simple Bootstrap Routines (1.1-7)
$t0
[1] 1.141418
$t
[,1]
[1,] 1.0219516
[2,] 1.2196019
[3,] 1.1821639
[4,] 1.2338789
[5,] 1.1830193
[6,] 1.0560445
[7,] 1.0591619
[8,] 1.2529843
[9,] 1.2346992
[10,] 1.1147462
[11,] 1.0599891
[12,] 1.1564735
[13,] 1.0929717
[14,] 1.0904788
[15,] 1.0693141
[16,] 1.0964003
[17,] 1.1508746
[18,] 1.0235503
[19,] 1.0784670
[20,] 1.1427606
[21,] 1.1044024
[22,] 0.9973107
[23,] 1.0994921
[24,] 1.1149875
[25,] 1.0891179
[26,] 1.1554459
[27,] 1.1350064
[28,] 1.1657122
[29,] 0.9502535
[30,] 0.9629885
[31,] 1.2165819
[32,] 1.1844438
[33,] 1.1749877
[34,] 1.1328742
[35,] 1.1087894
[36,] 1.2029912
[37,] 1.0975033
[38,] 1.1066056
[39,] 1.0771606
[40,] 1.1864058
[41,] 1.1599496
[42,] 1.2604339
[43,] 1.2979220
[44,] 0.9684300
[45,] 0.9566766
[46,] 1.1752132
[47,] 1.1549297
[48,] 1.1749862
[49,] 1.0430862
[50,] 1.1837348
[51,] 0.8871475
[52,] 1.1350670
[53,] 0.9105496
[54,] 1.1004107
[55,] 1.1357671
[56,] 1.1515115
[57,] 1.1018604
[58,] 1.0044363
[59,] 1.0369992
[60,] 1.0858716
[61,] 1.0332090
[62,] 1.1925114
[63,] 1.0454457
[64,] 1.1534857
[65,] 1.1208712
[66,] 1.1406828
[67,] 0.9540632
[68,] 1.1807294
[69,] 1.0378775
[70,] 1.1430674
[71,] 1.1882890
[72,] 1.0788544
[73,] 1.0173413
[74,] 1.0876894
[75,] 1.0944733
[76,] 1.1023795
[77,] 1.0923641
[78,] 1.2302176
[79,] 1.1549165
[80,] 1.1323395
[81,] 0.9594158
[82,] 1.0646302
[83,] 1.2100950
[84,] 1.3172579
[85,] 1.0539528
[86,] 1.1784292
[87,] 1.0454329
[88,] 1.1618101
[89,] 1.1461579
[90,] 1.1124846
[91,] 1.2783179
[92,] 1.3308517
[93,] 1.3072195
[94,] 1.0648809
[95,] 1.2042161
[96,] 1.1365066
[97,] 1.2310378
[98,] 1.0954906
[99,] 1.1537080
[100,] 1.0290199
[101,] 1.1582958
[102,] 1.2334902
[103,] 1.2498898
[104,] 1.1110916
[105,] 1.1459877
[106,] 1.1630460
[107,] 1.2351136
[108,] 1.1022337
[109,] 0.9780342
[110,] 1.0612678
[111,] 1.1597928
[112,] 1.0620767
[113,] 1.1818118
[114,] 1.0486785
[115,] 1.1168716
[116,] 1.2272822
[117,] 1.0970429
[118,] 1.0583942
[119,] 1.0922989
[120,] 1.0908989
[121,] 1.2875274
[122,] 1.0118986
[123,] 1.2360866
[124,] 1.0665311
[125,] 1.1816442
[126,] 1.2130479
[127,] 1.0517596
[128,] 1.2376329
[129,] 1.1997994
[130,] 1.1861908
[131,] 1.0540896
[132,] 0.9866427
[133,] 1.1745762
[134,] 1.0161659
[135,] 1.1447022
[136,] 1.2377750
[137,] 1.0086735
[138,] 1.2040854
[139,] 1.2940602
[140,] 1.1743100
[141,] 1.2517427
[142,] 1.0486897
[143,] 1.1927318
[144,] 1.1285868
[145,] 1.1452846
[146,] 1.2199602
[147,] 1.0240343
[148,] 1.3027213
[149,] 1.1826222
[150,] 1.2727795
[151,] 1.1732931
[152,] 1.0192386
[153,] 0.9428618
[154,] 1.1075708
[155,] 1.2908623
[156,] 1.1779530
[157,] 1.3381477
[158,] 1.1247151
[159,] 1.2786261
[160,] 0.9652429
[161,] 1.1841272
[162,] 1.1524632
[163,] 1.2416679
[164,] 1.1082957
[165,] 1.1392683
[166,] 1.3306291
[167,] 1.1584514
[168,] 1.0256946
[169,] 1.1138315
[170,] 1.0177383
[171,] 1.2401879
[172,] 1.1898319
[173,] 1.3749130
[174,] 1.1350515
[175,] 1.3388232
[176,] 1.2500780
[177,] 1.3412292
[178,] 1.2125515
[179,] 1.1815461
[180,] 1.3253936
[181,] 1.2314906
[182,] 1.1598213
[183,] 1.1223625
[184,] 1.2325135
[185,] 1.2409039
[186,] 1.1904821
[187,] 1.1521904
[188,] 0.9959229
[189,] 1.1907599
[190,] 1.1398663
[191,] 1.0992113
[192,] 1.2625212
[193,] 1.2960263
[194,] 1.2230995
[195,] 1.1599114
[196,] 0.8814247
[197,] 1.1600584
[198,] 1.0387332
[199,] 1.0014843
[200,] 1.2044329
[201,] 1.1109786
[202,] 0.9631266
[203,] 0.9243843
[204,] 1.2919869
[205,] 0.9515291
[206,] 1.2181320
[207,] 1.1160335
[208,] 1.0852239
[209,] 1.2054177
[210,] 1.1269074
[211,] 1.4703001
[212,] 1.1577931
[213,] 0.9681569
[214,] 1.4253971
[215,] 0.9563384
[216,] 1.3512714
[217,] 1.1843107
[218,] 1.1137115
[219,] 1.1226371
[220,] 1.1257098
[221,] 1.1931442
[222,] 1.1663128
[223,] 1.2212343
[224,] 1.0363272
[225,] 1.1039092
[226,] 1.1948889
[227,] 0.9897214
[228,] 1.1518323
[229,] 1.3750174
[230,] 1.2072474
[231,] 1.2405840
[232,] 1.0806256
[233,] 1.1499386
[234,] 1.0931435
[235,] 1.1332376
[236,] 1.2273520
[237,] 1.0480677
[238,] 1.2660945
[239,] 1.2410832
[240,] 1.2658881
[241,] 1.1311166
[242,] 1.3178531
[243,] 1.1448205
[244,] 1.1256489
[245,] 1.1407650
[246,] 1.0438767
[247,] 1.1069316
[248,] 1.1386095
[249,] 1.0152740
[250,] 1.0216005
[251,] 1.1111656
[252,] 1.3285302
[253,] 1.1967816
[254,] 1.0170785
[255,] 0.9160759
[256,] 1.4435306
[257,] 1.1067014
[258,] 1.1601081
[259,] 1.0785882
[260,] 1.1737186
[261,] 1.0998500
[262,] 1.1921395
[263,] 0.9341092
[264,] 1.0845798
[265,] 1.1879519
[266,] 1.2004181
[267,] 1.1707971
[268,] 0.9582416
[269,] 1.0193227
[270,] 1.1257769
[271,] 1.1830017
[272,] 1.1542156
[273,] 1.0921255
[274,] 1.2140357
[275,] 1.1291363
[276,] 1.0226527
[277,] 1.3564711
[278,] 1.0543403
[279,] 1.1670479
[280,] 1.1372472
[281,] 1.2775079
[282,] 0.9814272
[283,] 1.1107539
[284,] 1.0676285
[285,] 1.0814195
[286,] 1.1394936
[287,] 0.9960645
[288,] 1.1333409
[289,] 1.2879462
[290,] 1.2295620
[291,] 1.0453684
[292,] 1.0162422
[293,] 1.1006044
[294,] 1.1311635
[295,] 1.1789526
[296,] 1.1487440
[297,] 1.2344014
[298,] 1.1275723
[299,] 1.1109295
[300,] 1.1412574
[301,] 1.0660551
[302,] 1.2561396
[303,] 1.1161273
[304,] 1.3003692
[305,] 1.1555687
[306,] 1.0673619
[307,] 1.1153419
[308,] 1.2701549
[309,] 1.1082970
[310,] 1.1280595
[311,] 1.2025060
[312,] 0.9861541
[313,] 1.0093709
[314,] 1.2713430
[315,] 0.9800962
[316,] 1.1214336
[317,] 1.2496352
[318,] 1.1991890
[319,] 1.1665606
[320,] 1.0797960
[321,] 1.1882395
[322,] 1.0574934
[323,] 1.0650006
[324,] 1.0263107
[325,] 1.3237456
[326,] 1.0487850
[327,] 1.1618059
[328,] 1.1186326
[329,] 1.1044700
[330,] 1.2098880
[331,] 1.1887319
[332,] 1.0876685
[333,] 1.2244676
[334,] 1.1262187
[335,] 1.3187054
[336,] 1.2783481
[337,] 1.0636821
[338,] 1.0702041
[339,] 0.9836167
[340,] 1.2017886
[341,] 1.2947470
[342,] 1.1359625
[343,] 1.1933908
[344,] 0.9516974
[345,] 1.2034354
[346,] 1.1066587
[347,] 1.2642359
[348,] 1.2049810
[349,] 1.2170557
[350,] 1.1362857
[351,] 1.2093391
[352,] 1.1143736
[353,] 0.9994299
[354,] 1.1997917
[355,] 1.2564765
[356,] 1.0355687
[357,] 0.9865839
[358,] 1.0046446
[359,] 1.2007707
[360,] 1.1082438
[361,] 1.1057556
[362,] 0.9978254
[363,] 1.1227038
[364,] 1.1747249
[365,] 1.2227679
[366,] 1.1316945
[367,] 1.3791049
[368,] 1.1111571
[369,] 1.0096134
[370,] 1.1542012
[371,] 1.1178881
[372,] 1.0544766
[373,] 1.1463136
[374,] 1.2265085
[375,] 1.1234785
[376,] 1.1707285
[377,] 0.9963210
[378,] 1.2877894
[379,] 0.9688100
[380,] 1.2770239
[381,] 1.0229010
[382,] 1.1800517
[383,] 1.0483758
[384,] 1.1273695
[385,] 1.1096304
[386,] 1.0550706
[387,] 1.3111883
[388,] 1.0407517
[389,] 1.1297430
[390,] 1.0266622
[391,] 1.0821925
[392,] 1.2830618
[393,] 1.1271598
[394,] 0.9652525
[395,] 1.1657509
[396,] 1.2087072
[397,] 0.9312888
[398,] 1.0313231
[399,] 1.1975512
[400,] 1.1090394
[401,] 1.0377311
[402,] 1.0330151
[403,] 1.1002084
[404,] 1.1627962
[405,] 1.1684280
[406,] 1.1462411
[407,] 1.1528141
[408,] 0.9215105
[409,] 1.0524952
[410,] 1.1401680
[411,] 1.0790763
[412,] 1.1710066
[413,] 1.0066826
[414,] 1.1480604
[415,] 1.0532913
[416,] 1.1375827
[417,] 1.0537580
[418,] 1.0411006
[419,] 1.0925236
[420,] 0.9902160
[421,] 1.1075848
[422,] 1.0588966
[423,] 1.0738688
[424,] 1.0926526
[425,] 0.9318529
[426,] 1.2804156
[427,] 1.1409126
[428,] 1.1616623
[429,] 1.0814007
[430,] 1.1179050
[431,] 1.0270392
[432,] 1.1448280
[433,] 0.9297235
[434,] 1.1435239
[435,] 1.1011026
[436,] 0.9741027
[437,] 1.1325980
[438,] 1.0036246
[439,] 1.1818911
[440,] 1.1278391
[441,] 1.1043831
[442,] 1.0528631
[443,] 1.2459736
[444,] 1.2203617
[445,] 1.2701358
[446,] 1.1939465
[447,] 1.1356561
[448,] 1.0416680
[449,] 1.1476477
[450,] 1.1261690
[451,] 1.0613960
[452,] 1.0541807
[453,] 1.0631082
[454,] 1.0179112
[455,] 1.0835580
[456,] 0.9492498
[457,] 1.0521260
[458,] 1.2904965
[459,] 1.1203708
[460,] 1.0403799
[461,] 1.0809312
[462,] 1.1961582
[463,] 1.0638742
[464,] 1.3402565
[465,] 1.1460701
[466,] 1.2328666
[467,] 1.0438423
[468,] 1.2137275
[469,] 1.1028465
[470,] 1.2314836
[471,] 1.2542518
[472,] 1.1759681
[473,] 1.2966959
[474,] 1.1194930
[475,] 1.0640547
[476,] 1.0648786
[477,] 1.1208084
[478,] 1.2988805
[479,] 0.9986156
[480,] 1.1177867
[481,] 1.0324511
[482,] 1.0508431
[483,] 1.1292981
[484,] 1.0276901
[485,] 1.2011979
[486,] 1.2075388
[487,] 1.2257225
[488,] 1.0303713
[489,] 1.0872661
[490,] 1.2282104
[491,] 1.0599433
[492,] 1.2793819
[493,] 1.0837638
[494,] 1.0915101
[495,] 0.9578676
[496,] 1.2596087
[497,] 1.0641717
[498,] 1.1408272
[499,] 1.1315033
[500,] 1.0922203
$R
[1] 500
$data
[1] 1.66010192 0.17151571 3.01949976 2.56908550 0.74297025 1.35432487
[7] 1.86074189 0.23852757 0.06796258 0.44118248 0.14600106 0.40308108
[13] 0.39204272 0.34348229 0.47279134 0.80841170 1.83230319 0.27808396
[19] 0.63589943 0.31720071 0.84428115 0.08872372 2.22137847 0.40671793
[25] 0.03364937 0.19200140 1.31799017 1.48895728 0.26672287 2.33535005
[31] 4.28638054 0.86115734 1.07356591 2.08785408 0.08190640 0.25305609
[37] 1.73178015 1.66335916 0.02967895 0.03700080 1.07085607 0.14206934
[43] 1.80520095 1.65708325 2.95591031 0.01743079 0.64220459 0.15071405
[49] 0.44344341 1.72285886 2.03721143 3.28091085 5.06389603 1.75504778
[55] 0.41849274 0.16756711 0.85524188 1.60116223 0.20206898 1.11844795
[61] 1.35278299 0.58043853 0.42524822 1.55205789 2.30757416 2.04565254
[67] 3.44015575 1.38604791 0.79173681 1.05587315 0.46642192 0.79256723
[73] 1.10244260 3.13847418 0.73065445 0.02588435 1.67620686 2.18362909
[79] 1.97829218 0.34102672 0.69222196 0.82509289 0.80764854 1.59490145
[85] 1.17314966 0.67864863 1.03963966 0.95525157 0.45496006 0.88490774
[91] 0.27390549 0.64842677 0.15044882 1.50222075 0.76108461 1.87995125
[97] 0.76415221 1.42043610 4.02319597 0.07382617
$seed
[1] 403 342 1919896738 -1966359555 -536885001 -1117352418
[7] -437141288 -1094376389 -923496999 1557216104 -861866346 -703746031
[13] 1573343683 -1201562158 551048292 525173671 -1718014723 375130356
[19] -819077158 -1847692507 1338565183 1019873734 -1303404976 -73018765
[25] 1736304273 -2924864 1859250334 -754269079 31387803 137294250
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$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
}
<environment: 0x3525570>
$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
$weights
[1] 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
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$student
[1] FALSE
attr(,"class")
[1] "simpleboot"
attr(,"boot_type")
[1] "boot"
BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
Based on 500 bootstrap replicates
CALL :
boot.ci(boot.out = b.mean)
Intervals :
Level Normal Basic
95% ( 0.956, 1.343 ) ( 0.949, 1.333 )
Level Percentile BCa
95% ( 0.950, 1.334 ) ( 0.969, 1.376 )
Calculations and Intervals on Original Scale
Some BCa intervals may be unstable
Warning message:
In boot.ci(b.mean) : bootstrap variances needed for studentized intervals
$t0
[1] 0.834687
$t
[,1]
[1,] 0.9974456
[2,] 0.9974456
[3,] 0.9622737
[4,] 0.9622737
[5,] 0.9474408
[6,] 0.8263464
[7,] 0.8401674
[8,] 1.3179902
[9,] 0.8552419
[10,] 0.8497615
[11,] 0.7946225
[12,] 1.1731497
[13,] 0.8442811
[14,] 0.9703904
[15,] 1.0633646
[16,] 0.8084117
[17,] 1.0633646
[18,] 1.0396397
[19,] 1.2102164
[20,] 1.1184479
[21,] 0.8084117
[22,] 1.3361575
[23,] 0.8497615
[24,] 0.9974456
[25,] 1.4613284
[26,] 0.9974456
[27,] 0.9052467
[28,] 1.3179902
[29,] 0.9474408
[30,] 1.0396397
[31,] 1.0396397
[32,] 0.9474408
[33,] 1.1184479
[34,] 1.0552479
[35,] 0.7610846
[36,] 0.9703904
[37,] 1.5271393
[38,] 1.3179902
[39,] 1.0866493
[40,] 0.9052467
[41,] 1.3179902
[42,] 1.0880043
[43,] 1.3179902
[44,] 1.1104453
[45,] 0.7925672
[46,] 0.8442811
[47,] 1.1377961
[48,] 1.1024426
[49,] 1.0880043
[50,] 1.0396397
[51,] 1.0866493
[52,] 1.0866493
[53,] 1.0396397
[54,] 1.1104453
[55,] 1.1731497
[56,] 1.0396397
[57,] 0.9052467
[58,] 1.0633646
[59,] 1.0396397
[60,] 0.8167523
[61,] 1.5076688
[62,] 1.0722110
[63,] 0.8552419
[64,] 0.8431251
[65,] 1.0396397
[66,] 1.5520579
[67,] 1.0396397
[68,] 1.0960069
[69,] 1.0396397
[70,] 1.0552479
[71,] 0.8700748
[72,] 0.8552419
[73,] 1.2182191
[74,] 1.2455699
[75,] 1.3179902
[76,] 1.0552479
[77,] 1.1024426
[78,] 0.8552419
[79,] 0.9474408
[80,] 1.4613284
[81,] 1.0477564
[82,] 0.9474408
[83,] 1.0477564
[84,] 0.8997664
[85,] 1.3361575
[86,] 1.3179902
[87,] 1.3179902
[88,] 1.0477564
[89,] 0.9974456
[90,] 1.0552479
[91,] 1.0477564
[92,] 0.8849077
[93,] 1.2182191
[94,] 1.0791579
[95,] 0.7626184
[96,] 1.1184479
[97,] 1.3701864
[98,] 1.1024426
[99,] 0.8552419
[100,] 1.2182191
$R
[1] 100
$data
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[97] 0.76415221 1.42043610 4.02319597 0.07382617
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[433] -117316768 1401815010 -1778573400 -904485684 1149144572 1193239474
[439] -1996713488 -1074974268 515088728 1973585274 -366845520 1215311804
[445] -228376492 -1272085566 747805728 560602300 -1309883824 -1868249118
[451] -7491992 274610764 1903829244 -1378856974 -2112613120 1196756852
[457] 1046109221 -2077554385 -1633833240 2017147846 448593523 -1300533035
[463] -1095476558 517722752 -898190743 -1514912037 -1813859796 -2076497350
[469] -1396460305 -1684215943 -740189170 -1279168036 -1517591987 -82570825
[475] -149393600 -1691773410 -1009458965 234197101 1987467482 -1260832616
[481] 1053286145 2130273459 674942932 781581026 -181146761 -53446975
[487] 1068183110 1499672964 1933487925 -1720840929 1723278456 697606486
[493] -1739411485 626727333 314264642 -55381456 -666469799 775244747
[499] 390290492 -1999592854 766058655 -1604990679 307232190 1719381164
[505] 14801405 -866197177 -504587728 -1525834930 1383399291 -158005027
[511] -1281867510 -511101912 -157382511 1779833987 -939651708 1046170546
[517] 1365619975 -310341807 751804118 -1564842060 -2016745531 -1339271409
[523] -1288624760 -627101530 318201939 1290308021 -2007875822 520996064
[529] 1761864009 213832571 952692300 767508442 -660128305 911281049
[535] -921117778 -1648492356 2007988525 1419078359 -400829664 -202333058
[541] -853022133 791623757 -1379199366 974094008 818478177 -11031021
[547] -758701004 -1858180542 1181556311 658019745 1989932646 723179108
[553] -59517547 130197183 522124888 281815670 45409923 -1371137339
[559] -1172486174 1652150864 644822905 -1198584661 768713756 973009738
[565] -667421889 -342647415 14296478 -2026349492 -1099092451 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
}
<bytecode: 0x3ec3f38>
<environment: 0x34d9cc8>
$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,] 0.0120271 0.002090341 0.003813014 0.02020619 0.009490542 0.01679103
[,37] [,38] [,39] [,40] [,41] [,42]
[1,] 0.01847308 0.02143723 0.015392 0.01123905 0.00618613 0.005140154
[,43] [,44] [,45] [,46] [,47] [,48]
[1,] 0.0003241207 0.01636927 0.005611193 0.003619452 0.0003827014 0.01091563
[,49] [,50] [,51] [,52] [,53] [,54]
[1,] 0.002310676 0.01799914 0.007956617 0.02102807 0.005521055 0.01062463
[,55] [,56] [,57] [,58] [,59] [,60]
[1,] 0.004301597 0.01309655 0.0103177 0.01049647 0.008978443 0.01134644
[,61] [,62] [,63] [,64] [,65] [,66]
[1,] 0.0007160647 0.002570428 0.01053018 0.008914539 0.01871934 0.01709138
[,67] [,68] [,69] [,70] [,71] [,72]
[1,] 0.01287501 0.01004902 0.001881817 0.004920867 0.001696969 0.01200082
[,73] [,74] [,75] [,76] [,77] [,78]
[1,] 0.01440199 0.01180574 0.0008820835 0.01225756 0.008370585 0.02158653
[,79] [,80] [,81] [,82] [,83] [,84]
[1,] 0.005778736 0.004669661 0.01934278 0.01042816 0.005004601 0.01400212
[,85] [,86] [,87] [,88] [,89] [,90]
[1,] 0.004573013 0.0004417765 0.01791934 0.00615997 0.003741292 0.003820843
[,91] [,92] [,93] [,94] [,95] [,96]
[1,] 0.01096965 0.01597505 0.0132922 0.01161302 0.008569643 0.01874537
[,97] [,98] [,99] [,100]
[1,] 0.005591602 0.01213947 0.01670329 0.0006739314
$student
[1] FALSE
attr(,"class")
[1] "simpleboot"
attr(,"boot_type")
[1] "boot"
BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
Based on 100 bootstrap replicates
CALL :
boot.ci(boot.out = bw.stud, type = "stud")
Intervals :
Level Studentized
95% ( 0.3289, 0.8903 )
Calculations and Intervals on Original Scale
Some studentized intervals may be unstable
R Package Documentation
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