Ngày 2 (22/10/2025)
DUC CHIEN NGO
2025-10-22
# NGÀY 2 (22.10.2025)
# 1.1 Nhập liệu
# Tìm kiếm file dữ liệu cần thực hiện
file.choose()## [1] "D:\\TAP HUAN R DAU\\Obesity data.csv"# Đọc dữ liệu "Obesity data.csAv" vào R và gọi dữ liệu là "ob"
ob = read.csv("D:\\TAP HUAN R DAU\\Obesity data.csv")
# 1.2 Mô tả đặc điểm age, gender, weight, height, pcfat, hypertension và diabetes
# Mô tả biến liên tục: age, weight, height, pcfat
library(table1)##
## Attaching package: 'table1'## The following objects are masked from 'package:base':
##
## units, units<-table1(~age+gender+weight+height+pcfat+hypertension+diabetes, data=ob)| Overall (N=1217) |
|
|---|---|
| age | |
| Mean (SD) | 47.2 (17.3) |
| Median [Min, Max] | 48.0 [13.0, 88.0] |
| gender | |
| F | 862 (70.8%) |
| M | 355 (29.2%) |
| weight | |
| Mean (SD) | 55.1 (9.40) |
| Median [Min, Max] | 54.0 [34.0, 95.0] |
| height | |
| Mean (SD) | 157 (7.98) |
| Median [Min, Max] | 155 [136, 185] |
| pcfat | |
| Mean (SD) | 31.6 (7.18) |
| Median [Min, Max] | 32.4 [9.20, 48.4] |
| hypertension | |
| Mean (SD) | 0.507 (0.500) |
| Median [Min, Max] | 1.00 [0, 1.00] |
| diabetes | |
| Mean (SD) | 0.111 (0.314) |
| Median [Min, Max] | 0 [0, 1.00] |
# 1.3 Làm kết quả tốt hơn
ob$hyper=as.factor(ob$hypertension)
ob$dia=as.factor(ob$diabetes)
table1(~age+gender+weight+height+pcfat+hypertension+hyper+diabetes+dia, data=ob)| Overall (N=1217) |
|
|---|---|
| age | |
| Mean (SD) | 47.2 (17.3) |
| Median [Min, Max] | 48.0 [13.0, 88.0] |
| gender | |
| F | 862 (70.8%) |
| M | 355 (29.2%) |
| weight | |
| Mean (SD) | 55.1 (9.40) |
| Median [Min, Max] | 54.0 [34.0, 95.0] |
| height | |
| Mean (SD) | 157 (7.98) |
| Median [Min, Max] | 155 [136, 185] |
| pcfat | |
| Mean (SD) | 31.6 (7.18) |
| Median [Min, Max] | 32.4 [9.20, 48.4] |
| hypertension | |
| Mean (SD) | 0.507 (0.500) |
| Median [Min, Max] | 1.00 [0, 1.00] |
| hyper | |
| 0 | 600 (49.3%) |
| 1 | 617 (50.7%) |
| diabetes | |
| Mean (SD) | 0.111 (0.314) |
| Median [Min, Max] | 0 [0, 1.00] |
| dia | |
| 0 | 1082 (88.9%) |
| 1 | 135 (11.1%) |
# Trình bày kết quả trung vị (Q1, Q3) thay vi trung vị (min, max) cho biến liên tục
quantile(ob$age, c(0.25, 0.75))## 25% 75%
## 35 58quantile(ob$weight, c(0.25, 0.75))## 25% 75%
## 49 61quantile(ob$height, c(0.25, 0.75))## 25% 75%
## 151 162quantile(ob$pcfat, c(0.25, 0.75))## 25% 75%
## 27.0 36.8# 1.5 Mô tả theo biến gender1
table1(~age+weight+height+pcfat+hypertension|gender,data=ob)| F (N=862) |
M (N=355) |
Overall (N=1217) |
|
|---|---|---|---|
| age | |||
| Mean (SD) | 48.6 (16.4) | 43.7 (18.8) | 47.2 (17.3) |
| Median [Min, Max] | 49.0 [14.0, 85.0] | 44.0 [13.0, 88.0] | 48.0 [13.0, 88.0] |
| weight | |||
| Mean (SD) | 52.3 (7.72) | 62.0 (9.59) | 55.1 (9.40) |
| Median [Min, Max] | 51.0 [34.0, 95.0] | 62.0 [38.0, 95.0] | 54.0 [34.0, 95.0] |
| height | |||
| Mean (SD) | 153 (5.55) | 165 (6.73) | 157 (7.98) |
| Median [Min, Max] | 153 [136, 170] | 165 [146, 185] | 155 [136, 185] |
| pcfat | |||
| Mean (SD) | 34.7 (5.19) | 24.2 (5.76) | 31.6 (7.18) |
| Median [Min, Max] | 34.7 [14.6, 48.4] | 24.6 [9.20, 39.0] | 32.4 [9.20, 48.4] |
| hypertension | |||
| Mean (SD) | 0.501 (0.500) | 0.521 (0.500) | 0.507 (0.500) |
| Median [Min, Max] | 1.00 [0, 1.00] | 1.00 [0, 1.00] | 1.00 [0, 1.00] |
# 1.6 Kiểm định sự khác biệt
library(compareGroups)
createTable(compareGroups(gender~age+weight+height+pcfat+hypertension+hyper+diabetes+dia,data=ob))##
## --------Summary descriptives table by 'gender'---------
##
## ______________________________________________
## F M p.overall
## N=862 N=355
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## age 48.6 (16.4) 43.7 (18.8) <0.001
## weight 52.3 (7.72) 62.0 (9.59) <0.001
## height 153 (5.55) 165 (6.73) <0.001
## pcfat 34.7 (5.19) 24.2 (5.76) <0.001
## hypertension 0.50 (0.50) 0.52 (0.50) 0.527
## hyper: 0.569
## 0 430 (49.9%) 170 (47.9%)
## 1 432 (50.1%) 185 (52.1%)
## diabetes 0.12 (0.32) 0.09 (0.29) 0.181
## dia: 0.238
## 0 760 (88.2%) 322 (90.7%)
## 1 102 (11.8%) 33 (9.30%)
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯# 2.1 Nhập dữ liệu
A <- c(14, 4, 10, 6, 3, 11, 12) # Nhóm A (n = 7)
B <- c(16, 17, 13, 12, 7, 16, 11, 8, 7) # Nhóm B (n = 9)
# 2.2 Kiểm tra phân phối chuẩn
shapiro.test(A)##
## Shapiro-Wilk normality test
##
## data: A
## W = 0.92541, p-value = 0.5126shapiro.test(B)##
## Shapiro-Wilk normality test
##
## data: B
## W = 0.89641, p-value = 0.2319# 2.3 Mô tả đặc điểm
mean(A)## [1] 8.571429sd(A)## [1] 4.237025mean(B)## [1] 11.88889sd(B)## [1] 3.95109# 2.4 Kiểm định t sự khác biệt giữa A và B
t.test(A,B)##
## Welch Two Sample t-test
##
## data: A and B
## t = -1.6, df = 12.554, p-value = 0.1345
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -7.813114 1.178194
## sample estimates:
## mean of x mean of y
## 8.571429 11.888889# 2.5 Thực hiện bootstrap
library(simpleboot)## Simple Bootstrap Routines (1.1-8)library(boot)
b = two.boot(A, B, mean, R = 1000)
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% (-7.020, 0.518 ) (-6.998, 0.476 )
##
## Level Percentile BCa
## 95% (-7.111, 0.363 ) (-7.075, 0.368 )
## Calculations and Intervals on Original Scalehist(b, breaks=50)
# 2.6 Thực hiện bootstrap bằng AI
# Bootstrap 2000 lần sự khác biệt trung vị giữa hai nhóm
boot_median <- two.boot(A, B, median, R = 1000)
# Xem kết quả
boot_median## $t0
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## [789,] -1
## [790,] -1
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## [799,] -5
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## [801,] -1
## [802,] -10
## [803,] -10
## [804,] -2
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## [808,] -1
## [809,] 2
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## [812,] -1
## [813,] -6
## [814,] -1
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## [837,] -1
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## [841,] 1
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## [846,] 1
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## [850,] 0
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## [856,] 3
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## [891,] 3
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## [895,] -5
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## [900,] 3
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## [910,] 2
## [911,] 2
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## [913,] -2
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## [915,] -1
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## [917,] -3
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## [921,] -2
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## [923,] -9
## [924,] 4
## [925,] -1
## [926,] -7
## [927,] -10
## [928,] -1
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## [930,] 2
## [931,] -3
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## [935,] 0
## [936,] -2
## [937,] -2
## [938,] -6
## [939,] -1
## [940,] -1
## [941,] -1
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## [943,] 1
## [944,] -5
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## [946,] 3
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## [948,] 1
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## [950,] -7
## [951,] -5
## [952,] -6
## [953,] -2
## [954,] -1
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## [956,] -8
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## [958,] 2
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## [960,] -5
## [961,] -5
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## [963,] -2
## [964,] -2
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## [966,] -2
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## [969,] -7
## [970,] 0
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## [972,] -3
## [973,] -5
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## [975,] -1
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## [978,] -2
## [979,] -10
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## [983,] 3
## [984,] -1
## [985,] -9
## [986,] -8
## [987,] -2
## [988,] 2
## [989,] -3
## [990,] -7
## [991,] -10
## [992,] -6
## [993,] 1
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## [995,] -6
## [996,] 0
## [997,] -2
## [998,] -6
## [999,] -3
## [1000,] -7
##
## $R
## [1] 1000
##
## $data
## [1] 14 4 10 6 3 11 12 16 17 13 12 7 16 11 8 7
##
## $seed
## [1] 10403 264 1823725018 1854852476 591667950 -800975646
## [7] 1551751744 783178947 884584755 554978769 -1243063944 -1540491401
## [13] -593853011 257508256 775607333 -798397112 1700286192 1247831210
## [19] 323846728 -1348885590 -30369857 -375695410 710466276 408804469
## [25] -1045069908 222827735 -762326798 676841057 -885419253 -1358035952
## [31] -566892989 650145564 1462555114 1048573782 2144191185 491910940
## [37] 1034519962 1409779886 1840530471 -2098900975 775818034 -2041380611
## [43] 93016371 -2103886604 -1114637316 564640634 -74987805 320860219
## [49] 1390634877 -1131143718 1519704873 1802094849 1509905807 -343805562
## [55] 771292214 1192635532 1036762894 694283746 1223772825 -449176931
## [61] -1356947258 -1242673619 -1080174108 -1577633527 -1710061279 -339265628
## [67] 1336355788 178384061 -1137583081 -558495450 -1614162901 12488937
## [73] -564962775 1804619854 1617299831 -1487082424 1559566886 2066701398
## [79] -634728434 614497190 -1292367613 272114047 -840056454 1113076392
## [85] -1791280809 641229605 125339488 -1293056222 -1857670209 -301341066
## [91] -870717232 1438079280 1626248303 339984659 359023311 40227508
## [97] 443400265 -381215171 339643359 1356320355 -748726849 -888602056
## [103] -862764530 1584445165 -1301253157 -337103721 -300432711 -944887432
## [109] 605671939 50596545 -520235134 1191930125 -49413181 -1785382309
## [115] -331633041 624660919 -362371827 353456820 -133188854 1524887130
## [121] 353094463 -1403675025 1086078152 1060584304 344328531 -335965636
## [127] 1753477298 816274287 630989421 -85085753 243246733 -1895468840
## [133] 1957061631 573565792 717732415 796204918 -2103810752 730592061
## [139] 1903268831 -426365449 298928761 1558582730 970978795 -992971178
## [145] 871039540 452604587 825602459 -1973546091 395973623 -1225235847
## [151] 690107095 -832778002 -274292572 714501697 1872889009 767029840
## [157] 650717094 160799047 -1750480603 -1312311902 -1684794587 715337576
## [163] 1162403050 -789141730 -726218453 1992604090 -570622173 667071075
## [169] -572441842 525109691 -988265616 -865375922 -769288558 -794019704
## [175] 703373914 2102420766 -537157624 -1066663693 974633065 -572837714
## [181] -631743080 725705386 -571291506 1687949825 -1197636668 870019865
## [187] -1763384756 13965160 -1984622229 -972351712 -38946678 1559540168
## [193] -1897642903 1951102548 402014717 -381144635 -71590729 810159771
## [199] 1284008839 1528970982 -1222305559 1563979490 -335670023 178020264
## [205] -770951485 -1269647605 759646670 39916926 1853206360 1106896481
## [211] 231999787 -1868331738 1911922674 -18649474 145918741 -2088241622
## [217] -1475393207 -347154230 1691589944 1831647523 591643080 -411720744
## [223] 355455447 -1777333687 -504163216 1780883893 1599364598 1809954620
## [229] -761564052 1711930229 1315840492 -1834586891 1136209580 -1750912022
## [235] 561452373 1145009964 753037008 -1305486803 8609733 1034522581
## [241] -1900593122 2125795292 -1024340163 -340945725 -1985818243 1517889439
## [247] -1210882842 -1940413425 -1255542446 2065250961 1884001681 -1022676798
## [253] 1420718730 -1898232415 672347911 -1175934574 -1243566055 -41014720
## [259] -1348668235 1682054703 -141864108 1881284918 -1952815537 -1007774001
## [265] 857895593 -1647952720 634466023 -1680075252 285320134 -1753010123
## [271] -1145737096 1337209251 -446466400 1363794395 -1478919001 -2081823675
## [277] 1210387222 -2042962021 -1626166150 522031553 1290264289 2006276398
## [283] 1952530269 -176477641 -1500463411 -2141963025 -1727676092 1086432134
## [289] 61090489 -1600504306 -2029427550 -573933981 -763303779 -1435324257
## [295] 788929068 999887264 852098599 -1244063120 -1940512576 -469830782
## [301] -1126096024 -1190468258 -1506279043 1696918384 -1458450163 -1959415586
## [307] 22801974 1121831103 1870981692 -58094323 770898507 -939016532
## [313] -1150325400 109347901 -1091599121 -589514620 -2074406251 89531640
## [319] -144879288 858864979 -2120776803 1764133634 615813366 1286325156
## [325] 1121448658 -1052671622 -767075863 -1267300627 -1850578943 150833815
## [331] 2015768974 1872585169 2124915128 -1257492435 1191548521 -1217433979
## [337] 350559274 267588404 -1557701445 -1400249278 -2027532036 325084292
## [343] -449019455 459407243 1082683084 1166787525 560997034 -598772189
## [349] -520073262 -41583746 78398979 -1897714573 3647959 -2143895883
## [355] -2047136399 -1737924921 473890426 -404536893 1503291883 388521143
## [361] -1257169297 892453349 -1155534845 653467304 364550053 141132880
## [367] 890428026 -586276840 354913017 -879830520 965169418 275174348
## [373] -1382785796 1166396874 741016161 -2047385257 -1793807608 -592065752
## [379] -1175917950 -412866399 1453535672 505118696 687458099 304131871
## [385] 2000778629 -1617403463 -780592160 1074448840 -1912751662 828765360
## [391] -1064251950 1778221903 -641782887 -939442952 47070658 831234432
## [397] -356125787 1771784644 -293673837 1132835453 -1947929962 1712958628
## [403] -2003647752 572426197 395666258 1107069664 -637532982 -1169188804
## [409] 984827735 687744386 497313934 -1404992311 -2128621959 -1791359877
## [415] 1933569530 -1756442679 -1818823153 -381708461 -1243103303 579339325
## [421] 1350889146 -938490094 -1908211382 814436628 -1139732156 1396242406
## [427] 1051207242 -9759812 -1809629088 -1918965904 1122307880 1098690199
## [433] 1767639405 16513838 -241923451 1891117857 -1770347884 -453499852
## [439] -504261505 1419772454 1793078688 -1824264786 1401711111 -228243978
## [445] -1838631879 645254076 -2066022806 -1998166277 736385857 -1768385505
## [451] 651984355 375397792 1212508958 1830681077 -1393117412 1237895208
## [457] -413926801 -1180262667 -1775985085 1894537570 -1839876473 45451243
## [463] -2012515914 -1765821482 -59065064 1300948443 1197228621 -385494793
## [469] -809581475 -1296628705 1582990636 -1018130792 -586451747 -1930066171
## [475] -1585656596 2094298725 -209588767 -2096973108 -1482647096 640357469
## [481] 1334680429 -691705561 143117677 1863952481 1292179537 -2111650827
## [487] -421690711 -2066647100 1562788686 -2050856228 1329819637 988863468
## [493] -955860209 2123481524 -393455548 -2010104715 75329486 961095159
## [499] 1273050259 2078516471 1776511288 -135824529 -1929769794 -234261972
## [505] -74739150 621756739 -742074692 1565948982 -516632675 1640531425
## [511] 568617748 -834781951 -616917039 -1385839439 -63619970 1371464330
## [517] -622607995 1279835317 760404796 1417426170 478753443 -1151241367
## [523] -1706912495 562525764 -248162179 1914871979 1151389866 183903337
## [529] -1501058022 -135909995 -1417859074 462775710 1057682152 36028492
## [535] 584637219 556413412 698902342 -1873558274 -1369812908 72808157
## [541] 91917153 1227279186 949705016 1400457593 -1925759015 1215968316
## [547] 394787283 1940692083 -1817192454 124709918 -1282058780 -920215597
## [553] 1479790212 894175839 1332815731 -1092484347 1891855980 -1909300304
## [559] -2070284927 377958872 -1910662907 -1392831632 113637991 -2024610055
## [565] 1560316200 -891864760 1698251269 1156151466 -1520086187 232431499
## [571] 651013684 1776131916 -278510311 990039641 -109707163 851725436
## [577] -566941304 -746930017 -1590292621 -1886059394 -1666257243 -1830802488
## [583] 290087397 930456461 -1694681094 301199795 922375763 -381696602
## [589] 1647831790 -2011355304 -2032423866 1956170017 -568209265 -409903940
## [595] 1258246392 -533801760 1727367114 -1678414186 -158966946 -200954956
## [601] 1835511224 -844036534 -2100399234 -1678468358 197843191 764863571
## [607] 1503141554 1524416804 1696103357 -2136973691 911880827 -574472394
## [613] -1685917824 366549640 24831653 671807571 -991403947 -1365412868
## [619] -287759879 546197237 -1335639480 -50861603 -119882618 988816987
## [625] 594183717 -668529026
##
## $statistic
## function (x, idx)
## {
## d1 <- x[idx[ind == 1]]
## d2 <- x[idx[ind == 2]]
## fval <- func(d1, ...) - func(d2, ...)
## if (student) {
## b <- two.boot(d1, d2, FUN, R = M, student = FALSE, M = NULL,
## weights = NULL, ...)
## fval <- c(fval, var(b$t))
## }
## fval
## }
## <bytecode: 0x000001fcd4c28c80>
## <environment: 0x000001fcd2d98e40>
##
## $sim
## [1] "ordinary"
##
## $call
## boot(data = c(sample1, sample2), statistic = boot.func, R = R,
## strata = ind, weights = weights)
##
## $stype
## [1] "i"
##
## $strata
## [1] 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2
##
## $weights
## [1] 0.1428571 0.1428571 0.1428571 0.1428571 0.1428571 0.1428571 0.1428571
## [8] 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111
## [15] 0.1111111 0.1111111
##
## $student
## [1] FALSE
##
## attr(,"class")
## [1] "simpleboot"
## attr(,"boot_type")
## [1] "boot"# Khoảng tin cậy
boot.ci(boot_median, type = c("perc", "bca"))## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
## Based on 1000 bootstrap replicates
##
## CALL :
## boot.ci(boot.out = boot_median, type = c("perc", "bca"))
##
## Intervals :
## Level Percentile BCa
## 95% (-10.000, 3.000 ) (-10.894, 3.000 )
## Calculations and Intervals on Original Scale# Đồ thị phân bố
hist(boot_median$t,
main = "Phân phối Bootstrap của sự khác biệt trung vị",
xlab = "Hiệu trung vị (A - B)",
col = "lightblue", border = "white")
abline(v = boot_median$t0, col = "red", lwd = 2)