├── README.md ├── biostat_ex_data.csv ├── chapter1.R ├── chapter3.R ├── chapter4.R ├── chapter5.R ├── chapter6.R ├── chapter7.R ├── chapter8.R ├── chapter9.R └── 바이오통계학_정오표(2023-11-29).pdf /README.md: -------------------------------------------------------------------------------- 1 | # 바이오통계학 2 | 3 | 한국방송통신대학교 통계데이터과학과 바이오통계학(학부과목)을 위한 페이지입니다. 4 | -------------------------------------------------------------------------------- /biostat_ex_data.csv: -------------------------------------------------------------------------------- 1 | id,age,sex,weight,Recur,OP_date,Recur_date,SBP,CA19.9,CRP,CEA,post.CEA,stage,smoking,obesity,Recur_1y,post.CA19.9,post.CA19.9.binary,post.CA19.9.3grp 2 | 1,82,1,73.99458773,1,2014-07-10,2015-08-12,144.2868693,4490.5,0.1,3,5.1,3,1,1,0,36.79519432,0,1 3 | 2,50,0,64.81969411,0,2013-05-08,2018-07-07,108.5909644,36.7,0.1,1.1,1.5,3,1,0,0,32.76504319,0,1 4 | 3,28,1,63.20467506,0,2016-05-06,2020-01-13,5.629379042,36.7,0.1,10.7,12.4,3,1,0,0,31.13307217,0,1 5 | 4,35,1,52.49919214,1,2015-11-24,2017-08-07,143.9313002,64.9,1.3,1.3,0.4,3,1,1,0,36.13405075,0,1 6 | 5,49,1,72.76626407,1,2015-02-12,2016-06-02,172.6531431,38.2,10.7,3.7,0.4,3,1,0,0,33.92165713,0,1 7 | 6,60,1,67.39817162,1,2015-06-26,2016-02-12,119.4545516,3.4,3.8,1.3,0.4,3,1,1,1,83.62253514,1,3 8 | 7,59,0,59.6871504,0,2017-02-12,2020-01-07,84.7922663,22.6,0.1,17.1,34,3,1,1,0,51.9946109,1,2 9 | 8,50,1,88.08956957,1,2012-09-20,2014-02-20,81.57315212,52.2,0.2,8.2,10.3,3,1,1,0,32.96320877,0,1 10 | 9,69,0,53.92028668,1,2016-05-16,2016-10-26,108.2372874,19.9,0.1,4.6,15.1,2,1,0,1,62.09547454,1,2 11 | 10,39,1,64.03527271,0,2017-05-16,2019-10-18,88.06102003,84.2,9.6,4.3,6.1,2,1,0,0,50.11849843,1,2 12 | 11,64,1,87.01967654,1,2016-01-06,2017-07-27,93.90121782,48599.6,9.4,1.4,3.9,2,1,1,0,34.95666629,0,1 13 | 12,69,1,70.20880076,1,2015-11-25,2016-12-14,163.5876912,49.5,0.1,7.2,8.2,2,1,1,0,34.62685303,0,1 14 | 13,38,0,52.87121831,1,2012-08-16,2013-05-27,37.44441978,15.7,4.4,2.9,1.1,3,1,0,1,48.0298491,1,2 15 | 14,62,0,66.58098455,1,2016-07-14,2016-12-19,73.84247725,18.7,8.8,0.7,0.1,3,1,0,1,46.23422765,1,2 16 | 15,44,0,67.92354845,1,2015-08-21,2016-07-26,85.98477787,1,0.1,1.9,1.5,3,1,1,1,93.94980348,1,3 17 | 16,57,1,69.38252335,1,2015-12-16,2017-12-25,103.6706542,60.7,6.2,19.5,46.9,3,1,0,0,40.24676538,1,2 18 | 17,68,1,78.92354499,1,2015-02-01,2015-09-09,130.1152614,13.2,1.2,5.5,1.3,3,1,1,1,103.1384957,1,3 19 | 18,72,1,70.04703722,1,2015-07-24,2016-04-02,93.50121132,81.6,0.3,2.1,1.3,3,1,1,1,64.28291572,1,2 20 | 19,42,1,81.74443763,1,2015-10-18,2016-06-12,70.66395107,2499.2,7.4,4.5,2.8,3,1,0,1,47.68939453,1,2 21 | 20,58,1,82.96029912,1,2011-12-23,2012-04-16,161.7436673,7.3,8,2.4,0.2,3,1,0,1,35.90101627,0,1 22 | 21,54,1,67.76523259,1,2016-10-21,2017-02-13,116.7727163,70.8,1.4,1,0.2,3,1,0,1,36.54465508,0,1 23 | 22,58,1,83.38053227,1,2012-01-28,2012-08-31,116.2343912,4.1,3.4,6.3,3.3,3,1,1,1,33.77377179,0,1 24 | 23,64,1,82.15015741,0,2017-11-17,2019-12-25,169.4101542,67.3,3.5,1.8,0.9,2,1,0,0,34.89029731,0,1 25 | 24,65,1,80.42510191,0,2017-07-26,2019-02-16,118.9590089,12.9,0.1,2.1,4.8,1,1,0,0,42.96218755,1,2 26 | 25,60,1,85.76092752,1,2015-05-23,2016-04-18,164.6779413,10.8,1.3,2.2,0.4,3,1,1,1,62.10198704,1,2 27 | 26,59,0,60.34647146,0,2015-02-21,2019-11-11,121.9180676,12.7,1.7,1.2,0.2,2,1,0,0,44.34438046,1,2 28 | 27,41,0,53.72965281,0,2017-05-17,2019-07-22,95.66085804,7.6,0.1,8.3,19.4,2,1,1,0,34.0434679,0,1 29 | 28,26,1,81.86659818,1,2016-03-16,2016-08-05,101.3140237,3.4,0.5,1.2,0.6,3,0,0,1,76.16352142,1,3 30 | 29,48,1,70.06016384,0,2017-03-08,2019-11-10,108.7657149,15,3.8,6.8,3.6,2,0,0,0,44.85561174,1,2 31 | 30,34,1,71.46807425,0,2016-09-20,2018-07-09,51.73704063,49.6,3.9,4.2,1.5,2,0,0,0,43.40793171,1,2 32 | 31,74,1,89.62288749,1,2014-08-04,2014-10-26,159.8793205,25.1,12.3,4.7,0.5,3,1,1,1,56.92409485,1,2 33 | 32,76,1,77.92933712,1,2014-08-07,2014-11-03,144.4670616,25.8,11.6,3.1,0.6,3,1,0,1,30.76011479,0,1 34 | 33,57,1,69.65817651,1,2016-10-10,2018-07-15,79.75343653,133.8,8.1,6.3,4.8,3,0,1,0,41.61261289,1,2 35 | 34,65,1,52.82635627,1,2016-07-26,2016-11-25,120.9084595,11,0.6,12.9,5,3,0,0,1,47.32047363,1,2 36 | 35,65,1,67.45409634,1,2016-01-25,2016-01-29,87.50597384,472.2,0.9,3.2,1.1,3,0,1,1,37.92530864,1,2 37 | 36,42,0,42.75124586,1,2016-05-24,2016-10-28,80.12106074,161.8,0.1,3.6,5.1,2,0,0,1,61.63342617,1,2 38 | 37,71,0,43.78648698,0,2017-06-30,2019-12-20,158.9163391,23.8,0.8,2.9,1.1,1,1,0,0,48.11506586,1,2 39 | 38,42,0,40,0,2016-09-27,2019-08-30,51.71326898,19.5,8.6,1.2,0.5,2,0,1,0,43.09481715,1,2 40 | 39,57,1,64.6670317,0,2017-09-03,2019-11-21,142.8340459,4,0.4,0.4,0.2,1,1,0,0,34.89564966,0,1 41 | 40,74,1,93.17904358,0,2016-03-01,2019-12-29,126.749763,54.5,2.3,7.1,4.9,2,0,0,0,35.50508854,0,1 42 | 41,74,0,59.6891793,0,2013-12-17,2018-06-02,93.76868531,8,1.8,0.5,0.2,1,0,0,0,54.99245445,1,2 43 | 42,56,0,63.32189906,0,2012-03-13,2019-02-22,90.59974755,5.9,1.1,2.5,2.8,1,0,0,0,39.43203078,1,2 44 | 43,51,0,57.93021605,0,2012-03-14,2019-02-16,181.3709025,3.9,1.3,1.9,1.7,1,1,1,0,34.73436705,0,1 45 | 44,61,1,95,0,2015-09-16,2019-11-18,155.7151354,12.4,1.3,1.6,0.7,1,1,0,0,31.16438885,0,1 46 | 45,64,0,40,1,2014-07-27,2019-01-05,89.37148037,2646.2,9.5,1.5,1,3,0,0,0,33.93997625,0,1 47 | 46,50,0,53.51889249,0,2015-01-27,2019-11-29,111.1327544,25.9,6.7,0.8,0.2,1,0,1,0,33.12339112,0,1 48 | 47,60,1,71.70147523,0,2014-05-24,2018-07-06,106.969789,18.6,1,2.6,0.8,2,0,0,0,63.61324878,1,2 49 | 48,57,1,90.31984878,0,2017-08-23,2019-11-19,200.8805289,33.4,4.4,1.5,0.1,2,1,0,0,143.8906677,1,3 50 | 49,62,1,65.26823587,0,2014-05-11,2019-10-03,70.42580036,6.6,2,2.1,0.7,1,0,0,0,35.61321105,0,1 51 | 50,55,0,45.70061265,1,2015-07-27,2016-10-14,86.31951343,6,0.9,0.8,0.3,1,0,1,0,56.48700049,1,2 52 | 51,54,0,59.54214352,0,2015-02-01,2019-11-16,76.41133263,1,0.1,1.8,3.9,1,0,0,0,33.28386032,0,1 53 | 52,63,1,74.46092669,0,2015-09-05,2019-08-02,90.93077881,99.1,0.1,1.3,2.9,1,0,0,0,37.06446455,1,2 54 | 53,80,1,67.3270515,1,2014-07-05,2015-08-10,132.2743862,4489.1,0.8,2.4,0.8,3,0,1,0,33.89848347,0,1 55 | 54,58,0,40,0,2013-05-04,2018-07-10,79.41264907,38.2,0.1,0.4,1.2,3,0,0,0,34.7666512,0,1 56 | 55,32,1,69.73972804,0,2016-05-04,2020-01-12,73.72364187,36,0.1,2.4,6.1,3,0,0,0,42.27055145,1,2 57 | 56,41,1,81.08642641,1,2015-11-21,2017-08-06,135.3091829,64.8,0.3,2,1.6,3,0,1,0,52.85612645,1,2 58 | 57,50,1,88.55063037,1,2015-02-12,2016-06-03,136.1092733,37.7,8.1,1.4,1.9,3,0,0,0,49.60211861,1,2 59 | 58,73,1,86.70765183,1,2015-06-21,2016-02-10,137.2173347,4.9,1.3,4.3,15.9,3,1,1,1,34.03050936,0,1 60 | 59,61,0,40,0,2017-02-13,2020-01-04,108.9496919,23.4,0.1,1.1,1.2,3,0,1,0,36.52340322,0,1 61 | 60,50,1,73.50475448,1,2012-09-20,2014-02-20,95.04481922,52.2,2,6,1.3,3,1,1,0,41.34000319,1,2 62 | 61,63,0,66.39976634,1,2016-05-25,2016-10-29,76.97791238,19.7,1.7,8.3,3.5,2,0,0,1,32.68727769,0,1 63 | 62,34,1,80.14003895,0,2017-05-22,2019-10-18,160.5528052,84.9,9.9,2.4,2.5,2,1,0,0,41.23154525,1,2 64 | 63,60,1,80.02471632,1,2016-01-13,2017-07-29,192.2267344,48599.8,9.7,4.7,9.5,2,0,1,0,41.42114529,1,2 65 | 64,70,1,80.22559784,1,2015-11-25,2016-12-23,132.9708119,49.9,0.1,20.4,54.7,2,0,1,0,36.02391817,0,1 66 | 65,38,0,58.15702952,1,2012-08-19,2013-05-25,52.95757019,14.2,3.2,1.3,1.6,3,0,0,1,92.80135401,1,3 67 | 66,62,0,65.426861,1,2016-07-13,2016-12-10,130.8362347,18.6,7.6,1.1,0.8,3,0,0,1,57.5417477,1,2 68 | 67,41,0,52.87017503,1,2015-08-25,2016-07-20,88.1847752,1,0.1,1,0.8,3,0,1,1,86.97918923,1,3 69 | 68,60,1,72.54885487,1,2015-12-18,2017-12-25,115.8296264,60.6,8.3,2.3,0.7,3,0,0,0,39.99768426,1,2 70 | 69,77,1,78.62824191,1,2015-02-05,2015-09-14,206.847716,11.6,2,3.7,0.4,3,0,1,1,46.23253406,1,2 71 | 70,76,1,83.33229357,1,2015-07-17,2016-03-31,150.9093735,81.5,0.1,3.6,2.8,3,0,1,1,59.87734144,1,2 72 | 71,39,1,95,1,2015-10-26,2016-06-16,155.7649773,2500.5,7.2,9.5,6.8,3,1,0,1,34.2902607,0,1 73 | 72,59,1,87.91164342,1,2011-12-25,2012-04-07,96.70675638,6.6,5,1.6,2.4,3,0,0,1,47.84637307,1,2 74 | 73,54,1,79.0246173,1,2016-10-19,2017-02-12,83.50585087,72.4,0.1,3,11,3,0,0,1,69.83308645,1,2 75 | 74,57,1,84.0411388,1,2012-01-27,2012-08-22,131.885796,3.5,4.7,1.5,0.2,3,0,1,1,32.57095566,0,1 76 | 75,65,1,82.97263533,0,2017-11-15,2019-12-14,121.1420816,69.1,4.4,2.1,0.4,2,0,0,0,44.79601366,1,2 77 | 76,60,1,61.90940812,0,2017-07-26,2019-02-17,111.3003907,10.4,0.3,12.4,7,1,0,0,0,109.2863247,1,3 78 | 77,64,1,69.92674981,1,2015-05-26,2016-04-16,178.703797,12.9,0.1,3.8,2.9,3,0,1,1,48.00367663,1,2 79 | 78,66,0,58.97100382,0,2015-02-28,2019-11-20,88.06312855,11.5,0.1,10.6,47.5,2,0,0,0,51.67089867,1,2 80 | 79,43,0,40,0,2017-05-14,2019-07-23,43.39291588,5.6,1.7,1.1,0.2,2,0,1,0,40.33343521,1,2 81 | 80,28,1,89.20082447,1,2016-03-14,2016-08-03,170.0159082,4.1,0.1,3.5,5.2,3,0,0,1,34.75980335,0,1 82 | 81,47,1,94.01792147,0,2017-03-10,2019-11-08,131.8744295,13.2,1,6.5,56.8,2,0,0,0,45.6963505,1,2 83 | 82,41,1,95,0,2016-09-23,2018-07-11,145.4482252,49.4,3.8,1,0.4,2,0,0,0,44.35913432,1,2 84 | 83,77,1,50.21451539,1,2014-08-11,2014-10-30,72.2786174,24.4,10.6,4.7,2.5,3,0,1,1,44.50748941,1,2 85 | 84,71,1,70.10265636,1,2014-08-03,2014-10-26,174.3815396,24,10.3,17.5,13.6,3,0,0,1,42.11832915,1,2 86 | 85,55,1,78.6121748,1,2016-10-12,2018-07-19,129.9056472,134,8.6,3.1,1.3,3,0,1,0,32.4821307,0,1 87 | 86,68,1,83.35935693,1,2016-07-25,2016-11-23,89.68241278,11.5,0.8,0.8,0.3,3,0,0,1,96.97028671,1,3 88 | 87,71,1,86.85820642,1,2016-01-25,2016-02-04,147.2557033,472,0.1,6.9,11.9,3,0,1,1,42.19493988,1,2 89 | 88,43,0,53.81800298,1,2016-05-22,2016-10-29,106.5251408,159.8,2.1,2.5,0.2,2,0,0,1,59.65395428,1,2 90 | 89,76,0,61.84697885,0,2017-06-28,2019-12-16,149.4365603,24.4,0.1,2.8,5.3,1,0,0,0,34.84152208,0,1 91 | 90,42,0,40,0,2016-09-30,2019-09-06,116.9814201,22.8,9.6,0.5,0.1,2,0,1,0,54.12870086,1,2 92 | 91,59,1,71.5912412,0,2017-09-01,2019-11-20,93.02791091,1.4,0.5,1.5,0.8,1,0,0,0,66.04711343,1,2 93 | 92,76,1,51.93279038,0,2016-02-27,2019-12-23,115.3820814,56.8,1.6,1.6,2,2,0,0,0,102.3022587,1,3 94 | 93,76,0,53.90575994,0,2013-12-18,2018-06-03,167.2320659,8.7,1.6,0.4,0.2,1,0,0,0,30.93663927,0,1 95 | 94,53,0,40.60206355,0,2012-03-15,2019-02-18,61.34012209,6.1,0.9,4.7,6.4,1,0,0,0,34.9130161,0,1 96 | 95,63,0,51.74665027,0,2012-03-15,2019-02-17,137.6089346,3.3,0.6,2.3,4.1,1,0,1,0,39.98430706,1,2 97 | 96,63,1,60.91536241,0,2015-09-04,2019-11-16,111.1487861,11.8,1,3.4,1.8,1,0,0,0,44.82942048,1,2 98 | 97,58,0,49.94719112,1,2014-07-28,2019-01-04,117.6686959,2646,8.8,4.8,6.2,3,0,0,0,49.84960025,1,2 99 | 98,54,0,67.04249039,0,2015-01-23,2019-11-22,79.90455776,24,5.4,2.2,2.6,1,0,1,0,35.56188326,0,1 100 | 99,60,1,87.53139292,0,2014-05-23,2018-07-05,127.6850646,16.8,0.1,1.9,3.6,2,0,0,0,31.08083353,0,1 101 | 100,59,1,73.40486278,0,2017-08-24,2019-11-20,155.8469289,34.2,0.1,2.9,8.6,2,0,0,0,32.2848039,0,1 102 | 101,65,1,85.1385245,0,2014-05-16,2019-10-02,128.1268282,7,2.6,2.3,0.4,1,0,0,0,50.99496702,1,2 103 | 102,50,0,50.01021101,1,2015-07-22,2016-10-11,145.8516068,5.1,0.7,2,0.8,1,0,1,0,36.46932579,0,1 104 | 103,47,0,58.89595482,0,2015-02-06,2019-11-18,108.0174175,1,0.9,3.7,1.2,1,0,0,0,47.19944372,1,2 105 | 104,61,1,70.54088942,0,2015-09-07,2019-08-07,147.6980103,98.2,1.2,1.4,0.3,1,0,0,0,35.09575302,0,1 106 | 105,82,1,87.57100623,1,2014-07-03,2015-08-06,193.893314,4491,0.1,1.6,1.8,3,0,1,0,51.06438821,1,2 107 | 106,57,0,58.37331875,0,2013-05-08,2018-07-07,49.81109002,38.4,0.1,1.9,1.6,3,0,0,0,48.64197349,1,2 108 | 107,33,1,86.10498813,0,2016-05-05,2020-01-07,96.2265563,35,0.1,18.4,118.5,3,0,0,0,31.68437516,0,1 109 | 108,37,1,57.25673601,1,2015-11-28,2017-08-08,87.11916208,66.5,0.5,3.8,2.5,3,0,1,0,32.93693071,0,1 110 | 109,51,1,63.92838072,1,2015-02-07,2016-06-02,138.0395121,37.7,8.7,16.1,12.3,3,0,0,0,36.78760098,0,1 111 | 110,73,1,73.98117075,1,2015-06-19,2016-02-16,178.884557,4.8,4,2,0.5,3,1,1,1,58.1439349,1,2 112 | 111,64,0,50.1073845,0,2017-02-06,2020-01-02,124.8882593,21.8,1.6,14.5,2.3,3,0,1,0,33.10647908,0,1 113 | 112,46,1,64.60187946,1,2012-09-23,2014-02-19,73.2150672,52,1,3.8,2.1,3,1,1,0,44.51296464,1,2 114 | 113,61,0,49.29228939,1,2016-05-18,2016-10-26,31.25031901,19.5,1.8,0.3,0.1,2,0,0,1,42.31401999,1,2 115 | 114,37,1,83.10050835,0,2017-05-19,2019-10-10,124.3457695,85.2,9.3,3.3,6.3,2,0,0,0,36.81615203,0,1 116 | 115,61,1,74.03154113,1,2016-01-09,2017-07-28,176.5946496,48599.3,10.4,6.6,6.6,2,0,1,0,36.51695596,0,1 117 | 116,66,1,73.49288867,1,2015-11-19,2016-12-22,144.7895238,50,1.3,2.3,0.6,2,0,1,0,35.01116746,0,1 118 | 117,42,0,59.28364064,1,2012-08-20,2013-05-24,76.25718571,15.6,3.8,4.8,3.3,3,0,0,1,41.35012707,1,2 119 | 118,63,0,46.27538163,1,2016-07-15,2016-12-17,43.51005984,20.1,6.7,1.6,2.7,3,0,0,1,60.82617184,1,2 120 | 119,47,0,54.95308987,1,2015-08-30,2016-07-22,87.19861939,3.3,1.3,1.4,0.3,3,0,1,1,38.39417626,1,2 121 | 120,54,1,70.34002698,1,2015-12-19,2017-12-25,107.9509181,58.3,7.4,3.6,2.8,3,0,0,0,37.5144467,1,2 122 | 121,69,1,75.20192874,1,2015-02-03,2015-09-07,109.1673946,14.5,0.4,0.4,0.2,3,0,1,1,49.99153605,1,2 123 | 122,77,1,61.48432093,1,2015-07-22,2016-03-29,143.4323251,83.5,0.1,3.5,4.2,3,0,1,1,62.96265154,1,2 124 | 123,35,1,77.62022174,1,2015-10-19,2016-06-15,142.8841681,2500.2,6.3,9,17.2,3,1,0,1,174.4334257,1,3 125 | 124,53,1,95,1,2011-12-28,2012-04-05,151.6042581,7.5,7.1,1.6,0.3,3,0,0,1,36.06866032,0,1 126 | 125,56,1,73.56505758,1,2016-10-16,2017-02-15,113.913624,69.4,0.1,5.9,5.5,3,0,0,1,52.88401155,1,2 127 | 126,56,1,80.83415946,1,2012-01-27,2012-08-29,109.803219,4.8,3.4,7.9,4.5,3,0,1,1,57.91208196,1,2 128 | 127,56,1,76.69642222,0,2017-11-11,2019-12-12,184.7698867,68.2,4.8,2.1,0.3,2,0,0,0,32.395743,0,1 129 | 128,65,1,78.69561176,0,2017-07-24,2019-02-14,138.3301356,11.2,0.1,2.7,3,1,0,0,0,39.19216795,1,2 130 | 129,56,1,86.92897605,1,2015-05-20,2016-04-13,148.4442596,11.4,0.1,0.3,0.4,3,0,1,1,69.17968222,1,2 131 | 130,68,0,59.95375149,0,2015-02-28,2019-11-15,94.15003804,11.8,0.1,1.5,7.2,2,0,0,0,72.2260304,1,3 132 | 131,42,0,74.72818679,0,2017-05-14,2019-07-27,172.8839652,5.9,0.2,0.7,0.5,2,0,1,0,36.55626752,0,1 133 | 132,25,1,68.78625871,1,2016-03-14,2016-08-05,116.9425067,5.9,0.1,1.9,2.2,3,0,0,1,68.05349593,1,2 134 | 133,50,1,65.31436333,0,2017-03-12,2019-11-09,106.9369231,15,3.4,1.6,1.3,2,0,0,0,42.01528939,1,2 135 | 134,29,1,51.92185244,0,2016-09-20,2018-07-03,25.99919533,47.7,1.9,1.1,2.5,2,0,0,0,38.51107723,1,2 136 | 135,69,1,58.33033789,1,2014-08-13,2014-10-26,113.2468555,26,10.1,8.6,7.4,3,0,1,1,169.8333416,1,3 137 | 136,73,1,80.14404024,1,2014-08-05,2014-11-03,158.8978097,25.5,11.1,3.5,1.1,3,0,0,1,41.53586566,1,2 138 | 137,63,1,71.47462297,1,2016-10-08,2018-07-16,73.80067949,133,8.4,0.8,0.4,3,0,1,0,40.05397066,1,2 139 | 138,69,1,95,1,2016-07-24,2016-11-23,146.2066296,11.2,0.1,1.2,2.3,3,0,0,1,39.33983831,1,2 140 | 139,63,1,61.02587086,1,2016-01-27,2016-01-28,152.9276755,472.9,0.2,3,2.1,3,0,1,1,40.52028858,1,2 141 | 140,39,0,43.03137876,1,2016-05-22,2016-11-04,77.20862262,161,0.8,2,0.7,2,0,0,1,37.18840381,1,2 142 | 141,73,0,56.30438481,0,2017-07-01,2019-12-17,92.69774647,23.6,0.1,2,2.5,1,0,0,0,49.96700853,1,2 143 | 142,43,0,58.2905964,0,2016-09-24,2019-08-28,95.73675111,20.6,8.7,2.1,0.7,2,0,1,0,34.40256896,0,1 144 | 143,61,1,89.89041146,0,2017-09-03,2019-11-21,152.2784148,4.2,0.1,3,3.2,1,0,0,0,33.29650762,0,1 145 | 144,78,1,57.05899372,0,2016-03-05,2019-12-28,129.1289889,54,2.8,2.7,1,2,0,0,0,31.07984881,0,1 146 | 145,76,0,61.68158302,0,2013-12-23,2018-05-28,92.21512154,10.4,1.3,0.3,0.2,1,0,0,0,31.8420176,0,1 147 | 146,56,0,61.38992957,0,2012-03-15,2019-02-16,102.8320854,4.2,0.9,2.9,3.8,1,0,0,0,41.34378504,1,2 148 | 147,56,0,74.40451296,0,2012-03-18,2019-02-14,96.6776592,5.4,2.6,2.6,0.6,1,0,1,0,35.50809364,0,1 149 | 148,62,1,72.22036406,0,2015-09-10,2019-11-22,106.5218703,12.9,0.5,1,0.9,1,0,0,0,102.2403524,1,3 150 | 149,58,0,49.45460935,1,2014-08-03,2019-01-11,96.31954602,2647.2,11.7,0.9,0.1,3,0,0,0,32.30594193,0,1 151 | 150,55,0,47.34288941,0,2015-01-30,2019-11-26,87.48111786,22.9,6.5,1.4,0.6,1,0,1,0,55.55588498,1,2 152 | 151,59,1,65.10159917,0,2014-05-20,2018-07-06,127.8979439,17.9,0.1,13.7,16.8,2,0,0,0,31.65684658,0,1 153 | 152,54,1,59.10992235,0,2017-08-21,2019-11-19,79.69812619,34.1,0.8,17.5,25,2,0,0,0,35.86125457,0,1 154 | 153,62,1,75.94816406,0,2014-05-16,2019-09-29,134.0970069,6.8,3,8.5,1,1,0,0,0,33.2385745,0,1 155 | 154,51,0,48.53305639,1,2015-07-25,2016-10-08,116.6321218,6.1,0.1,2.4,1.8,1,0,1,0,31.96565763,0,1 156 | 155,51,0,44.72469146,0,2015-02-01,2019-11-14,151.1782016,1.6,1.2,2.3,0.6,1,0,0,0,37.99657777,1,2 157 | 156,58,1,73.68910116,0,2015-08-29,2019-08-09,112.3212076,100.4,0.7,2.4,1,1,0,0,0,36.62439091,0,1 158 | -------------------------------------------------------------------------------- /chapter1.R: -------------------------------------------------------------------------------- 1 | #setwd("C:\\Users\\user\\Dropbox\\KNOU_강의개편\\학부_바이오통계학\\R") 2 | 3 | ### Program 1-1 4 | setwd("C:/Users/KNOU_stat/R_codes") 5 | 6 | ### Program 1-2 7 | install.packages("dplyr") 8 | library(dplyr) 9 | 10 | ### Program 1-3 11 | setwd("C:/Users/KNOU_stat/R_codes") 12 | dat0<-read.csv("biostat_ex_data.csv") 13 | summary(dat0) 14 | 15 | ### Program 1-4 16 | library(dplyr) 17 | dat1<-dat0 %>% mutate_at(vars(sex, Recur, stage, smoking, obesity, Recur_1y, 18 | post.CA19.9.binary, post.CA19.9.3grp), 19 | as.factor) 20 | summary(dat1) 21 | 22 | ### Program 1-5 23 | summary(dat1$stage) 24 | table(dat1$stage) 25 | 26 | ### Program 1-6 27 | library(ggplot2) 28 | ggplot(dat1) + geom_bar(aes(x=stage)) 29 | 30 | ### Program 1-7 31 | mean(dat1$age) 32 | median(dat1$age) 33 | var(dat1$age) 34 | sd(dat1$age) 35 | fivenum(dat1$age) 36 | summary(dat1$age) 37 | 38 | 39 | ### Program 1-8 40 | ggplot(dat1) + geom_histogram(aes(x=age)) 41 | ggplot(dat1) + geom_histogram(aes(x=age), breaks=seq(20, 80, 10), 42 | color="black", fill="skyblue") 43 | ggplot(dat1) + geom_boxplot(aes(x=1, y=age))+ 44 | scale_x_continuous(breaks = NULL) + 45 | theme(axis.title.x = element_blank()) 46 | 47 | ### Program 1-9 48 | ggplot(dat1) + geom_histogram(aes(x=CA19.9), color="black", fill="grey")+ 49 | geom_vline(xintercept=mean(dat1$CA19.9), color="red")+ 50 | geom_vline(xintercept=median(dat1$CA19.9), color="blue") 51 | mean(dat1$CA19.9) 52 | median(dat1$CA19.9) 53 | 54 | ### Program 1-10 55 | library(tableone) 56 | t1<-CreateTableOne(vars=c("age", "sex", "CA19.9", "CRP", "CEA", 57 | "stage", "smoking", "obesity"), data=dat1) 58 | summary(t1, digits=4) 59 | print(t1) 60 | print(t1, nonnormal=c("CA19.9", "CRP", "CEA")) 61 | 62 | 63 | 64 | -------------------------------------------------------------------------------- /chapter3.R: -------------------------------------------------------------------------------- 1 | #### Program 3-1 2 | 3 | pop<-(-4):4 4 | sample(pop, size=4, replace=TRUE) 5 | 6 | #### Program 3-2 7 | set.seed(1000) 8 | sam<-sample(pop, size=4, replace=TRUE) 9 | mean(sam) 10 | 11 | #### Program 3-3 12 | m<-10 13 | xbar.vec<-rep(NA, m) 14 | for(i in 1:m){ 15 | set.seed(1000+i) 16 | xx<-sample(pop, size=4, replace=TRUE) 17 | xbar.vec[i]<-mean(xx) 18 | } 19 | table(xbar.vec) 20 | hist(xbar.vec, main="n=4, m=10", xlab=bquote(bar(X))) 21 | mean(xbar.vec) 22 | sd(xbar.vec) 23 | 24 | 25 | #### Figure 3-2 26 | m<-100 27 | xbar.vec<-rep(NA, m) 28 | for(i in 1:m){ 29 | set.seed(1000+i) 30 | xx<-sample(pop, size=4, replace=TRUE) 31 | xbar.vec[i]<-mean(xx) 32 | } 33 | table(xbar.vec) 34 | hist(xbar.vec, main="n=4, m=100", xlab=bquote(bar(X))) 35 | mean(xbar.vec) 36 | sd(xbar.vec) 37 | 38 | m<-10000 39 | xbar.vec<-rep(NA, m) 40 | for(i in 1:m){ 41 | set.seed(1000+i) 42 | xx<-sample(pop, size=4, replace=TRUE) 43 | xbar.vec[i]<-mean(xx) 44 | } 45 | table(xbar.vec) 46 | hist(xbar.vec, main="n=4, m=10000", xlab=bquote(bar(X))) 47 | mean(xbar.vec) 48 | sd(xbar.vec) 49 | 50 | #### Program 3-4 51 | m<-10000 52 | xbar.vec<-rep(NA, m) 53 | for(i in 1:m){ 54 | set.seed(1000+i) 55 | xx<-sample(pop, size=40, replace=TRUE) 56 | xbar.vec[i]<-mean(xx) 57 | } 58 | table(xbar.vec) 59 | hist(xbar.vec, main="n=40, m=10000", xlab=bquote(bar(X)), xlim=c(-4, 4)) 60 | mean(xbar.vec) 61 | sd(xbar.vec) 62 | 63 | 64 | 65 | #setwd("C:\\Users\\KNOU_stat\\Dropbox\\KNOU_강의개편\\학부_바이오통계학\\R") 66 | #setwd("C:\\Users\\user\\Dropbox\\KNOU_강의개편\\학부_바이오통계학\\R") 67 | 68 | #### Program 3-5 69 | setwd("C:/Users/KNOU_stat/R_codes") 70 | dat0<-read.csv("biostat_ex_data.csv") 71 | library(dplyr) 72 | dat1<-dat0 %>% mutate_at(vars(sex, Recur, stage, smoking, obesity, Recur_1y, 73 | post.CA19.9.binary, post.CA19.9.3grp), 74 | as.factor) 75 | mean(dat1$weight) 76 | t.test(dat1$weight) 77 | t.test(dat1$weight, conf.level=0.99)$conf.int 78 | 79 | #### Program 3-6 80 | table(dat1$sex) 81 | prop.test(x=sum(dat1$sex==1), n=nrow(dat1)) 82 | binom.test(x=sum(dat1$sex==1), n=nrow(dat1)) 83 | 84 | #### Program 3-7 85 | t.test(dat1$weight, mu=65) 86 | t.test(dat1$weight, mu=68, alternative = "greater") 87 | 88 | #### Program 3-8 89 | prop.test(x=sum(dat1$sex==1), n=nrow(dat1), p=0.6) 90 | binom.test(x=sum(dat1$sex==1), n=nrow(dat1), p=0.6) 91 | binom.test(x=sum(dat1$sex==1), n=nrow(dat1), p=0.6, alternative="greater") 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | -------------------------------------------------------------------------------- /chapter4.R: -------------------------------------------------------------------------------- 1 | setwd("C:\\Users\\user\\Dropbox\\KNOU_강의개편\\학부_바이오통계학\\R") 2 | setwd("C:\\Users\\KNOU_stat\\Dropbox\\KNOU_강의개편\\학부_바이오통계학\\R") 3 | 4 | #### 5 | #### Program 4-1 6 | 7 | setwd("C:/Users/KNOU_stat/R_codes") 8 | dat0<-read.csv("biostat_ex_data.csv") 9 | library(dplyr) 10 | dat1<-dat0 %>% mutate_at(vars(sex, Recur, stage, smoking, obesity, Recur_1y, 11 | post.CA19.9.binary, post.CA19.9.3grp), 12 | as.factor) 13 | library(ggplot2) 14 | ggplot(dat1) + geom_histogram(aes(x=weight), color="black", fill="skyblue", 15 | breaks=seq(40, 100, 10)) 16 | 17 | 18 | #### Program 4-2 19 | t.test(weight~sex, data=dat1) 20 | 21 | #### Program 4-3 22 | wilcox.test(weight~sex, data=dat1) 23 | 24 | #### Program 4-4 25 | ggplot(dat1) + geom_histogram(aes(x=CEA), color="black", fill="skyblue") 26 | 27 | #### Program 4-5 28 | ggplot(dat1) + geom_histogram(aes(x=log(CEA)), color="black", fill="skyblue") 29 | 30 | #### Program 4-6 31 | fit<-aov(log(CEA)~stage, data=dat1) 32 | summary(fit) 33 | oneway.test(log(CEA)~stage, data=dat1) 34 | 35 | #### Program 4-7 36 | kruskal.test(CEA~stage, data=dat1) 37 | 38 | #### Program 4-8 39 | ggplot(dat1) + 40 | geom_histogram(aes(x=log(post.CEA) - log(CEA)), color="black", fill="skyblue") 41 | 42 | #### Program 4-9 43 | t.test(log(dat1$post.CEA), log(dat1$CEA), paired=TRUE) 44 | 45 | #### Program 4-10 46 | wilcox.test(log(dat1$post.CEA), log(dat1$CEA), paired=TRUE) 47 | 48 | 49 | 50 | -------------------------------------------------------------------------------- /chapter5.R: -------------------------------------------------------------------------------- 1 | setwd("C:\\Users\\user\\Dropbox\\KNOU_강의개편\\학부_바이오통계학\\R") 2 | setwd("C:\\Users\\KNOU_stat\\Dropbox\\KNOU_강의개편\\학부_바이오통계학\\R") 3 | 4 | #### Program 5-1 5 | 6 | setwd("C:/Users/KNOU_stat/R_codes") 7 | dat0<-read.csv("biostat_ex_data.csv") 8 | library(dplyr) 9 | dat2<-dat0 %>% mutate_at(vars(sex, Recur, stage, smoking, obesity, Recur_1y, 10 | post.CA19.9.binary, post.CA19.9.3grp), 11 | as.factor) %>% 12 | mutate(HTN=as.factor(ifelse(SBP>=140, 1, 0))) 13 | 14 | #### Program 5-2 15 | xtabs(~smoking+HTN, data=dat2) 16 | 17 | #### Program 5-3 18 | library(epiR) 19 | epi.2by2(xtabs(~smoking+HTN, data=dat2)[2:1, 2:1]) 20 | 21 | 22 | 23 | #### Program 5-4 24 | chisq.test(dat2$HTN, dat2$smoking) 25 | chisq.test(dat2$HTN, dat2$smoking)$expected 26 | chisq.test(xtabs(~smoking+HTN, data=dat2)) 27 | 28 | #### Program 5-5 29 | fisher.test(dat2$HTN, dat2$smoking) 30 | 31 | #### Program 5-6 32 | dat3<-dat2 %>% mutate(CEA.grp=as.factor(ifelse(CEA>5, 1, 0)), 33 | post.CEA.grp=as.factor(ifelse(post.CEA>5, 1, 0))) 34 | xtabs(~CEA.grp+post.CEA.grp, data=dat3) 35 | mcnemar.test(xtabs(~CEA.grp+post.CEA.grp, data=dat3)) 36 | mcnemar.test(dat3$CEA.grp, dat3$post.CEA.grp) 37 | 38 | 39 | 40 | -------------------------------------------------------------------------------- /chapter6.R: -------------------------------------------------------------------------------- 1 | 2 | setwd("C:\\Users\\user\\Dropbox\\KNOU_강의개편\\학부_바이오통계학\\R") 3 | setwd("C:\\Users\\KNOU_stat\\Dropbox\\KNOU_강의개편\\학부_바이오통계학\\R") 4 | setwd("C:\\Users\\SYP\\Dropbox\\KNOU_강의개편\\학부_바이오통계학\\R") 5 | 6 | #### Program 6-1 7 | 8 | setwd("C:/Users/KNOU_stat/R_codes") 9 | dat0<-read.csv("biostat_ex_data.csv") 10 | library(dplyr) 11 | dat3<-dat0 %>% mutate_at(vars(sex, Recur, stage, smoking, obesity, Recur_1y, 12 | post.CA19.9.binary, post.CA19.9.3grp), 13 | as.factor) %>% 14 | mutate(HTN=as.factor(ifelse(SBP>=140, 1, 0)), 15 | CEA.grp=as.factor(ifelse(CEA>5, 1, 0)), 16 | post.CEA.grp=as.factor(ifelse(post.CEA>5, 1, 0))) 17 | library(ggplot2) 18 | ggplot(dat3) + geom_point(aes(age, SBP)) 19 | cor(dat3$age, dat3$SBP) 20 | cor(dat3$age, dat3$SBP, method="spearman") 21 | 22 | #### Program 6-2 23 | 24 | ggplot(dat3) + geom_point(aes(log(CEA), log(post.CEA))) 25 | cor(log(dat3$CEA), log(dat3$post.CEA)) 26 | cor(log(dat3$CEA), log(dat3$post.CEA), method="spearman") 27 | 28 | #### Program 6-3 29 | dat4<-dat3 %>% mutate(log.CEA=log(CEA), 30 | log.post.CEA=log(post.CEA)) 31 | obj<-lm(log.post.CEA~log.CEA, data=dat4) 32 | summary(obj) 33 | 34 | #### Program 6-4 35 | ggplot(dat4, aes(log.CEA, log.post.CEA)) + geom_point() + 36 | geom_smooth(method="lm") 37 | 38 | #### Program 6-5 39 | dat.new<-data.frame(log.CEA=c(1, 2, 3)) 40 | predict(obj, newdata=dat.new) 41 | 42 | #### Program 6-6 43 | obj2<-lm(SBP~age+weight, data=dat4) 44 | summary(obj2) 45 | 46 | #### Program 6-7 47 | anova(obj) 48 | 49 | #### Program 6-8 50 | anova(obj2) 51 | 52 | #### Program 6-9 53 | library(broom) 54 | tidy(obj2, conf.int=TRUE) 55 | 56 | #### Program 6-10 57 | std.res<-rstandard(obj2) 58 | yhat<-predict(obj2) 59 | plot(yhat, std.res) 60 | abline(h=0) 61 | abline(h=2, lty=2) 62 | abline(h=-2, lty=2) 63 | 64 | #### Program 6-11 65 | qqnorm(std.res) 66 | qqline(std.res) 67 | 68 | #### Program 6-12 69 | levels(dat4$stage) 70 | 71 | #### Program 6-13 72 | model.1<-lm(log.CEA~stage, data=dat4) 73 | summary(model.1) 74 | 75 | #### Program 6-14 76 | dat5<-dat4 %>% mutate(stage.new=relevel(stage, ref=2)) 77 | levels(dat5$stage.new) 78 | model.2<-lm(log.CEA~stage.new, data=dat5) 79 | summary(model.2) 80 | 81 | #### Program 6-15 82 | model.3<-lm(log.CEA~age+sex+stage, data=dat4) 83 | summary(model.3) 84 | 85 | #### Program 6-16 86 | model.4<-lm(log.CEA~age+sex, data=dat4) 87 | library(lmtest) 88 | lrtest(model.3, model.4) 89 | 90 | #### Program 6-17 91 | anova(model.3) 92 | 93 | 94 | 95 | 96 | 97 | -------------------------------------------------------------------------------- /chapter7.R: -------------------------------------------------------------------------------- 1 | setwd("C:\\Users\\user\\Dropbox\\KNOU_강의개편\\학부_바이오통계학\\R") 2 | setwd("C:\\Users\\KNOU_stat\\Dropbox\\KNOU_강의개편\\학부_바이오통계학\\R") 3 | 4 | #### Program 7-1 5 | setwd("C:/Users/KNOU_stat/R_codes") 6 | dat0<-read.csv("biostat_ex_data.csv") 7 | library(dplyr) 8 | dat4<-dat0 %>% mutate_at(vars(sex, Recur, stage, smoking, obesity, Recur_1y, 9 | post.CA19.9.binary, post.CA19.9.3grp), 10 | as.factor) %>% 11 | mutate(HTN=as.factor(ifelse(SBP>=140, 1, 0)), 12 | CEA.grp=as.factor(ifelse(CEA>5, 1, 0)), 13 | post.CEA.grp=as.factor(ifelse(post.CEA>5, 1, 0)), 14 | log.CEA=log(CEA), 15 | log.post.CEA=log(post.CEA)) 16 | xtabs(~post.CA19.9.binary+Recur_1y, data=dat4) 17 | library(epiR) 18 | epi.tests(xtabs(~post.CA19.9.binary+Recur_1y, data=dat4)[2:1, 2:1]) 19 | 20 | #### Program 7-2 21 | library(pROC) 22 | fit<-roc(Recur_1y~post.CA19.9, data=dat4) 23 | fit 24 | plot(fit) 25 | 26 | #### Program 7-3 27 | coords(fit) 28 | 29 | #### Program 7-4 30 | coords(fit, x="best", best.method="youden") 31 | 32 | 33 | 34 | 35 | -------------------------------------------------------------------------------- /chapter8.R: -------------------------------------------------------------------------------- 1 | setwd("C:\\Users\\user\\Dropbox\\KNOU_강의개편\\학부_바이오통계학\\R") 2 | setwd("C:\\Users\\KNOU_stat\\Dropbox\\KNOU_강의개편\\학부_바이오통계학\\R") 3 | 4 | #### Program 8-1 5 | setwd("C:/Users/KNOU_stat/R_codes") 6 | dat0<-read.csv("biostat_ex_data.csv") 7 | library(dplyr) 8 | dat4<-dat0 %>% mutate_at(vars(sex, Recur, stage, smoking, obesity, Recur_1y, 9 | post.CA19.9.binary, post.CA19.9.3grp), 10 | as.factor) %>% 11 | mutate(HTN=as.factor(ifelse(SBP>=140, 1, 0)), 12 | CEA.grp=as.factor(ifelse(CEA>5, 1, 0)), 13 | post.CEA.grp=as.factor(ifelse(post.CEA>5, 1, 0)), 14 | log.CEA=log(CEA), 15 | log.post.CEA=log(post.CEA)) 16 | model.1<-glm(Recur_1y~post.CA19.9.binary, family=binomial, data=dat4) 17 | summary(model.1) 18 | 19 | #### Program 8-2 20 | coef(model.1) 21 | exp(coef(model.1)) 22 | 23 | #### Program 8-3 24 | library(lmtest) 25 | coefci(model.1) 26 | exp(coefci(model.1)) 27 | 28 | #### Program 8-4 29 | library(epiR) 30 | epi.2by2(xtabs(~post.CA19.9.binary + Recur_1y, data=dat4)[2:1, 2:1]) 31 | 32 | 33 | #### Program 8-5 34 | model.2<-glm(Recur_1y~age+sex+post.CA19.9, family=binomial, data=dat4) 35 | summary(model.2) 36 | exp(coef(model.2)) 37 | exp(coefci(model.2)) 38 | 39 | 40 | #### Program 8-6 41 | model.3<-glm(Recur_1y~post.CA19.9.3grp+sex, family=binomial, data=dat4) 42 | summary(model.3) 43 | exp(coef(model.3)) 44 | 45 | #### Program 8-7 46 | model.30<-glm(Recur_1y~sex, family=binomial, data=dat4) 47 | lrtest(model.3, model.30) 48 | 49 | 50 | #### Program 8-8 51 | predict(model.2) 52 | predict(model.2, type="response") 53 | 54 | #### Program 8-9 55 | predict(model.2, newdata=data.frame(age=60, sex=as.factor(0), post.CA19.9=30), 56 | type="response") 57 | 58 | #### Program 8-10 59 | library(pROC) 60 | roc(dat4$Recur_1y~predict(model.2)) 61 | 62 | 63 | 64 | 65 | -------------------------------------------------------------------------------- /chapter9.R: -------------------------------------------------------------------------------- 1 | setwd("C:\\Users\\user\\Dropbox\\KNOU_강의개편\\학부_바이오통계학\\R") 2 | setwd("C:\\Users\\KNOU_stat\\Dropbox\\KNOU_강의개편\\학부_바이오통계학\\R") 3 | 4 | #### Program 9-1 5 | setwd("C:/Users/KNOU_stat/R_codes") 6 | dat0<-read.csv("biostat_ex_data.csv") 7 | library(dplyr) 8 | dat4<-dat0 %>% mutate_at(vars(sex, Recur, stage, smoking, obesity, Recur_1y, 9 | post.CA19.9.binary, post.CA19.9.3grp), 10 | as.factor) %>% 11 | mutate(HTN=as.factor(ifelse(SBP>=140, 1, 0)), 12 | CEA.grp=as.factor(ifelse(CEA>5, 1, 0)), 13 | post.CEA.grp=as.factor(ifelse(post.CEA>5, 1, 0)), 14 | log.CEA=log(CEA), 15 | log.post.CEA=log(post.CEA)) 16 | 17 | dat5<-dat4 %>% mutate(OP_date=as.Date(OP_date, format="%Y-%m-%d"), 18 | Recur_date=as.Date(Recur_date, format="%Y-%m-%d"), 19 | rfs=as.double(Recur_date-OP_date) 20 | ) 21 | 22 | #### Program 9-2 23 | library(survival) 24 | Surv.obj<-Surv(time=dat5$rfs, event=dat5$Recur==1) 25 | 26 | #### Program 9-3 27 | fit<-survfit(Surv.obj~1, data=dat5) 28 | fit 29 | summary(fit) 30 | 31 | #### Program 9-4 32 | library(survminer) 33 | ggsurvplot(fit) 34 | 35 | #### Program 9-5 36 | ggsurvplot(fit, xscale=365.25, break.x.by=365.25, xlab="Year", legend="none", 37 | risk.table = TRUE, conf.int=FALSE, title="Recurrence Free Survival") 38 | 39 | #### Program 9-6 40 | survdiff(Surv.obj~stage, data=dat5) 41 | 42 | #### Program 9-7 43 | fit<-survfit(Surv.obj~stage, data=dat5) 44 | ggsurvplot(fit, xscale=365.25, break.x.by=365.25, xlab="Year", 45 | risk.table = TRUE, conf.int=FALSE, title="Recurrence Free Survival", 46 | pval=T) 47 | 48 | #### Program 9-8 49 | m1<-coxph(Surv(time=rfs, event=Recur==1)~age+stage, data=dat5) 50 | summary(m1) 51 | 52 | #### Program 9-9 53 | m0<-coxph(Surv(time=rfs, event=Recur==1)~age, data=dat5) 54 | anova(m0, m1) 55 | 56 | #### Program 9-10 57 | cox.zph(m1) 58 | ggcoxzph(cox.zph(m1)) 59 | -------------------------------------------------------------------------------- /바이오통계학_정오표(2023-11-29).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/biostat81/biostatistics/9397821c4f472f28e8f7c9d1de18b1941317da05/바이오통계학_정오표(2023-11-29).pdf --------------------------------------------------------------------------------