我请求帮助制作一个斯皮尔曼相关矩阵,我可以在每个条件下可视化TAC(因变量)和浓度之间是否存在相关性?如果可能,包括p.adjust
。
我所寻找的矩阵类型是一个易于阅读的,其中包括斯皮尔曼的p和p值。我感谢任何人谁可以帮助我或指出我在正确的方向。
以下是我数据框:
> str(table5)
'data.frame': 280 obs. of 5 variables:
$ treatment : chr "control" "control" "control" "control" ...
$ concentration: num 0 0 0 0 0 0 0 0 0 0 ...
$ day : chr "day 00" "day 00" "day 00" "day 00" ...
$ TAC : num 0.0135 0.0162 0.0146 0.0153 0.0128 ...
$ conditions : Factor w/ 15 levels "controlday 00",..: 1 1 1 1 1 1 1 1 2 2 ...
> dput(table5)
structure(list(treatment = c("control", "control", "control",
"control", "control", "control", "control", "control", "control",
"control", "control", "control", "control", "control", "control",
"control", "control", "control", "control", "control", "control",
"control", "control", "control", "control", "control", "control",
"control", "control", "control", "control", "control", "control",
"control", "control", "control", "control", "control", "control",
"control", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn",
"nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn",
"nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn",
"nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn",
"nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn",
"nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn",
"nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn",
"nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn",
"nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn",
"nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn",
"nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn",
"nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn",
"nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn",
"nZn", "nZn", "nZn", "nZn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn",
"Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn",
"Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn",
"Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn",
"Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn",
"Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn",
"Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn",
"Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn",
"Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn",
"Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn",
"Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn",
"Zn", "Zn", "Zn", "Zn"), concentration = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 1, 1, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,
100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,
100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,
100, 100, 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,
1, 1, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 100, 100, 100, 100, 100,
100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,
100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,
100, 100, 100, 100, 100, 100, 100, 100, 100), day = c("day 00",
"day 00", "day 00", "day 00", "day 00", "day 00", "day 00", "day 00",
"day 07", "day 07", "day 07", "day 07", "day 07", "day 07", "day 07",
"day 07", "day 14", "day 14", "day 14", "day 14", "day 14", "day 14",
"day 14", "day 14", "day 21", "day 21", "day 21", "day 21", "day 21",
"day 21", "day 21", "day 21", "day 28", "day 28", "day 28", "day 28",
"day 28", "day 28", "day 28", "day 28", "day 00", "day 00", "day 00",
"day 00", "day 00", "day 00", "day 00", "day 00", "day 07", "day 07",
"day 07", "day 07", "day 07", "day 07", "day 07", "day 07", "day 14",
"day 14", "day 14", "day 14", "day 14", "day 14", "day 14", "day 14",
"day 21", "day 21", "day 21", "day 21", "day 21", "day 21", "day 21",
"day 21", "day 28", "day 28", "day 28", "day 28", "day 28", "day 28",
"day 28", "day 28", "day 00", "day 00", "day 00", "day 00", "day 00",
"day 00", "day 00", "day 00", "day 07", "day 07", "day 07", "day 07",
"day 07", "day 07", "day 07", "day 07", "day 14", "day 14", "day 14",
"day 14", "day 14", "day 14", "day 14", "day 14", "day 21", "day 21",
"day 21", "day 21", "day 21", "day 21", "day 21", "day 21", "day 28",
"day 28", "day 28", "day 28", "day 28", "day 28", "day 28", "day 28",
"day 00", "day 00", "day 00", "day 00", "day 00", "day 00", "day 00",
"day 00", "day 07", "day 07", "day 07", "day 07", "day 07", "day 07",
"day 07", "day 07", "day 14", "day 14", "day 14", "day 14", "day 14",
"day 14", "day 14", "day 14", "day 21", "day 21", "day 21", "day 21",
"day 21", "day 21", "day 21", "day 21", "day 28", "day 28", "day 28",
"day 28", "day 28", "day 28", "day 28", "day 28", "day 00", "day 00",
"day 00", "day 00", "day 00", "day 00", "day 00", "day 00", "day 07",
"day 07", "day 07", "day 07", "day 07", "day 07", "day 07", "day 07",
"day 14", "day 14", "day 14", "day 14", "day 14", "day 14", "day 14",
"day 14", "day 21", "day 21", "day 21", "day 21", "day 21", "day 21",
"day 21", "day 21", "day 28", "day 28", "day 28", "day 28", "day 28",
"day 28", "day 28", "day 28", "day 00", "day 00", "day 00", "day 00",
"day 00", "day 00", "day 00", "day 00", "day 07", "day 07", "day 07",
"day 07", "day 07", "day 07", "day 07", "day 07", "day 14", "day 14",
"day 14", "day 14", "day 14", "day 14", "day 14", "day 14", "day 21",
"day 21", "day 21", "day 21", "day 21", "day 21", "day 21", "day 21",
"day 28", "day 28", "day 28", "day 28", "day 28", "day 28", "day 28",
"day 28", "day 00", "day 00", "day 00", "day 00", "day 00", "day 00",
"day 00", "day 00", "day 07", "day 07", "day 07", "day 07", "day 07",
"day 07", "day 07", "day 07", "day 14", "day 14", "day 14", "day 14",
"day 14", "day 14", "day 14", "day 14", "day 21", "day 21", "day 21",
"day 21", "day 21", "day 21", "day 21", "day 21", "day 28", "day 28",
"day 28", "day 28", "day 28", "day 28", "day 28", "day 28"),
TAC = c(0.0134723395589115, 0.0161888871061509, 0.0146337654145718,
0.0153067871292595, 0.012800314735395, 0.0160841665978896,
0.0140621616691814, 0.0135425580967982, 0.0132198270328205,
0.0138496077219653, 0.0135775493518084, 0.0126333962864469,
0.0164821881641534, 0.0132516331108305, 0.0157791571175251,
0.0129960024291699, 0.0146323678504021, 0.0134451215151322,
0.0143262838325461, 0.0153573779185249, 0.0139773746147923,
0.0159350865128266, 0.0156720782857077, 0.0155096081292032,
0.013476349735956, 0.0140104181996115, 0.0129878390010014,
0.0147239859165112, 0.015160930718777, 0.0148955399340424,
0.013274378116328, 0.0153663044374496, 0.0145472559523844,
0.0132898660703847, 0.0139871399975842, 0.0124985111701027,
0.0149240276338179, 0.0129573902698069, 0.0147729343794709,
0.0128674264777598, 0.0147815872982594, 0.0139767796824041,
0.0144185398405766, 0.0155799146991459, 0.0135417909851351,
0.015988596586438, 0.0139603963976125, 0.0126397298299191,
0.013297964384596, 0.012347536157165, 0.0152573470818857,
0.0136566619097667, 0.0125192707022401, 0.0141156296691061,
0.0139603724286662, 0.0141388938152221, 0.0127749097766803,
0.0142082519110294, 0.0149398326676766, 0.0143207529313558,
0.0144381103787128, 0.0149147414885484, 0.0139224295866318,
0.0161358891403436, 0.0151690152511571, 0.0120945286936824,
0.0153132383654698, 0.0131770823852777, 0.0136750345235747,
0.0129352436377984, 0.0162120454010317, 0.0155409171425954,
0.0135940425474181, 0.0142951343511937, 0.0143779323175896,
0.0136891451722703, 0.0140286347004686, 0.0122667606250391,
0.0152446224172418, 0.013442306549535, 0.0129068996979612,
0.0147404146947943, 0.013688825582269, 0.0130193063055386,
0.01285971255513, 0.0151660181611206, 0.0138280467330508,
0.0135147736966651, 0.0158580706409006, 0.0149366602534351,
0.0106554950909403, 0.0179654260106192, 0.0120425346368713,
0.0145387164119486, 0.0139546280207597, 0.0121871897075845,
0.0150418870034593, 0.0148117380734173, 0.0139690179111281,
0.0170751257982307, 0.0129661477952429, 0.0144612227917873,
0.0146065893466387, 0.0126241343210384, 0.0170751257982307,
0.0130964557093226, 0.0134570968344701, 0.0165480203562944,
0.0151921149184481, 0.0130666062376204, 0.012722050697886,
0.0155582048904096, 0.0125288074742436, 0.016985639190516,
0.0176528351294189, 0.0138432089287227, 0.013890319218671,
0.017035215335001, 0.0168839977227436, 0.0133203267470888,
0.013892777179513, 0.0155216139064973, 0.0130076218759369,
0.013903958340264, 0.0135000204009635, 0.0148519977852621,
0.0153029154169557, 0.0141832966293512, 0.0176005510379328,
0.0180687740940438, 0.0177789446952697, 0.0182099087520794,
0.0184723827329167, 0.022483746075728, 0.0196648164641345,
0.0170131886149416, 0.0215058343136062, 0.0211259597744559,
0.0196373761289472, 0.0206737739206, 0.020532594441278, 0.0193494766153245,
0.0211617300063814, 0.0213333413267872, 0.0202163436360403,
0.0236752367085596, 0.0231873026647459, 0.0228522660496144,
0.0238366734630018, 0.0264524093818515, 0.0268093919646026,
0.0252668406573153, 0.0258403852690662, 0.0223986018317785,
0.0272147558779617, 0.0225116847733454, 0.0247724813762193,
0.022691182948792, 0.0235805783268122, 0.0270689051186104,
0.0126334908832258, 0.0164665820507107, 0.0129386884401034,
0.0119158011756844, 0.0130928729787235, 0.0149940706645974,
0.0129535502638655, 0.0162831996423606, 0.0176755444192191,
0.0161755659998132, 0.0174173101524856, 0.0155714069341957,
0.01433383826834, 0.0143819293817603, 0.0185494616259894,
0.0140319779691521, 0.0144114680062016, 0.0174497227904159,
0.0180907703704672, 0.0157478259355293, 0.0158958906812569,
0.0147163839619763, 0.0146701443994308, 0.0180369287296324,
0.0149336258279806, 0.0186097801562105, 0.0137231521985133,
0.0153650910635747, 0.0138998273293687, 0.0155199902217533,
0.0163903022171882, 0.015754928008943, 0.0171808546793322,
0.0154244829039175, 0.0134954450270778, 0.0147187179502944,
0.0160939056001929, 0.0145497150558122, 0.0154571534643691,
0.015511148172344, 0.0132885919777709, 0.0138910418368534,
0.0152496449072613, 0.0132820365830201, 0.013480084079182,
0.016683045565325, 0.0176337406920335, 0.0151657804062655,
0.0125455114843902, 0.0118102856445592, 0.0116410665300014,
0.0146556231989517, 0.014464999427952, 0.0121229802720933,
0.0146834533301593, 0.0121645122630423, 0.0136816673389857,
0.0135984961089614, 0.0164906141382343, 0.0149265724276527,
0.0163311308492402, 0.017967595623527, 0.0143263172313383,
0.0145117513172078, 0.0149694356038913, 0.0136478358101476,
0.0148523043836901, 0.0140267859486034, 0.0136857372651645,
0.0161384954212, 0.0171836598216303, 0.0165288287203719,
0.0163703032374203, 0.0149628937118673, 0.0167639896711626,
0.0144140290861155, 0.0164700832677882, 0.017097353142466,
0.0177233791174971, 0.016410406871025, 0.0145656397252108,
0.0127795571441824, 0.0139787766512734, 0.0145603577832239,
0.0130325210010334, 0.0157142193796273, 0.0165295708322065,
0.0154878492755022, 0.0176888974165639, 0.0186435561581489,
0.0177330425080685, 0.0182856446463086, 0.0219973970170363,
0.0217533371623466, 0.0176290655250839, 0.0202192044566584,
0.01917805317661, 0.0186277616395779, 0.0170154664932417,
0.0195884686724334, 0.0201420675026667, 0.0183148068985733,
0.020836323932372, 0.0207067552945439, 0.018534989031893,
0.019680916901509, 0.0219673944081694, 0.0236890701508884,
0.0235543150426157, 0.0234233849979097, 0.0210565415662947,
0.0232511101944444, 0.0227186732866978, 0.0225332903957415,
0.0234773944195847, 0.0229988542468931, 0.022618525386521,
0.0197686090869307, 0.0186686467858637, 0.0189525178016395
), conditions = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L), levels = c("controlday 00",
"controlday 07", "controlday 14", "controlday 21", "controlday 28",
"nZnday 00", "nZnday 07", "nZnday 14", "nZnday 21", "nZnday 28",
"Znday 00", "Znday 07", "Znday 14", "Znday 21", "Znday 28"
), class = "factor")), class = "data.frame", row.names = c(NA,
-280L))
3条答案
按热度按时间p4tfgftt1#
您可以按
conditions
对 Dataframe 进行split
,使用lapply
从每个子 Dataframe 中获取cor.test
,并从每个子 Dataframe 中创建一行 Dataframe (组/相关性/p值),然后将其rbind
到单个 Dataframe 中。前四组都具有NA值,因为
concentration
的值对于整个组为0,因此标准差为0。ih99xse12#
使用
tidyverse
-假设OP表示cor.test
,因为正在询问P值。按"治疗"、"天"条件分组,将cor.test
应用于TAC、浓度,将list
输出转换为tibble,broom::tidy
和unnest
位于tibble
列dzjeubhm3#
你可以试试这个R基溶液:
或者,在底数R中,可以使用
by
来避免split
ting:无论哪种方式,输出都是: