如何为我的每个自变量做斯皮尔曼相关矩阵？

问题描述 投票：0回答：3

我正在寻求帮助来制作一个斯皮尔曼相关矩阵，如果TAC（因变量）和浓度之间存在相关性，我可以在每个conditions中可视化？如果可能的话，包括

p.adjust
。

我正在寻找的矩阵类型是一种易于阅读的矩阵，其中包含 Spearman 的 p 和 p 值。感谢任何能帮助我或为我指明正确方向的人。

这是我的数据框：

> 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))
r matrix correlation
3个回答
1
投票

您可以通过 

split
conditions
数据框，使用 
lapply
从每个子数据框获取 
cor.test
并从每个子数据框创建一个组/相关/pvalue 的单行数据框，然后您
rbind
到单个数据框中。

do.call(rbind, lapply(split(table5, table5$conditions), function(d) { 
  x <- cor.test(d$concentration, d$TAC, method = 'spearman')
  data.frame(group = d$conditions[1], cor = x$estimate, 
             p = scales::pvalue(x$p.value, add_p = TRUE))
})) |> `rownames<-`(NULL)
#>            group         cor       p
#> 1  controlday 00          NA    <NA>
#> 2  controlday 07          NA    <NA>
#> 3  controlday 14          NA    <NA>
#> 4  controlday 21          NA    <NA>
#> 5  controlday 28          NA    <NA>
#> 6      nZnday 00 -0.05160468 p=0.811
#> 7      nZnday 07  0.70034929 p<0.001
#> 8      nZnday 14  0.71509349 p<0.001
#> 9      nZnday 21  0.73720978 p<0.001
#> 10     nZnday 28  0.78144237 p<0.001
#> 11      Znday 00  0.23590713 p=0.267
#> 12      Znday 07  0.46444216 p=0.022
#> 13      Znday 14  0.56765153 p=0.004
#> 14      Znday 21  0.65611670 p<0.001
#> 15      Znday 28  0.81830286 p<0.001

前四组都有NA值，因为整组

concentration
的值为0，因此标准差为0.

1
投票

使用

tidyverse
-假设OP意味着
cor.test
作为被问到的P值。按“治疗”、“日”条件分组，在 TAC 上应用 
cor.test
，集中注意力，将 
list
输出转换为带有 
broom::tidy
和 
unnest
tibble
列

的小标题 
library(dplyr) # version >= 1.1.0
library(tidyr)
table5 %>%
   reframe(cor = broom::tidy(cor.test(TAC, concentration,
    method = "spearman")), .by = c("treatment", "day", "conditions")) %>% 
   unnest(where(is_tibble))

-输出

# A tibble: 15 × 8
   treatment day    conditions    estimate statistic     p.value method                          alternative
   <chr>     <chr>  <fct>            <dbl>     <dbl>       <dbl> <chr>                           <chr>      
 1 control   day 00 controlday 00  NA            NA  NA          Spearman's rank correlation rho two.sided  
 2 control   day 07 controlday 07  NA            NA  NA          Spearman's rank correlation rho two.sided  
 3 control   day 14 controlday 14  NA            NA  NA          Spearman's rank correlation rho two.sided  
 4 control   day 21 controlday 21  NA            NA  NA          Spearman's rank correlation rho two.sided  
 5 control   day 28 controlday 28  NA            NA  NA          Spearman's rank correlation rho two.sided  
 6 nZn       day 00 nZnday 00      -0.0516     2419.  0.811      Spearman's rank correlation rho two.sided  
 7 nZn       day 07 nZnday 07       0.700       689.  0.000139   Spearman's rank correlation rho two.sided  
 8 nZn       day 14 nZnday 14       0.715       655.  0.0000860  Spearman's rank correlation rho two.sided  
 9 nZn       day 21 nZnday 21       0.737       604.  0.0000396  Spearman's rank correlation rho two.sided  
10 nZn       day 28 nZnday 28       0.781       503.  0.00000654 Spearman's rank correlation rho two.sided  
11 Zn        day 00 Znday 00        0.236      1757.  0.267      Spearman's rank correlation rho two.sided  
12 Zn        day 07 Znday 07        0.464      1232.  0.0222     Spearman's rank correlation rho two.sided  
13 Zn        day 14 Znday 14        0.568       994.  0.00381    Spearman's rank correlation rho two.sided  
14 Zn        day 21 Znday 21        0.656       791.  0.000499   Spearman's rank correlation rho two.sided  
15 Zn        day 28 Znday 28        0.818       418.  0.00000103 Spearman's rank correlation rho two.sided
1
投票

你可以试试这个基本的 R 解决方案：

ll <- split(df, df$condition)
sprtest <- lapply(ll, function(x) cor.test(x$TAC, x$concentration, method = "spearman")[c("estimate", "p.value")])
do.call(rbind, lapply(sprtest, unlist))

或者，在基础 R 中，您也可以使用 

by
避免 
split
ting：

by(df, df$condition, FUN = function(x) 
  unlist(cor.test(x$TAC, x$concentration, method = "spearman")[c("estimate", "p.value")]))
do.call(rbind, xx)

无论哪种方式，你的输出是：

#               estimate.rho      p.value
# controlday 00           NA           NA
# controlday 07           NA           NA
# controlday 14           NA           NA
# controlday 21           NA           NA
# controlday 28           NA           NA
# nZnday 00      -0.05160468 8.107384e-01
# nZnday 07       0.70034929 1.386591e-04
# nZnday 14       0.71509349 8.597702e-05
# nZnday 21       0.73720978 3.964055e-05
# nZnday 28       0.78144237 6.541909e-06
# Znday 00        0.23590713 2.671011e-01
# Znday 07        0.46444216 2.222910e-02
# Znday 14        0.56765153 3.812867e-03
# Znday 21        0.65611670 4.987260e-04
# Znday 28        0.81830286 1.031562e-06
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