我需要获取矩阵的相关性和 p 值，因此我尝试了 Hmisc 包中的 rcorr 。问题是我的一对 pvalue 为 0，这给了我 1.036733e-59 和 cor.test 函数。
我对每个函数都使用了 pearson。
这是一个可重现的示例。
library(Hmisc)
A <- c(6.32451531850417,6.396670829087,6.86361544161307,6.69938199174752,13.165463943882,6.90981191583279,13.1464308794264,6.52552248884005,6.08631376117103,6.61690829991384,6.77722148405774,6.49620534991742,6.5518962525994,6.2497899855603,6.8646997315825,6.69081335429442,6.70869072803975,6.87789921476436,6.56641042358147,6.84013201548233,6.69568254965127,6.70640778501988,6.62246110671344,6.67829056829049,6.65799888115762,6.24141301052501,6.69746610666983,6.68902124059975,6.64539017705934,6.81328563238539,7.06715007760963,6.75490752235589,6.49012918187287,12.7208750140264,6.73382593460874,6.35761704581448,7.28515563569873,6.67369856749724,6.66992764180906,6.40471908440153,6.30772659143547,6.45098473518897,6.60578485189021,6.50990123242974,6.40449000722152,6.50128036958986,6.66306523170188,6.45702244416934,6.28566653849103,6.42625738798208,6.88706148070107,6.35110357440403,6.68612827575002,6.58213967827742,6.37737813156623,6.38199814182166,6.7091244513399,6.46266526892103,6.46052608198987,6.57481275927555,6.60832078443043,6.75222494716443,6.55842628702281,6.67547286814189,6.51448496000258,6.67177207392162,6.69153307744344,6.69836071289889,6.6615217504183,6.63805883879528,6.36018525770115,6.50727400620095,6.38370628835495,6.81563624248625,6.62298705858254,6.41641698679136,6.29103959938316,6.64396635235954,6.76381424552375,6.51339014760998,6.63043442032935,6.93781852637583,6.59535594885934,7.0997394675434,6.19171479075248,6.28123555799257,6.43057159585162,6.5869614799051)
B <- c(3.9995637786823,3.48601398807956,4.03812725251975,3.81851391342822,11.7502675850666,3.98407110753864,11.8300601767337,3.59623022007111,3.47594268425866,3.39485541409169,3.96156903705731,3.84385038139104,4.40329546631133,3.69704624452915,3.87264919566187,3.90460396437268,3.45875737518772,3.95915349682997,4.37078882304428,3.79514714207642,3.85315638891753,3.82101134020264,3.76652556154815,3.78168978057991,4.38817115664092,3.67188258569308,3.93083752538972,4.09512848434603,3.76017250778245,3.93149619446255,3.73967734750671,3.79329645229012,3.72530038941995,11.6296199270195,3.76362438867387,3.52361952114506,5.05916028061208,3.78174363576111,3.77862237861648,3.96446993299436,3.95420785742674,3.65350904888874,3.74208843021492,3.7864852643104,3.34969922277165,3.76699340323312,3.90279649287842,3.48792692835044,3.54100392028721,3.73429478892297,4.07755160257201,3.71095088992823,3.96086251069495,3.62303629160269,3.50473542630912,3.45494734742944,3.62764756649661,3.50045819158303,4.75363129670227,3.66796624908498,4.41415957987699,3.61676764219056,4.35208487010989,3.63812742107392,3.61369595503799,3.6738293973286,3.79492785542216,4.89013596433762,3.70204962677728,3.52121048655018,3.59305187045436,3.58411383210404,3.50590908471792,3.73025791048932,4.0143103011064,3.6584374210132,3.72337929455363,3.7766291637304,3.55626499087112,4.11134785621808,4.03941383606145,3.81034661576582,4.15448306192498,4.73600249458588,3.57585585559837,3.76987550019725,3.72667260885011,3.55088410428519)
C <- c(5.0828344582765,5.44171716119668,5.50349616275127,6.20354287213394,6.99263387581131,5.53286444824163,6.66169084634442,5.16933585666197,4.99851910038837,5.18468358275925,5.94408569177543,5.38879105718559,5.1963019014005,4.90579273695638,5.40610158292019,5.97408534843616,5.6634072236913,5.71035471476711,5.30554917408064,5.60513350695794,5.59638539269362,5.54571609819415,5.26763404068582,5.88068748660826,5.20462261826007,5.28737753968805,5.41548211806564,5.23988511286448,5.45891799263611,5.33513304020226,5.38926947164349,5.78004326608306,5.53385821088178,6.70309488254236,5.72120763082083,5.43668800424696,5.31591419975872,5.55446882414854,5.39756885193532,5.34660398519912,4.98008442404427,5.07080405039416,5.03893220932515,5.11538656986937,5.19477846798434,5.27241946711687,5.15677380833086,5.20070799769709,5.611572982919,4.83939324104591,5.36607457080691,5.21935359026813,5.70039096365024,5.29675323855941,5.02690902121225,5.04349564595055,5.17171742744466,5.21386568112692,5.25785400845595,4.77688955147304,5.89435056128631,5.44712026551928,5.31047450193841,5.11339046941959,5.23862350238358,5.39464522888807,5.24658651925926,5.13548147374252,5.04054442277134,5.0942112627996,4.8247921182326,5.21145125468889,5.25857361278046,5.01095797437389,5.6214184959344,5.07188964223442,5.17615526987811,5.3227559035551,5.43031530449701,5.27626866543545,5.45060599999116,5.2667271685913,5.12852923511888,5.2622926326258,5.35048009048626,4.92554974015645,5.14856255783588,5.75558021935756)
mtx <- cbind(A,B,C)
rcorrelation <- rcorr(mtx, type="pearson")
rcorrelation$P["A","B"] # 0
rcorrelation$P["A","C"] # 2.220446e-16
cor.test(A,B, method="pearson")$p.value # 1.036733e-59
cor.test(A,C, method="pearson")$p.value # 2.181905e-16
如何使用 rcorr 将 1.036733e-59 作为 p 值？
我有一个最大的矩阵，并且某些 p 值有很多 0，我需要对它们进行排名，即使它们都接近 0...
谢谢
PS：具有 2 个功能的一对 A/C 存在很小（较小？）的差异，但这不是我目前的问题...
差异（顺便说一下，差异是无限小的）可能是由于相关测试的实施方式不同所致。 AFAICT
rcorrcor.testrcorrcor.test如果您想模仿
rcorrcombnapplyget_corr_p <- function(x) {
# the x argument is a vector of length two with the variable names
cor.test(get(x[1]), get(x[2]))$p.value
}
apply(combn(c("A", "B", "C"), 2), 2, get_corr_p)
但正如 @Roland 所指出的，对 p 值进行排名没有意义。从概念上讲，您可以使用 p 值来检查是否存在错误拒绝假设的风险（在本例中相关系数等于 0）。通常，此风险的阈值设置为 5%，因此如果 p<0.05 then the risk of saying "there is a correlation" and that not being true is low enough, and we can make that statement. There is no such thing as "more significant", either the difference is significant or it isn't (given the threshold you selected). It is a yes/no thing, so it makes no sense to rank p-values.