我声明我以前从未使用过均方根偏差。 我只是想重现我在文章中发现的内容。

通常，我必须量化“方法”的噪声（由于两种仪器的耦合，这是不同噪声的结果），在正常操作之外的三个不同点上测量方法的噪声，我们知道它只能测量噪音。 最后，在我要遵循的过程中，您必须计算这三个点之间的标准偏差，并将其乘以第95个百分位数置信区间的1.96因子（这样就可以得出方法）。
时间分辨率为30分钟，然后是三点之间的标准差，然后是随后的三点，依此类推。我已经有一个以此方式组织的数据集，并且已经计算出标准差。

因为我正在按照文章的方法进行操作，所以他们比较了使用标准偏差和使用均方根偏差计算出的检出限。 因为最后我必须使用此检测限制来按噪声过滤数据，所以我想像他们一样比较哪种方法最适合我的情况。

如何计算均方根差以及标准差？每三个点（三个不同的列），然后是三个随后的点（相同的三列，但下一行），依此类推？

我已经尝试使用rmse包的Metrics函数，但是问题在于它只需要两个值：实际值和预测值。 当然，就像我进行标准差计算一样，我应该使用聚合来对函数每行每三列进行迭代。

编辑

我在此文章的后面贴了一篇文章，试图使您更好地理解我的需求

“对于时滞与真实时滞有很大不同，通量的标准偏差提供了一种影响通量的随机误差的度量” .......”将该随机误差的度量乘以α得出在给定的置信区间（第95个百分位数，α= 1.96；第99个百分位数，α= 3）下，测量精度的估计值可用作检测的通量极限（LoD）（即LoDσ=α×REσ）。 .....“LoDσ方法的一种修改是基于通量从零开始的均方根偏差（RMSE）计算随机误差，这反映了这些区域的交叉协方差函数的可变性，也反映了其与零

我与您分享部分数据集以及我尝试使用的代码。

数据集

structure(list(`101_LOD` = c(-0.00647656063436054,0.00645714072316343,0.00174533523902105,-0.000354643362187957,-0.000599093190801188,0.00086188829059792),`101_LOD.1` = c(0.00380625456526623,-0.00398115037246045,0.00158673927930099,-0.00537583996746438,-0.00280048350643599,0.00348232298529063),`101_LOD.2` = c(-0.00281100080425964,-0.00335537844222041,0.00611652518452308,-0.000738139825060029,0.00485039477849737,0.00412428118507656),`107_LOD` = c(0.00264717678436649,0.00339296025595841,0.00392733001719888,0.0106686039973083,0.00886643251752075,0.0426091484273961),`107_LOD.1` = c(0.000242380702002215,-0.00116108069669281,0.0119784744970561,0.00380805756323248,0.00190407945251567,0.00199684331869391),`107_LOD.2` = c(-0.0102716279438754,-0.00706528150567528,-0.0108745954674186,-0.0122962259781756,-0.00590383880635847,-0.00166664119985051),`111_LOD` = c(-0.00174374098054644,0.00383270191075735,-0.00118363208946644,0.00107908760333878,-9.30127551375776e-05,-0.00141500588842743),`111_LOD.1` = c(0.000769378300959002,0.00253820252869653,0.00110643824418424,-0.000338050323261079,-0.00313666295753596,0.0043919374295125),`111_LOD.2` = c(0.000177265973907964,0.00199829884609846,-0.000490950219515303,-0.00100263695578483,0.00122606902671889,0.00934018452187161),`113_LOD` = c(0.000997977666838309,0.0062400770296875,-0.00153620247996209,0.00136849054508488,-0.00145700847633675,-0.000591288575933268),`113_LOD.1` = c(-0.00114161441697546,0.00152607521404826,0.000811193628975422,-0.000799514037634276,-0.000319008435039752,-0.0010086036089075),`113_LOD.2` = c(-0.000722312098377764,0.00364767954707251,0.000547744649351312,0.000352509651080838,-0.000852173274761947,0.00360487150682726),`135_LOD` = c(-0.00634051802134062,0.00426062889500736,0.00484049067127332,0.00216220020394825,0.00165634168942681,-0.00537970105199375),`135_LOD.1` = c(-0.00209301968088832,0.00535855274344209,-0.00119679744329422,0.0041216882161451,0.00512978202611836,0.0014048506490567),`135_LOD.2` = c(0.00022377545723911,0.00400550696583795,0.00198972253447825,0.00301341644871015,0.00256802839330668,0.00946109288597202),`137_LOD` = c(-0.0108508893475138,-0.0231919072487789,-0.00346546003410657,-0.00154066625155414,0.0247266017774909,-0.0254464953061609),`137_LOD.1` = c(-0.00363025194918789,-0.00291104074373261,0.0024998477144967,0.000877707284759669,0.0095477003599792,0.0501795740749602),`137_LOD.2` = c(0.00930498343499501,-0.011839104725282,0.000274929503053888,0.000715665078729413,0.0145503185102915,0.0890428314632625),`149_LOD` = c(-0.000194406250680231,0.000355157226357547,-0.000353931679163222,0.000101471293242973,-0.000429409422518444,0.000344585379249552),`149_LOD.1` = c(-0.000494386150759807,0.000384907974061922,0.000582537329068263,-0.000173285705433721,-6.92758935962043e-05,0.00237942557324254),`149_LOD.2` = c(0.000368606958615297,0.000432568466833549,3.33092313366271e-05,0.000715304544370804,-0.000656902381786168,0.000855422043674721),`155_LOD` = c(-0.000696168382693618,-0.000917607266525328,4.77049670728094e-06,0.000140297660927979,-5.99898679530658e-06,6.71169142984434e-06),`155_LOD.1` = c(-0.000213644203677328,-3.44396001911029e-07,-0.000524232671878577,-0.000830180665933627,1.47799998238307e-06,-5.97640014667251e-05),`155_LOD.2` = c(-0.000749882784933487,0.000345737159390042,-0.00076916001239521,-0.000135205762575321,-2.55352420251723e-06,-3.07199008030628e-05),`31_LOD` = c(-0.00212014938530172,0.0247411322547065,-0.00107990654365844,-0.000409195814154659,-0.00768439381433953,0.001860128524035),`31_LOD.1` = c(-0.00248488588195854,-0.011146734518705,-0.000167943850441196,-0.0021998906531997,0.0166775965182051,-0.0156939303287719),`31_LOD.2` = c(0.00210626277375321,-0.00327815351414411,-0.00271043947479133,0.00118991079627845,-0.00838520090692615,0.0255825346347586),`33_LOD` = c(0.0335175783154054,0.0130192144768818,0.0890608024914352,-0.0142431454793663,0.00961009674973182,-0.0429774973256228),`33_LOD.1` = c(0.018600175159935,0.04588362587764,0.0517479021554752,0.0453766081395813,-0.0483559729403664,0.123771869764484),`33_LOD.2` = c(0.01906507758481,-0.00984821669825455,0.134177176083007,-0.00544320457445977,0.0516083894733814,-0.0941500564321804),`39_LOD` = c(-0.148517395684098,-0.21311281527214,0.112875846920874,-0.134256453140454,0.0429030528286934,-0.0115143877745049
),`39_LOD.1` = c(-0.0431568202849291,-0.159003698955288,0.0429009071238143,-0.126060096927082,-0.078848020069061,-0.0788748111534866),`39_LOD.2` = c(-0.16276833960171,0.0236589399437796,0.0828435027244962,-0.50219849047847,-0.105196237549017,-0.161206838628339
    ),`42_LOD` = c(-0.00643926654994104,-0.0069253267922805,7.63419856289838e-05,-0.0185223126108671,0.00120855708103566,-0.00275288147011515),`42_LOD.1` = c(-0.000866169150506504,-0.00147791175852563,-0.000670310173141084,-0.00757733007180311,0.0151353172950393,-0.00114193461500327),`42_LOD.2` = c(0.00719928454572906,0.00311615354837406,0.00270759483782046,-0.0108062423259522,0.00158765505419478,-0.0034831499672973),`45_LOD` = c(0.00557787518897268,0.022337270533665,0.00657118689440082,-0.00247269227623608,0.0191646343214611,0.0233090596023039),`45_LOD.1` = c(-0.0305395220788143,0.077105031761457,-0.00101713990356452,0.0147500116150713,-5.43009569586179e-05,-0.0235006181977403),`45_LOD.2` = c(-0.0216498682456909,-0.0413426968184435,-0.0210779895848601,-0.0147549519865421,0.00305229143870313,-0.0483293292336662),`47_LOD` = c(-0.00467568767221499,-0.0199796182799552,0.00985966068611855,-0.031010117051163,0.0319279109813341,0.0350743318265918),`47_LOD.1` = c(0.00820166533285921,-0.00748186905620154,-0.010483251821707,-0.00921919551377505,0.0129546148757833,0.000223462281435923),`47_LOD.2` = c(0.00172469728530889,0.0181683409295075,0.00264937907258855,-0.0569837400476351,0.00514558635349483,0.0963339573489031),`59_LOD` = c(-0.00664210061621158,-0.062069664217766,0.0104345353700492,0.0115323589989968,-0.000701276829098035,-0.0397759501000331),`59_LOD.1` = c(-0.00844888486350536,0.0207426674766074,-0.0227755432761471,-0.00370561240222376,0.0152046240483297,-0.0127327412801225),`59_LOD.2` = c(-0.000546590647534814,0.0178115310450356,0.00776130696191998,0.00162470375408126,-0.036140754156005,0.0197791914089296),`61_LOD` = c(0.00797528044191513,-0.00358928087671818,0.000662870138322471,-0.0412142836466128,-0.00571822580078707,-0.0333870884803465),`61_LOD.1` = c(0.000105849888219735,-0.00694734283847093,-0.00656216592134899,0.00161225110022219,0.0125744958934939,-0.0178560868664668),`61_LOD.2` = c(0.0049288443167774,0.0059411543659837,-0.00165857112209555,-0.0093669075333705,0.00655185371925189,0.00516436591134869),`69_LOD` = c(0.0140014747729604,0.0119645827116724,0.0059880663080946,-0.00339119330845176,0.00406436116298777,0.00374425148741196),`69_LOD.1` = c(0.00465076983995792,0.00664902297016735,-0.00183936649215524,0.00496509351837152,-0.0224812403463345,-0.0193087796456654),`69_LOD.2` = c(-0.00934638876711703,-0.00802183076602164,0.00406752039394799,-0.000421337136630527,-0.00406768983408334,-0.0046016148041856),`71_LOD` = c(-0.00206064862123214,0.0058604630066848,-0.00353440181333921,-0.000305197461077327,0.00266085011303462,-0.00105635261106644),`71_LOD.1` = c(3.66652318354654e-06,0.00542612739642576,0.000860385212430484,0.00157520645492044,-0.00280256517377998,-0.00474358065422048),`71_LOD.2` = c(-0.00167098030843413,0.0059622082597603,-0.00121597491543965,-0.000791592953383716,-0.0022790991468459,0.00508978650148816),`75_LOD` = c(NA,-0.00562613898652477,-0.000103076958936504,-3.76628574664693e-05,-0.000325767611573817,0.000117404893823389),`75_LOD.1` = c(NA,NA,-0.000496324358203359,-0.000517476831074487,-0.00213096062838051,-0.00111202867609916),`75_LOD.2` = c(NA,-0.000169651845347418,-4.72864955070539e-05,-0.00144880109085214,0.00421635976535877
    ),`79_LOD` = c(-0.0011901810540199,0.00731686066269579,0.00538551997145174,-0.00578723012473479,-0.0030246805255648,0.00146141135533218),`79_LOD.1` = c(-0.00424278455960268,-0.010593752642875,0.0065136497427927,-0.00427355522802769,0.000539975609490915,-0.0206849687839064),`79_LOD.2` = c(-0.00366739576561779,-0.00374066839898667,-0.00132764684703939,-0.00534145222725701,0.00920940542227595,-0.0101871763957068),`85_LOD` = c(-0.0120254177480422,0.00369546541331518,-0.00420718877886963,0.00414911885475517,-0.00130381692844529,-0.00812757789798261),`85_LOD.1` = c(-0.00302024868281014,0.00537704163310547,0.00184264538884543,-0.00159032685888543,-0.0062127769817834,0.00349476605688194),`85_LOD.2` = c(0.0122689407380797,-0.00509605601025503,-0.00641413996554198,0.000592176121486696,0.00131237912317341,-0.00535018996837309),`87_LOD` = c(0.00613621268007298,0.000410268892659307,-0.00239014321624482,-0.00171179729894864,-0.00107159765522861,-0.00708388174601732),`87_LOD.1` = c(0.00144787264098156,-0.0025946273860992,-0.00194897899110034,0.00157863310440493,-0.0048913305554607,-0.000585669821053749),`87_LOD.2` = c(-0.00224691693198253,-0.00277315666829267,0.00166487067514155,-0.00173757960229744,-0.00362252480121682,-0.0101992979591839),`93_LOD` = c(-0.0234225447373586,0.0390095666365413,0.00606244490932179,0.0264258422783391,0.0161211132913951,-0.0617678157059),`93_LOD.1` = c(-0.0124876313221369,-0.0309636779639578,0.00610883313140442,-0.0192442672220773,0.0129557286224975,-0.00869066964782635),`93_LOD.2` = c(-0.0219837540560547,-0.00521242297372905,0.0179965615561871,0.0081370991723329,1.45427765512579e-06,-0.0111199632179688),`99_LOD` = c(0.00412086456443205,-0.00259940538393106,0.00742537463584133,-0.00302091572866969,-0.00320466045653491,-0.00168702410433936),`99_LOD.1` = c(0.00280546156134205,-0.00472591065687533,0.00518402193979284,-0.00130887074314965,0.00148769905391341,0.00366250488078969),`99_LOD.2` = c(-0.00240469207099292,-9.57307699040024e-05,-0.000145493235845501,0.000667454164326723,-0.0057445759245933,0.00433464631989088),H_LOD = c(-6248.9128518109,-10081.9540490064,-6696.91582671427,-5414.20614601348,-3933.64339240365,-13153.7509294302),H_LOD.1 = c(-6.2489128518109,-10.0819540490064,-6.69691582671427,-5.41420614601348,-3.93364339240365,-13.1537509294302),H_LOD.2 = c(-6248.9128518109,-13153.7509294302)),row.names = c(NA,6L),class = "data.frame")

代码

LOD_rdu=sapply(split.default(LOD_ut,rep(seq((ncol(LOD_ut) / 3)),each = 3)),function(i)
  apply(i,1,rmse))

我收到此错误Error in mse(actual,predicted) : argument "predicted" is missing,with no default