我有一个x和y值的数据框形式的数据，绘制时看起来像这样：

(The以绿色突出显示的部分对应于下面的dput（）数据）。
我想使用基线包来试验“Liland，K. H.，Almøy，T.，& Mevik，B.-H.（2010）.光谱多元校准的基线校正的最佳选择.应用光谱学，64（9），1007- 1016.doi：10.1366/000370210792434350”中描述的不同基线校正算法.
该包规定“数据必须组织为矩阵或data.frame中的行向量”，然而，简单地将y值转换为矩阵是行不通的：

> baseline(as.matrix(y, byrow=T))
Error in rep(3, p - 4) : invalid 'times' argument

随包提供的示例数据具有一种结构，我无法解释或弄清楚如何使用自己的数据进行模拟。

> library(baseline)
> data(milk)
> str(milk)
'data.frame':   45 obs. of  2 variables:
 $ cow    : num  0 0.25 0.375 0.875 0.5 0.75 0.5 0.125 0 0.125 ...
 $ spectra: num [1:45, 1:21451] 1029 371 606 368 554 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : NULL
  .. ..$ : chr [1:21451] "4999.94078628963" "5001.55954267662" "5003.17856106153" "5004.79784144435" ...
 - attr(*, "terms")=Classes 'terms', 'formula'  language cow ~ spectra
  .. ..- attr(*, "variables")= language list(cow, spectra)
  .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:2] "cow" "spectra"
  .. .. .. ..$ : chr "spectra"
  .. ..- attr(*, "term.labels")= chr "spectra"
  .. ..- attr(*, "order")= int 1
  .. ..- attr(*, "intercept")= int 1
  .. ..- attr(*, "response")= int 1
  .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> 
  .. ..- attr(*, "predvars")= language list(cow, spectra)
  .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "nmatrix.21451"
  .. .. ..- attr(*, "names")= chr [1:2] "cow" "spectra"

如果有人能告诉我如何对自己的数据调用baseline（），我将非常感激。
下面是我正在处理的数据的一个子部分，对应于上图中绿色突出显示的部分：

> dput(fid_df[fid_df$rt>12.5&fid_df$rt<12.8,])
structure(list(rt = c(12.5002866914485, 12.5011200182712, 12.5019533450939, 
12.5027866719166, 12.5036199987393, 12.5044533255619, 12.5052866523846, 
12.5061199792073, 12.50695330603, 12.5077866328527, 12.5086199596754, 
12.5094532864981, 12.5102866133207, 12.5111199401434, 12.5119532669661, 
12.5127865937888, 12.5136199206115, 12.5144532474342, 12.5152865742568, 
12.5161199010795, 12.5169532279022, 12.5177865547249, 12.5186198815476, 
12.5194532083703, 12.5202865351929, 12.5211198620156, 12.5219531888383, 
12.522786515661, 12.5236198424837, 12.5244531693064, 12.5252864961291, 
12.5261198229517, 12.5269531497744, 12.5277864765971, 12.5286198034198, 
12.5294531302425, 12.5302864570652, 12.5311197838878, 12.5319531107105, 
12.5327864375332, 12.5336197643559, 12.5344530911786, 12.5352864180013, 
12.536119744824, 12.5369530716466, 12.5377863984693, 12.538619725292, 
12.5394530521147, 12.5402863789374, 12.5411197057601, 12.5419530325827, 
12.5427863594054, 12.5436196862281, 12.5444530130508, 12.5452863398735, 
12.5461196666962, 12.5469529935189, 12.5477863203415, 12.5486196471642, 
12.5494529739869, 12.5502863008096, 12.5511196276323, 12.551952954455, 
12.5527862812776, 12.5536196081003, 12.554452934923, 12.5552862617457, 
12.5561195885684, 12.5569529153911, 12.5577862422138, 12.5586195690364, 
12.5594528958591, 12.5602862226818, 12.5611195495045, 12.5619528763272, 
12.5627862031499, 12.5636195299725, 12.5644528567952, 12.5652861836179, 
12.5661195104406, 12.5669528372633, 12.567786164086, 12.5686194909087, 
12.5694528177313, 12.570286144554, 12.5711194713767, 12.5719527981994, 
12.5727861250221, 12.5736194518448, 12.5744527786674, 12.5752861054901, 
12.5761194323128, 12.5769527591355, 12.5777860859582, 12.5786194127809, 
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12.5827860468943, 12.583619373717, 12.5844527005397, 12.5852860273623, 
12.586119354185, 12.5869526810077, 12.5877860078304, 12.5886193346531, 
12.5894526614758, 12.5902859882984, 12.5911193151211, 12.5919526419438, 
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12.599452583348, 12.6002859101707, 12.6011192369933, 12.601952563816, 
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12.6094525052202, 12.6102858320429, 12.6111191588656, 12.6119524856882, 
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12.6194524270924, 12.6202857539151, 12.6211190807378, 12.6219524075605, 
12.6227857343831, 12.6236190612058, 12.6244523880285, 12.6252857148512, 
12.6261190416739, 12.6269523684966, 12.6277856953192, 12.6286190221419, 
12.6294523489646, 12.6302856757873, 12.63111900261, 12.6319523294327, 
12.6327856562554, 12.633618983078, 12.6344523099007, 12.6352856367234, 
12.6361189635461, 12.6369522903688, 12.6377856171915, 12.6386189440141, 
12.6394522708368, 12.6402855976595, 12.6411189244822, 12.6419522513049, 
12.6427855781276, 12.6436189049503, 12.6444522317729, 12.6452855585956, 
12.6461188854183, 12.646952212241, 12.6477855390637, 12.6486188658864, 
12.649452192709, 12.6502855195317, 12.6511188463544, 12.6519521731771, 
12.6527854999998, 12.6536188268225, 12.6544521536452, 12.6552854804678, 
12.6561188072905, 12.6569521341132, 12.6577854609359, 12.6586187877586, 
12.6594521145813, 12.6602854414039, 12.6611187682266, 12.6619520950493, 
12.662785421872, 12.6636187486947, 12.6644520755174, 12.6652854023401, 
12.6661187291627, 12.6669520559854, 12.6677853828081, 12.6686187096308, 
12.6694520364535, 12.6702853632762, 12.6711186900988, 12.6719520169215, 
12.6727853437442, 12.6736186705669, 12.6744519973896, 12.6752853242123, 
12.6761186510349, 12.6769519778576, 12.6777853046803, 12.678618631503, 
12.6794519583257, 12.6802852851484, 12.6811186119711, 12.6819519387937, 
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12.6861185729072, 12.6869518997298, 12.6877852265525, 12.6886185533752, 
12.6894518801979, 12.6902852070206, 12.6911185338433, 12.691951860666, 
12.6927851874886, 12.6936185143113, 12.694451841134, 12.6952851679567, 
12.6961184947794, 12.6969518216021, 12.6977851484247, 12.6986184752474, 
12.6994518020701, 12.7002851288928, 12.7011184557155, 12.7019517825382, 
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12.739451489559, 12.7402848163817, 12.7411181432043, 12.741951470027, 
12.7427847968497, 12.7436181236724, 12.7444514504951, 12.7452847773178, 
12.7461181041404, 12.7469514309631, 12.7477847577858, 12.7486180846085, 
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12.7627846405941, 12.7636179674168, 12.7644512942395, 12.7652846210622, 
12.7661179478849, 12.7669512747076, 12.7677846015302, 12.7686179283529, 
12.7694512551756, 12.7702845819983, 12.771117908821, 12.7719512356437, 
12.7727845624663, 12.773617889289, 12.7744512161117, 12.7752845429344, 
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12.78945109892, 12.7902844257427, 12.7911177525654, 12.7919510793881, 
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12.7961177135015, 12.7969510403242, 12.7977843671469, 12.7986176939696, 
12.7994510207923), value = c(17.3893229166667, 17.394140625, 
17.3994791666667, 17.3966145833333, 17.397265625, 17.4009114583333, 
17.4036458333333, 17.400390625, 17.3940104166667, 17.3908854166667, 
17.3873697916667, 17.3858072916667, 17.3841145833333, 17.3834635416667, 
17.383984375, 17.3837239583333, 17.3838541666667, 17.3850260416667, 
17.3904947916667, 17.3954427083333, 17.39375, 17.3897135416667, 
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17.4138020833333, 17.4260416666667, 17.4364583333333, 17.4458333333333, 
17.4639322916667, 17.491015625, 17.5239583333333, 17.5647135416667, 
17.6229166666667, 17.6997395833333, 17.7994791666667, 17.916015625, 
18.0544270833333, 18.2184895833333, 18.4170572916667, 18.6494791666667, 
18.914453125, 19.2151041666667, 19.5665364583333, 19.9688802083333, 
20.4143229166667, 20.8876302083333, 21.3850260416667, 21.9104166666667, 
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24.6528645833333, 25.1217447916667, 25.5454427083333, 25.9471354166667, 
26.2817708333333, 26.5583333333333, 26.7454427083333, 26.8537760416667, 
26.9045572916667, 26.889453125, 26.82734375, 26.6755208333333, 
26.442578125, 26.1438802083333, 25.7912760416667, 25.437109375, 
25.0861979166667, 24.7126302083333, 24.262890625, 23.73359375, 
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21.3856770833333, 20.9497395833333, 20.56640625, 20.2286458333333, 
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18.3026041666667, 18.2216145833333, 18.1454427083333, 18.0764322916667, 
18.0162760416667, 17.9623697916667, 17.9102864583333, 17.8688802083333, 
17.8309895833333, 17.8029947916667, 17.7690104166667, 17.7415364583333, 
17.7149739583333, 17.693359375, 17.6713541666667, 17.6526041666667, 
17.631640625, 17.6157552083333, 17.6013020833333, 17.5877604166667, 
17.5740885416667, 17.5670572916667, 17.55703125, 17.545703125, 
17.5307291666667, 17.527734375, 17.5272135416667, 17.5216145833333, 
17.5140625, 17.5040364583333, 17.4944010416667, 17.4796875, 17.469921875, 
17.4647135416667, 17.4657552083333, 17.46640625, 17.4638020833333, 
17.4580729166667, 17.4485677083333, 17.4446614583333, 17.4430989583333, 
17.4475260416667, 17.4428385416667, 17.4401041666667, 17.433984375, 
17.4352864583333, 17.4342447916667, 17.4309895833333, 17.42890625, 
17.4265625, 17.4248697916667, 17.42109375, 17.4109375, 17.4052083333333, 
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17.42265625, 17.42265625, 17.4223958333333, 17.4248697916667, 
17.418359375, 17.4127604166667, 17.4125, 17.4184895833333, 17.4180989583333, 
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17.4015625, 17.4002604166667, 17.4006510416667, 17.4001302083333, 
17.398046875, 17.399609375, 17.4014322916667, 17.4, 17.39765625, 
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17.3979166666667, 17.3924479166667, 17.3860677083333, 17.3852864583333, 
17.3834635416667, 17.3805989583333, 17.3751302083333, 17.3782552083333, 
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17.3842447916667, 17.3846354166667, 17.3779947916667, 17.3703125, 
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17.3813802083333, 17.38203125, 17.3881510416667, 17.38984375, 
17.394921875, 17.3963541666667, 17.3998697916667, 17.3963541666667, 
17.3932291666667, 17.3854166666667, 17.3725260416667, 17.3647135416667, 
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17.3888020833333, 17.3864583333333, 17.3825520833333, 17.3834635416667, 
17.386328125, 17.3854166666667, 17.384375, 17.3852864583333, 
17.390234375, 17.3916666666667, 17.38828125, 17.3891927083333, 
17.3901041666667, 17.3950520833333, 17.3963541666667, 17.3970052083333, 
17.3907552083333, 17.3846354166667, 17.3760416666667, 17.3743489583333
)), row.names = 15002:15361, class = "data.frame")

(p.s这里的问题是：R-package(baseline) application to sample dataset似乎也有类似的问题，但没有解决如何将简单的x，y数据转换为基线包所需的格式）。