R - 线条之间的颜色或阴影区域

Question

我正在尝试用 R 复制我在 Excel 上制作的图表，该图表应该代表围绕时间序列预测的 95% 置信区间 (CI)。 Excel 图表如下所示:



因此，基本上，原始历史时间序列以及从某个时间点开始对其各自 CI 的预测。

他们在 Excel 上完成的方式有点低效:

我有四个时间序列，它们大部分时间都重叠；

实际/历史时间序列(上面的蓝线)在预测开始时停止；

预测(上面的红色虚线)只是隐藏在蓝色的下面，直到预测期开始；

然后我有一个时间序列，表示 CI 的上限和下限之间的差异，它使用 Excel 堆积区域图表，成为上图中的阴影区域。

显然，生成预测和 CI 的计算要快得多，而且更容易泛化和使用 R，虽然我可以在 R 上完成任务，然后简单地将输出复制到 Excel 上绘制图表，但在 R 中做所有事情都会要好得多。

在问题的最后，我按照@MLavoie 的建议提供了带有 dput() 的原始数据。

这里是我加载的包(不确定你是否需要它们，但它们是我通常使用的包):

    require(zoo)
    require(xts)
    require(lattice)
    require(latticeExtra)

我的前 100 行数据如下所示:

    > head(data)
               fifth_percentile   Median nintyfifth_percentile
    2017-06-18         1.146267 1.146267              1.146267
    2017-06-19         1.134643 1.134643              1.134643
    2017-06-20         1.125664 1.125664              1.125664
    2017-06-21         1.129037 1.129037              1.129037
    2017-06-22         1.147542 1.147542              1.147542
    2017-06-23         1.159989 1.159989              1.159989

然后在 100 个数据点之后，时间序列开始发散，最后它们看起来像这样:

    > tail(data)
               fifth_percentile   Median nintyfifth_percentile
    2017-12-30        0.9430930 1.125844              1.341603
    2017-12-31        0.9435227 1.127391              1.354928
    2018-01-01        0.9417235 1.124625              1.355527
    2018-01-02        0.9470077 1.124088              1.361420
    2018-01-03        0.9571596 1.127299              1.364005
    2018-01-04        0.9515535 1.127978              1.369536

DaveTurek 提供的解决方案

感谢 DaveTurek 我找到了答案。但是，唯一的区别是对于我的 xts 数据框，显然，我需要首先将每一列转换为数字(使用 as.numeric() )。不知道这是否源于我在 xts 和格子上做错了什么，或者这是使用 DaveTurek 建议实现它的唯一方法。将尝试进一步调查。

下面是生成图表的代码:

    x = index(data[1:100,2])
    y = as.numeric(data[1:100,2])
    ex.x = index(data[101:200,2])
    ex.y = as.numeric(data[101:200,2])
    ex.lo = as.numeric(data[101:200,1])
    ex.hi = as.numeric(data[101:200,3])

    xyplot(y~x, ylim = c(0.9,1.4),
   panel=function(x,y,...) {
     panel.lines(x,y,lwd=2,col=4)
     panel.polygon(c(ex.x,rev(ex.x)),c(ex.lo,rev(ex.hi)),border=NA,col=5)
     panel.lines(ex.x,ex.y,lwd=2,col=2)
   })

这里是最终结果:



这是我试图绘制的来自 dput() 的最终数据集:

    > dput(data)
    structure(c(1.14626724930899, 1.13464279067717, 1.12566420479952, 
    1.12903662366847, 1.14754211999921, 1.15998855701439, 1.15274364578958, 
    1.16226441955745, 1.16169992687419, 1.16520028734587, 1.16823402018407, 
    1.19832130049664, 1.18411773220697, 1.18531274215286, 1.16421444455115, 
    1.17108139956539, 1.18392357740377, 1.20103911352579, 1.17791736605905, 
    1.18277944964829, 1.20162550199013, 1.19665058179752, 1.19411188122108, 
    1.19367558590966, 1.19803272562951, 1.20600155861871, 1.22189449901607, 
    1.22072774140118, 1.22312376195254, 1.25355505518571, 1.25895911759195, 
    1.2613354420716, 1.24440525381363, 1.24444079462029, 1.24168652168112, 
    1.24154936710117, 1.23440527301777, 1.22592718438811, 1.21709102449773, 
    1.21448030929365, 1.23109601090898, 1.24401127451953, 1.23953314346685, 
    1.21863565024168, 1.20834325548551, 1.20281193695583, 1.20405850724191, 
    1.19608032796923, 1.22008184095742, 1.21675995421116, 1.20198916403093, 
    1.20029121301547, 1.18822375424598, 1.19007923345344, 1.19285965857709, 
    1.1971013197471, 1.1776860331227, 1.18028531916998, 1.18394951589397, 
    1.16712430930941, 1.17827461393349, 1.18751430033172, 1.21482260909863, 
    1.2167262724184, 1.21729489152574, 1.21847062594996, 1.21932070698031, 
    1.19678189566773, 1.17678214957629, 1.17586968485613, 1.16903708967946, 
    1.16967697995898, 1.14498266161799, 1.12782282645368, 1.11540004479973, 
    1.12639853863918, 1.11402516325222, 1.10511837662567, 1.10600107687395, 
    1.10243149863659, 1.10404564773364, 1.12949458422398, 1.11679224666313, 
    1.11338078540871, 1.10762728498848, 1.12437898939299, 1.11572706259347, 
    1.1148111967932, 1.12358625045939, 1.11169207274881, 1.13009253108247, 
    1.13772927166761, 1.12550770863279, 1.13062401691547, 1.12821231512428, 
    1.13174620070443, 1.13072790983063, 1.1428325334377, 1.12739171867048,
    1.1214997813059, 1.11870510839984, 1.096148222775, 1.08805136310032, 
    1.08701594286129, 1.08047984136855, 1.07939438148434, 1.0684082570972, 
    1.06497159411023, 1.05820047926833, 1.06322519359802, 1.06234781015662, 
    1.05431808916504, 1.054405104791, 1.05330182895869, 1.04787681441803, 
    1.041698698458, 1.03870702538097, 1.03300007904201, 1.02741553353049, 
    1.03525701392318, 1.0339774223954, 1.0328464056954, 1.03100871401712, 
    1.03348765946373, 1.03473218333386, 1.02942612874379, 1.02109481188296, 
    1.02301597272716, 1.01553904377803, 1.0031650628692, 1.00779708136199, 
    1.01322764666693, 1.01964272925677, 1.02125480865504, 1.02300342204156, 
    1.02563993245866, 1.02972111884963, 1.02048756192688, 1.00481457379443, 
    1.00512607721887, 1.01094340128446, 1.01377432300649, 1.01170553705668, 
    1.00551128145228, 1.00612634442438, 1.00735643866839, 1.0080606590012, 
    0.985706701720841, 0.982234200010558, 0.975314534071082, 0.973611418201841, 
    0.968118612511537, 0.973092829667201, 0.975599110408158, 0.967214930243667, 
    0.968569928969912, 0.963572085616274, 0.964901787179726, 0.957782708788541, 
    0.951868416101986, 0.956694066411684, 0.956937537219092, 0.956303331651844, 
    0.947880835881923, 0.956308493824626, 0.948146077843001, 0.945939091828748, 
    0.945082701640947, 0.937222489932819, 0.937989843132858, 0.948712728941467, 
    0.939050882255992, 0.946264846068344, 0.944926693194716, 0.946825914432391, 
    0.939070104432721, 0.950666108330947, 0.949365988007735, 0.943616625744159, 
    0.946600795357699, 0.941276090147603, 0.939957902451166, 0.941523527816784, 
    0.946611480333791, 0.959236316317354, 0.96165367272139, 0.957508302724503, 
    0.954774123925477, 0.960811125123549, 0.956525507301749, 0.948237690612711, 
    0.951299123137395, 0.945212566792479, 0.94507842203255, 0.942735006048921, 
    0.943093032220433, 0.943522672031737, 0.941723495992432, 0.947007713852018, 
    0.95715960245335, 0.951553478810637, 1.14626724930899, 1.13464279067717, 
    1.12566420479952, 1.12903662366847, 1.14754211999921, 1.15998855701439, 
    1.15274364578958, 1.16226441955745, 1.16169992687419, 1.16520028734587, 
    1.16823402018407, 1.19832130049664, 1.18411773220697, 1.18531274215286, 
    1.16421444455115, 1.17108139956539, 1.18392357740377, 1.20103911352579, 
    1.17791736605905, 1.18277944964829, 1.20162550199013, 1.19665058179752, 
    1.19411188122108, 1.19367558590966, 1.19803272562951, 1.20600155861871, 
    1.22189449901607, 1.22072774140118, 1.22312376195254, 1.25355505518571, 
    1.25895911759195, 1.2613354420716, 1.24440525381363, 1.24444079462029, 
    1.24168652168112, 1.24154936710117, 1.23440527301777, 1.22592718438811, 
    1.21709102449773, 1.21448030929365, 1.23109601090898, 1.24401127451953, 
    1.23953314346685, 1.21863565024168, 1.20834325548551, 1.20281193695583, 
    1.20405850724191, 1.19608032796923, 1.22008184095742, 1.21675995421116, 
    1.20198916403093, 1.20029121301547, 1.18822375424598, 1.19007923345344, 
    1.19285965857709, 1.1971013197471, 1.1776860331227, 1.18028531916998, 
    1.18394951589397, 1.16712430930941, 1.17827461393349, 1.18751430033172, 
    1.21482260909863, 1.2167262724184, 1.21729489152574, 1.21847062594996, 
    1.21932070698031, 1.19678189566773, 1.17678214957629, 1.17586968485613, 
    1.16903708967946, 1.16967697995898, 1.14498266161799, 1.12782282645368, 
    1.11540004479973, 1.12639853863918, 1.11402516325222, 1.10511837662567, 
    1.10600107687395, 1.10243149863659, 1.10404564773364, 1.12949458422398, 
    1.11679224666313, 1.11338078540871, 1.10762728498848, 1.12437898939299, 
    1.11572706259347, 1.1148111967932, 1.12358625045939, 1.11169207274881, 
    1.13009253108247, 1.13772927166761, 1.12550770863279, 1.13062401691547, 
    1.12821231512428, 1.13174620070443, 1.13072790983063, 1.1428325334377, 
    1.12739171867048, 1.1214997813059, 1.11870510839984, 1.11811303551412, 
    1.11855383782522, 1.11981261957516, 1.12096887905804, 1.12162710713999, 
    1.12015553029278, 1.12189306008921, 1.1236834173899, 1.12204149206779, 
    1.12075809542535, 1.12116672935174, 1.12216772364685, 1.11821915571021, 
    1.12117719223463, 1.11896003906963, 1.11563621625852, 1.1183625095638, 
    1.12053072892388, 1.1216348268255, 1.12317377733957, 1.11873136428952, 
    1.12267083202989, 1.12642930089215, 1.13027646770951, 1.13129632891931, 
    1.12700346009603, 1.12060488827701, 1.12390899402613, 1.13129350591169, 
    1.12786650327192, 1.1274201121913, 1.13101906643359, 1.12727135093377, 
    1.12458327192256, 1.12259738972645, 1.12097982776572, 1.12073621452193, 
    1.12364872830763, 1.12644326299714, 1.12556263098661, 1.12797963752343, 
    1.12734519199847, 1.1261793072762, 1.12911407446825, 1.12754878937943, 
    1.12777579027467, 1.12554965831588, 1.12324469267853, 1.12231558194992, 
    1.12135908710208, 1.11923353817423, 1.12345300992675, 1.12186883237389, 
    1.12173652640663, 1.12488148969114, 1.12664301925369, 1.12294230775256, 
    1.12393650688095, 1.13038044949978, 1.12822226676967, 1.12934384230215, 
    1.1217648908055, 1.12218158739803, 1.12302651609468, 1.12682187689922, 
    1.13537701046932, 1.13172108462183, 1.1374053505525, 1.13498257452656, 
    1.12692005654471, 1.13210629725645, 1.12868775509168, 1.13073909215368, 
    1.13098804355869, 1.13353301668386, 1.13336476594698, 1.13233873705211, 
    1.12667020676157, 1.12133152301322, 1.12418759586717, 1.12048022460741, 
    1.12798162212357, 1.13053093896994, 1.12019367019997, 1.12422483586498, 
    1.11303086301782, 1.11986711815552, 1.12504718249418, 1.11341517044014, 
    1.12495096618792, 1.12995127061511, 1.13538401552385, 1.13145536081928, 
    1.1264465959783, 1.12584386458867, 1.1273908895838, 1.12462482614994, 
    1.1240880626286, 1.12729907535003, 1.12797751377714, 1.14626724930899, 
    1.13464279067717, 1.12566420479952, 1.12903662366847, 1.14754211999921, 
    1.15998855701439, 1.15274364578958, 1.16226441955745, 1.16169992687419, 
    1.16520028734587, 1.16823402018407, 1.19832130049664, 1.18411773220697, 
    1.18531274215286, 1.16421444455115, 1.17108139956539, 1.18392357740377, 
    1.20103911352579, 1.17791736605905, 1.18277944964829, 1.20162550199013, 
    1.19665058179752, 1.19411188122108, 1.19367558590966, 1.19803272562951, 
    1.20600155861871, 1.22189449901607, 1.22072774140118, 1.22312376195254, 
    1.25355505518571, 1.25895911759195, 1.2613354420716, 1.24440525381363, 
    1.24444079462029, 1.24168652168112, 1.24154936710117, 1.23440527301777, 
    1.22592718438811, 1.21709102449773, 1.21448030929365, 1.23109601090898, 
    1.24401127451953, 1.23953314346685, 1.21863565024168, 1.20834325548551, 
    1.20281193695583, 1.20405850724191, 1.19608032796923, 1.22008184095742, 
    1.21675995421116, 1.20198916403093, 1.20029121301547, 1.18822375424598, 
    1.19007923345344, 1.19285965857709, 1.1971013197471, 1.1776860331227, 
    1.18028531916998, 1.18394951589397, 1.16712430930941, 1.17827461393349, 
    1.18751430033172, 1.21482260909863, 1.2167262724184, 1.21729489152574, 
    1.21847062594996, 1.21932070698031, 1.19678189566773, 1.17678214957629, 
    1.17586968485613, 1.16903708967946, 1.16967697995898, 1.14498266161799, 
    1.12782282645368, 1.11540004479973, 1.12639853863918, 1.11402516325222, 
    1.10511837662567, 1.10600107687395, 1.10243149863659, 1.10404564773364, 
    1.12949458422398, 1.11679224666313, 1.11338078540871, 1.10762728498848, 
    1.12437898939299, 1.11572706259347, 1.1148111967932, 1.12358625045939, 
    1.11169207274881, 1.13009253108247, 1.13772927166761, 1.12550770863279, 
    1.13062401691547, 1.12821231512428, 1.13174620070443, 1.13072790983063, 
    1.1428325334377, 1.12739171867048, 1.1214997813059, 1.11870510839984, 
    1.14162401974592, 1.15630966411729, 1.15992199767135, 1.16683144867851, 
    1.16928280999155, 1.17287782220285, 1.18184525262982, 1.17555305757354, 
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    1.19421117710041, 1.19403330560815, 1.19510080390052, 1.2058940348108, 
    1.19848571699109, 1.20138771250604, 1.20660682710938, 1.20790011589089, 
    1.20963951875753, 1.21572259411602, 1.21379678812156, 1.220302087399, 
    1.22062959185172, 1.22743877731977, 1.23135277550334, 1.24075667733246, 
    1.24169498945046, 1.23529301399753, 1.2399941777708, 1.24823732280171, 
    1.23861121958778, 1.24816319854615, 1.25252933549084, 1.25133386983018, 
    1.24512546001264, 1.2617641352045, 1.25486018976211, 1.25424601859098, 
    1.25820538036104, 1.25968528498312, 1.26939611029084, 1.27883933177157, 
    1.27926882841012, 1.27951234203094, 1.28997494816278, 1.29391898267335, 
    1.2971442938215, 1.29733541086814, 1.30376525837809, 1.31025722802128, 
    1.29718190520268, 1.27919305871102, 1.28685138548374, 1.28594279969497, 
    1.28695233433419, 1.30277136510213, 1.29178316107299, 1.29586799884087, 
    1.30076586308517, 1.30881154838964, 1.32171887794143, 1.3197588324899, 
    1.3121332301804, 1.31744410759858, 1.31402945919721, 1.30926303329755, 
    1.32019231597949, 1.31449633135152, 1.31730801686101, 1.31834557852015, 
    1.3175761022299, 1.33430488507454, 1.34091614601639, 1.33606628597812, 
    1.33180446732765, 1.33630738683041, 1.33449101077219, 1.32521028784732, 
    1.32241490851887, 1.31488015995544, 1.31913131799656, 1.32901121011698, 
    1.33177659436063, 1.32577077582349, 1.31960627618725, 1.31307169067904, 
    1.32148403094167, 1.33104893196281, 1.33491831741272, 1.3386091981919, 
    1.35730874062825, 1.3460340606746, 1.34160318929376, 1.35492848895938, 
    1.35552729646417, 1.36141957863605, 1.36400538435282, 1.369536167295),
    .indexCLASS = "Date", tclass = "Date", .indexTZ = "UTC", tzone = "UTC",
    class = c("xts", "zoo"), index = structure(c(1497744000, 1497830400, 1497916800, 
    1498003200, 1498089600, 1498176000, 1498262400, 1498348800, 1498435200, 
    1498521600, 1498608000, 1498694400, 1498780800, 1498867200, 1498953600, 
    1499040000, 1499126400, 1499212800, 1499299200, 1499385600, 1499472000, 
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    1504224000, 1504310400, 1504396800, 1504483200, 1504569600, 1504656000, 
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    1509926400, 1510012800, 1510099200, 1510185600, 1510272000, 1510358400, 
    1510444800, 1510531200, 1510617600, 1510704000, 1510790400, 1510876800, 
    1510963200, 1511049600, 1511136000, 1511222400, 1511308800, 1511395200, 
    1511481600, 1511568000, 1511654400, 1511740800, 1511827200, 1511913600, 
    1.512e+09, 1512086400, 1512172800, 1512259200, 1512345600, 1512432000, 
    1512518400, 1512604800, 1512691200, 1512777600, 1512864000, 1512950400, 
    1513036800, 1513123200, 1513209600, 1513296000, 1513382400, 1513468800, 
    1513555200, 1513641600, 1513728000, 1513814400, 1513900800, 1513987200, 
    1514073600, 1514160000, 1514246400, 1514332800, 1514419200, 1514505600, 
    1514592000, 1514678400, 1514764800, 1514851200, 1514937600, 1515024000
    ), tzone = "UTC", tclass = "Date"), .Dim = c(201L, 3L), .Dimnames = list(
NULL, c("fifth_percentile", "Median", "nintyfifth_percentile"
)))

Answer 1

我还没有尝试过你的数据，但如果问题是如何为预测区域着色，也许这个简单的例子会有所帮助。

library(lattice)

x = 1:12 # base data
y = x
ex.x = 12:16 # extrapolated data
ex.y = 12:16
ex.lo = 12+0:4*.3 # lower bound
ex.hi = 12+0:4*1.6 # upper bound

xyplot(y~x,xlim=c(0:18),ylim=c(0:20),
  panel=function(x,y,...) {
    panel.lines(x,y,lwd=2,col=4)
    panel.polygon(c(ex.x,rev(ex.x)),c(ex.lo,rev(ex.hi)),border=NA,col=5)
    panel.lines(ex.x,ex.y,lwd=2,col=2)
})

您可以在面板函数中将阴影多边形添加到点阵图中。我用过 c(ex.x,rev(ex.x))和 c(ex.lo,rev(ex.hi))构建多边形边界。