आर - लाइनों के बीच रंग या छाया क्षेत्र

Question

मैं एक्सेल पर बनाए गए चार्ट के साथ दोहराने की कोशिश कर रहा हूं, जो एक समय श्रृंखला पूर्वानुमान के आसपास 95% विश्वास अंतराल (सीआई) का प्रतिनिधित्व करना चाहिए। एक्सेल चार्ट इस तरह दिखता है:

तो, मूल रूप से, मूल ऐतिहासिक समय श्रृंखला और एक निश्चित बिंदु से समय में क्या यह अपने संबंधित सीआई के साथ हो सकता है की पूर्वानुमान।

वे जिस तरह से यह एक्सेल पर किया है एक सा अक्षम है:

1. मैं चार समय श्रृंखला जो काफी समय ओवरलैप है;
2. वास्तविक/ऐतिहासिक समय श्रृंखला (ऊपर की नीली रेखा) पूर्वानुमान शुरू होने पर बस बंद हो जाती है;
3. पूर्वानुमान (अवधि ऊपर लाल रंग) पूर्वानुमान अवधि शुरू होने तक नीले रंग के नीचे बस छिपा हुआ है;
4. फिर मेरे पास ऊपरी बाउंड और सीआई की निचली सीमा के बीच अंतर का प्रतिनिधित्व करने वाली एक समय श्रृंखला है, जो एक्सेल स्टैक्ड एरिया चार्ट के साथ खेलती है, उपरोक्त चार्ट में छायांकित क्षेत्र बन जाती है।

जाहिर है, गणना पूर्वानुमान तैयार करने में और सीआईएस सामान्यीकरण और अनुसंधान के साथ उपयोग करने के लिए बहुत तेज और आसान है, और जब तक मैं आर पर कार्य को पूरा कर सकता है और उसके बाद बस एक्सेल पर आउटपुट की प्रति चार्ट आकर्षित करने के लिए , आर में सबकुछ करना बहुत अच्छा होगा।

प्रश्न के अंत में मैंने dput() के साथ कच्चे डेटा को @MLavoie द्वारा सुझाए गए अनुसार प्रदान किया।

यहाँ संकुल मैं लोड (यकीन नहीं आप उन सभी को यहां की जरूरत है, लेकिन वे लोगों को मैं आमतौर पर के साथ काम कर रहे हैं):

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

मेरे डेटा पहली 100 पंक्तियों के लिए इस तरह दिखता है:

> 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 

फिर 100 डेटा बिंदु के बाद, समय श्रृंखला वितरित हो जाते हैं के लिए शुरू और अंत में वे इस तरह दिखेगा:

> 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 

डेवटेरेक

डेवटेरेक के लिए धन्यवाद मुझे जवाब मिला है। हालांकि, केवल अंतर यह है कि मेरे एक्सटीएस डेटाफ्रेम के लिए, जाहिर है, मुझे प्रत्येक कॉलम को संख्याओं में बदलने की आवश्यकता है (as.numeric() के साथ)। कोई विचार नहीं है कि अगर यह xts और जाली के साथ कुछ गलत कर रहा है, या डेवटेक सुझाव का उपयोग करके इसे हासिल करने का यही एकमात्र तरीका है। आगे की जांच करने की कोशिश करेंगे।,

इधर, अंतिम डाटासेट है dput (से) है कि मैं कर रहा हूँ:

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) 
    }) 

और यहाँ अंतिम परिणाम:

यहाँ चार्ट उत्पन्न करने के लिए कोड है साजिश करने की कोशिश कर रहा है:

> 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, 1.629725645, 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, 
    1.18031492211593, 1.18142628277888, 1.18307577052783, 1.18257404220722, 
    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, 
    1499558400, 1499644800, 1499731200, 1499817600, 1499904000, 1499990400, 
    1500076800, 1500163200, 1500249600, 1500336000, 1500422400, 1500508800, 
    1500595200, 1500681600, 1500768000, 1500854400, 1500940800, 1501027200, 
    1501113600, 1501200000, 1501286400, 1501372800, 1501459200, 1501545600, 
    1501632000, 1501718400, 1501804800, 1501891200, 1501977600, 1502064000, 
    1502150400, 1502236800, 1502323200, 1502409600, 1502496000, 1502582400, 
    1502668800, 1502755200, 1502841600, 1502928000, 1503014400, 1503100800, 
    1503187200, 1503273600, 1503360000, 1503446400, 1503532800, 1503619200, 
    1503705600, 1503792000, 1503878400, 1503964800, 1504051200, 1504137600, 
    1504224000, 1504310400, 1504396800, 1504483200, 1504569600, 1504656000, 
    1504742400, 1504828800, 1504915200, 1505001600, 1505088000, 1505174400, 
    1505260800, 1505347200, 1505433600, 1505520000, 1505606400, 1505692800, 
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    1506816000, 1506902400, 1506988800, 1507075200, 1507161600, 1507248000, 
    1507334400, 1507420800, 1507507200, 1507593600, 1507680000, 1507766400, 
    1507852800, 1507939200, 1508025600, 1508112000, 1508198400, 1508284800, 
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    1508889600, 1508976000, 1509062400, 1509148800, 1509235200, 1509321600, 
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    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

मैंने आपके डेटा के साथ प्रयास नहीं किया है, लेकिन अगर क्यू शेर भविष्य क्षेत्र को छायांकन करने का तरीका है, शायद यह सरल उदाहरण मदद करेगा।

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) 
}) 

आप एक पैनल फ़ंक्शन में जाली साजिश में छायांकित बहुभुज जोड़ सकते हैं। बहुभुज सीमा बनाने के लिए मैंने c(ex.x,rev(ex.x)) और c(ex.lo,rev(ex.hi)) का उपयोग किया था।