Opgave 9 - Superbenzine Loodvrij 98 - Stijn Cappaert

R Software Module: rwasp_decompose.wasp (opens new window with default values)

Title produced by software: Classical Decomposition

Date of computation: Fri, 15 Aug 2008 06:39:40 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Aug/15/t1218804023plf60a0q1a0x7lm.htm/, Retrieved Fri, 15 Aug 2008 12:40:23 +0000

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

0.91 0.9 0.89 0.89 0.89 0.89 0.89 0.89 0.88 0.88 0.88 0.86 0.85 0.85 0.86 0.9 0.92 0.91 0.93 0.96 0.96 0.97 0.98 1.01 0.99 1.03 1.08 1.06 1.1 1.17 1.16 1.12 1.17 1.13 1.12 1.07 1.04 1.08 1.06 1.12 1.18 1.13 1.08 1.06 1.09 1.02 1.01 1.01 1 1.01 1.03 1.09 1.07 1.05 1.06 1.06 1.08 1.07 1.04 1.04 1.06 1.09 1.09 1.05 1.01 1.02 1.03 1.06 1.08 1.05 1.05 1.05 1.04 1.05 1.07 1.1 1.16 1.16 1.17 1.17 1.18 1.21 1.18 1.13 1.12 1.17 1.19 1.26 1.25 1.28 1.35 1.39 1.45 1.41 1.32 1.31

Text written by user:

Classical decomposition, multiplicatief

Output produced by software:

Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 3 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 0.91 NA NA 0.965231570419191 NA 2 0.9 NA NA 0.981820722865626 NA 3 0.89 NA NA 0.990897587224616 NA 4 0.89 NA NA 1.00789664496506 NA 5 0.89 NA NA 1.02216222286161 NA 6 0.89 NA NA 1.01777307274688 NA 7 0.89 0.896470324638234 0.885 1.01296081880026 0.992782444147446 8 0.89 0.889038733684688 0.880416666666667 1.00979316651361 1.00108124233387 9 0.88 0.899295136528292 0.877083333333334 1.02532462121990 0.978544155589699 10 0.88 0.879187381984438 0.87625 1.00335221909779 1.00092428307345 11 0.88 0.867768835124282 0.877916666666667 0.988441008209908 1.01409495752860 12 0.86 0.857424783666478 0.88 0.974346345075543 1.00300343118438 13 0.85 0.851816860894937 0.8825 0.965231570419191 0.997867075684522 14 0.85 0.870956799575382 0.887083333333333 0.981820722865626 0.975938187077018 15 0.86 0.885201844587324 0.893333333333333 0.990897587224616 0.971529832725244 16 0.9 0.907526937403958 0.900416666666667 1.00789664496506 0.991706100288892 17 0.92 0.928464019099295 0.908333333333333 1.02216222286161 0.990883848027298 18 0.91 0.935079010586195 0.91875 1.01777307274688 0.973179795180652 19 0.93 0.942897695499913 0.930833333333333 1.01296081880026 0.986321214314694 20 0.96 0.95341304804993 0.944166666666666 1.00979316651361 1.00690881246438 21 0.96 0.985166073555458 0.960833333333333 1.02532462121990 0.974454993700063 22 0.97 0.979940667318841 0.976666666666667 1.00335221909779 0.989855847756539 23 0.98 0.979380298967984 0.990833333333333 0.988441008209908 1.00063274810885 24 1.01 0.983277853238735 1.00916666666667 0.974346345075543 1.02717659781846 25 0.99 0.993786337710759 1.02958333333333 0.965231570419191 0.996189988162364 26 1.03 1.0268208393303 1.04583333333333 0.981820722865626 1.00309612012917 27 1.08 1.05159006444212 1.06125 0.990897587224616 1.02701616962590 28 1.06 1.08516872107905 1.07666666666667 1.00789664496506 0.976806628692704 29 1.1 1.11330502106677 1.08916666666667 1.02216222286161 0.988049078361274 30 1.17 1.1170059473397 1.0975 1.01777307274688 1.04744294583795 31 1.16 1.11636723571946 1.10208333333333 1.01296081880026 1.03908459768834 32 1.12 1.11708369045568 1.10625 1.00979316651361 1.00261064553107 33 1.17 1.13554701800104 1.1075 1.02532462121990 1.03034042752330 34 1.13 1.11288483634930 1.10916666666667 1.00335221909779 1.01537909682267 35 1.12 1.10211172415405 1.115 0.988441008209908 1.0162309096745 36 1.07 1.08802008533436 1.11666666666667 0.974346345075543 0.983437727320247 37 1.04 1.07301576244933 1.11166666666667 0.965231570419191 0.969230869103012 38 1.08 1.08573008270224 1.10583333333333 0.981820722865626 0.994722369036716 39 1.06 1.08998734594708 1.1 0.990897587224616 0.972488354054219 40 1.12 1.10070712768893 1.09208333333333 1.00789664496506 1.01752770725813 41 1.18 1.10691650717388 1.08291666666667 1.02216222286161 1.06602439511243 42 1.13 1.09495419743018 1.07583333333333 1.01777307274688 1.03200663795076 43 1.08 1.08555634414762 1.07166666666667 1.01296081880026 0.994881570010095 44 1.06 1.07753345810056 1.06708333333333 1.00979316651361 0.983728154361473 45 1.09 1.08983462863832 1.06291666666667 1.02532462121990 1.00015173986707 46 1.02 1.06397141566828 1.06041666666667 1.00335221909779 0.958672371249126 47 1.01 1.04239341324137 1.05458333333333 0.988441008209908 0.968924004286792 48 1.01 1.01981584117907 1.04666666666667 0.974346345075543 0.990374888501713 49 1 1.00625391216201 1.0425 0.965231570419191 0.99378495617615 50 1.01 1.02272991965169 1.04166666666667 0.981820722865626 0.987552999665807 51 1.03 1.03177211269763 1.04125 0.990897587224616 0.998282457263748 52 1.09 1.05115220931148 1.04291666666667 1.00789664496506 1.03695734104385 53 1.07 1.06943722566896 1.04625 1.02216222286161 1.00052623409540 54 1.05 1.06738951004329 1.04875 1.01777307274688 0.98370837460958 55 1.06 1.06614126178728 1.0525 1.01296081880026 0.994239729754964 56 1.06 1.06869776789357 1.05833333333333 1.00979316651361 0.991861339889658 57 1.08 1.09111628441485 1.06416666666667 1.02532462121990 0.98981200759843 58 1.07 1.06857011333915 1.065 1.00335221909779 1.00133813087509 59 1.04 1.04857116954268 1.06083333333333 0.988441008209908 0.991825858089904 60 1.04 1.02996528227360 1.05708333333333 0.974346345075543 1.00974277278963 61 1.06 1.01791712697124 1.05458333333333 0.965231570419191 1.04134214064555 62 1.09 1.03418449475179 1.05333333333333 0.981820722865626 1.05397054928928 63 1.09 1.04374545854326 1.05333333333333 0.990897587224616 1.04431592116462 64 1.05 1.06081121882573 1.0525 1.00789664496506 0.989808536491823 65 1.01 1.07539983863565 1.05208333333333 1.02216222286161 0.939185560304134 66 1.02 1.07163023117973 1.05291666666667 1.01777307274688 0.95182085230752 67 1.03 1.06614126178728 1.0525 1.01296081880026 0.96610086947888 68 1.06 1.06028282483929 1.05 1.00979316651361 0.999733255285608 69 1.08 1.07402754072785 1.0475 1.02532462121990 1.00556080644646 70 1.05 1.05226563977881 1.04875 1.00335221909779 0.9978468936995 71 1.05 1.04486451576189 1.05708333333333 0.988441008209908 1.00491497621045 72 1.05 1.04173863394327 1.06916666666667 0.974346345075543 1.00793036351686 73 1.04 1.04325445569474 1.08083333333333 0.965231570419191 0.996880477550824 74 1.05 1.07141186382711 1.09125 0.981820722865626 0.980015282124439 75 1.07 1.08998734594708 1.1 0.990897587224616 0.981662772488693 76 1.1 1.11960518978202 1.11083333333333 1.00789664496506 0.982489193547022 77 1.16 1.14780299608835 1.12291666666667 1.02216222286161 1.01062639142189 78 1.16 1.15177986065855 1.13166666666667 1.01777307274688 1.00713690143596 79 1.17 1.15308706540097 1.13833333333333 1.01296081880026 1.01466752607545 80 1.17 1.15789616426894 1.14666666666667 1.00979316651361 1.01045329979023 81 1.18 1.18595881187769 1.15666666666667 1.02532462121990 0.994975532187112 82 1.21 1.17224984264592 1.16833333333333 1.00335221909779 1.03220316691950 83 1.18 1.16512483842743 1.17875 0.988441008209908 1.01276701095193 84 1.13 1.15703628477721 1.1875 0.974346345075543 0.976633157375516 85 1.12 1.15827788450303 1.2 0.965231570419191 0.966952762359394 86 1.17 1.19454854615318 1.21666666666667 0.981820722865626 0.97944951987742 87 1.19 1.22582289019579 1.23708333333333 0.990897587224616 0.970776455161427 88 1.26 1.26659011717276 1.25666666666667 1.00789664496506 0.994796961476794 89 1.25 1.29899782488663 1.27083333333333 1.02216222286161 0.962280287196859 90 1.28 1.30699025425245 1.28416666666667 1.01777307274688 0.979349307185241 91 1.35 NA NA 1.01296081880026 NA 92 1.39 NA NA 1.00979316651361 NA 93 1.45 NA NA 1.02532462121990 NA 94 1.41 NA NA 1.00335221909779 NA 95 1.32 NA NA 0.988441008209908 NA 96 1.31 NA NA 0.974346345075543 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/15/t1218804023plf60a0q1a0x7lm/1b6g51218803974.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/15/t1218804023plf60a0q1a0x7lm/1b6g51218803974.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/15/t1218804023plf60a0q1a0x7lm/2r1xr1218803974.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/15/t1218804023plf60a0q1a0x7lm/2r1xr1218803974.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/15/t1218804023plf60a0q1a0x7lm/3xq4d1218803974.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/15/t1218804023plf60a0q1a0x7lm/3xq4d1218803974.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/15/t1218804023plf60a0q1a0x7lm/476fn1218803974.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/15/t1218804023plf60a0q1a0x7lm/476fn1218803974.ps (open in new window)

Parameters (Session):

par2 = grey ; par3 = FALSE ; par4 = Unknown ;

Parameters (R input):

par1 = multiplicative ; par2 = 12 ; par3 = FALSE ; par4 = Unknown ;

R code (references can be found in the software module):

par2 <- as.numeric(par2) x <- ts(x,freq=par2) m <- decompose(x,type=par1) m$figure bitmap(file='test1.png') plot(m) dev.off() mylagmax <- length(x)/2 bitmap(file='test2.png') op <- par(mfrow = c(2,2)) acf(as.numeric(x),lag.max = mylagmax,main='Observed') acf(as.numeric(m$trend),na.action=na.pass,lag.max = mylagmax,main='Trend') acf(as.numeric(m$seasonal),na.action=na.pass,lag.max = mylagmax,main='Seasonal') acf(as.numeric(m$random),na.action=na.pass,lag.max = mylagmax,main='Random') par(op) dev.off() bitmap(file='test3.png') op <- par(mfrow = c(2,2)) spectrum(as.numeric(x),main='Observed') spectrum(as.numeric(m$trend[!is.na(m$trend)]),main='Trend') spectrum(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal') spectrum(as.numeric(m$random[!is.na(m$random)]),main='Random') par(op) dev.off() bitmap(file='test4.png') op <- par(mfrow = c(2,2)) cpgram(as.numeric(x),main='Observed') cpgram(as.numeric(m$trend[!is.na(m$trend)]),main='Trend') cpgram(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal') cpgram(as.numeric(m$random[!is.na(m$random)]),main='Random') par(op) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Classical Decomposition by Moving Averages',6,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'t',header=TRUE) a<-table.element(a,'Observations',header=TRUE) a<-table.element(a,'Fit',header=TRUE) a<-table.element(a,'Trend',header=TRUE) a<-table.element(a,'Seasonal',header=TRUE) a<-table.element(a,'Random',header=TRUE) a<-table.row.end(a) for (i in 1:length(m$trend)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) if (par1 == 'additive') a<-table.element(a,m$trend[i]+m$seasonal[i]) else a<-table.element(a,m$trend[i]*m$seasonal[i]) a<-table.element(a,m$trend[i]) a<-table.element(a,m$seasonal[i]) a<-table.element(a,m$random[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab')

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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