prijs wagen Lode Merckx

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

Title produced by software: Classical Decomposition

Date of computation: Wed, 28 May 2008 09:34:09 -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/May/28/t1211989048hr53rcqqiw4n3ti.htm/, Retrieved Wed, 28 May 2008 17:37:33 +0200

User-defined keywords:

Dataseries X:

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101,6 101,8 102,1 102,1 101,9 102,1 102 102,1 102,2 102,3 102,7 102,8 103,1 103,1 103,3 103,5 103,3 103,5 103,8 103,9 103,9 104,2 104,6 104,9 105,2 105,2 105,6 105,6 106,2 106,3 106,4 106,9 107,2 107,3 107,3 107,4 107,55 107,87 108,37 108,38 107,92 108,03 108,14 108,3 108,64 108,66 109,04 109,03 109,03 109,54 109,75 109,83 109,65 109,82 109,95 110,12 110,15 110,2 109,99 110,14 110,14 110,81 110,97 110,99 109,73 109,81 110,02 110,18 110,21 110,25 110,36 110,51 110,64 110,95 111,18 111,19 111,69 111,7 111,83 111,77 111,73 112,01 111,86 112,04

Text written by user:

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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 101.6 NA NA 1.00000680830681 NA 2 101.8 NA NA 1.00148467633993 NA 3 102.1 NA NA 1.00296866013020 NA 4 102.1 NA NA 1.00230381267848 NA 5 101.9 NA NA 0.998362377729575 NA 6 102.1 NA NA 0.998394347124846 NA 7 102 102.080665132058 102.204166666667 0.998791619376815 0.999209790297177 8 102.1 102.281647783150 102.320833333333 0.999617032534751 0.998224043246403 9 102.2 102.393055176082 102.425 0.999688114972727 0.998114567675029 10 102.3 102.475785816639 102.533333333333 0.999438743335226 0.998284611186558 11 102.7 102.595864077827 102.65 0.999472616442546 1.00101501091792 12 102.8 102.712322731320 102.766666666667 0.999471191028087 1.00085361976391 13 103.1 102.900700574771 102.9 1.00000680830681 1.00193681310346 14 103.1 103.202995896830 103.05 1.00148467633993 0.999002006715646 15 103.3 103.502186689352 103.195833333333 1.00296866013020 0.99804654668834 16 103.5 103.583922774435 103.345833333333 1.00230381267848 0.999189808879727 17 103.3 103.334665938252 103.504166666667 0.998362377729575 0.999664527504524 18 103.5 103.504373961722 103.670833333333 0.998394347124846 0.999957741286145 19 103.8 103.720348040535 103.845833333333 0.998791619376815 1.00076794921122 20 103.9 103.980996738459 104.020833333333 0.999617032534751 0.999221042873225 21 103.9 104.171666947304 104.204166666667 0.999688114972727 0.99739212249103 22 104.2 104.328911819906 104.3875 0.999438743335226 0.998764371087005 23 104.6 104.540671210655 104.595833333333 0.999472616442546 1.00056751873369 24 104.9 104.777896526111 104.833333333333 0.999471191028087 1.00116535526993 25 105.2 105.059048602700 105.058333333333 1.00000680830681 1.00134163976520 26 105.2 105.447990712959 105.291666666667 1.00148467633993 0.997648217749033 27 105.6 105.867521112826 105.554166666667 1.00296866013020 0.997473057742223 28 105.6 106.064624710814 105.820833333333 1.00230381267848 0.995619418707406 29 106.2 105.888809687943 106.0625 0.998362377729575 1.00293884040225 30 106.3 106.108519217139 106.279166666667 0.998394347124846 1.00180457501691 31 106.4 106.352580120768 106.48125 0.998791619376815 1.00044587427196 32 106.9 106.649557708230 106.690416666667 0.999617032534751 1.00234827314011 33 107.2 106.883737495882 106.917083333333 0.999688114972727 1.00295893941892 34 107.3 107.088195617131 107.148333333333 0.999438743335226 1.00197784995488 35 107.3 107.279226179708 107.335833333333 0.999472616442546 1.00019364252551 36 107.4 107.422747165369 107.479583333333 0.999471191028087 0.999788246288897 37 107.55 107.624899405014 107.624166666667 1.00000680830681 0.999304069918503 38 107.87 107.914981299010 107.755 1.00148467633993 0.99958317836441 39 108.37 108.193572597111 107.873333333333 1.00296866013020 1.00163066436068 40 108.38 108.238788731149 107.99 1.00230381267848 1.00130462720903 41 107.92 107.942108311473 108.119166666667 0.998362377729575 0.99979518362371 42 108.03 108.085756022091 108.259583333333 0.998394347124846 0.999484150140193 43 108.14 108.258191297904 108.389166666667 0.998791619376815 0.99890824614298 44 108.3 108.478856877768 108.520416666667 0.999617032534751 0.998351228221646 45 108.64 108.613614471499 108.6475 0.999688114972727 1.00024293021302 46 108.66 108.704371351666 108.765416666667 0.999438743335226 0.999591816307717 47 109.04 108.840485695976 108.897916666667 0.999472616442546 1.00183308906377 48 109.03 108.986919579328 109.044583333333 0.999471191028087 1.00039528065238 49 109.03 109.195326763559 109.194583333333 1.00000680830681 0.99848595385481 50 109.54 109.508176504954 109.345833333333 1.00148467633993 1.00029060382578 51 109.75 109.809605850746 109.484583333333 1.00296866013020 0.999457189102133 52 109.83 109.864191414043 109.611666666667 1.00230381267848 0.999688784729558 53 109.65 109.535744256924 109.715416666667 0.998362377729575 1.00104309094580 54 109.82 109.624947307242 109.80125 0.998394347124846 1.00177927285302 55 109.95 109.760956521891 109.89375 0.998791619376815 1.00172231988586 56 110.12 109.950792958176 109.992916666667 0.999617032534751 1.00153893425661 57 110.15 110.062329164781 110.096666666667 0.999688114972727 1.00079655624122 58 110.2 110.133985187445 110.195833333333 0.999438743335226 1.00059940455658 59 109.99 110.189357281250 110.2475 0.999472616442546 0.998190775532516 60 110.14 110.192115257176 110.250416666667 0.999471191028087 0.999527050941398 61 110.14 110.253667302351 110.252916666667 1.00000680830681 0.998969038353719 62 110.81 110.422031272114 110.258333333333 1.00148467633993 1.00351350834083 63 110.97 110.590667694823 110.263333333333 1.00296866013020 1.00343005710232 64 110.99 110.521953291113 110.267916666667 1.00230381267848 1.00423487546998 65 109.73 110.104810812230 110.285416666667 0.998362377729575 0.996595872519418 66 109.81 110.139120396011 110.31625 0.998394347124846 0.997011775699426 67 110.02 110.21915217728 110.3525 0.998791619376815 0.998193125483675 68 110.18 110.336895036992 110.379166666667 0.999617032534751 0.998578036504114 69 110.21 110.359319842271 110.39375 0.999688114972727 0.99864696663151 70 110.25 110.348864517262 110.410833333333 0.999438743335226 0.999104073089521 71 110.36 110.442557010748 110.500833333333 0.999472616442546 0.999252489140211 72 110.51 110.602731338157 110.66125 0.999471191028087 0.999161581843097 73 110.64 110.816171132023 110.815416666667 1.00000680830681 0.998410239857383 74 110.95 111.121818689706 110.957083333333 1.00148467633993 0.998453780799015 75 111.18 111.416445224996 111.086666666667 1.00296866013020 0.997877824727589 76 111.19 111.479571058810 111.223333333333 1.00230381267848 0.997402474228602 77 111.69 111.176802415317 111.359166666667 0.998362377729575 1.00461604915354 78 111.7 111.306409786858 111.485416666667 0.998394347124846 1.00353609656349 79 111.83 NA NA 0.998791619376815 NA 80 111.77 NA NA 0.999617032534751 NA 81 111.73 NA NA 0.999688114972727 NA 82 112.01 NA NA 0.999438743335226 NA 83 111.86 NA NA 0.999472616442546 NA 84 112.04 NA NA 0.999471191028087 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/28/t1211989048hr53rcqqiw4n3ti/192rt1211988842.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/28/t1211989048hr53rcqqiw4n3ti/192rt1211988842.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/28/t1211989048hr53rcqqiw4n3ti/22aoi1211988842.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/28/t1211989048hr53rcqqiw4n3ti/22aoi1211988842.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/28/t1211989048hr53rcqqiw4n3ti/3cpkw1211988842.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/28/t1211989048hr53rcqqiw4n3ti/3cpkw1211988842.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/28/t1211989048hr53rcqqiw4n3ti/45nco1211988842.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/28/t1211989048hr53rcqqiw4n3ti/45nco1211988842.ps (open in new window)

Parameters (Session):

par1 = multiplicative ; par2 = 12 ;

Parameters (R input):

par1 = multiplicative ; par2 = 12 ;

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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