timothy van den broeck multiplicatief decompositiemodel gemiddelde prijs rode tafelwijn

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

Title produced by software: Classical Decomposition

Date of computation: Sun, 18 May 2008 04:05:29 -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/18/t1211105278cq3mh8qxza72uao.htm/, Retrieved Sun, 18 May 2008 12:08:02 +0200

User-defined keywords:

multiplicatief decompositiemodel gemiddelde prijs rode tafelwijn jan 00 - dec 05

Dataseries X:

» Textbox « » Textfile « » CSV «

2,17 2,18 2,18 2,18 2,17 2,17 2,18 2,17 2,18 2,17 2,17 2,17 2,17 2,17 2,17 2,17 2,17 2,17 2,18 2,18 2,18 2,18 2,18 2,18 2,18 2,18 2,18 2,18 2,18 2,18 2,18 2,19 2,19 2,19 2,2 2,2 2,21 2,21 2,21 2,2 2,21 2,2 2,21 2,21 2,22 2,22 2,23 2,24 2,24 2,25 2,25 2,32 2,36 2,37 2,37 2,37 2,38 2,38 2,41 2,42 2,43 2,44 2,44 2,44 2,43 2,43 2,43 2,42 2,42 2,42 2,42 2,42

Text written by user:

Het multiplicatief decompositiemodel van de gemiddelde prijzen van rode tafelwijnen vanaf januari 2000 tot en met december 2005

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 2 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 2.17 NA NA 0.998524672160648 NA 2 2.18 NA NA 0.997842788875492 NA 3 2.18 NA NA 0.996026941483364 NA 4 2.18 NA NA 1.00063859803664 NA 5 2.17 NA NA 1.00402525046030 NA 6 2.17 NA NA 1.00171608657884 NA 7 2.18 2.17779776671448 2.17416666666667 1.00167011117569 1.00101122028830 8 2.17 2.17465698502576 2.17375 1.00041724440518 0.99785851973078 9 2.18 2.17329395123630 2.17291666666667 1.00017363048267 1.00308566117339 10 2.17 2.16718693023228 2.17208333333333 0.997745757252536 1.00129802820813 11 2.17 2.17323180681339 2.17166666666667 1.00072070920033 0.998512902855897 12 2.17 2.17274861247412 2.17166666666667 1.00049820988831 0.998734960658432 13 2.17 2.16846274637554 2.17166666666667 0.998524672160648 1.00070891401156 14 2.17 2.16739769100331 2.17208333333333 0.997842788875492 1.00120066059288 15 2.17 2.16386853037261 2.1725 0.996026941483364 1.00283356846376 16 2.17 2.17430428698378 2.17291666666667 1.00063859803664 0.998020384262889 17 2.17 2.18249988818807 2.17375 1.00402525046030 0.99427267407631 18 2.17 2.17831510660624 2.17458333333333 1.00171608657884 0.996182780635811 19 2.18 2.17904985435345 2.17541666666667 1.00167011117569 1.00043603667197 20 2.18 2.17715802813677 2.17625 1.00041724440518 1.00130535855758 21 2.18 2.17746134136331 2.17708333333333 1.00017363048267 1.00116588000368 22 2.18 2.17300711381625 2.17791666666667 0.997745757252536 1.00321806870271 23 2.18 2.18032024517022 2.17875 1.00072070920033 0.999853120122638 24 2.18 2.1806692233024 2.17958333333333 1.00049820988831 0.999693111043505 25 2.18 2.17678378531021 2.18 0.998524672160648 1.00147750764752 26 2.18 2.17571304757727 2.18041666666667 0.997842788875492 1.00197036664716 27 2.18 2.17258376611059 2.18125 0.996026941483364 1.00341355486730 28 2.18 2.18347680746578 2.18208333333333 1.00063859803664 0.998407673736723 29 2.18 2.19212179683832 2.18333333333333 1.00402525046030 0.994470290448368 30 2.18 2.18874964917477 2.185 1.00171608657884 0.996002444053814 31 2.18 2.1907360056505 2.18708333333333 1.00167011117569 0.995099361300126 32 2.19 2.19049692472884 2.18958333333333 1.00041724440518 0.9997731452059 33 2.19 2.19246394582055 2.19208333333333 1.00017363048267 0.99887617498785 34 2.19 2.18922048237161 2.19416666666667 0.997745757252536 1.00035607086389 35 2.2 2.19783285758123 2.19625 1.00072070920033 1.00098603604514 36 2.2 2.19942856473781 2.19833333333333 1.00049820988831 1.00025981078511 37 2.21 2.19717033070016 2.20041666666667 0.998524672160648 1.00583917829245 38 2.21 2.19774874249827 2.2025 0.997842788875492 1.00557445774616 39 2.21 2.1958243947452 2.20458333333333 0.996026941483364 1.00645570988679 40 2.2 2.2084927724167 2.20708333333333 1.00063859803664 0.996154493905178 41 2.21 2.2184774596629 2.20958333333333 1.00402525046030 0.99617870372044 42 2.2 2.21629684155569 2.2125 1.00171608657884 0.992646814609793 43 2.21 2.21911665880048 2.21541666666667 1.00167011117569 0.995891762263006 44 2.21 2.21925892050549 2.21833333333333 1.00041724440518 0.995827922366365 45 2.22 2.22205241572233 2.22166666666667 1.00017363048267 0.99907634234557 46 2.22 2.22331012907773 2.22833333333333 0.997745757252536 0.9985111707834 47 2.23 2.24119742164657 2.23958333333333 1.00072070920033 0.995003821823805 48 2.24 2.25403909202754 2.25291666666667 1.00049820988831 0.99377158449594 49 2.24 2.2633225902308 2.26666666666667 0.998524672160648 0.989695419322252 50 2.25 2.27508155863612 2.28 0.997842788875492 0.988975534287589 51 2.25 2.28422178580185 2.29333333333333 0.996026941483364 0.985018186055942 52 2.32 2.30813969947118 2.30666666666667 1.00063859803664 1.00513846736900 53 2.36 2.33017526877661 2.32083333333333 1.00402525046030 1.01279935102867 54 2.37 2.33984182556708 2.33583333333333 1.00171608657884 1.01288897997437 55 2.37 2.35517684890184 2.35125 1.00167011117569 1.00629385903868 56 2.37 2.36807098561076 2.36708333333333 1.00041724440518 1.00081459314394 57 2.38 2.38333041363766 2.38291666666667 1.00017363048267 0.99860262193668 58 2.38 2.39043254341753 2.39583333333333 0.997745757252536 0.995635708923784 59 2.41 2.40548240474029 2.40375 1.00072070920033 1.0018780412822 60 2.42 2.41036693732259 2.40916666666667 1.00049820988831 1.00399651294923 61 2.43 2.41060497937450 2.41416666666667 0.998524672160648 1.00804570669664 62 2.44 2.41353224559260 2.41875 0.997842788875492 1.01096639767533 63 2.44 2.41287526574345 2.4225 0.996026941483364 1.01124166451605 64 2.44 2.42738246573722 2.42583333333333 1.00063859803664 1.00519800008482 65 2.43 2.43768963934673 2.42791666666667 1.00402525046030 0.996845521586254 66 2.43 2.43250056357562 2.42833333333333 1.00171608657884 0.998972019323215 67 2.43 NA NA 1.00167011117569 NA 68 2.42 NA NA 1.00041724440518 NA 69 2.42 NA NA 1.00017363048267 NA 70 2.42 NA NA 0.997745757252536 NA 71 2.42 NA NA 1.00072070920033 NA 72 2.42 NA NA 1.00049820988831 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/18/t1211105278cq3mh8qxza72uao/1kksz1211105126.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/18/t1211105278cq3mh8qxza72uao/1kksz1211105126.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/18/t1211105278cq3mh8qxza72uao/2n0bl1211105126.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/18/t1211105278cq3mh8qxza72uao/2n0bl1211105126.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/18/t1211105278cq3mh8qxza72uao/3mns61211105126.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/18/t1211105278cq3mh8qxza72uao/3mns61211105126.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/18/t1211105278cq3mh8qxza72uao/40e341211105126.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/18/t1211105278cq3mh8qxza72uao/40e341211105126.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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