opgave 9 - oefening 2 - Seghers Sanne 2MAR03

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

Title produced by software: Classical Decomposition

Date of computation: Sun, 01 Jun 2008 07:49:32 -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/Jun/01/t12123283598w92t15n5qwneho.htm/, Retrieved Sun, 01 Jun 2008 13:52:44 +0000

IsPrivate?

No (this computation is public)

User-defined keywords:

Additief decompositiemodel van PPI

Dataseries X:

» Textbox « » Textfile « » CSV «

121.3 124.0 122.9 120.1 118.3 118.1 118.4 116.6 116.4 116.7 117.7 119.5 123.3 124.6 125.4 127.0 126.8 131.8 128.1 130.1 133.5 142.7 140.0 137.9 132.6 133.7 137.0 141.1 145.3 146.1 141.8 140.0 137.4 139.5 140.3 142.7 143.3 146.0 147.2 146.1 147.1 141.7 138.8 138.3 140.2 143.1 142.0 142.4 141.2 138.0 137.9 136.8 135.9 138.8 139.5 138.0 139.7 137.5 137.8 137.4 141.7 145.3 148.9 151.3 151.4 149.2 143.8 143.6 144.3 142.0 140.8 141.8

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 1 121.3 NA NA -0.476128472222225 NA 2 124 NA NA -0.443836805555555 NA 3 122.9 NA NA 0.390538194444446 NA 4 120.1 NA NA 0.806163194444441 NA 5 118.3 NA NA 1.40512152777779 NA 6 118.1 NA NA 1.83428819444444 NA 7 118.4 118.156163194444 119.25 -1.09383680555554 0.243836805555546 8 116.6 117.407204861111 119.358333333333 -1.95112847222223 -0.807204861111146 9 116.4 118.175954861111 119.4875 -1.31154513888889 -1.77595486111112 10 116.7 121.069704861111 119.879166666667 1.19053819444445 -4.36970486111112 11 117.7 120.526996527778 120.520833333333 0.00616319444444387 -2.82699652777777 12 119.5 121.089496527778 121.445833333333 -0.35633680555556 -1.58949652777778 13 123.3 121.944704861111 122.420833333333 -0.476128472222225 1.35529513888889 14 124.6 122.943663194444 123.3875 -0.443836805555555 1.65633680555554 15 125.4 125.053038194444 124.6625 0.390538194444446 0.34696180555558 16 127 127.264496527778 126.458333333333 0.806163194444441 -0.26449652777778 17 126.8 129.875954861111 128.470833333333 1.40512152777779 -3.0759548611111 18 131.8 132.000954861111 130.166666666667 1.83428819444444 -0.200954861111086 19 128.1 130.226996527778 131.320833333333 -1.09383680555554 -2.12699652777775 20 130.1 130.136371527778 132.0875 -1.95112847222223 -0.0363715277777601 21 133.5 131.638454861111 132.95 -1.31154513888889 1.86154513888891 22 142.7 135.211371527778 134.020833333333 1.19053819444445 7.48862847222222 23 140 135.385329861111 135.379166666667 0.00616319444444387 4.6146701388889 24 137.9 136.389496527778 136.745833333333 -0.35633680555556 1.51050347222224 25 132.6 137.436371527778 137.9125 -0.476128472222225 -4.83637152777777 26 133.7 138.451996527778 138.895833333333 -0.443836805555555 -4.75199652777778 27 137 139.861371527778 139.470833333333 0.390538194444446 -2.86137152777778 28 141.1 140.306163194444 139.5 0.806163194444441 0.793836805555571 29 145.3 140.784288194444 139.379166666667 1.40512152777779 4.51571180555555 30 146.1 141.425954861111 139.591666666667 1.83428819444444 4.67404513888889 31 141.8 139.143663194444 140.2375 -1.09383680555554 2.65633680555555 32 140 139.244704861111 141.195833333333 -1.95112847222223 0.755295138888897 33 137.4 140.821788194444 142.133333333333 -1.31154513888889 -3.42178819444442 34 139.5 143.957204861111 142.766666666667 1.19053819444445 -4.45720486111111 35 140.3 143.056163194444 143.05 0.00616319444444387 -2.75616319444444 36 142.7 142.585329861111 142.941666666667 -0.35633680555556 0.114670138888897 37 143.3 142.157204861111 142.633333333333 -0.476128472222225 1.14279513888891 38 146 141.993663194444 142.4375 -0.443836805555555 4.00633680555555 39 147.2 142.873871527778 142.483333333333 0.390538194444446 4.32612847222222 40 146.1 143.556163194444 142.75 0.806163194444441 2.54383680555554 41 147.1 144.375954861111 142.970833333333 1.40512152777779 2.72404513888887 42 141.7 144.863454861111 143.029166666667 1.83428819444444 -3.16345486111112 43 138.8 141.835329861111 142.929166666667 -1.09383680555554 -3.03532986111108 44 138.3 140.557204861111 142.508333333333 -1.95112847222223 -2.25720486111112 45 140.2 140.475954861111 141.7875 -1.31154513888889 -0.275954861111103 46 143.1 142.203038194444 141.0125 1.19053819444445 0.89696180555552 47 142 140.164496527778 140.158333333333 0.00616319444444387 1.83550347222223 48 142.4 139.214496527778 139.570833333333 -0.35633680555556 3.18550347222225 49 141.2 139.003038194444 139.479166666667 -0.476128472222225 2.19696180555556 50 138 139.051996527778 139.495833333333 -0.443836805555555 -1.05199652777776 51 137.9 139.853038194444 139.4625 0.390538194444446 -1.95303819444445 52 136.8 140.014496527778 139.208333333333 0.806163194444441 -3.21449652777778 53 135.9 140.205121527778 138.8 1.40512152777779 -4.30512152777777 54 138.8 140.250954861111 138.416666666667 1.83428819444444 -1.45095486111111 55 139.5 137.135329861111 138.229166666667 -1.09383680555554 2.36467013888890 56 138 136.603038194444 138.554166666667 -1.95112847222223 1.39696180555555 57 139.7 138.005121527778 139.316666666667 -1.31154513888889 1.69487847222223 58 137.5 141.569704861111 140.379166666667 1.19053819444445 -4.06970486111109 59 137.8 141.635329861111 141.629166666667 0.00616319444444387 -3.83532986111109 60 137.4 142.351996527778 142.708333333333 -0.35633680555556 -4.95199652777777 61 141.7 142.844704861111 143.320833333333 -0.476128472222225 -1.14470486111111 62 145.3 143.289496527778 143.733333333333 -0.443836805555555 2.01050347222221 63 148.9 144.548871527778 144.158333333333 0.390538194444446 4.35112847222223 64 151.3 145.343663194444 144.5375 0.806163194444441 5.95633680555557 65 151.4 146.255121527778 144.85 1.40512152777779 5.14487847222222 66 149.2 146.992621527778 145.158333333333 1.83428819444444 2.20737847222222 67 143.8 NA NA -1.09383680555554 NA 68 143.6 NA NA -1.95112847222223 NA 69 144.3 NA NA -1.31154513888889 NA 70 142 NA NA 1.19053819444445 NA 71 140.8 NA NA 0.00616319444444387 NA 72 141.8 NA NA -0.35633680555556 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t12123283598w92t15n5qwneho/1jfio1212328166.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t12123283598w92t15n5qwneho/1jfio1212328166.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t12123283598w92t15n5qwneho/2k49n1212328166.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t12123283598w92t15n5qwneho/2k49n1212328166.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t12123283598w92t15n5qwneho/3ss031212328166.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t12123283598w92t15n5qwneho/3ss031212328166.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t12123283598w92t15n5qwneho/4v5g31212328166.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t12123283598w92t15n5qwneho/4v5g31212328166.ps (open in new window)

Parameters (Session):

par1 = additive ; par2 = 12 ;

Parameters (R input):

par1 = additive ; 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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