Opgave 9 Stap 2

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Sat, 11 Dec 2010 13:22:36 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/11/t1292073637w3xfx6vj0fpjao3.htm/, Retrieved Sat, 11 Dec 2010 14:20:42 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/11/t1292073637w3xfx6vj0fpjao3.htm/}, year = {2010}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

KDGP2W92

Dataseries X:

» Textbox « » Textfile « » CSV «

361.58 363.19 363.61 364.14 365.51 365.51 365.5 365.5 364.59 364.63 364.54 363.67 365.22 369.05 370.45 370.46 370.46 370.58 370.58 370.22 370.21 370.29 370.29 370.2 370.2 372.55 374.51 375.58 375.75 375.75 375.75 375.69 375.76 377.5 377.51 377.74 369.82 373.1 374.55 375.01 374.81 375.31 375.31 375.39 375.59 376.26 377.18 377.26 377.26 381.87 387.09 387.14 388.78 389.16 389.16 389.42 389.49 388.97 388.97 389.09 389.09 391.76 390.96 391.76 392.8 393.06 393.06 393.26 393.87 394.47 394.57 394.57 394.57 399.57 406.13 407.03 409.46 409.9 409.9 410.14 410.54 410.69 410.79 410.97

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 'George Udny Yule' @ 72.249.76.132 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 361.58 NA NA 0.991255293830792 NA 2 363.19 NA NA 0.999143125683757 NA 3 363.61 NA NA 1.00432002427416 NA 4 364.14 NA NA 1.00408381099648 NA 5 365.51 NA NA 1.00456443362750 NA 6 365.51 NA NA 1.00361787067363 NA 7 365.5 365.105895145224 364.4825 1.00171035686274 1.00107942616106 8 365.5 365.055636278391 364.878333333333 1.0004859234678 1.00121724931065 9 364.59 365.055515664539 365.4075 0.99903673478114 0.998724808571398 10 364.63 365.458719896930 365.955833333333 0.998641602644027 0.99773238439306 11 364.54 365.504294510262 366.425416666667 0.997486194694724 0.997361742324932 12 363.67 365.248847898123 366.842916666667 0.995654628463243 0.995677336404459 13 365.22 364.054201534844 367.265833333333 0.991255293830792 1.00320226620168 14 369.05 367.359116116504 367.674166666667 0.999143125683757 1.00460280910236 15 370.45 369.69522253544 368.105 1.00432002427416 1.00204162082318 16 370.46 370.080190638029 368.575 1.00408381099648 1.00102628935993 17 370.46 370.734922798744 369.050416666667 1.00456443362750 0.999258438356256 18 370.58 370.899111156711 369.562083333333 1.00361787067363 0.99913962814385 19 370.58 370.67456997075 370.041666666667 1.00171035686274 0.99974487062666 20 370.22 370.574983622855 370.395 1.0004859234678 0.999042073430362 21 370.21 370.352907950716 370.71 0.99903673478114 0.999614130339877 22 370.29 370.588408929179 371.0925 0.998641602644027 0.999194769933466 23 370.29 370.592305341701 371.52625 0.997486194694724 0.99918426438611 24 370.2 370.345769883664 371.962083333333 0.995654628463243 0.999606395170357 25 370.2 369.136450030922 372.392916666667 0.991255293830792 1.00288118382508 26 372.55 372.516776193211 372.83625 0.999143125683757 1.00008918741091 27 374.51 374.908061928100 373.295416666667 1.00432002427416 0.998938241215586 28 375.58 375.353722487033 373.827083333333 1.00408381099648 1.00060283806823 29 375.75 376.13738660909 374.428333333333 1.00456443362750 0.9989700927829 30 375.75 376.400191610340 375.043333333333 1.00361787067363 0.998272605527753 31 375.75 375.983634862122 375.341666666667 1.00171035686274 0.999378603640001 32 375.69 375.531140766234 375.34875 1.0004859234678 1.00042302546053 33 375.76 375.011749257246 375.373333333333 0.99903673478114 1.00199527279942 34 377.5 374.841373854439 375.35125 0.998641602644027 1.00709266994255 35 377.51 374.344931529992 375.288333333333 0.997486194694724 1.00845495211347 36 377.74 373.600315950453 375.230833333333 0.995654628463243 1.01108051538719 37 369.82 371.913203922766 375.194166666667 0.991255293830792 0.994371794545912 38 373.1 374.841865508604 375.163333333333 0.999143125683757 0.995353065735492 39 374.55 376.764380106300 375.14375 1.00432002427416 0.994122639444645 40 375.01 376.616776247616 375.085 1.00408381099648 0.995733657263958 41 374.81 376.731335330472 375.019583333333 1.00456443362750 0.994899985346887 42 375.31 376.342483582777 374.985833333333 1.00361787067363 0.997256531941471 43 375.31 375.917688930296 375.275833333333 1.00171035686274 0.998383452154048 44 375.39 376.133933535123 375.95125 1.0004859234678 0.998022157883679 45 375.59 376.476170604313 376.839166666667 0.99903673478114 0.997646144235663 46 376.26 377.353789686424 377.867083333333 0.998641602644027 0.99710142122242 47 377.18 378.001965291291 378.954583333333 0.997486194694724 0.997825499953001 48 377.26 378.46201453002 380.11375 0.995654628463243 0.996823949342676 49 377.26 377.933840763671 381.267916666667 0.991255293830792 0.998217040415569 50 381.87 382.101889245604 382.429583333333 0.999143125683757 0.999393121960058 51 387.09 385.250465844739 383.593333333333 1.00432002427416 1.00477490442802 52 387.14 386.273133931620 384.702083333333 1.00408381099648 1.00224417903352 53 388.78 387.483523318399 385.722916666667 1.00456443362750 1.00334588854385 54 389.16 388.10613954941 386.707083333333 1.00361787067363 1.00271539237131 55 389.16 388.356009907323 387.692916666667 1.00171035686274 1.00207023986282 56 389.42 388.786745513913 388.597916666667 1.0004859234678 1.00162879648906 57 389.49 388.796374870695 389.17125 0.99903673478114 1.00178403188439 58 388.97 388.995870269915 389.525 0.998641602644027 0.999933494744053 59 388.97 388.904905018553 389.885 0.997486194694724 1.00016738020171 60 389.09 388.519370845784 390.215 0.995654628463243 1.00146872768010 61 389.09 387.124842452677 390.54 0.991255293830792 1.00507628891722 62 391.76 390.527579962568 390.8625 0.999143125683757 1.00315578233310 63 390.96 392.895015096173 391.205 1.00432002427416 0.995074981809837 64 391.76 393.215955116406 391.616666666667 1.00408381099648 0.996297314243073 65 392.8 393.868785999643 392.079166666667 1.00456443362750 0.997286441480934 66 393.06 393.960995302452 392.540833333333 1.00361787067363 0.997712983485178 67 393.06 393.669665971165 392.9975 1.00171035686274 0.99845132601299 68 393.26 393.742485788157 393.55125 1.0004859234678 0.998774615883295 69 393.87 394.128733442589 394.50875 0.99903673478114 0.999343530626836 70 394.47 395.239460789779 395.777083333333 0.998641602644027 0.99805317822203 71 394.57 396.109249059735 397.1075 0.997486194694724 0.996114079478354 72 394.57 396.771688291364 398.503333333333 0.995654628463243 0.994450994472803 73 394.57 396.409600371559 399.906666666667 0.991255293830792 0.995359344552113 74 399.57 400.967793006691 401.311666666667 0.999143125683757 0.996513951915664 75 406.13 404.44929850877 402.709583333333 1.00432002427416 1.00415553073630 76 407.03 405.730186347459 404.08 1.00408381099648 1.00320364048887 77 409.46 407.282232599655 405.431666666667 1.00456443362750 1.00534707194675 78 409.9 408.262549959552 406.790833333333 1.00361787067363 1.00401077698802 79 409.9 NA NA 1.00171035686274 NA 80 410.14 NA NA 1.0004859234678 NA 81 410.54 NA NA 0.99903673478114 NA 82 410.69 NA NA 0.998641602644027 NA 83 410.79 NA NA 0.997486194694724 NA 84 410.97 NA NA 0.995654628463243 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292073637w3xfx6vj0fpjao3/1lv2h1292073753.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292073637w3xfx6vj0fpjao3/1lv2h1292073753.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292073637w3xfx6vj0fpjao3/2lv2h1292073753.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292073637w3xfx6vj0fpjao3/2lv2h1292073753.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292073637w3xfx6vj0fpjao3/3e5121292073753.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292073637w3xfx6vj0fpjao3/3e5121292073753.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292073637w3xfx6vj0fpjao3/4e5121292073753.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292073637w3xfx6vj0fpjao3/4e5121292073753.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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