*The author of this computation has been verified*

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

Title produced by software: Classical Decomposition

Date of computation: Fri, 04 Dec 2009 14:39:14 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/04/t12599627911941al9lpmyuxjw.htm/, Retrieved Fri, 04 Dec 2009 22:39:56 +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/2009/Dec/04/t12599627911941al9lpmyuxjw.htm/}, year = {2009}, } @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 = {2009}, 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:

Dataseries X:

» Textbox « » Textfile « » CSV «

267413 267366 264777 258863 254844 254868 277267 285351 286602 283042 276687 277915 277128 277103 275037 270150 267140 264993 287259 291186 292300 288186 281477 282656 280190 280408 276836 275216 274352 271311 289802 290726 292300 278506 269826 265861 269034 264176 255198 253353 246057 235372 258556 260993 254663 250643 243422 247105 248541 245039 237080 237085 225554 226839 247934 248333 246969 245098 246263 255765 264319 268347 273046 273963 267430 271993 292710

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 1 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 267413 NA NA 1.00909178270324 NA 2 267366 NA NA 1.00381811265289 NA 3 264777 NA NA 0.9850804341603 NA 4 258863 NA NA 0.980553315593723 NA 5 254844 NA NA 0.960661796720184 NA 6 254868 NA NA 0.94880601025182 NA 7 277267 280212.796606069 271654.375 1.03150481786303 0.98948728736964 8 285351 283235.217376375 272464.875 1.03952928749577 1.00747005490074 9 286602 282753.520226277 273298.083333333 1.03459752361824 1.0136107227618 10 283042 277561.719158669 274195.875 1.01227532747773 1.01974436841630 11 276687 272966.827150513 275178.5 0.991962770167411 1.01362866282442 12 277915 276697.741819751 276112.708333333 1.00211882129565 1.00439923424110 13 277128 279468.894220464 276950.916666667 1.00909178270324 0.99162377542233 14 277103 278670.322685362 277610.375 1.00381811265289 0.994375710085456 15 275037 273941.920926036 278090.916666667 0.9850804341603 1.00399748629294 16 270150 273125.935334317 278542.666666667 0.980553315593723 0.989104164235906 17 267140 267982.932551924 278956.583333333 0.960661796720184 0.996854528966092 18 264993 265052.477452801 279353.708333333 0.94880601025182 0.999775601219154 19 287259 288490.064037644 279678.833333333 1.03150481786303 0.995732733320469 20 291186 291010.116799877 279944.125 1.03952928749577 1.00060438861046 21 292300 289849.522883164 280156.791666667 1.03459752361824 1.00845430791971 22 288186 283885.360951284 280442.833333333 1.01227532747773 1.01514921035134 23 281477 278696.321447436 280954.416666667 0.991962770167411 1.00997744978521 24 282656 282114.653353314 281518.166666667 1.00211882129565 1.00191888879309 25 280190 284450.233760287 281887.375 1.00909178270324 0.98502292051594 26 280408 283050.775800206 281974.166666667 1.00381811265289 0.990663244809224 27 276836 277748.353813667 281955 0.9850804341603 0.996715178321887 28 275216 276076.420260939 281551.666666667 0.980553315593723 0.996883398226746 29 274352 269622.101770152 280662.875 0.960661796720184 1.01754269475238 30 271311 265170.050330904 279477.625 0.94880601025182 1.02315853416113 31 289802 287081.200373912 278313 1.03150481786303 1.00947745663089 32 290726 288128.238418897 277171.833333333 1.03952928749577 1.00901599091904 33 292300 285128.783707584 275593.916666667 1.03459752361824 1.02515079747182 34 278506 277142.131035429 273781.375 1.01227532747773 1.00492118956968 35 269826 269507.811639157 271691.458333333 0.991962770167411 1.00118062760002 36 265861 269585.036465801 269015.041666667 1.00211882129565 0.986186041648963 37 269034 268636.041660199 266215.666666667 1.00909178270324 1.00148140337887 38 264176 264681.615376487 263674.875 1.00381811265289 0.998089722341431 39 255198 256975.757473439 260867.791666667 0.9850804341603 0.993082003178364 40 253353 253118.684626555 258138.625 0.980553315593723 1.00092571346043 41 246057 245811.738890269 255877.5 0.960661796720184 1.00099775995580 42 235372 240992.773245586 253995.833333333 0.94880601025182 0.976676590049203 43 258556 260311.028608955 252360.458333333 1.03150481786303 0.993257955230198 44 260993 260619.564707379 250709.208333333 1.03952928749577 1.00143287512985 45 254663 257777.128975689 249156.916666667 1.03459752361824 0.987919296843505 46 250643 250765.061936649 247724.166666667 1.01227532747773 0.999513241853925 47 243422 244213.339644837 246192.041666667 0.991962770167411 0.996759637921548 48 247105 245501.281853406 244982.208333333 1.00211882129565 1.00653242270055 49 248541 246404.151958590 244184.083333333 1.00909178270324 1.00867212676582 50 245039 244142.618450761 243214 1.00381811265289 1.00367154884685 51 237080 238749.922415659 242365.916666667 0.9850804341603 0.993005558289766 52 237085 237111.805451698 241814.291666667 0.980553315593723 0.999886950159877 53 225554 232193.517342688 241701.625 0.960661796720184 0.971405242408689 54 226839 229782.630234461 242180.833333333 0.94880601025182 0.987189500653477 55 247934 250861.026158205 243199.083333333 1.03150481786303 0.988332080901403 56 248333 254505.529889252 244827.666666667 1.03952928749577 0.975746971423614 57 246969 255853.294880521 247297.416666667 1.03459752361824 0.965275823847922 58 245098 253405.497772097 250332.583333333 1.01227532747773 0.967216584308015 59 246263 251575.645993238 253614 0.991962770167411 0.97888251077618 60 255765 257785.296119799 257240.25 1.00211882129565 0.992162872940354 61 264319 NA 260987.333333333 NA NA 62 268347 NA NA NA NA 63 273046 NA NA NA NA 64 273963 NA NA NA NA 65 267430 NA NA NA NA 66 271993 NA NA NA NA 67 292710 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t12599627911941al9lpmyuxjw/16lkl1259962752.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599627911941al9lpmyuxjw/16lkl1259962752.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599627911941al9lpmyuxjw/2i2r61259962752.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599627911941al9lpmyuxjw/2i2r61259962752.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599627911941al9lpmyuxjw/3kkqv1259962752.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599627911941al9lpmyuxjw/3kkqv1259962752.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599627911941al9lpmyuxjw/4qowc1259962752.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599627911941al9lpmyuxjw/4qowc1259962752.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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