Brutoschuld van de schatkist van België - Tom Janssens

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Thu, 04 Jun 2009 08:13:36 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Jun/04/t1244124853yb335lor6m431ur.htm/, Retrieved Thu, 04 Jun 2009 16:14:18 +0200

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/Jun/04/t1244124853yb335lor6m431ur.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 «

321.935 310.215 309.030 305.333 294.735 289.351 288.225 289.648 290.155 288.301 289.148 289.741 287.595 285.226 287.816 283.519 290.304 282.166 280.041 282.500 279.913 277.793 281.229 275.363 273.547 270.601 273.338 271.917 273.985 273.911 270.798 271.115 271.344 274.525 276.663 273.784 274.027 269.160 270.491 270.846 270.333 272.599 272.764 270.674 268.175 268.351 272.482 268.714 269.419 265.518 264.101 267.179 271.322 270.157 271.296 269.907 271.244 266.844 270.911 269.829 269.285 263.018 266.680 265.814 268.457 269.508 270.223 264.676 265.521 262.971 266.003 267.722 266.433

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 1 321.935 NA NA 1.00141829633784 NA 2 310.215 NA NA 0.98775008885803 NA 3 309.03 NA NA 0.995697790077363 NA 4 305.333 NA NA 0.99505059438254 NA 5 294.735 NA NA 1.00748583486033 NA 6 289.351 NA NA 1.00465641433361 NA 7 288.225 296.805867731477 295.720583333333 1.00366996570178 0.97108929214553 8 289.648 292.205871668915 293.248541666667 0.996444415403311 0.991246337199502 9 290.155 290.690987940887 291.323416666667 0.99782911812234 0.998156159072271 10 288.301 289.045438671142 289.530583333333 0.998324375074282 0.997424492583018 11 289.148 291.211658328869 288.437041666667 1.00961948800393 0.992913544942838 12 289.741 288.544387459403 287.953041666667 1.00205361884463 1.00414706572924 13 287.595 287.720161169616 287.312666666667 1.00141829633784 0.999564989922476 14 285.226 283.162104348272 286.673833333333 0.98775008885803 1.00728874245542 15 287.816 284.719036299279 285.94925 0.995697790077363 1.01087726251456 16 283.519 283.673667016015 285.084666666667 0.99505059438254 0.999454771330585 17 290.304 286.445224174256 284.316875 1.00748583486033 1.0134712520932 18 282.166 284.707404502437 283.387833333333 1.00465641433361 0.991073626950874 19 280.041 283.239093526759 282.203416666667 1.00366996570178 0.988708855522246 20 282.5 280.009558098448 281.008708333333 0.996444415403311 1.00889413175202 21 279.913 279.188679086585 279.796083333333 0.99782911812234 1.00259437780853 22 277.793 278.242404221067 278.709416666667 0.998324375074282 0.99838484639922 23 281.229 280.215892485018 277.546041666667 1.00961948800393 1.00361545344911 24 275.363 277.089996046858 276.522125 1.00205361884463 0.993767382180891 25 273.547 276.184197927665 275.793041666667 1.00141829633784 0.990451307687214 26 270.601 271.565630211303 274.933541666667 0.98775008885803 0.996447892870125 27 273.338 272.922880118009 274.102125 0.995697790077363 1.00152101532056 28 271.917 272.25471515753 273.608916666667 0.99505059438254 0.998759561767978 29 273.985 275.328247665219 273.2825 1.00748583486033 0.995121286404104 30 273.911 274.297782647371 273.026458333333 1.00465641433361 0.998589916974034 31 270.798 273.982496350583 272.980666666667 1.00366996570178 0.988377008046135 32 271.115 271.970161517939 272.940625 0.996444415403311 0.996855678898131 33 271.344 272.169824341073 272.761958333333 0.99782911812234 0.996965775529774 34 274.525 272.141935142931 272.598708333333 0.998324375074282 1.00875669843318 35 276.663 275.02228363629 272.401916666667 1.00961948800393 1.00596575790884 36 273.784 272.754068285884 272.195083333333 1.00205361884463 1.00377604528720 37 274.027 272.608425271779 272.222333333333 1.00141829633784 1.00520370831095 38 269.16 268.950397226036 272.285875 0.98775008885803 1.00077933617546 39 270.491 270.964674464190 272.135458333333 0.995697790077363 0.998251895878579 40 270.846 270.401184662844 271.746166666667 0.99505059438254 1.00164501992738 41 270.333 273.345725435096 271.314708333333 1.00748583486033 0.988978333462869 42 272.599 272.190808843094 270.92925 1.00465641433361 1.00149965077308 43 272.764 271.51882114144 270.526 1.00366996570178 1.00458597622561 44 270.674 269.221594153601 270.18225 0.996444415403311 1.00539483413641 45 268.175 269.178623678435 269.76425 0.99782911812234 0.996271532766162 46 268.351 268.893886788627 269.345208333333 0.998324375074282 0.997981037073357 47 272.482 271.823514625943 269.233625 1.00961948800393 1.00242247391644 48 268.714 269.725862249735 269.173083333333 1.00205361884463 0.99624855310019 49 269.419 269.391702800892 269.010166666667 1.00141829633784 1.00010132902693 50 265.518 265.622831801688 268.917041666667 0.98775008885803 0.99960533587803 51 264.101 267.855608114674 269.012958333333 0.995697790077363 0.98598271605698 52 267.179 267.746265295707 269.078041666667 0.99505059438254 0.997881332555357 53 271.322 270.963105392804 268.949791666667 1.00748583486033 1.00132451466658 54 270.157 270.183044859732 268.930791666667 1.00465641433361 0.99990360290837 55 271.296 269.958783458084 268.971666666667 1.00366996570178 1.00495341001610 56 269.907 267.905955377130 268.861916666667 0.996444415403311 1.00746920545328 57 271.244 268.281533725029 268.865208333333 0.99782911812234 1.01104237863053 58 266.844 268.465189663231 268.915791666667 0.998324375074282 0.99396126676511 59 270.911 271.324678463912 268.739541666667 1.00961948800393 0.998475337863648 60 269.829 269.144712903039 268.593125 1.00205361884463 1.00254245045195 61 269.285 268.902217882795 268.521375 1.00141829633784 1.00142349929361 62 263.018 264.97256299319 268.258708333333 0.98775008885803 0.992623526862137 63 266.68 266.650149990153 267.802291666667 0.995697790077363 1.00011194447049 64 265.814 266.078975103936 267.402458333333 0.99505059438254 0.999004148659877 65 268.457 269.035575097834 267.036583333333 1.00748583486033 0.997849447614415 66 269.508 267.986363609792 266.744291666667 1.00465641433361 1.00567803663482 67 270.223 267.515850761566 266.537666666667 1.00366996570178 1.01011958443108 68 264.676 NA NA 0.996444415403311 NA 69 265.521 NA NA 0.99782911812234 NA 70 262.971 NA NA 0.998324375074282 NA 71 266.003 NA NA 1.00961948800393 NA 72 267.722 NA NA 1.00205361884463 NA 73 266.433 NA NA NA NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/04/t1244124853yb335lor6m431ur/1yg991244124814.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/04/t1244124853yb335lor6m431ur/1yg991244124814.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/04/t1244124853yb335lor6m431ur/2krln1244124814.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/04/t1244124853yb335lor6m431ur/2krln1244124814.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/04/t1244124853yb335lor6m431ur/3e31r1244124814.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/04/t1244124853yb335lor6m431ur/3e31r1244124814.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/04/t1244124853yb335lor6m431ur/424fh1244124814.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/04/t1244124853yb335lor6m431ur/424fh1244124814.ps (open in new window)

Parameters (Session):

par1 = 4 ;

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')

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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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