Agneta Peers - Classical Decomposition aantal werklozen jonger dan 25 jaar in België

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Mon, 01 Jun 2009 14:09:20 -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/01/t1243887044tlnks3yqv3ab3k3.htm/, Retrieved Mon, 01 Jun 2009 22:10:48 +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/01/t1243887044tlnks3yqv3ab3k3.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 «

122.860 117.702 113.537 108.366 111.078 150.739 159.129 157.928 147.768 137.507 136.919 136.151 133.001 125.554 119.647 114.158 116.193 152.803 161.761 160.942 149.470 139.208 134.588 130.322 126.611 122.401 117.352 112.135 112.879 148.729 157.230 157.221 146.681 136.524 132.111 125.326 122.716 116.615 113.719 110.737 112.093 143.565 149.946 149.147 134.339 122.683 115.614 116.566 111.272 104.609 101.802 94.542 93.051 124.129 130.374 123.946 114.971 105.531 104.919 104.782 101.281 94.545 93.248 84.031 87.486 115.867 120.327 117.008 108.811 104.519 106.758 109.337

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 'George Udny Yule' @ 72.249.76.132 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 122.86 NA NA -6.07842083333333 NA 2 117.702 NA NA -11.6454708333333 NA 3 113.537 NA NA -14.5710291666667 NA 4 108.366 NA NA -20.0044875 NA 5 111.078 NA NA -18.2584458333333 NA 6 150.739 NA NA 14.8945458333333 NA 7 159.129 157.7556625 133.729541666667 24.0261208333333 1.37333749999999 8 157.928 157.026970833333 134.47925 22.5477208333333 0.90102916666666 9 147.768 146.779770833333 135.061 11.7187708333333 0.988229166666656 10 137.507 137.292354166667 135.556916666667 1.73543750000001 0.214645833333321 11 136.919 134.685804166667 136.011375 -1.32557083333334 2.23319583333333 12 136.151 133.271329166667 136.3105 -3.03917083333334 2.87967083333336 13 133.001 130.427745833333 136.506166666667 -6.07842083333333 2.57325416666669 14 125.554 125.095945833333 136.741416666667 -11.6454708333333 0.45805416666667 15 119.647 122.3668875 136.937916666667 -14.5710291666667 -2.7198875 16 114.158 117.075220833333 137.079708333333 -20.0044875 -2.91722083333332 17 116.193 118.7950125 137.053458333333 -18.2584458333333 -2.6020125 18 152.803 151.608004166667 136.713458333333 14.8945458333333 1.19499583333334 19 161.761 160.230454166667 136.204333333333 24.0261208333333 1.53054583333338 20 160.942 158.354429166667 135.806708333333 22.5477208333333 2.58757083333336 21 149.47 147.298479166667 135.579708333333 11.7187708333333 2.17152083333335 22 139.208 137.135229166667 135.399791666667 1.73543750000001 2.07277083333335 23 134.588 133.851845833333 135.177416666667 -1.32557083333334 0.736154166666665 24 130.322 131.8304125 134.869583333333 -3.03917083333334 -1.50841249999999 25 126.611 128.432620833333 134.511041666667 -6.07842083333333 -1.82162083333333 26 122.401 122.5217375 134.167208333333 -11.6454708333333 -0.120737500000018 27 117.352 119.324929166667 133.895958333333 -14.5710291666667 -1.97292916666666 28 112.135 113.663429166667 133.667916666667 -20.0044875 -1.52842916666668 29 112.879 115.194429166667 133.452875 -18.2584458333333 -2.31542916666666 30 148.729 148.036045833333 133.1415 14.8945458333333 0.692954166666709 31 157.23 156.7971625 132.771041666667 24.0261208333333 0.432837500000034 32 157.221 154.9153875 132.367666666667 22.5477208333333 2.30561250000002 33 146.681 143.693979166667 131.975208333333 11.7187708333333 2.98702083333339 34 136.524 133.501020833333 131.765583333333 1.73543750000001 3.02297916666666 35 132.111 130.3490125 131.674583333333 -1.32557083333334 1.76198750000000 36 125.326 128.387495833333 131.426666666667 -3.03917083333334 -3.06149583333331 37 122.716 124.829579166667 130.908 -6.07842083333333 -2.11357916666665 38 116.615 118.6226125 130.268083333333 -11.6454708333333 -2.00761249999996 39 113.719 114.8463875 129.417416666667 -14.5710291666667 -1.12738749999997 40 110.737 108.321970833333 128.326458333333 -20.0044875 2.4150291666667 41 112.093 108.803929166667 127.062375 -18.2584458333333 3.28907083333335 42 143.565 140.904545833333 126.01 14.8945458333333 2.66045416666667 43 149.946 149.1942875 125.168166666667 24.0261208333333 0.751712499999996 44 149.147 146.738804166667 124.191083333333 22.5477208333333 2.40819583333334 45 134.339 134.9130625 123.194291666667 11.7187708333333 -0.574062499999997 46 122.683 123.758395833333 122.022958333333 1.73543750000001 -1.07539583333333 47 115.614 119.229179166667 120.55475 -1.32557083333334 -3.61517916666666 48 116.566 115.912329166667 118.9515 -3.03917083333334 0.653670833333337 49 111.272 111.247745833333 117.326166666667 -6.07842083333333 0.0242541666666654 50 104.609 103.815154166667 115.460625 -11.6454708333333 0.793845833333336 51 101.802 99.0325541666667 113.603583333333 -14.5710291666667 2.76944583333332 52 94.542 92.0774291666667 112.081916666667 -20.0044875 2.46457083333334 53 93.051 92.6631791666667 110.921625 -18.2584458333333 0.387820833333336 54 124.129 124.879545833333 109.985 14.8945458333333 -0.750545833333334 55 130.374 133.103829166667 109.077708333333 24.0261208333333 -2.72982916666666 56 123.946 130.789804166667 108.242083333333 22.5477208333333 -6.84380416666667 57 114.971 119.185104166667 107.466333333333 11.7187708333333 -4.21410416666666 58 105.531 108.407395833333 106.671958333333 1.73543750000001 -2.87639583333333 59 104.919 104.676554166667 106.002125 -1.32557083333334 0.242445833333335 60 104.782 102.386829166667 105.426 -3.03917083333334 2.39517083333332 61 101.281 98.5847041666667 104.663125 -6.07842083333333 2.69629583333334 62 94.545 92.3099458333333 103.955416666667 -11.6454708333333 2.23505416666667 63 93.248 88.8386375 103.409666666667 -14.5710291666667 4.4093625 64 84.031 83.1063458333333 103.110833333333 -20.0044875 0.92465416666667 65 87.486 84.8868458333333 103.145291666667 -18.2584458333333 2.59915416666669 66 115.867 118.306254166667 103.411708333333 14.8945458333333 -2.43925416666667 67 120.327 NA NA 24.0261208333333 NA 68 117.008 NA NA 22.5477208333333 NA 69 108.811 NA NA 11.7187708333333 NA 70 104.519 NA NA 1.73543750000001 NA 71 106.758 NA NA -1.32557083333334 NA 72 109.337 NA NA -3.03917083333334 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243887044tlnks3yqv3ab3k3/17eeb1243886958.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243887044tlnks3yqv3ab3k3/17eeb1243886958.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243887044tlnks3yqv3ab3k3/27abg1243886958.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243887044tlnks3yqv3ab3k3/27abg1243886958.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243887044tlnks3yqv3ab3k3/34iyz1243886958.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243887044tlnks3yqv3ab3k3/34iyz1243886958.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243887044tlnks3yqv3ab3k3/424s61243886958.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243887044tlnks3yqv3ab3k3/424s61243886958.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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