Opgave 9-oefening2-decompositie-veerle van rompay

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Wed, 14 Jan 2009 06:30:42 -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/Jan/14/t12319399490ngchqnf2s92ayk.htm/, Retrieved Wed, 14 Jan 2009 14:32:29 +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/Jan/14/t12319399490ngchqnf2s92ayk.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 «

113,9000 112,0000 113,8500 113,0800 111,7200 110,6900 113,5300 113,9900 112,7400 112,1500 115,8200 118,3800 118,8100 123,8500 117,9600 120,1600 118,7400 119,8400 124,8100 121,3300 120,2000 118,3200 129,5800 130,2000 127,1900 133,1000 129,1200 123,2800 123,3600 124,1300 126,9700 127,1400 123,7000 123,6700 130,1900 134,0100 124,9600 129,9600 128,3200 132,3800 126,2500 128,9100 131,4200 129,4400 126,8600 126,7100 131,6300 132,7800 126,6100 132,8400 123,1400 128,1300 125,4900 126,4800 130,8600 127,3200 126,5600 126,6400 129,2600 126,4700 135,4000 135,5000 132,2200 122,6200 125,1600 128,5000 133,8600 128,8700 125,0700 125,2500 132,1600 130,2400

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 4 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 113.9 NA NA 0.84165277777778 NA 2 112 NA NA 5.00423611111111 NA 3 113.85 NA NA -0.120513888888895 NA 4 113.08 NA NA -1.17043055555555 NA 5 111.72 NA NA -2.92976388888889 NA 6 110.69 NA NA -1.39276388888889 NA 7 113.53 115.183986111111 113.692083333333 1.49190277777777 -1.65398611111110 8 113.99 113.833319444444 114.390416666667 -0.55709722222223 0.156680555555553 9 112.74 112.317402777778 115.055416666667 -2.73801388888889 0.422597222222223 10 112.15 112.037069444444 115.521666666667 -3.48459722222222 0.112930555555550 11 115.82 118.231069444444 116.109166666667 2.12190277777779 -2.41106944444445 12 118.38 119.716402777778 116.782916666667 2.93348611111111 -1.33640277777776 13 118.81 118.475819444444 117.634166666667 0.84165277777778 0.334180555555562 14 123.85 123.414236111111 118.41 5.00423611111111 0.435763888888886 15 117.96 118.906152777778 119.026666666667 -0.120513888888895 -0.946152777777783 16 120.16 118.424152777778 119.594583333333 -1.17043055555555 1.73584722222222 17 118.74 117.495236111111 120.425 -2.92976388888889 1.24476388888890 18 119.84 120.098069444444 121.490833333333 -1.39276388888889 -0.258069444444445 19 124.81 123.824402777778 122.3325 1.49190277777777 0.98559722222224 20 121.33 122.509986111111 123.067083333333 -0.55709722222223 -1.17998611111111 21 120.2 121.179486111111 123.9175 -2.73801388888889 -0.9794861111111 22 118.32 121.027902777778 124.5125 -3.48459722222222 -2.70790277777776 23 129.58 126.956902777778 124.835 2.12190277777779 2.62309722222223 24 130.2 128.139736111111 125.20625 2.93348611111111 2.06026388888888 25 127.19 126.316652777778 125.475 0.84165277777778 0.873347222222236 26 133.1 130.811319444444 125.807083333333 5.00423611111111 2.28868055555557 27 129.12 126.074486111111 126.195 -0.120513888888895 3.04551388888891 28 123.28 125.393319444444 126.56375 -1.17043055555555 -2.11331944444444 29 123.36 123.882319444444 126.812083333333 -2.92976388888889 -0.522319444444435 30 124.13 125.603486111111 126.99625 -1.39276388888889 -1.47348611111111 31 126.97 128.553986111111 127.062083333333 1.49190277777777 -1.58398611111110 32 127.14 126.281236111111 126.838333333333 -0.55709722222223 0.858763888888888 33 123.7 123.936152777778 126.674166666667 -2.73801388888889 -0.236152777777761 34 123.67 123.535402777778 127.02 -3.48459722222222 0.134597222222212 35 130.19 129.641486111111 127.519583333333 2.12190277777779 0.548513888888891 36 134.01 130.772652777778 127.839166666667 2.93348611111111 3.23734722222224 37 124.96 129.065402777778 128.22375 0.84165277777778 -4.10540277777775 38 129.96 133.509236111111 128.505 5.00423611111111 -3.5492361111111 39 128.32 128.611986111111 128.7325 -0.120513888888895 -0.291986111111100 40 132.38 127.820402777778 128.990833333333 -1.17043055555555 4.55959722222224 41 126.25 126.247736111111 129.1775 -2.92976388888889 0.00226388888890483 42 128.91 127.793486111111 129.18625 -1.39276388888889 1.11651388888890 43 131.42 130.695652777778 129.20375 1.49190277777777 0.724347222222235 44 129.44 128.835402777778 129.3925 -0.55709722222223 0.604597222222253 45 126.86 126.558652777778 129.296666666667 -2.73801388888889 0.301347222222233 46 126.71 125.419152777778 128.90375 -3.48459722222222 1.29084722222225 47 131.63 130.816902777778 128.695 2.12190277777779 0.81309722222224 48 132.78 131.495569444444 128.562083333333 2.93348611111111 1.28443055555559 49 126.61 129.279152777778 128.4375 0.84165277777778 -2.66915277777778 50 132.84 133.330069444444 128.325833333333 5.00423611111111 -0.490069444444458 51 123.14 128.104486111111 128.225 -0.120513888888895 -4.9644861111111 52 128.13 127.039152777778 128.209583333333 -1.17043055555555 1.09084722222224 53 125.49 125.178152777778 128.107916666667 -2.92976388888889 0.311847222222241 54 126.48 126.353486111111 127.74625 -1.39276388888889 0.126513888888908 55 130.86 129.341486111111 127.849583333333 1.49190277777777 1.51851388888892 56 127.32 127.769569444444 128.326666666667 -0.55709722222223 -0.449569444444435 57 126.56 126.077819444444 128.815833333333 -2.73801388888889 0.482180555555573 58 126.64 125.479986111111 128.964583333333 -3.48459722222222 1.16001388888893 59 129.26 130.843152777778 128.72125 2.12190277777779 -1.58315277777777 60 126.47 131.725152777778 128.791666666667 2.93348611111111 -5.25515277777777 61 135.4 129.842486111111 129.000833333333 0.84165277777778 5.55751388888891 62 135.5 134.194652777778 129.190416666667 5.00423611111111 1.30534722222225 63 132.22 129.072402777778 129.192916666667 -0.120513888888895 3.14759722222223 64 122.62 127.902486111111 129.072916666667 -1.17043055555555 -5.28248611111107 65 125.16 126.206069444444 129.135833333333 -2.92976388888889 -1.04606944444444 66 128.5 128.020986111111 129.41375 -1.39276388888889 0.479013888888886 67 133.86 NA NA 1.49190277777777 NA 68 128.87 NA NA -0.55709722222223 NA 69 125.07 NA NA -2.73801388888889 NA 70 125.25 NA NA -3.48459722222222 NA 71 132.16 NA NA 2.12190277777779 NA 72 130.24 NA NA 2.93348611111111 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jan/14/t12319399490ngchqnf2s92ayk/1fmb41231939836.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jan/14/t12319399490ngchqnf2s92ayk/1fmb41231939836.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jan/14/t12319399490ngchqnf2s92ayk/2ypi81231939836.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jan/14/t12319399490ngchqnf2s92ayk/2ypi81231939836.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jan/14/t12319399490ngchqnf2s92ayk/3q8421231939836.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jan/14/t12319399490ngchqnf2s92ayk/3q8421231939836.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jan/14/t12319399490ngchqnf2s92ayk/4opts1231939836.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jan/14/t12319399490ngchqnf2s92ayk/4opts1231939836.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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