Paper klassieke decompositie

*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: Mon, 20 Dec 2010 20:22:07 +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/20/t1292876435xw1y19i1mrxqvsi.htm/, Retrieved Mon, 20 Dec 2010 21:20:36 +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/20/t1292876435xw1y19i1mrxqvsi.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:

Dataseries X:

» Textbox « » Textfile « » CSV «

37 30 47 35 30 43 82 40 47 19 52 136 80 42 54 66 81 63 137 72 107 58 36 52 79 77 54 84 48 96 83 66 61 53 30 74 69 59 42 65 70 100 63 105 82 81 75 102 121 98 76 77 63 37 35 23 40 29 37 51 20 28 13 22 25 13 16 13 16 17 9 17 25 14 8 7 10 7 10 3

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 'RServer@AstonUniversity' @ vre.aston.ac.uk Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 37 NA NA 12.0368055555556 NA 2 30 NA NA -0.188194444444443 NA 3 47 NA NA -12.7048611111111 NA 4 35 NA NA 2.57013888888889 NA 5 30 NA NA -2.45486111111111 NA 6 43 NA NA 3.29513888888889 NA 7 82 61.3701388888889 51.625 9.7451388888889 20.6298611111111 8 40 53.3534722222222 53.9166666666667 -0.563194444444445 -13.3534722222222 9 47 60.1618055555556 54.7083333333333 5.45347222222222 -13.1618055555556 10 19 49.0201388888889 56.2916666666667 -7.27152777777778 -30.0201388888889 11 52 43.3201388888889 59.7083333333333 -16.3881944444444 8.67986111111111 12 136 69.1368055555556 62.6666666666667 6.47013888888889 66.8631944444444 13 80 77.8284722222222 65.7916666666667 12.0368055555556 2.17152777777777 14 42 69.2284722222222 69.4166666666667 -0.188194444444443 -27.2284722222222 15 54 60.5451388888889 73.25 -12.7048611111111 -6.54513888888887 16 66 79.9451388888889 77.375 2.57013888888889 -13.9451388888889 17 81 75.8784722222222 78.3333333333333 -2.45486111111111 5.12152777777779 18 63 77.4618055555556 74.1666666666667 3.29513888888889 -14.4618055555556 19 137 80.3701388888889 70.625 9.7451388888889 56.6298611111111 20 72 71.4784722222222 72.0416666666667 -0.563194444444445 0.521527777777791 21 107 78.9534722222222 73.5 5.45347222222222 28.0465277777778 22 58 66.9784722222222 74.25 -7.27152777777778 -8.97847222222222 23 36 57.2368055555555 73.625 -16.3881944444444 -21.2368055555555 24 52 80.0951388888889 73.625 6.47013888888889 -28.0951388888889 25 79 84.7868055555556 72.75 12.0368055555556 -5.78680555555556 26 77 70.0618055555556 70.25 -0.188194444444443 6.93819444444445 27 54 55.3784722222222 68.0833333333333 -12.7048611111111 -1.37847222222221 28 84 68.5284722222222 65.9583333333333 2.57013888888889 15.4715277777778 29 48 63.0451388888889 65.5 -2.45486111111111 -15.0451388888889 30 96 69.4618055555556 66.1666666666667 3.29513888888889 26.5381944444444 31 83 76.4118055555555 66.6666666666667 9.7451388888889 6.58819444444445 32 66 64.9368055555556 65.5 -0.563194444444445 1.06319444444445 33 61 69.7034722222222 64.25 5.45347222222222 -8.70347222222222 34 53 55.6868055555556 62.9583333333333 -7.27152777777778 -2.68680555555555 35 30 46.6951388888889 63.0833333333333 -16.3881944444444 -16.6951388888889 36 74 70.6368055555555 64.1666666666667 6.47013888888889 3.36319444444446 37 69 75.5368055555555 63.5 12.0368055555556 -6.53680555555555 38 59 64.1034722222222 64.2916666666667 -0.188194444444443 -5.10347222222222 39 42 54.0868055555556 66.7916666666667 -12.7048611111111 -12.0868055555556 40 65 71.4034722222222 68.8333333333333 2.57013888888889 -6.40347222222222 41 70 69.4201388888889 71.875 -2.45486111111111 0.579861111111114 42 100 78.2118055555555 74.9166666666667 3.29513888888889 21.7881944444445 43 63 87.9951388888889 78.25 9.7451388888889 -24.9951388888889 44 105 81.4784722222222 82.0416666666667 -0.563194444444445 23.5215277777778 45 82 90.5368055555555 85.0833333333333 5.45347222222222 -8.53680555555555 46 81 79.7284722222222 87 -7.27152777777778 1.27152777777779 47 75 70.8201388888889 87.2083333333333 -16.3881944444444 4.17986111111111 48 102 90.7618055555555 84.2916666666667 6.47013888888889 11.2381944444445 49 121 92.5368055555555 80.5 12.0368055555556 28.4631944444445 50 98 75.7284722222222 75.9166666666667 -0.188194444444443 22.2715277777778 51 76 58.0451388888889 70.75 -12.7048611111111 17.9548611111111 52 77 69.4034722222222 66.8333333333333 2.57013888888889 7.59652777777778 53 63 60.6284722222222 63.0833333333333 -2.45486111111111 2.37152777777779 54 37 62.6701388888889 59.375 3.29513888888889 -25.6701388888889 55 35 62.7868055555556 53.0416666666667 9.7451388888889 -27.7868055555556 56 23 45.3534722222222 45.9166666666667 -0.563194444444445 -22.3534722222222 57 40 45.8284722222222 40.375 5.45347222222222 -5.82847222222222 58 29 28.1868055555556 35.4583333333333 -7.27152777777778 0.813194444444441 59 37 15.1951388888889 31.5833333333333 -16.3881944444444 21.8048611111111 60 51 35.4701388888889 29 6.47013888888889 15.5298611111111 61 20 39.2451388888889 27.2083333333333 12.0368055555556 -19.2451388888889 62 28 25.8118055555555 26 -0.188194444444443 2.18819444444445 63 13 11.8784722222222 24.5833333333333 -12.7048611111111 1.12152777777778 64 22 25.6534722222222 23.0833333333333 2.57013888888889 -3.65347222222222 65 25 18.9618055555556 21.4166666666667 -2.45486111111111 6.03819444444445 66 13 22.1284722222222 18.8333333333333 3.29513888888889 -9.12847222222222 67 16 27.3701388888889 17.625 9.7451388888889 -11.3701388888889 68 13 16.6868055555556 17.25 -0.563194444444445 -3.68680555555556 69 16 21.9118055555556 16.4583333333333 5.45347222222222 -5.91180555555556 70 17 8.35347222222222 15.625 -7.27152777777778 8.64652777777778 71 9 -2.01319444444445 14.375 -16.3881944444444 11.0131944444444 72 17 19.9701388888889 13.5 6.47013888888889 -2.97013888888889 73 25 NA 13 NA NA 74 14 NA 12.3333333333333 NA NA 75 8 NA NA NA NA 76 7 NA NA NA NA 77 10 NA NA NA NA 78 7 NA NA NA NA 79 10 NA NA NA NA 80 3 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/20/t1292876435xw1y19i1mrxqvsi/10o6x1292876524.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t1292876435xw1y19i1mrxqvsi/10o6x1292876524.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t1292876435xw1y19i1mrxqvsi/20o6x1292876524.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t1292876435xw1y19i1mrxqvsi/20o6x1292876524.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t1292876435xw1y19i1mrxqvsi/3axnh1292876524.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t1292876435xw1y19i1mrxqvsi/3axnh1292876524.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t1292876435xw1y19i1mrxqvsi/4axnh1292876524.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t1292876435xw1y19i1mrxqvsi/4axnh1292876524.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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