opgave 9 oefening 2:

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Mon, 24 May 2010 18:46:57 +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/May/24/t1274726955nll7s7gjbumbscn.htm/, Retrieved Mon, 24 May 2010 20:49:20 +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/2010/May/24/t1274726955nll7s7gjbumbscn.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 «

164 96 73 49 39 59 169 169 210 278 298 245 200 188 90 79 78 91 167 169 289 247 275 203 223 104 107 85 75 99 135 211 335 488 326 346 261 224 141 148 145 223 272 445 560 612 467 404 518 404 300 210 196 186 247 343 464 680 711 610 513 292 273 322 189 257 324 404 677 858 895 664 628 308 324 248 272

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 1 164 NA NA 62.1069444444445 NA 2 96 NA NA -41.7430555555556 NA 3 73 NA NA -107.793055555556 NA 4 49 NA NA -129.918055555556 NA 5 39 NA NA -171.926388888889 NA 6 59 NA NA -145.793055555556 NA 7 169 60.5319444444445 155.583333333333 -95.0513888888889 108.468055555556 8 169 146.698611111111 160.916666666667 -14.2180555555555 22.3013888888889 9 210 298.890277777778 165.458333333333 133.431944444444 -88.8902777777778 10 278 409.490277777778 167.416666666667 242.073611111111 -131.490277777778 11 298 367.140277777778 170.291666666667 196.848611111111 -69.1402777777778 12 245 245.231944444444 173.25 71.9819444444444 -0.231944444444451 13 200 236.606944444444 174.5 62.1069444444445 -36.6069444444445 14 188 132.673611111111 174.416666666667 -41.7430555555556 55.326388888889 15 90 69.9152777777778 177.708333333333 -107.793055555556 20.0847222222222 16 79 49.7902777777778 179.708333333333 -129.918055555556 29.2097222222222 17 78 5.53194444444446 177.458333333333 -171.926388888889 72.4680555555555 18 91 28.9569444444444 174.75 -145.793055555556 62.0430555555556 19 167 78.9069444444445 173.958333333333 -95.0513888888889 88.0930555555555 20 169 157.198611111111 171.416666666667 -14.2180555555555 11.8013888888889 21 289 302.056944444444 168.625 133.431944444444 -13.0569444444445 22 247 411.656944444444 169.583333333333 242.073611111111 -164.656944444444 23 275 366.556944444444 169.708333333333 196.848611111111 -91.5569444444444 24 203 241.898611111111 169.916666666667 71.9819444444444 -38.8986111111111 25 223 231.023611111111 168.916666666667 62.1069444444445 -8.0236111111111 26 104 127.590277777778 169.333333333333 -41.7430555555556 -23.5902777777777 27 107 65.2069444444445 173 -107.793055555556 41.7930555555556 28 85 55.0402777777778 184.958333333333 -129.918055555556 29.9597222222222 29 75 25.1986111111111 197.125 -171.926388888889 49.8013888888889 30 99 59.4152777777778 205.208333333333 -145.793055555556 39.5847222222222 31 135 117.698611111111 212.75 -95.0513888888889 17.3013888888889 32 211 205.115277777778 219.333333333333 -14.2180555555555 5.8847222222222 33 335 359.181944444444 225.75 133.431944444444 -24.1819444444445 34 488 471.865277777778 229.791666666667 242.073611111111 16.1347222222223 35 326 432.181944444444 235.333333333333 196.848611111111 -106.181944444444 36 346 315.398611111111 243.416666666667 71.9819444444444 30.6013888888889 37 261 316.398611111111 254.291666666667 62.1069444444445 -55.3986111111111 38 224 228.006944444444 269.75 -41.7430555555556 -4.0069444444444 39 141 181.081944444444 288.875 -107.793055555556 -40.0819444444444 40 148 173.498611111111 303.416666666667 -129.918055555556 -25.4986111111111 41 145 142.531944444444 314.458333333333 -171.926388888889 2.46805555555557 42 223 176.956944444444 322.75 -145.793055555556 46.0430555555556 43 272 240.823611111111 335.875 -95.0513888888889 31.1763888888889 44 445 339.865277777778 354.083333333333 -14.2180555555555 105.134722222222 45 560 501.640277777778 368.208333333333 133.431944444444 58.3597222222223 46 612 619.490277777778 377.416666666667 242.073611111111 -7.49027777777775 47 467 578.973611111111 382.125 196.848611111111 -111.973611111111 48 404 454.690277777778 382.708333333333 71.9819444444444 -50.6902777777778 49 518 442.231944444444 380.125 62.1069444444445 75.7680555555556 50 404 333.090277777778 374.833333333333 -41.7430555555556 70.9097222222222 51 300 258.790277777778 366.583333333333 -107.793055555556 41.2097222222223 52 210 235.498611111111 365.416666666667 -129.918055555556 -25.4986111111111 53 196 206.490277777778 378.416666666667 -171.926388888889 -10.4902777777779 54 186 251.373611111111 397.166666666667 -145.793055555556 -65.3736111111111 55 247 310.490277777778 405.541666666667 -95.0513888888889 -63.4902777777777 56 343 386.448611111111 400.666666666667 -14.2180555555555 -43.448611111111 57 464 528.306944444444 394.875 133.431944444444 -64.3069444444444 58 680 640.490277777778 398.416666666667 242.073611111111 39.5097222222223 59 711 599.640277777778 402.791666666667 196.848611111111 111.359722222222 60 610 477.440277777778 405.458333333333 71.9819444444444 132.559722222222 61 513 473.731944444444 411.625 62.1069444444445 39.2680555555555 62 292 375.631944444444 417.375 -41.7430555555556 -83.6319444444445 63 273 320.998611111111 428.791666666667 -107.793055555556 -47.9986111111111 64 322 315.165277777778 445.083333333333 -129.918055555556 6.8347222222223 65 189 288.240277777778 460.166666666667 -171.926388888889 -99.2402777777777 66 257 324.290277777778 470.083333333333 -145.793055555556 -67.2902777777777 67 324 382.073611111111 477.125 -95.0513888888889 -58.0736111111111 68 404 468.365277777778 482.583333333333 -14.2180555555555 -64.3652777777778 69 677 618.806944444445 485.375 133.431944444444 58.1930555555555 70 858 726.490277777778 484.416666666667 242.073611111111 131.509722222222 71 895 681.640277777778 484.791666666667 196.848611111111 213.359722222222 72 664 NA NA 71.9819444444444 NA 73 628 NA NA NA NA 74 308 NA NA NA NA 75 324 NA NA NA NA 76 248 NA NA NA NA 77 272 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/24/t1274726955nll7s7gjbumbscn/1s6hh1274726815.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/24/t1274726955nll7s7gjbumbscn/1s6hh1274726815.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/24/t1274726955nll7s7gjbumbscn/2s6hh1274726815.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/24/t1274726955nll7s7gjbumbscn/2s6hh1274726815.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/24/t1274726955nll7s7gjbumbscn/3lyyk1274726815.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/24/t1274726955nll7s7gjbumbscn/3lyyk1274726815.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/24/t1274726955nll7s7gjbumbscn/4lyyk1274726815.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/24/t1274726955nll7s7gjbumbscn/4lyyk1274726815.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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