IKO opgave 9 oe 3

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Sun, 06 Jun 2010 12:19:48 +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/Jun/06/t12758268303j8q1hn8np3dyqb.htm/, Retrieved Sun, 06 Jun 2010 14:20:30 +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/Jun/06/t12758268303j8q1hn8np3dyqb.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 «

278,1 283,3 287 287,9 279,7 273,2 272,6 274,8 277,2 277,5 278,7 279 280,1 280,8 280,1 270,5 260 258,2 260,1 262,2 263,4 264,7 267,4 266,8 265,2 262,5 255 252,8 249,6 254,2 259,4 260,8 261,6 257,2 249,8 235,2 227,4 223,1 221,6 218,5 220,1 222,1 222,9 222,8 222,1 209 201,7 204,5 208,7 213 218,6 221,9 224,6 226,8 229,6 231,4 229,1 232,7 236,6 242,2 251,5 261,5 266,4 280,9 294,4 308,8 328,8 348,2 365,9 387,5 395,9 395,8

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 278.1 NA NA -0.921250000000002 NA 2 283.3 NA NA -0.401249999999992 NA 3 287 NA NA -1.59208333333333 NA 4 287.9 NA NA -2.66791666666667 NA 5 279.7 NA NA -3.74125000000001 NA 6 273.2 NA NA -1.41125 NA 7 272.6 280.847083333333 279.166666666667 1.68041666666666 -8.24708333333325 8 274.8 282.709583333333 279.145833333333 3.56375 -7.90958333333339 9 277.2 282.95125 278.754166666667 4.19708333333334 -5.75125000000003 10 277.5 279.70875 277.741666666667 1.96708333333332 -2.20875000000001 11 278.7 276.71875 276.195833333333 0.522916666666675 1.98124999999999 12 279 273.55375 274.75 -1.19625000000000 5.44625000000002 13 280.1 272.682916666667 273.604166666667 -0.921250000000002 7.41708333333332 14 280.8 272.157083333333 272.558333333333 -0.401249999999992 8.64291666666668 15 280.1 269.86625 271.458333333333 -1.59208333333333 10.2337500000000 16 270.5 267.682083333333 270.35 -2.66791666666667 2.81791666666669 17 260 265.604583333333 269.345833333333 -3.74125000000001 -5.60458333333332 18 258.2 266.955416666667 268.366666666667 -1.41125 -8.75541666666669 19 260.1 268.917916666667 267.2375 1.68041666666666 -8.81791666666663 20 262.2 269.417916666667 265.854166666667 3.56375 -7.21791666666667 21 263.4 268.242916666667 264.045833333333 4.19708333333334 -4.84291666666667 22 264.7 264.229583333333 262.2625 1.96708333333332 0.470416666666665 23 267.4 261.614583333333 261.091666666667 0.522916666666675 5.78541666666666 24 266.8 259.295416666667 260.491666666667 -1.19625000000000 7.50458333333336 25 265.2 259.374583333333 260.295833333333 -0.921250000000002 5.82541666666668 26 262.5 259.807083333333 260.208333333333 -0.401249999999992 2.69291666666669 27 255 258.482916666667 260.075 -1.59208333333333 -3.48291666666665 28 252.8 257.019583333333 259.6875 -2.66791666666667 -4.21958333333333 29 249.6 254.900416666667 258.641666666667 -3.74125000000001 -5.30041666666659 30 254.2 255.180416666667 256.591666666667 -1.41125 -0.980416666666656 31 259.4 255.380416666667 253.7 1.68041666666666 4.01958333333334 32 260.8 254.047083333333 250.483333333333 3.56375 6.75291666666664 33 261.6 251.647083333333 247.45 4.19708333333334 9.95291666666668 34 257.2 246.59625 244.629166666667 1.96708333333332 10.6037500000000 35 249.8 242.49375 241.970833333333 0.522916666666675 7.30625 36 235.2 238.207916666667 239.404166666667 -1.19625000000000 -3.00791666666669 37 227.4 235.624583333333 236.545833333333 -0.921250000000002 -8.22458333333333 38 223.1 233.040416666667 233.441666666667 -0.401249999999992 -9.94041666666666 39 221.6 228.620416666667 230.2125 -1.59208333333333 -7.02041666666665 40 218.5 223.890416666667 226.558333333333 -2.66791666666667 -5.39041666666665 41 220.1 218.804583333333 222.545833333333 -3.74125000000001 1.29541666666668 42 222.1 217.85125 219.2625 -1.41125 4.24874999999997 43 222.9 218.884583333333 217.204166666667 1.68041666666666 4.01541666666665 44 222.8 219.567916666667 216.004166666667 3.56375 3.23208333333338 45 222.1 219.655416666667 215.458333333333 4.19708333333334 2.44458333333336 46 209 217.442083333333 215.475 1.96708333333332 -8.44208333333327 47 201.7 216.327083333333 215.804166666667 0.522916666666675 -14.6270833333333 48 204.5 214.99125 216.1875 -1.19625000000000 -10.4912500000000 49 208.7 215.74125 216.6625 -0.921250000000002 -7.04124999999996 50 213 216.89875 217.3 -0.401249999999992 -3.89874999999998 51 218.6 216.357916666667 217.95 -1.59208333333333 2.24208333333334 52 221.9 216.56125 219.229166666667 -2.66791666666667 5.33874999999998 53 224.6 217.929583333333 221.670833333333 -3.74125000000001 6.67041666666665 54 226.8 223.284583333333 224.695833333333 -1.41125 3.51541666666665 55 229.6 229.730416666667 228.05 1.68041666666666 -0.130416666666690 56 231.4 235.417916666667 231.854166666667 3.56375 -4.01791666666665 57 229.1 240.06375 235.866666666667 4.19708333333334 -10.96375 58 232.7 242.28375 240.316666666667 1.96708333333332 -9.58375 59 236.6 246.20625 245.683333333333 0.522916666666675 -9.60624999999996 60 242.2 250.812083333333 252.008333333333 -1.19625000000000 -8.61208333333329 61 251.5 258.637083333333 259.558333333333 -0.921250000000002 -7.13708333333332 62 261.5 268.157083333333 268.558333333333 -0.401249999999992 -6.65708333333328 63 266.4 277.532916666667 279.125 -1.59208333333333 -11.1329166666667 64 280.9 288.607083333333 291.275 -2.66791666666667 -7.70708333333334 65 294.4 300.62125 304.3625 -3.74125000000001 -6.22125 66 308.8 315.98875 317.4 -1.41125 -7.18874999999991 67 328.8 NA NA 1.68041666666666 NA 68 348.2 NA NA 3.56375 NA 69 365.9 NA NA 4.19708333333334 NA 70 387.5 NA NA 1.96708333333332 NA 71 395.9 NA NA 0.522916666666675 NA 72 395.8 NA NA -1.19625000000000 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jun/06/t12758268303j8q1hn8np3dyqb/1a1tj1275826785.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/06/t12758268303j8q1hn8np3dyqb/1a1tj1275826785.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/06/t12758268303j8q1hn8np3dyqb/2a1tj1275826785.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/06/t12758268303j8q1hn8np3dyqb/2a1tj1275826785.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/06/t12758268303j8q1hn8np3dyqb/3ktt41275826785.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/06/t12758268303j8q1hn8np3dyqb/3ktt41275826785.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/06/t12758268303j8q1hn8np3dyqb/4ktt41275826785.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/06/t12758268303j8q1hn8np3dyqb/4ktt41275826785.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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