Tijdreeks 2 - Stap 24

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Mon, 02 Aug 2010 14:07:26 +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/Aug/02/t12807584657ochlszk4xg5i6j.htm/, Retrieved Mon, 02 Aug 2010 16:14:32 +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/Aug/02/t12807584657ochlszk4xg5i6j.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:

Van Puyenbroeck Cassandra

Dataseries X:

» Textbox « » Textfile « » CSV «

408 407 406 404 402 401 402 404 405 405 406 408 405 400 402 404 410 402 400 392 390 397 394 397 400 395 391 392 395 386 385 372 367 364 364 368 370 357 350 353 353 348 337 322 315 316 317 326 329 310 301 299 300 295 274 258 250 247 248 256 253 237 225 214 221 221 207 194 191 185 180 185 189 179 162 148 152 151 134 122 119 115 113 109

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 'George Udny Yule' @ 72.249.76.132 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 408 NA NA 9.84664351851853 NA 2 407 NA NA 2.33275462962965 NA 3 406 NA NA -1.55613425925924 NA 4 404 NA NA -1.05613425925926 NA 5 402 NA NA 6.49247685185187 NA 6 401 NA NA 5.9369212962963 NA 7 402 403.950810185185 404.708333333333 -0.757523148148149 -1.95081018518516 8 404 396.138310185185 404.291666666667 -8.15335648148151 7.8616898148149 9 405 394.95775462963 403.833333333333 -8.87557870370374 10.0422453703704 10 405 397.596643518519 403.666666666667 -6.07002314814816 7.40335648148147 11 406 400.610532407407 404 -3.38946759259259 5.38946759259261 12 408 409.624421296296 404.375 5.2494212962963 -1.62442129629630 13 405 414.179976851852 404.333333333333 9.84664351851853 -9.17997685185185 14 400 406.08275462963 403.75 2.33275462962965 -6.08275462962956 15 402 401.068865740741 402.625 -1.55613425925924 0.931134259259295 16 404 400.610532407407 401.666666666667 -1.05613425925926 3.38946759259261 17 410 407.325810185185 400.833333333333 6.49247685185187 2.67418981481484 18 402 405.811921296296 399.875 5.9369212962963 -3.81192129629625 19 400 398.450810185185 399.208333333333 -0.757523148148149 1.54918981481484 20 392 390.638310185185 398.791666666667 -8.15335648148151 1.36168981481484 21 390 389.249421296296 398.125 -8.87557870370374 0.750578703703752 22 397 391.096643518518 397.166666666667 -6.07002314814816 5.90335648148152 23 394 392.652199074074 396.041666666667 -3.38946759259259 1.34780092592604 24 397 399.999421296296 394.75 5.2494212962963 -2.99942129629625 25 400 403.304976851852 393.458333333333 9.84664351851853 -3.30497685185185 26 395 394.332754629630 392 2.33275462962965 0.667245370370495 27 391 388.652199074074 390.208333333333 -1.55613425925924 2.34780092592598 28 392 386.818865740741 387.875 -1.05613425925926 5.1811342592593 29 395 391.742476851852 385.25 6.49247685185187 3.25752314814821 30 386 388.728587962963 382.791666666667 5.9369212962963 -2.72858796296293 31 385 379.575810185185 380.333333333333 -0.757523148148149 5.42418981481484 32 372 369.346643518518 377.5 -8.15335648148151 2.65335648148152 33 367 365.332754629630 374.208333333333 -8.87557870370374 1.66724537037032 34 364 364.804976851852 370.875 -6.07002314814816 -0.804976851851848 35 364 364.110532407407 367.5 -3.38946759259259 -0.110532407407391 36 368 369.416087962963 364.166666666667 5.2494212962963 -1.41608796296293 37 370 370.429976851852 360.583333333333 9.84664351851853 -0.429976851851848 38 357 358.832754629630 356.5 2.33275462962965 -1.83275462962956 39 350 350.693865740741 352.25 -1.55613425925924 -0.693865740740705 40 353 347.027199074074 348.083333333333 -1.05613425925926 5.97280092592592 41 353 350.617476851852 344.125 6.49247685185187 2.38252314814821 42 348 346.353587962963 340.416666666667 5.9369212962963 1.64641203703707 43 337 336.200810185185 336.958333333333 -0.757523148148149 0.799189814814781 44 322 325.138310185185 333.291666666667 -8.15335648148151 -3.13831018518522 45 315 320.416087962963 329.291666666667 -8.87557870370374 -5.41608796296293 46 316 318.929976851852 325 -6.07002314814816 -2.92997685185185 47 317 317.152199074074 320.541666666667 -3.38946759259259 -0.152199074074019 48 326 321.374421296296 316.125 5.2494212962963 4.62557870370375 49 329 321.138310185185 311.291666666667 9.84664351851853 7.86168981481484 50 310 308.332754629630 306 2.33275462962965 1.66724537037038 51 301 299.068865740741 300.625 -1.55613425925924 1.93113425925924 52 299 293.985532407407 295.041666666667 -1.05613425925926 5.01446759259255 53 300 295.784143518518 289.291666666667 6.49247685185187 4.21585648148152 54 295 289.436921296296 283.5 5.9369212962963 5.56307870370370 55 274 276.659143518518 277.416666666667 -0.757523148148149 -2.65914351851848 56 258 263.054976851852 271.208333333333 -8.15335648148151 -5.05497685185185 57 250 256.124421296296 265 -8.87557870370374 -6.12442129629625 58 247 252.221643518519 258.291666666667 -6.07002314814816 -5.22164351851853 59 248 248.068865740741 251.458333333333 -3.38946759259259 -0.0688657407407618 60 256 250.332754629630 245.083333333333 5.2494212962963 5.66724537037035 61 253 249.054976851852 239.208333333333 9.84664351851853 3.94502314814815 62 237 236.082754629630 233.75 2.33275462962965 0.917245370370324 63 225 227.068865740741 228.625 -1.55613425925924 -2.06886574074076 64 214 222.527199074074 223.583333333333 -1.05613425925926 -8.52719907407405 65 221 224.659143518519 218.166666666667 6.49247685185187 -3.65914351851853 66 221 218.311921296296 212.375 5.9369212962963 2.68807870370370 67 207 205.992476851852 206.75 -0.757523148148149 1.00752314814818 68 194 193.513310185185 201.666666666667 -8.15335648148151 0.486689814814866 69 191 187.749421296296 196.625 -8.87557870370374 3.25057870370375 70 185 185.179976851852 191.25 -6.07002314814816 -0.179976851851819 71 180 182.235532407407 185.625 -3.38946759259259 -2.23553240740742 72 185 185.082754629630 179.833333333333 5.2494212962963 -0.0827546296296475 73 189 183.721643518519 173.875 9.84664351851853 5.2783564814815 74 179 170.166087962963 167.833333333333 2.33275462962965 8.83391203703701 75 162 160.277199074074 161.833333333333 -1.55613425925924 1.72280092592590 76 148 154.860532407407 155.916666666667 -1.05613425925926 -6.86053240740742 77 152 156.700810185185 150.208333333333 6.49247685185187 -4.70081018518519 78 151 150.186921296296 144.25 5.9369212962963 0.813078703703695 79 134 NA NA -0.757523148148149 NA 80 122 NA NA -8.15335648148151 NA 81 119 NA NA -8.87557870370374 NA 82 115 NA NA -6.07002314814816 NA 83 113 NA NA -3.38946759259259 NA 84 109 NA NA 5.2494212962963 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Aug/02/t12807584657ochlszk4xg5i6j/1jrrq1280758042.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/02/t12807584657ochlszk4xg5i6j/1jrrq1280758042.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/02/t12807584657ochlszk4xg5i6j/2u18s1280758042.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/02/t12807584657ochlszk4xg5i6j/2u18s1280758042.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/02/t12807584657ochlszk4xg5i6j/3u18s1280758042.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/02/t12807584657ochlszk4xg5i6j/3u18s1280758042.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/02/t12807584657ochlszk4xg5i6j/44a8d1280758042.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/02/t12807584657ochlszk4xg5i6j/44a8d1280758042.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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