Tijdreeks2-Stap24

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Fri, 02 Jul 2010 13:10:16 +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/Jul/02/t1278076342uzquhtfnjbyk65f.htm/, Retrieved Fri, 02 Jul 2010 15:12:28 +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/Jul/02/t1278076342uzquhtfnjbyk65f.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:

Steffi Poppe

Dataseries X:

» Textbox « » Textfile « » CSV «

59 58 57 55 53 52 53 55 56 56 57 59 68 68 68 70 66 64 72 80 84 82 83 86 99 100 95 98 103 107 114 123 132 137 141 146 156 156 143 143 151 149 156 166 177 180 189 195 205 209 199 191 198 200 208 224 234 243 249 258 274 270 261 258 261 258 264 270 267 287 297 306 322 323 309 303 306 301 316 327 330 342 359 379

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 59 NA NA 1.08778011529030 NA 2 58 NA NA 1.06469246425813 NA 3 57 NA NA 0.993668582652538 NA 4 55 NA NA 0.966966789703818 NA 5 53 NA NA 0.958328967926301 NA 6 52 NA NA 0.930099442449319 NA 7 53 53.7011063642466 56.2083333333333 0.95539403464931 0.986944284546185 8 55 56.5499557920575 57 0.992104487579956 0.972591388086016 9 56 58.3071932360772 57.875 1.00746770170328 0.96043038417686 10 56 59.4086635503426 58.9583333333333 1.00763810968779 0.942623460171696 11 57 60.8775615169933 60.125 1.01251661566725 0.936305571045073 12 59 62.5944611090912 61.1666666666667 1.02334268843201 0.942575412498134 13 68 67.9409330341735 62.4583333333333 1.08778011529030 1.00086938702765 14 68 68.4508530145957 64.2916666666667 1.06469246425813 0.993413478506987 15 68 66.0789607463937 66.5 0.993668582652538 1.02907187449541 16 70 66.4789667921375 68.75 0.966966789703818 1.0529646199056 17 66 67.9614959754402 70.9166666666667 0.958328967926301 0.97113812832859 18 64 68.0135217291065 73.125 0.930099442449319 0.940989355835858 19 72 72.1720577008 75.5416666666667 0.95539403464931 0.99761600671671 20 80 77.5495007791666 78.1666666666667 0.992104487579956 1.03159916177683 21 84 81.2270834498267 80.625 1.00746770170328 1.03413783226485 22 82 83.5499932616125 82.9166666666667 1.00763810968779 0.981448313744812 23 83 86.6967352165081 85.625 1.01251661566725 0.95736015655865 24 86 91.034859991764 88.9583333333333 1.02334268843201 0.94469305503167 25 99 100.619660664353 92.5 1.08778011529030 0.983903139270606 26 100 102.254838754791 96.0416666666667 1.06469246425813 0.97794883076195 27 95 99.2012468348117 99.8333333333333 0.993668582652538 0.957649253725535 28 98 100.68541697791 104.125 0.966966789703818 0.973328640248873 29 103 104.298136009312 108.833333333333 0.958328967926301 0.987553602979093 30 107 105.79881157861 113.75 0.930099442449319 1.01135351525662 31 114 113.333617360274 118.625 0.95539403464931 1.00587983208554 32 123 122.359553468195 123.333333333333 0.992104487579956 1.00523413590237 33 132 128.620043250785 127.666666666667 1.00746770170328 1.02627861617668 34 137 132.546396345181 131.541666666667 1.00763810968779 1.03360033752423 35 141 137.111625038273 135.416666666667 1.01251661566725 1.02835919245098 36 146 142.415190806788 139.166666666667 1.02334268843201 1.02517153663808 37 156 155.18996311475 142.666666666667 1.08778011529030 1.00521964738564 38 156 155.666910711741 146.208333333333 1.06469246425813 1.00213975652716 39 143 148.926078825049 149.875 0.993668582652538 0.960207917432576 40 143 148.469692502440 153.541666666667 0.966966789703818 0.963159535052243 41 151 150.777090953738 157.333333333333 0.958328967926301 1.00147840129327 42 149 150.094797525259 161.375 0.930099442449319 0.992705959544836 43 156 158.077904649684 165.458333333333 0.95539403464931 0.986855186028126 44 166 168.368399079715 169.708333333333 0.992104487579956 0.985933232764222 45 177 175.551247021796 174.25 1.00746770170328 1.00825259291963 46 180 179.947372421744 178.583333333333 1.00763810968779 1.00029246094315 47 189 184.826470551592 182.541666666667 1.01251661566725 1.02258079936252 48 195 190.981329228624 186.625 1.02334268843201 1.02104221804094 49 205 207.675353677507 190.916666666667 1.08778011529030 0.987117615883965 50 209 208.147376762465 195.5 1.06469246425813 1.00409624781632 51 199 199.023536533781 200.291666666667 0.993668582652538 0.999881739947993 52 191 198.510223869613 205.291666666667 0.966966789703818 0.962167067654178 53 198 201.648387001159 210.416666666667 0.958328967926301 0.981907184801145 54 200 200.475183991264 215.541666666667 0.930099442449319 0.997629711659053 55 208 211.181889742275 221.041666666667 0.95539403464931 0.984932942184779 56 224 224.670328749878 226.458333333333 0.992104487579956 0.997016389508987 57 234 233.312728586117 231.583333333333 1.00746770170328 1.00294570904060 58 243 238.768247074769 236.958333333333 1.00763810968779 1.01772326503660 59 249 245.408714722349 242.375 1.01251661566725 1.01463389465086 60 258 253.192036829553 247.416666666667 1.02334268843201 1.01898939331052 61 274 274.301885739038 252.166666666667 1.08778011529030 0.998899439797052 62 270 273.004892710189 256.416666666667 1.06469246425813 0.98899326425853 63 261 258.064011486386 259.708333333333 0.993668582652538 1.01137697773782 64 258 254.231685126295 262.916666666667 0.966966789703818 1.01482236516598 65 261 255.634252194341 266.75 0.958328967926301 1.02098994074386 66 258 251.824424043153 270.75 0.930099442449319 1.02452333994334 67 264 262.494511019898 274.75 0.95539403464931 1.00573531604243 68 270 276.755814347825 278.958333333333 0.992104487579956 0.97558925956535 69 267 285.281270865645 283.166666666667 1.00746770170328 0.935918433025159 70 287 289.234122401632 287.041666666667 1.00763810968779 0.99227573018328 71 297 294.431394197572 290.791666666667 1.01251661566725 1.00872395353569 72 306 301.331782464542 294.458333333333 1.02334268843201 1.01549195208444 73 322 324.611716071215 298.416666666667 1.08778011529030 0.991954338238853 74 323 322.557454484203 302.958333333333 1.06469246425813 1.00137198973282 75 309 306.008520599371 307.958333333333 0.993668582652538 1.00977580426444 76 303 302.539734328582 312.875 0.966966789703818 1.00152133957690 77 306 304.509029558582 317.75 0.958328967926301 1.00489630945781 78 301 300.770907202049 323.375 0.930099442449319 1.00076168536406 79 316 NA NA 0.95539403464931 NA 80 327 NA NA 0.992104487579956 NA 81 330 NA NA 1.00746770170328 NA 82 342 NA NA 1.00763810968779 NA 83 359 NA NA 1.01251661566725 NA 84 379 NA NA 1.02334268843201 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jul/02/t1278076342uzquhtfnjbyk65f/1e2n61278076214.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jul/02/t1278076342uzquhtfnjbyk65f/1e2n61278076214.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jul/02/t1278076342uzquhtfnjbyk65f/2e2n61278076214.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jul/02/t1278076342uzquhtfnjbyk65f/2e2n61278076214.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jul/02/t1278076342uzquhtfnjbyk65f/3e2n61278076214.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jul/02/t1278076342uzquhtfnjbyk65f/3e2n61278076214.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jul/02/t1278076342uzquhtfnjbyk65f/47u491278076214.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jul/02/t1278076342uzquhtfnjbyk65f/47u491278076214.ps (open in new window)

Parameters (Session):

par1 = multiplicative ; par2 = 12 ;

Parameters (R input):

par1 = multiplicative ; 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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