classical decomposition- Verkoopcijfers X - Lehaen Joni

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Thu, 20 May 2010 10:24:04 +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/20/t1274351248r6z2y163btlsyxz.htm/, Retrieved Thu, 20 May 2010 12:27:29 +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/20/t1274351248r6z2y163btlsyxz.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:

KDGP2W52

Dataseries X:

» Textbox « » Textfile « » CSV «

154 96 73 49 36 59 95 169 210 278 298 245 200 118 90 79 78 91 167 169 289 347 375 203 223 104 107 85 75 99 135 211 335 460 488 326 346 261 224 141 148 145 223 272 445 560 612 467 518 404 300 210 196 186 247 343 464 680 711 610 613 392 273 322 189 257 324 404 677 858 895 664 628 308 324 248 272

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 3 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 154 NA NA 1.32627607042365 NA 2 96 NA NA 0.851641354896029 NA 3 73 NA NA 0.668051351344534 NA 4 49 NA NA 0.529649653606985 NA 5 36 NA NA 0.442226831184652 NA 6 59 NA NA 0.488719413524296 NA 7 95 104.241412589297 148.75 0.700782605642333 0.911346053744423 8 169 131.275388780494 151.583333333333 0.866027853417223 1.28737002091523 9 210 208.69219661221 153.208333333333 1.36214651038701 1.00626666166258 10 278 272.631695217002 155.166666666667 1.75702488861656 1.01969068482197 11 298 293.138209776579 158.166666666667 1.85335011449892 1.01658531730519 12 245 186.099165583822 161.25 1.15410335245781 1.31650241005324 13 200 219.609212660983 165.583333333333 1.32627607042365 0.910708606331308 14 118 143.572538412889 168.583333333333 0.851641354896029 0.82188419390241 15 90 114.821326012342 171.875 0.668051351344534 0.783826516603076 16 79 94.2997070776102 178.041666666667 0.529649653606985 0.837754458080996 17 78 81.425015291874 184.125 0.442226831184652 0.957936571708377 18 91 90.6981778265507 185.583333333333 0.488719413524296 1.00332776446762 19 167 129.498785667656 184.791666666667 0.700782605642333 1.28958738214416 20 169 160.359490857756 185.166666666667 0.866027853417223 1.05388211883205 21 289 252.394397153793 185.291666666667 1.36214651038701 1.14503334170252 22 347 327.245885504833 186.25 1.75702488861656 1.0603647451967 23 375 345.418127589736 186.375 1.85335011449892 1.08564076418537 24 203 215.336450512754 186.583333333333 1.15410335245781 0.942710811460956 25 223 246.134734069456 185.583333333333 1.32627607042365 0.906007845024717 26 104 158.405292010661 186 0.851641354896029 0.656543721992573 27 107 126.70707297168 189.666666666667 0.668051351344534 0.844467459396804 28 85 103.965813255938 196.291666666667 0.529649653606985 0.817576444968033 29 75 90.9697443982761 205.708333333333 0.442226831184652 0.824449936581566 30 99 105.339396923383 215.541666666667 0.488719413524296 0.939819316338087 31 135 158.230872498992 225.791666666667 0.700782605642333 0.85318369208171 32 211 205.645530692698 237.458333333333 0.866027853417223 1.0260373726055 33 335 339.004212772567 248.875 1.36214651038701 0.988188309697339 34 460 449.944790226556 256.083333333333 1.75702488861656 1.02234765240504 35 488 484.573832020029 261.458333333333 1.85335011449892 1.00707047668193 36 326 307.472368150636 266.416666666667 1.15410335245781 1.06025787605177 37 346 360.747091155234 272 1.32627607042365 0.959120692815544 38 261 236.933721943366 278.208333333333 0.851641354896029 1.1015738825999 39 224 190.617318916974 285.333333333333 0.668051351344534 1.17512931811598 40 141 155.761135631587 294.083333333333 0.529649653606985 0.905232229004153 41 148 134.17899102861 303.416666666667 0.442226831184652 1.10300426963595 42 145 153.681892244494 314.458333333333 0.488719413524296 0.943507383220645 43 223 229.506303347864 327.5 0.700782605642333 0.971650872969696 44 272 294.990737570242 340.625 0.866027853417223 0.922062849296184 45 445 476.410742007857 349.75 1.36214651038701 0.934067939199955 46 560 625.134813495698 355.791666666667 1.75702488861656 0.895806773051927 47 612 668.44160796261 360.666666666667 1.85335011449892 0.915562395742177 48 467 420.526409051816 364.375 1.15410335245781 1.11051289514247 49 518 486.85384085135 367.083333333333 1.32627607042365 1.06397435233167 50 404 315.994427722881 371.041666666667 0.851641354896029 1.27850355751937 51 300 250.380079389337 374.791666666667 0.668051351344534 1.19817838835934 52 210 201.575830668592 380.583333333333 0.529649653606985 1.04179156451181 53 196 172.339481336252 389.708333333333 0.442226831184652 1.13729018145055 54 186 195.385948865234 399.791666666667 0.488719413524296 0.95196200689074 55 247 287.116473386711 409.708333333333 0.700782605642333 0.860278050529415 56 343 357.813841436883 413.166666666667 0.866027853417223 0.958599026305426 57 464 560.580045128854 411.541666666667 1.36214651038701 0.827714086564294 58 680 729.311747516588 415.083333333333 1.75702488861656 0.932385913589762 59 711 777.403150110858 419.458333333333 1.85335011449892 0.91458337916255 60 610 487.175877656254 422.125 1.15410335245781 1.25211454010128 61 613 568.032988661864 428.291666666667 1.32627607042365 1.07916267582287 62 392 369.647833081331 434.041666666667 0.851641354896029 1.06046881631185 63 273 297.589041551017 445.458333333333 0.668051351344534 0.917372489850902 64 322 244.565727553025 461.75 0.529649653606985 1.31661947576112 65 189 210.868494003215 476.833333333333 0.442226831184652 0.896293212949671 66 257 237.884174532951 486.75 0.488719413524296 1.08035770140901 67 324 343.120683287627 489.625 0.700782605642333 0.94427417460113 68 404 421.539057650834 486.75 0.866027853417223 0.95839280528695 69 677 661.151862479095 485.375 1.36214651038701 1.023970495162 70 858 851.132139794003 484.416666666667 1.75702488861656 1.00806908808268 71 895 898.488690924788 484.791666666667 1.85335011449892 0.996117156554083 72 664 NA NA 1.15410335245781 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/20/t1274351248r6z2y163btlsyxz/1n6sy1274351039.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/20/t1274351248r6z2y163btlsyxz/1n6sy1274351039.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/20/t1274351248r6z2y163btlsyxz/2n6sy1274351039.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/20/t1274351248r6z2y163btlsyxz/2n6sy1274351039.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/20/t1274351248r6z2y163btlsyxz/3xf911274351039.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/20/t1274351248r6z2y163btlsyxz/3xf911274351039.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/20/t1274351248r6z2y163btlsyxz/4xf911274351039.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/20/t1274351248r6z2y163btlsyxz/4xf911274351039.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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