Workshop 8 - blog 1

*The author of this computation has been verified*

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

Title produced by software: Classical Decomposition

Date of computation: Tue, 30 Nov 2010 18:49:04 +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/Nov/30/t1291144137vuag0s7ze1sg5uh.htm/, Retrieved Tue, 30 Nov 2010 20:08:57 +0100

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/Nov/30/t1291144137vuag0s7ze1sg5uh.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 «

219,3 211,1 215,2 240,2 242,2 240,7 255,4 253 218,2 203,7 205,6 215,6 188,5 202,9 214 230,3 230 241 259,6 247,8 270,3 289,7 322,7 315 320,2 329,5 360,6 382,2 435,4 464 468,8 403 351,6 252 188 146,5 152,9 148,1 165,1 177 206,1 244,9 228,6 253,4 241,1 261,4 273,7 263,7 272,5 263,2 279,8 298,1 267,6 264,3 264,3 268,7 269,1 288,6

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 1 219.3 NA NA -43.7393518518519 NA 2 211.1 NA NA -37.0726851851852 NA 3 215.2 NA NA -17.6629629629630 NA 4 240.2 NA NA -2.18240740740742 NA 5 242.2 NA NA 23.4037037037037 NA 6 240.7 NA NA 47.9231481481482 NA 7 255.4 273.855092592593 225.4 48.4550925925926 -18.4550925925925 8 253 252.625925925926 223.775 28.8509259259259 0.374074074074116 9 218.2 236.749537037037 223.383333333333 13.3662037037037 -18.5495370370371 10 203.7 214.464814814815 222.920833333333 -8.45601851851854 -10.7648148148148 11 205.6 205.846759259259 222 -16.1532407407407 -0.246759259259278 12 215.6 184.771759259259 221.504166666667 -36.7324074074074 30.8282407407407 13 188.5 177.952314814815 221.691666666667 -43.7393518518519 10.5476851851852 14 202.9 184.577314814815 221.65 -37.0726851851852 18.3226851851852 15 214 205.941203703704 223.604166666667 -17.6629629629630 8.0587962962963 16 230.3 227.175925925926 229.358333333333 -2.18240740740742 3.12407407407409 17 230 261.224537037037 237.820833333333 23.4037037037037 -31.224537037037 18 241 294.764814814815 246.841666666667 47.9231481481482 -53.7648148148148 19 259.6 304.925925925926 256.470833333333 48.4550925925926 -45.3259259259259 20 247.8 296.084259259259 267.233333333333 28.8509259259259 -48.2842592592592 21 270.3 291.982870370370 278.616666666667 13.3662037037037 -21.6828703703704 22 289.7 282.598148148148 291.054166666667 -8.45601851851854 7.10185185185185 23 322.7 289.788425925926 305.941666666667 -16.1532407407407 32.9115740740741 24 315 287.059259259259 323.791666666667 -36.7324074074074 27.9407407407408 25 320.2 298.060648148148 341.8 -43.7393518518519 22.1393518518519 26 329.5 319.910648148148 356.983333333333 -37.0726851851852 9.58935185185186 27 360.6 349.174537037037 366.8375 -17.6629629629630 11.4254629629631 28 382.2 366.471759259259 368.654166666667 -2.18240740740742 15.7282407407408 29 435.4 384.874537037037 361.470833333333 23.4037037037037 50.525462962963 30 464 396.760648148148 348.8375 47.9231481481482 67.239351851852 31 468.8 383.300925925926 334.845833333333 48.4550925925926 85.4990740740741 32 403 349.167592592593 320.316666666667 28.8509259259259 53.8324074074074 33 351.6 317.978703703704 304.6125 13.3662037037037 33.6212962962964 34 252 279.460648148148 287.916666666667 -8.45601851851854 -27.4606481481482 35 188 253.659259259259 269.8125 -16.1532407407407 -65.6592592592593 36 146.5 214.396759259259 251.129166666667 -36.7324074074074 -67.8967592592593 37 152.9 188.252314814815 231.991666666667 -43.7393518518519 -35.3523148148148 38 148.1 178.677314814815 215.75 -37.0726851851852 -30.5773148148148 39 165.1 187.249537037037 204.9125 -17.6629629629630 -22.1495370370371 40 177 198.517592592593 200.7 -2.18240740740742 -21.5175925925926 41 206.1 228.066203703704 204.6625 23.4037037037037 -21.9662037037037 42 244.9 261.039814814815 213.116666666667 47.9231481481482 -16.1398148148148 43 228.6 271.438425925926 222.983333333333 48.4550925925926 -42.8384259259259 44 253.4 261.613425925926 232.7625 28.8509259259259 -8.21342592592589 45 241.1 255.703703703704 242.3375 13.3662037037037 -14.6037037037037 46 261.4 243.706481481481 252.1625 -8.45601851851854 17.6935185185185 47 273.7 243.617592592593 259.770833333333 -16.1532407407407 30.0824074074074 48 263.7 226.409259259259 263.141666666667 -36.7324074074074 37.2907407407408 49 272.5 NA 265.4375 NA NA 50 263.2 NA 267.5625 NA NA 51 279.8 NA 269.366666666667 NA NA 52 298.1 NA 271.666666666667 NA NA 53 267.6 NA NA NA NA 54 264.3 NA NA NA NA 55 264.3 NA NA NA NA 56 268.7 NA NA NA NA 57 269.1 NA NA NA NA 58 288.6 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291144137vuag0s7ze1sg5uh/1udag1291142939.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291144137vuag0s7ze1sg5uh/1udag1291142939.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291144137vuag0s7ze1sg5uh/2udag1291142939.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291144137vuag0s7ze1sg5uh/2udag1291142939.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291144137vuag0s7ze1sg5uh/3mm911291142939.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291144137vuag0s7ze1sg5uh/3mm911291142939.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291144137vuag0s7ze1sg5uh/4mm911291142939.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291144137vuag0s7ze1sg5uh/4mm911291142939.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$trend)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) a<-table.element(a,m$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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