Paper

*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: Mon, 21 Dec 2009 14:42:08 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/21/t1261431823ld7mae6awcowg5j.htm/, Retrieved Mon, 21 Dec 2009 22:43:48 +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/2009/Dec/21/t1261431823ld7mae6awcowg5j.htm/}, year = {2009}, } @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 = {2009}, 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 «

25,6 23,7 22 21,3 20,7 20,4 20,3 20,4 19,8 19,5 23,1 23,5 23,5 22,9 21,9 21,5 20,5 20,2 19,4 19,2 18,8 18,8 22,6 23,3 23 21,4 19,9 18,8 18,6 18,4 18,6 19,9 19,2 18,4 21,1 20,5 19,1 18,1 17 17,1 17,4 16,8 15,3 14,3 13,4 15,3 22,1 23,7 22,2 19,5 16,6 17,3 19,8 21,2 21,5 20,6 19,1 19,6 23,5 24

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 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 25.6 NA NA 2.35920138888889 NA 2 23.7 NA NA 0.869618055555555 NA 3 22 NA NA -0.750173611111111 NA 4 21.3 NA NA -0.91892361111111 NA 5 20.7 NA NA -0.524131944444444 NA 6 20.4 NA NA -0.458506944444445 NA 7 20.3 20.1050347222222 21.6041666666667 -1.49913194444444 0.194965277777779 8 20.4 20.1133680555556 21.4833333333333 -1.36996527777778 0.286631944444448 9 19.8 19.5258680555556 21.4458333333333 -1.91996527777778 0.274131944444445 10 19.5 19.8279513888889 21.45 -1.62204861111111 -0.327951388888888 11 23.1 24.1039930555556 21.45 2.65399305555556 -1.00399305555556 12 23.5 24.6133680555556 21.4333333333333 3.18003472222222 -1.11336805555556 13 23.5 23.7467013888889 21.3875 2.35920138888889 -0.246701388888887 14 22.9 22.1696180555556 21.3 0.869618055555555 0.730381944444442 15 21.9 20.4581597222222 21.2083333333333 -0.750173611111111 1.44184027777778 16 21.5 20.2185763888889 21.1375 -0.91892361111111 1.28142361111111 17 20.5 20.5633680555556 21.0875 -0.524131944444444 -0.06336805555555 18 20.2 20.5998263888889 21.0583333333333 -0.458506944444445 -0.399826388888886 19 19.4 19.5300347222222 21.0291666666667 -1.49913194444444 -0.130034722222224 20 19.2 19.5758680555556 20.9458333333333 -1.36996527777778 -0.375868055555554 21 18.8 18.8800347222222 20.8 -1.91996527777778 -0.0800347222222229 22 18.8 18.9821180555555 20.6041666666667 -1.62204861111111 -0.182118055555549 23 22.6 23.0664930555556 20.4125 2.65399305555556 -0.466493055555556 24 23.3 23.4383680555556 20.2583333333333 3.18003472222222 -0.138368055555553 25 23 22.5092013888889 20.15 2.35920138888889 0.49079861111111 26 21.4 21.0154513888889 20.1458333333333 0.869618055555555 0.384548611111111 27 19.9 19.4414930555556 20.1916666666667 -0.750173611111111 0.458506944444444 28 18.8 19.2727430555556 20.1916666666667 -0.91892361111111 -0.472743055555554 29 18.6 19.5883680555556 20.1125 -0.524131944444444 -0.988368055555554 30 18.4 19.4748263888889 19.9333333333333 -0.458506944444445 -1.07482638888889 31 18.6 18.1550347222222 19.6541666666667 -1.49913194444444 0.444965277777779 32 19.9 17.9842013888889 19.3541666666667 -1.36996527777778 1.91579861111111 33 19.2 17.1758680555556 19.0958333333333 -1.91996527777778 2.02413194444444 34 18.4 17.2821180555556 18.9041666666667 -1.62204861111111 1.11788194444444 35 21.1 21.4373263888889 18.7833333333333 2.65399305555556 -0.337326388888886 36 20.5 21.8467013888889 18.6666666666667 3.18003472222222 -1.34670138888889 37 19.1 20.8217013888889 18.4625 2.35920138888889 -1.72170138888889 38 18.1 18.9612847222222 18.0916666666667 0.869618055555555 -0.86128472222222 39 17 16.8664930555556 17.6166666666667 -0.750173611111111 0.133506944444445 40 17.1 16.3269097222222 17.2458333333333 -0.91892361111111 0.77309027777778 41 17.4 16.6342013888889 17.1583333333333 -0.524131944444444 0.765798611111112 42 16.8 16.8748263888889 17.3333333333333 -0.458506944444445 -0.0748263888888872 43 15.3 16.0967013888889 17.5958333333333 -1.49913194444444 -0.796701388888891 44 14.3 16.4133680555556 17.7833333333333 -1.36996527777778 -2.11336805555556 45 13.4 15.9050347222222 17.825 -1.91996527777778 -2.50503472222222 46 15.3 16.1946180555556 17.8166666666667 -1.62204861111111 -0.894618055555554 47 22.1 20.5789930555556 17.925 2.65399305555556 1.52100694444444 48 23.7 21.3883680555556 18.2083333333333 3.18003472222222 2.31163194444445 49 22.2 21.0092013888889 18.65 2.35920138888889 1.19079861111111 50 19.5 20.0404513888889 19.1708333333333 0.869618055555555 -0.540451388888886 51 16.6 18.9206597222222 19.6708333333333 -0.750173611111111 -2.32065972222222 52 17.3 19.1685763888889 20.0875 -0.91892361111111 -1.86857638888888 53 19.8 19.8008680555556 20.325 -0.524131944444444 -0.000868055555557135 54 21.2 19.9373263888889 20.3958333333333 -0.458506944444445 1.26267361111111 55 21.5 NA NA -1.49913194444444 NA 56 20.6 NA NA -1.36996527777778 NA 57 19.1 NA NA -1.91996527777778 NA 58 19.6 NA NA -1.62204861111111 NA 59 23.5 NA NA 2.65399305555556 NA 60 24 NA NA 3.18003472222222 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/21/t1261431823ld7mae6awcowg5j/1nigs1261431725.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261431823ld7mae6awcowg5j/1nigs1261431725.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261431823ld7mae6awcowg5j/2a40q1261431725.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261431823ld7mae6awcowg5j/2a40q1261431725.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261431823ld7mae6awcowg5j/3o2zj1261431725.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261431823ld7mae6awcowg5j/3o2zj1261431725.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261431823ld7mae6awcowg5j/4eth11261431725.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261431823ld7mae6awcowg5j/4eth11261431725.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = 2 ; par3 = 1 ; par4 = 1 ;

Parameters (R input):

par1 = additive ; par2 = 12 ; par3 = 1 ; par4 = 1 ;

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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