*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: Wed, 15 Dec 2010 12:49:58 +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/Dec/15/t129241736315zgyc7hrhbwomj.htm/, Retrieved Wed, 15 Dec 2010 13:49:27 +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/Dec/15/t129241736315zgyc7hrhbwomj.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 «

94.6 95.9 104.7 102.8 98.1 113.9 80.9 95.7 113.2 105.9 108.8 102.3 99 100.7 115.5 100.7 109.9 114.6 85.4 100.5 114.8 116.5 112.9 102 106 105.3 118.8 106.1 109.3 117.2 92.5 104.2 112.5 122.4 113.3 100 110.7 112.8 109.8 117.3 109.1 115.9 96 99.8 116.8 115.7 99.4 94.3 91 93.2 103.1 94.1 91.8 102.7 82.6 89.1 104.5 105.1 95.1 88.7

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 94.6 NA NA -3.68072916666667 NA 2 95.9 NA NA -2.30468750000000 NA 3 104.7 NA NA 6.6546875 NA 4 102.8 NA NA -0.496354166666658 NA 5 98.1 NA NA 0.129687500000000 NA 6 113.9 NA NA 7.9890625 NA 7 80.9 84.2723958333333 101.583333333333 -17.3109375 -3.3723958333333 8 95.7 96.0713541666667 101.966666666667 -5.8953125 -0.371354166666663 9 113.2 111.041145833333 102.616666666667 8.42447916666666 2.15885416666667 10 105.9 112.3109375 102.979166666667 9.33177083333333 -6.4109375 11 108.8 106.346354166667 103.383333333333 2.96302083333333 2.45364583333331 12 102.3 98.0994791666667 103.904166666667 -5.80468750000001 4.20052083333333 13 99 100.440104166667 104.120833333333 -3.68072916666667 -1.44010416666666 14 100.7 102.203645833333 104.508333333333 -2.30468750000000 -1.50364583333332 15 115.5 111.4296875 104.775 6.6546875 4.0703125 16 100.7 104.786979166667 105.283333333333 -0.496354166666658 -4.08697916666664 17 109.9 106.025520833333 105.895833333333 0.129687500000000 3.87447916666669 18 114.6 114.043229166667 106.054166666667 7.9890625 0.556770833333331 19 85.4 89.0223958333333 106.333333333333 -17.3109375 -3.62239583333331 20 100.5 100.921354166667 106.816666666667 -5.8953125 -0.421354166666646 21 114.8 115.5703125 107.145833333333 8.42447916666666 -0.770312499999974 22 116.5 116.840104166667 107.508333333333 9.33177083333333 -0.340104166666663 23 112.9 110.671354166667 107.708333333333 2.96302083333333 2.22864583333336 24 102 101.986979166667 107.791666666667 -5.80468750000001 0.0130208333333428 25 106 104.515104166667 108.195833333333 -3.68072916666667 1.48489583333334 26 105.3 106.341145833333 108.645833333333 -2.30468750000000 -1.04114583333332 27 118.8 115.358854166667 108.704166666667 6.6546875 3.44114583333334 28 106.1 108.3578125 108.854166666667 -0.496354166666658 -2.2578125 29 109.3 109.246354166667 109.116666666667 0.129687500000000 0.0536458333333201 30 117.2 117.0390625 109.05 7.9890625 0.160937500000017 31 92.5 91.8515625 109.1625 -17.3109375 0.6484375 32 104.2 103.775520833333 109.670833333333 -5.8953125 0.424479166666657 33 112.5 118.0328125 109.608333333333 8.42447916666666 -5.53281249999999 34 122.4 119.031770833333 109.7 9.33177083333333 3.36822916666668 35 113.3 113.121354166667 110.158333333333 2.96302083333333 0.178645833333348 36 100 104.291145833333 110.095833333333 -5.80468750000001 -4.29114583333333 37 110.7 106.506770833333 110.1875 -3.68072916666667 4.19322916666667 38 112.8 107.8453125 110.15 -2.30468750000000 4.95468750000001 39 109.8 116.800520833333 110.145833333333 6.6546875 -7.00052083333333 40 117.3 109.549479166667 110.045833333333 -0.496354166666658 7.75052083333335 41 109.1 109.3171875 109.1875 0.129687500000000 -0.217187499999994 42 115.9 116.359895833333 108.370833333333 7.9890625 -0.459895833333334 43 96 90.0015625 107.3125 -17.3109375 5.9984375 44 99.8 99.7796875 105.675 -5.8953125 0.0203124999999886 45 116.8 113.003645833333 104.579166666667 8.42447916666666 3.79635416666667 46 115.7 112.665104166667 103.333333333333 9.33177083333333 3.03489583333334 47 99.4 104.608854166667 101.645833333333 2.96302083333333 -5.20885416666665 48 94.3 94.5703125 100.375 -5.80468750000001 -0.270312500000003 49 91 95.5859375 99.2666666666667 -3.68072916666667 -4.5859375 50 93.2 95.9578125 98.2625 -2.30468750000000 -2.75781249999999 51 103.1 103.958854166667 97.3041666666666 6.6546875 -0.85885416666666 52 94.1 95.8536458333333 96.35 -0.496354166666658 -1.75364583333334 53 91.8 95.8588541666667 95.7291666666667 0.129687500000000 -4.05885416666666 54 102.7 103.305729166667 95.3166666666666 7.9890625 -0.605729166666634 55 82.6 NA NA -17.3109375 NA 56 89.1 NA NA -5.8953125 NA 57 104.5 NA NA 8.42447916666666 NA 58 105.1 NA NA 9.33177083333333 NA 59 95.1 NA NA 2.96302083333333 NA 60 88.7 NA NA -5.80468750000001 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/15/t129241736315zgyc7hrhbwomj/1ypu71292417395.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/15/t129241736315zgyc7hrhbwomj/1ypu71292417395.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/15/t129241736315zgyc7hrhbwomj/2ypu71292417395.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/15/t129241736315zgyc7hrhbwomj/2ypu71292417395.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/15/t129241736315zgyc7hrhbwomj/3ryta1292417395.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/15/t129241736315zgyc7hrhbwomj/3ryta1292417395.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/15/t129241736315zgyc7hrhbwomj/4ryta1292417395.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/15/t129241736315zgyc7hrhbwomj/4ryta1292417395.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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