*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Fri, 04 Dec 2009 10:37:01 -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/04/t1259948263w0drygcwt9m2l35.htm/, Retrieved Fri, 04 Dec 2009 18:37:49 +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/04/t1259948263w0drygcwt9m2l35.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 «

95.5 76.7 79.4 55.2 60 64.8 82.3 210.5 106 80.8 97.3 189.5 90 69.3 87.3 57.4 56.2 61.6 77.7 177.2 97.6 81.6 96.8 191.3 106 75.1 72 63.5 57.4 62.3 79.4 178.1 109.3 85.2 102.7 193.7 108.4 73.4 85.9 58.5 58.6 62.7 77.5 180.5 102.2 82.6 97.8 197.8 93.8 72.4 77.7 58.7 53.1 64.3 76.4 188.4 105.5 79.8 96.1 202.5

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 95.5 NA NA 1.01620313949398 NA 2 76.7 NA NA 0.742857619514316 NA 3 79.4 NA NA 0.828646739957447 NA 4 55.2 NA NA 0.610976910802136 NA 5 60 NA NA 0.578256310761869 NA 6 64.8 NA NA 0.643264605442063 NA 7 82.3 80.3097622423321 99.6041666666667 0.806289183775767 1.02478201531294 8 210.5 188.176009627254 99.0666666666667 1.89948865707188 1.11863356235987 9 106 104.753516212879 99.0875 1.05718194739880 1.01189920713103 10 80.8 83.7079616735362 99.5083333333333 0.841215593402926 0.965260632138227 11 97.3 99.9891645999712 99.4416666666667 1.00550571960082 0.973105439867111 12 189.5 195.336760740939 99.15 1.97011357277800 0.97011949661293 13 90 100.426275260492 98.825 1.01620313949398 0.896179807192411 14 69.3 72.239808257686 97.2458333333333 0.742857619514316 0.959304871806977 15 87.3 79.1426690554358 95.5083333333333 0.828646739957447 1.10307121356812 16 57.4 58.1599104341066 95.1916666666667 0.610976910802136 0.986934119594844 17 56.2 55.0524101858248 95.2041666666667 0.578256310761869 1.02084540550180 18 61.6 61.2763142067352 95.2583333333333 0.643264605442063 1.00528239659084 19 77.7 77.4037616424736 96 0.806289183775767 1.00382718295908 20 177.2 184.076279942407 96.9083333333333 1.89948865707188 0.96264439967736 21 97.6 102.031272698327 96.5125 1.05718194739880 0.956569465604642 22 81.6 80.8653539808288 96.1291666666666 0.841215593402926 1.00908480558121 23 96.8 96.9642682268388 96.4333333333333 1.00550571960082 0.99830588906777 24 191.3 190.140586192737 96.5125 1.97011357277800 1.00609766610316 25 106 98.1779258143619 96.6125 1.01620313949398 1.07967243268541 26 75.1 71.8498080074409 96.7208333333333 0.742857619514316 1.04523591757159 27 72 80.5824427661119 97.2458333333333 0.828646739957447 0.893494879634983 28 63.5 59.8044566190157 97.8833333333333 0.610976910802136 1.06179377909119 29 57.4 56.8305483414175 98.2791666666667 0.578256310761869 1.01002016829332 30 62.3 63.4419717117235 98.625 0.643264605442063 0.98199974431891 31 79.4 79.6815285866402 98.825 0.806289183775767 0.996466827486448 32 178.1 187.772368287626 98.8541666666667 1.89948865707188 0.948488862467718 33 109.3 105.044241248413 99.3625 1.05718194739880 1.04051396536363 34 85.2 83.8972351820518 99.7333333333333 0.841215593402926 1.01552810191088 35 102.7 100.123232029251 99.575 1.00550571960082 1.02573596475587 36 193.7 196.305399914221 99.6416666666667 1.97011357277800 0.986727823506842 37 108.4 101.192661794861 99.5791666666667 1.01620313949398 1.07122392155026 38 73.4 73.9886189036259 99.6 0.742857619514316 0.992044466941698 39 85.9 82.37093864652 99.4041666666667 0.828646739957447 1.04284352480945 40 58.5 60.4867141694114 99 0.610976910802136 0.96715453638551 41 58.6 57.066669668312 98.6875 0.578256310761869 1.02686910486629 42 62.7 63.4607335960489 98.6541666666667 0.643264605442063 0.988012530695103 43 77.5 79.1910359998432 98.2166666666667 0.806289183775767 0.978646118484337 44 180.5 185.326776641646 97.5666666666667 1.89948865707188 0.973955319737852 45 102.2 102.740465588040 97.1833333333333 1.05718194739880 0.994739506143496 46 82.6 81.4717302210734 96.85 0.841215593402926 1.01384860461248 47 97.8 97.161179763594 96.6291666666666 1.00550571960082 1.00657485055205 48 197.8 190.050289320651 96.4666666666667 1.97011357277800 1.04077715801986 49 93.8 98.0509004219251 96.4875 1.01620313949398 0.956645982814712 50 72.4 71.8869508884166 96.7708333333333 0.742857619514316 1.00713688792253 51 77.7 80.5755373766122 97.2375 0.828646739957447 0.96431252623024 52 58.7 59.4225960497644 97.2583333333333 0.610976910802136 0.987839709171251 53 53.1 56.1318219659136 97.0708333333333 0.578256310761869 0.945987465581383 54 64.3 62.5226393797792 97.1958333333333 0.643264605442063 1.02842747263795 55 76.4 NA NA 0.806289183775767 NA 56 188.4 NA NA 1.89948865707188 NA 57 105.5 NA NA 1.05718194739880 NA 58 79.8 NA NA 0.841215593402926 NA 59 96.1 NA NA 1.00550571960082 NA 60 202.5 NA NA 1.97011357277800 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259948263w0drygcwt9m2l35/1oi5w1259948219.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259948263w0drygcwt9m2l35/1oi5w1259948219.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259948263w0drygcwt9m2l35/2kbl71259948219.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259948263w0drygcwt9m2l35/2kbl71259948219.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259948263w0drygcwt9m2l35/3i3y41259948219.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259948263w0drygcwt9m2l35/3i3y41259948219.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259948263w0drygcwt9m2l35/40mds1259948219.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259948263w0drygcwt9m2l35/40mds1259948219.ps (open in new window)

Parameters (Session):

par1 = FALSE ; par2 = -0.4 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;

Parameters (R input):

par1 = multiplicative ; par2 = 12 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 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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