TG 8

*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, 02 Dec 2009 11:03:32 -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/02/t1259777034pixedkbe0j8lcz8.htm/, Retrieved Wed, 02 Dec 2009 19:03:59 +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/02/t1259777034pixedkbe0j8lcz8.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 «

101.3 106.3 94 102.8 102 105.1 92.4 81.4 105.8 120.3 100.7 88.8 94.3 99.9 103.4 103.3 98.8 104.2 91.2 74.7 108.5 114.5 96.9 89.6 97.1 100.3 122.6 115.4 109 129.1 102.8 96.2 127.7 128.9 126.5 119.8 113.2 114.1 134.1 130 121.8 132.1 105.3 103 117.1 126.3 138.1 119.5 138 135.5 178.6 162.2 176.9 204.9 132.2 142.5 164.3 174.9 175.4 143

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 1 101.3 NA NA 0.942724391828947 NA 2 106.3 NA NA 0.954412978450807 NA 3 94 NA NA 1.11947962014922 NA 4 102.8 NA NA 1.05783668893803 NA 5 102 NA NA 1.02827152273714 NA 6 105.1 NA NA 1.14065121022909 NA 7 92.4 89.7675137836764 99.7833333333333 0.89962432387182 1.02932560015717 8 81.4 80.0460353352085 99.225 0.806712374252543 1.01691482481451 9 105.8 103.252653452573 99.35 1.03928186665901 1.02467100323574 10 120.3 109.520568119282 99.7625 1.09781298703703 1.09842381267579 11 100.7 100.756341065575 99.65 1.01110226859584 0.999440818662337 12 88.8 89.7391383046095 99.4791666666667 0.902089767250525 0.989534796941979 13 94.3 93.6989485111988 99.3916666666667 0.942724391828947 1.00641470900529 14 99.9 94.546535677783 99.0625 0.954412978450807 1.05662253284945 15 103.4 110.711869934341 98.8958333333333 1.11947962014922 0.933955862739224 16 103.3 104.479003644113 98.7666666666667 1.05783668893803 0.988715401152478 17 98.8 101.147642119910 98.3666666666667 1.02827152273714 0.976789947143533 18 104.2 112.059475978256 98.2416666666667 1.14065121022909 0.929863352388146 19 91.2 88.5155365996214 98.3916666666667 0.89962432387182 1.03032759562337 20 74.7 79.4813366732318 98.525 0.806712374252543 0.939843278015202 21 108.5 103.243992770351 99.3416666666667 1.03928186665901 1.05090860096180 22 114.5 110.490302924498 100.645833333333 1.09781298703703 1.03629003604273 23 96.9 102.702712932622 101.575 1.01110226859584 0.943499906020702 24 89.6 92.949074393076 103.0375 0.902089767250525 0.96396871711801 25 97.1 98.569691202315 104.558333333333 0.942724391828947 0.985089826452855 26 100.3 101.108124904632 105.9375 0.954412978450807 0.992007319833153 27 122.6 120.493323115395 107.633333333333 1.11947962014922 1.01748376449530 28 115.4 115.339460317210 109.033333333333 1.05783668893803 1.00052488266049 29 109 114.001036154124 110.866666666667 1.02827152273714 0.95613166052839 30 129.1 129.302320106219 113.358333333333 1.14065121022909 0.998435294076295 31 102.8 103.715439238372 115.2875 0.89962432387182 0.99117354903865 32 96.2 94.0088820128963 116.533333333333 0.806712374252543 1.02330756350026 33 127.7 122.206556495767 117.5875 1.03928186665901 1.04495211764210 34 128.9 130.282956236620 118.675 1.09781298703703 0.989384979612316 35 126.5 121.146903482258 119.816666666667 1.01110226859584 1.04418682082556 36 119.8 108.679264709507 120.475 0.902089767250525 1.10232619184734 37 113.2 113.790762112053 120.704166666667 0.942724391828947 0.994808347346584 38 114.1 115.571458248906 121.091666666667 0.954412978450807 0.987267978866057 39 134.1 135.382402063379 120.933333333333 1.11947962014922 0.990527557172616 40 130 127.345906736656 120.383333333333 1.05783668893803 1.02084160638812 41 121.8 124.172355299866 120.758333333333 1.02827152273714 0.980894658121472 42 132.1 138.280195673397 121.229166666667 1.14065121022909 0.9553067187727 43 105.3 109.979073593330 122.25 0.89962432387182 0.957454873545928 44 103 100.173509072809 124.175 0.806712374252543 1.02821595203514 45 117.1 131.906520584584 126.920833333333 1.03928186665901 0.887749896525476 46 126.3 142.843766496635 130.116666666667 1.09781298703703 0.884182790034281 47 138.1 135.239141350812 133.754166666667 1.01110226859584 1.02115407285652 48 119.5 125.465651795094 139.083333333333 0.902089767250525 0.952451912457787 49 138 135.033485074599 143.2375 0.942724391828947 1.02196873556039 50 135.5 139.348271574561 146.004166666667 0.954412978450807 0.972383786816457 51 178.6 167.492809167993 149.616666666667 1.11947962014922 1.06631443395798 52 162.2 162.492530726622 153.608333333333 1.05783668893803 0.998199728164032 53 176.9 161.631429980244 157.1875 1.02827152273714 1.09446535257172 54 204.9 182.185761840465 159.720833333333 1.14065121022909 1.12467625312798 55 132.2 NA NA 0.89962432387182 NA 56 142.5 NA NA 0.806712374252543 NA 57 164.3 NA NA 1.03928186665901 NA 58 174.9 NA NA 1.09781298703703 NA 59 175.4 NA NA 1.01110226859584 NA 60 143 NA NA 0.902089767250525 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/02/t1259777034pixedkbe0j8lcz8/18bxz1259777010.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259777034pixedkbe0j8lcz8/18bxz1259777010.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259777034pixedkbe0j8lcz8/26ize1259777010.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259777034pixedkbe0j8lcz8/26ize1259777010.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259777034pixedkbe0j8lcz8/33mkd1259777010.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259777034pixedkbe0j8lcz8/33mkd1259777010.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259777034pixedkbe0j8lcz8/41n2i1259777010.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259777034pixedkbe0j8lcz8/41n2i1259777010.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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