*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: Fri, 04 Dec 2009 05:03:52 -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/t1259928281faf7clkx745wcr7.htm/, Retrieved Fri, 04 Dec 2009 13:04:46 +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/t1259928281faf7clkx745wcr7.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:

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17 14 15 16 16 15 13 12 13 13 12 10 14 14 15 16 16 15 15 13 15 15 15 13 16 16 14 16 15 14 15 15 14 13 12 13 12 9 10 8 11 8 8 8 4 6 8 10 5 6 5 9 8 6 9 11 11 8 11 11 13

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 1 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 17 NA NA 0.9875681601825 NA 2 14 NA NA 0.95887584418625 NA 3 15 NA NA 0.934432428137803 NA 4 16 NA NA 1.07109105799535 NA 5 16 NA NA 1.11135449870623 NA 6 15 NA NA 0.929353341197457 NA 7 13 14.2353303759759 13.7083333333333 1.03844355326268 0.913220814456077 8 12 13.5925510409086 13.5833333333333 1.00067860423867 0.882836486240468 9 13 11.9975446063643 13.5833333333333 0.88325481764645 1.08355504618036 10 13 13.1237031707292 13.5833333333333 0.9661621966181 0.99057406517658 11 12 13.9866617096931 13.5833333333333 1.02969288660317 0.85796026593563 12 10 14.7935079691441 13.5833333333333 1.08909261122533 0.675972191373252 13 14 13.4967648558275 13.6666666666667 0.9875681601825 1.03728561248181 14 14 13.2244960177354 13.7916666666667 0.95887584418625 1.05864147724228 15 15 13.0041846249178 13.9166666666667 0.934432428137803 1.15347485695166 16 16 15.0845324001012 14.0833333333333 1.07109105799535 1.06068915996977 17 16 15.8831080440099 14.2916666666667 1.11135449870623 1.00735951399853 18 15 13.5143465032464 14.5416666666667 0.929353341197457 1.10993158244069 19 15 15.3170424106245 14.75 1.03844355326268 0.979301329713329 20 13 14.9267891798935 14.9166666666667 1.00067860423867 0.87091737166832 21 15 13.2120199806282 14.9583333333333 0.88325481764645 1.13532979983329 22 15 14.4119194328867 14.9166666666667 0.9661621966181 1.04080515228051 23 15 15.3166816882222 14.875 1.02969288660317 0.97932439318983 24 13 16.1094948743747 14.7916666666667 1.08909261122533 0.806977506208406 25 16 14.5666303626919 14.75 0.9875681601825 1.09840090684111 26 16 14.2233250220960 14.8333333333333 0.95887584418625 1.12491277357045 27 14 13.8996823685498 14.875 0.934432428137803 1.00721726071073 28 16 15.7985931054314 14.75 1.07109105799535 1.01274840697678 29 15 16.1609466686865 14.5416666666667 1.11135449870623 0.928163449054879 30 14 13.3981773355967 14.4166666666667 0.929353341197457 1.04491824890274 31 15 14.7978206339931 14.25 1.03844355326268 1.01366277987871 32 15 13.8010257501250 13.7916666666667 1.00067860423867 1.08687573457097 33 14 11.7767309019527 13.3333333333333 0.88325481764645 1.18878491124211 34 13 12.3990815232656 12.8333333333333 0.9661621966181 1.04846475729729 35 12 12.6995456014392 12.3333333333333 1.02969288660317 0.94491569829397 36 13 12.9783536171019 11.9166666666667 1.08909261122533 1.00166788358036 37 12 11.2335878220759 11.375 0.9875681601825 1.06822505775207 38 9 10.3478684851766 10.7916666666667 0.95887584418625 0.869744335550124 39 10 9.42219365038951 10.0833333333333 0.934432428137803 1.06132397306296 40 8 10.0414786687064 9.375 1.07109105799535 0.796695413488402 41 11 9.90957761346392 8.91666666666667 1.11135449870623 1.11003722147093 42 8 8.01567256782807 8.625 0.929353341197457 0.998044759476457 43 8 8.52389083303114 8.20833333333333 1.03844355326268 0.938538533248104 44 8 7.79695412469299 7.79166666666667 1.00067860423867 1.02604169167341 45 4 6.58760884827978 7.45833333333333 0.88325481764645 0.607200593132442 46 6 7.04493268367365 7.29166666666667 0.9661621966181 0.851675987466106 47 8 7.42236955759788 7.20833333333333 1.02969288660317 1.07782291597308 48 10 7.62364827857733 7 1.08909261122533 1.31170794278381 49 5 6.87182844793657 6.95833333333333 0.9875681601825 0.727608385145495 50 6 6.83199038982703 7.125 0.95887584418625 0.87822137585763 51 5 7.04717789553926 7.54166666666667 0.934432428137803 0.709503871495129 52 9 8.4794708757965 7.91666666666667 1.07109105799535 1.06138698178553 53 8 9.02975530198815 8.125 1.11135449870623 0.885959777696144 54 6 7.70588812076225 8.29166666666667 0.929353341197457 0.778625371400603 55 9 8.99984412827653 8.66666666666667 1.03844355326268 1.00001731938034 56 11 NA NA 1.00067860423867 NA 57 11 NA NA 0.88325481764645 NA 58 8 NA NA 0.9661621966181 NA 59 11 NA NA 1.02969288660317 NA 60 11 NA NA 1.08909261122533 NA 61 13 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259928281faf7clkx745wcr7/1uroo1259928230.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259928281faf7clkx745wcr7/1uroo1259928230.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259928281faf7clkx745wcr7/20ebs1259928230.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259928281faf7clkx745wcr7/20ebs1259928230.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259928281faf7clkx745wcr7/3kbcy1259928230.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259928281faf7clkx745wcr7/3kbcy1259928230.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259928281faf7clkx745wcr7/4v9qn1259928230.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259928281faf7clkx745wcr7/4v9qn1259928230.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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