ws9

*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 12:51:43 -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/t1259956364l5dcrusotlpdkuc.htm/, Retrieved Fri, 04 Dec 2009 20:52:49 +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/t1259956364l5dcrusotlpdkuc.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:

ws9.8

Dataseries X:

» Textbox « » Textfile « » CSV «

126.51 131.02 136.51 138.04 132.92 129.61 122.96 124.04 121.29 124.56 118.53 113.14 114.15 122.17 129.23 131.19 129.12 128.28 126.83 138.13 140.52 146.83 135.14 131.84 125.7 128.98 133.25 136.76 133.24 128.54 121.08 120.23 119.08 125.75 126.89 126.6 121.89 123.44 126.46 129.49 127.78 125.29 119.02 119.96 122.86 131.89 132.73 135.01 136.71 142.73 144.43 144.93 138.75 130.22 122.19 128.4 140.43 153.5 149.33 142.97

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 1 126.51 NA NA 0.969637405271937 NA 2 131.02 NA NA 1.0060251944183 NA 3 136.51 NA NA 1.03540392750211 NA 4 138.04 NA NA 1.04900960884383 NA 5 132.92 NA NA 1.01866562184587 NA 6 129.61 NA NA 0.98277443952445 NA 7 122.96 120.961555636422 126.079166666667 0.959409542706017 1.01652131830699 8 124.04 122.888334516291 125.195416666667 0.98157215166656 1.00937164205408 9 121.29 122.341431798437 124.523333333333 0.982477970381214 0.991405758597227 10 124.56 127.708142921957 123.934583333333 1.03044799512074 0.97534892568377 11 118.53 123.394273764207 123.490833333333 0.999218083103665 0.96057942061799 12 113.14 121.472067628367 123.277083333333 0.985358059615301 0.931407542564778 13 114.15 119.636691171550 123.382916666667 0.969637405271937 0.954138725186885 14 122.17 124.879164914637 124.13125 1.0060251944183 0.97830570923109 15 129.23 129.963469561762 125.519583333333 1.03540392750211 0.994356340560655 16 131.19 133.485161463367 127.24875 1.04900960884383 0.982805868171371 17 129.12 131.274165355250 128.86875 1.01866562184587 0.983590332877602 18 128.28 128.094820447617 130.34 0.98277443952445 1.00144564434172 19 126.83 126.258695574088 131.600416666667 0.959409542706017 1.00452487191725 20 138.13 129.926206843741 132.365416666667 0.98157215166656 1.06314194307332 21 140.52 130.489449099465 132.816666666667 0.982477970381214 1.0768686738258 22 146.83 137.272417730003 133.21625 1.03044799512074 1.06962492850382 23 135.14 133.515520264312 133.62 0.999218083103665 1.01216697304158 24 131.84 131.843371771676 133.8025 0.985358059615301 0.999974425929563 25 125.7 129.518104362442 133.57375 0.969637405271937 0.970520689897083 26 128.98 133.387203819265 132.588333333333 1.0060251944183 0.966959320736367 27 133.25 135.585281469796 130.949166666667 1.03540392750211 0.982776290726542 28 136.76 135.508438746424 129.1775 1.04900960884383 1.00923603921020 29 133.24 130.343784087298 127.955416666667 1.01866562184587 1.02221982377589 30 128.54 125.198911765818 127.393333333333 0.98277443952445 1.02668624021614 31 121.08 121.860602328733 127.01625 0.959409542706017 0.993594301079955 32 120.23 124.293209658364 126.626666666667 0.98157215166656 0.967309479982596 33 119.08 123.903162405522 126.112916666667 0.982477970381214 0.961073129112427 34 125.75 129.349131354187 125.527083333333 1.03044799512074 0.972175063593341 35 126.89 124.898929661014 124.996666666667 0.999218083103665 1.01594145237585 36 126.6 122.808870062579 124.63375 0.985358059615301 1.03087016382033 37 121.89 120.635013683395 124.4125 0.969637405271937 1.01040316802134 38 123.44 125.064441221275 124.315416666667 1.0060251944183 0.987011166360218 39 126.46 128.868098490126 124.461666666667 1.03540392750211 0.981313463003334 40 129.49 130.995074904374 124.875 1.04900960884383 0.988510446629596 41 127.78 127.714353450908 125.374166666667 1.01866562184587 1.00051401073817 42 125.29 123.798048700146 125.967916666667 0.98277443952445 1.01205149285889 43 119.02 121.783449811341 126.935833333333 0.959409542706017 0.977308494580984 44 119.96 125.991738469144 128.357083333333 0.98157215166656 0.952125920775186 45 122.86 127.633303766402 129.909583333333 0.982477970381214 0.962601424349724 46 131.89 135.299539172678 131.301666666667 1.03044799512074 0.97480006810425 47 132.73 132.298555907265 132.402083333333 0.999218083103665 1.00326113984976 48 135.01 131.116259636852 133.064583333333 0.985358059615301 1.02969685357051 49 136.71 129.351649941204 133.402083333333 0.969637405271937 1.05688640277987 50 142.73 134.692521509023 133.885833333333 1.0060251944183 1.05967278955750 51 144.43 139.748036676657 134.969583333333 1.03540392750211 1.03350289159465 52 144.93 143.296898004753 136.602083333333 1.04900960884383 1.01139663187401 53 138.75 140.773646722972 138.194166666667 1.01866562184587 0.985624818493518 54 130.22 136.819400534495 139.2175 0.98277443952445 0.951765608468433 55 122.19 NA NA 0.959409542706017 NA 56 128.4 NA NA 0.98157215166656 NA 57 140.43 NA NA 0.982477970381214 NA 58 153.5 NA NA 1.03044799512074 NA 59 149.33 NA NA 0.999218083103665 NA 60 142.97 NA NA 0.985358059615301 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956364l5dcrusotlpdkuc/1n6ha1259956301.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956364l5dcrusotlpdkuc/1n6ha1259956301.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956364l5dcrusotlpdkuc/2tzki1259956301.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956364l5dcrusotlpdkuc/2tzki1259956301.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956364l5dcrusotlpdkuc/3dr7w1259956301.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956364l5dcrusotlpdkuc/3dr7w1259956301.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956364l5dcrusotlpdkuc/4dmz01259956301.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956364l5dcrusotlpdkuc/4dmz01259956301.ps (open in new window)

Parameters (Session):

par1 = FALSE ; par2 = 0.5 ; 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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