Paper

*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: Mon, 21 Dec 2009 15:08:02 -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/21/t1261433330czdgder54880y4b.htm/, Retrieved Mon, 21 Dec 2009 23:08:56 +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/21/t1261433330czdgder54880y4b.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 «

25.6 23.7 22 21.3 20.7 20.4 20.3 20.4 19.8 19.5 23.1 23.5 23.5 22.9 21.9 21.5 20.5 20.2 19.4 19.2 18.8 18.8 22.6 23.3 23 21.4 19.9 18.8 18.6 18.4 18.6 19.9 19.2 18.4 21.1 20.5 19.1 18.1 17 17.1 17.4 16.8 15.3 14.3 13.4 15.3 22.1 23.7 22.2 19.5 16.6 17.3 19.8 21.2 21.5 20.6 19.1 19.6 23.5 24

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 25.6 NA NA 1.12034415488227 NA 2 23.7 NA NA 1.04253957080632 NA 3 22 NA NA 0.960252958391418 NA 4 21.3 NA NA 0.953716807272655 NA 5 20.7 NA NA 0.974840929211212 NA 6 20.4 NA NA 0.976292136523275 NA 7 20.3 19.9377618820680 21.6041666666667 0.922866509488199 1.01816844438582 8 20.4 19.9367495119937 21.4833333333333 0.928010062621896 1.02323600884525 9 19.8 19.287227589078 21.4458333333333 0.899346147538122 1.02658611293685 10 19.5 19.6639197888598 21.45 0.916732857289501 0.99166393116836 11 23.1 24.4363777804586 21.45 1.13922507135005 0.945311952840766 12 23.5 24.9876828981309 21.4333333333333 1.16583279462508 0.940463351316094 13 23.5 23.9613606125446 21.3875 1.12034415488227 0.980745642119212 14 22.9 22.2060928581747 21.3 1.04253957080632 1.03124850221321 15 21.9 20.3653648258846 21.2083333333333 0.960252958391418 1.07535515259539 16 21.5 20.1591890137257 21.1375 0.953716807272655 1.06651115703917 17 20.5 20.5569580947414 21.0875 0.974840929211212 0.997229254713712 18 20.2 20.5590852416193 21.0583333333333 0.976292136523275 0.982533987412418 19 19.4 19.4071136391123 21.0291666666667 0.922866509488199 0.999633451978252 20 19.2 19.4379441033345 20.9458333333333 0.928010062621896 0.987758782406744 21 18.8 18.7063998687929 20.8 0.899346147538122 1.00500364216865 22 18.8 18.8885165804024 20.6041666666667 0.916732857289501 0.995313735727968 23 22.6 23.2544317689329 20.4125 1.13922507135005 0.971857761331877 24 23.3 23.6178293644464 20.2583333333333 1.16583279462508 0.986542820699483 25 23 22.5749347208778 20.15 1.12034415488227 1.01882908120789 26 21.4 21.0028284368690 20.1458333333333 1.04253957080632 1.01891038458581 27 19.9 19.3891076515200 20.1916666666667 0.960252958391418 1.02634945133434 28 18.8 19.2571318668470 20.1916666666667 0.953716807272655 0.976261684761373 29 18.6 19.6064881887605 20.1125 0.974840929211212 0.94866555504124 30 18.4 19.4607565880306 19.9333333333333 0.976292136523275 0.945492530918195 31 18.6 18.1381721885660 19.6541666666667 0.922866509488199 1.02546165107668 32 19.9 17.9608614203279 19.3541666666667 0.928010062621896 1.10796467576312 33 19.2 17.1737641423634 19.0958333333333 0.899346147538122 1.11798437668294 34 18.4 17.3300707230103 18.9041666666667 0.916732857289501 1.06173830990598 35 21.1 21.3984442568584 18.7833333333333 1.13922507135005 0.986052992765454 36 20.5 21.7622121663348 18.6666666666667 1.16583279462508 0.94199982259674 37 19.1 20.6843539595139 18.4625 1.12034415488227 0.923403265936417 38 18.1 18.8612784018377 18.0916666666667 1.04253957080632 0.95963802741157 39 17 16.9164562836621 17.6166666666667 0.960252958391418 1.00493860622680 40 17.1 16.447641105423 17.2458333333333 0.953716807272655 1.03966276321301 41 17.4 16.7266456103824 17.1583333333333 0.974840929211212 1.04025639122764 42 16.8 16.9223970330701 17.3333333333333 0.976292136523275 0.992767157464105 43 15.3 16.2386052898694 17.5958333333333 0.922866509488199 0.94219914376175 44 14.3 16.5031122802927 17.7833333333333 0.928010062621896 0.866503224187381 45 13.4 16.0308450798670 17.825 0.899346147538122 0.835888559414058 46 15.3 16.3331237407079 17.8166666666667 0.916732857289501 0.936746714400196 47 22.1 20.4206094039497 17.925 1.13922507135005 1.08223998426440 48 23.7 21.2278721354650 18.2083333333333 1.16583279462508 1.11645669659018 49 22.2 20.8944184885543 18.65 1.12034415488227 1.06248470193898 50 19.5 19.9863523553329 19.1708333333333 1.04253957080632 0.975665776991913 51 16.6 18.8889759023578 19.6708333333333 0.960252958391418 0.878819481045973 52 17.3 19.1577863660895 20.0875 0.953716807272655 0.903027086188942 53 19.8 19.8136418862179 20.325 0.974840929211212 0.99931149021991 54 21.2 19.9122917011726 20.3958333333333 0.976292136523275 1.06466901540778 55 21.5 NA NA 0.922866509488199 NA 56 20.6 NA NA 0.928010062621896 NA 57 19.1 NA NA 0.899346147538122 NA 58 19.6 NA NA 0.916732857289501 NA 59 23.5 NA NA 1.13922507135005 NA 60 24 NA NA 1.16583279462508 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/21/t1261433330czdgder54880y4b/16lez1261433280.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261433330czdgder54880y4b/16lez1261433280.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261433330czdgder54880y4b/2ofxg1261433280.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261433330czdgder54880y4b/2ofxg1261433280.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261433330czdgder54880y4b/37ceu1261433280.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261433330czdgder54880y4b/37ceu1261433280.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261433330czdgder54880y4b/4tj2m1261433280.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261433330czdgder54880y4b/4tj2m1261433280.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = 2 ; par3 = 1 ; par4 = 1 ;

Parameters (R input):

par1 = multiplicative ; par2 = 12 ; par3 = 1 ; par4 = 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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