ws9: classical decomposition

*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 10:14:02 -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/04/t125994693834mgjb7nmkymtn9.htm/, Retrieved Fri, 04 Dec 2009 18:15:44 +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/t125994693834mgjb7nmkymtn9.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 «

6,3 6,2 6,1 6,3 6,5 6,6 6,5 6,2 6,2 5,9 6,1 6,1 6,1 6,1 6,1 6,4 6,7 6,9 7 7 6,8 6,4 5,9 5,5 5,5 5,6 5,8 5,9 6,1 6,1 6 6 5,9 5,5 5,6 5,4 5,2 5,2 5,2 5,5 5,8 5,8 5,5 5,3 5,1 5,2 5,8 5,8 5,5 5 4,9 5,3 6,1 6,5 6,8 6,6 6,4 6,4 6,6

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 6.3 NA NA 0.932947965393243 NA 2 6.2 NA NA 0.942710772479635 NA 3 6.1 NA NA 0.958746622447501 NA 4 6.3 NA NA 1.00292315303771 NA 5 6.5 NA NA 1.05077040273922 NA 6 6.6 NA NA 1.06226047029432 NA 7 6.5 6.52474566460572 6.24166666666667 1.04535311048423 0.996207413150224 8 6.2 6.46166727738326 6.22916666666667 1.03732451275718 0.959504681044298 9 6.2 6.31489569292848 6.225 1.01444107516923 0.98180560716828 10 5.9 6.11383075159805 6.22916666666667 0.9814845353736 0.96502507833699 11 6.1 6.25017793794432 6.24166666666667 1.00136362156651 0.975972214001685 12 6.1 6.07258191108831 6.2625 0.969673758257614 1.00451506283705 13 6.1 5.87368489878829 6.29583333333333 0.932947965393243 1.03853034425772 14 6.1 5.98621340524568 6.35 0.942710772479635 1.01900810864087 15 6.1 6.14396793885107 6.40833333333333 0.958746622447501 0.992843722609124 16 6.4 6.47303318356424 6.45416666666666 1.00292315303771 0.988717316674712 17 6.7 6.79498193771365 6.46666666666667 1.05077040273922 0.986021752731015 18 6.9 6.83387569222677 6.43333333333333 1.06226047029432 1.00967595998980 19 7 6.67283735525765 6.38333333333333 1.04535311048423 1.04902901529356 20 7 6.57404409959862 6.3375 1.03732451275718 1.06479358731825 21 6.8 6.39520561137938 6.30416666666667 1.01444107516923 1.06329654013005 22 6.4 6.15472594057195 6.27083333333333 0.9814845353736 1.03985133729695 23 5.9 6.23348854425154 6.225 1.00136362156651 0.946500496169343 24 5.5 5.97965484258862 6.16666666666667 0.969673758257614 0.919785530232883 25 5.5 5.68320802252051 6.09166666666667 0.932947965393243 0.967763273525355 26 5.6 5.66412055798181 6.00833333333333 0.942710772479635 0.988679520973216 27 5.8 5.68456851559498 5.92916666666667 0.958746622447501 1.02030611190425 28 5.9 5.87127929174161 5.85416666666667 1.00292315303771 1.00489172918393 29 6.1 6.09884654589891 5.80416666666667 1.05077040273922 1.00018912659835 30 6.1 6.14783247182836 5.7875 1.06226047029432 0.992219620159211 31 6 6.03255857508606 5.77083333333333 1.04535311048423 0.994602858027682 32 6 5.95597157741413 5.74166666666667 1.03732451275718 1.00739231576471 33 5.9 5.78231412846463 5.7 1.01444107516923 1.02035272884191 34 5.5 5.55356666265562 5.65833333333333 0.9814845353736 0.99035454764308 35 5.6 5.63684271973483 5.62916666666667 1.00136362156651 0.993463943990163 36 5.4 5.43421335356871 5.60416666666667 0.969673758257614 0.99370408349053 37 5.2 5.19729762387819 5.57083333333333 0.932947965393243 1.00051995793148 38 5.2 5.20454905639799 5.52083333333333 0.942710772479635 0.99912594610048 39 5.2 5.23315864752595 5.45833333333333 0.958746622447501 0.993663741201192 40 5.5 5.42832156581662 5.4125 1.00292315303771 1.01320452985592 41 5.8 5.68291659481464 5.40833333333333 1.05077040273922 1.02060269638520 42 5.8 5.77161522193246 5.43333333333333 1.06226047029432 1.00491799556555 43 5.5 5.71024136602009 5.4625 1.04535311048423 0.963181702393322 44 5.3 5.67070733640591 5.46666666666667 1.03732451275718 0.934627672631601 45 5.1 5.52447702185912 5.44583333333333 1.01444107516923 0.9231643067426 46 5.2 5.32455360440178 5.425 0.9814845353736 0.976607690774526 47 5.8 5.43656999542152 5.42916666666667 1.00136362156651 1.06684913555505 48 5.8 5.30492351913436 5.47083333333333 0.969673758257614 1.09332396199115 49 5.5 NA 5.55416666666667 NA NA 50 5 NA 5.6625 NA NA 51 4.9 NA 5.77083333333333 NA NA 52 5.3 NA 5.875 NA NA 53 6.1 NA 5.95833333333333 NA NA 54 6.5 NA NA NA NA 55 6.8 NA NA NA NA 56 6.6 NA NA NA NA 57 6.4 NA NA NA NA 58 6.4 NA NA NA NA 59 6.6 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t125994693834mgjb7nmkymtn9/1kxs21259946841.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t125994693834mgjb7nmkymtn9/1kxs21259946841.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t125994693834mgjb7nmkymtn9/2yc4n1259946841.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t125994693834mgjb7nmkymtn9/2yc4n1259946841.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t125994693834mgjb7nmkymtn9/31mhd1259946841.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t125994693834mgjb7nmkymtn9/31mhd1259946841.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t125994693834mgjb7nmkymtn9/4ph041259946841.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t125994693834mgjb7nmkymtn9/4ph041259946841.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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