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 02:04:33 -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/t12599175396vx5yy6yrlcglgk.htm/, Retrieved Fri, 04 Dec 2009 10:05:44 +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/t12599175396vx5yy6yrlcglgk.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 «

5.7 6.1 6 5.9 5.8 5.7 5.6 5.4 5.4 5.5 5.6 5.7 5.9 6.1 6 5.8 5.8 5.7 5.5 5.3 5.2 5.2 5 5.1 5.1 5.2 4.9 4.8 4.5 4.5 4.4 4.4 4.2 4.1 3.9 3.8 3.9 4.2 4.1 3.8 3.6 3.7 3.5 3.4 3.1 3.1 3.1 3.2 3.3 3.5 3.6 3.5 3.3 3.2 3.1 3.2 3 3 3.1 3.4

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 5.7 NA NA 1.01069177570925 NA 2 6.1 NA NA 1.07078568749111 NA 3 6 NA NA 1.06408636874514 NA 4 5.9 NA NA 1.03482005530154 NA 5 5.8 NA NA 1.00184825661367 NA 6 5.7 NA NA 1.00770313813477 NA 7 5.6 5.65555196028078 5.70833333333333 0.990753628078384 0.990177446751277 8 5.4 5.58868361762068 5.71666666666667 0.977612294627524 0.96623827174153 9 5.4 5.43737320839893 5.71666666666667 0.9511440014692 0.99312660599769 10 5.5 5.48614752866397 5.7125 0.96037593499588 1.00252499067217 11 5.6 5.44618391246845 5.70833333333333 0.954076013863086 1.02824291100038 12 5.7 5.57192040670635 5.70833333333333 0.976102844970456 1.02298661573476 13 5.9 5.76515433727484 5.70416666666667 1.01069177570925 1.02338977498890 14 6.1 6.09901681166809 5.69583333333333 1.07078568749111 1.00016120439774 15 6 6.04755752903486 5.68333333333333 1.06408636874514 0.99213607662159 16 5.8 5.85966856314499 5.6625 1.03482005530154 0.989817075402475 17 5.8 5.63539644345188 5.625 1.00184825661367 1.02920886901211 18 5.7 5.61794499510132 5.575 1.00770313813477 1.01460587545272 19 5.5 5.46565751489909 5.51666666666667 0.990753628078384 1.00628332181577 20 5.3 5.32391362115906 5.44583333333333 0.977612294627524 0.995508262744156 21 5.2 5.10050970787859 5.3625 0.9511440014692 1.01950595093815 22 5.2 5.06598305710327 5.275 0.96037593499588 1.02645428170330 23 5 4.9413186884659 5.17916666666667 0.954076013863086 1.01187563790837 24 5.1 4.95372193822506 5.075 0.976102844970456 1.02952892059730 25 5.1 5.03240279988563 4.97916666666667 1.01069177570925 1.01343239060989 26 5.2 5.2423882616752 4.89583333333333 1.07078568749111 0.991914322335664 27 4.9 5.12534934278908 4.81666666666667 1.06408636874514 0.956032393556524 28 4.8 4.89383651153022 4.72916666666667 1.03482005530154 0.98082557287945 29 4.5 4.64607129004589 4.6375 1.00184825661367 0.968560256413017 30 4.5 4.5724529892865 4.5375 1.00770313813477 0.984154459443048 31 4.4 4.39234108448084 4.43333333333333 0.990753628078384 1.00174369780758 32 4.4 4.24446671250783 4.34166666666667 0.977612294627524 1.03664377600932 33 4.2 4.05821440626859 4.26666666666667 0.9511440014692 1.03493792578145 34 4.1 4.02557579419107 4.19166666666667 0.96037593499588 1.01848784114718 35 3.9 3.92363760701194 4.1125 0.954076013863086 0.993975588629872 36 3.8 3.94508233175559 4.04166666666667 0.976102844970456 0.963224511035482 37 3.9 4.01328859271214 3.97083333333333 1.01069177570925 0.971771630647777 38 4.2 4.16714096715289 3.89166666666667 1.07078568749111 1.0078852702863 39 4.1 4.04796189443462 3.80416666666667 1.06408636874514 1.01285538424582 40 3.8 3.84608120553740 3.71666666666667 1.03482005530154 0.98801866027398 41 3.6 3.64839740116811 3.64166666666667 1.00184825661367 0.986734613627174 42 3.7 3.61093624498291 3.58333333333333 1.00770313813477 1.02466500347128 43 3.5 3.50066281921029 3.53333333333333 0.990753628078384 0.9998106589396 44 3.4 3.40127610839159 3.47916666666667 0.977612294627524 0.999624814819225 45 3.1 3.26163130503813 3.42916666666667 0.9511440014692 0.950444642596952 46 3.1 3.26127661259018 3.39583333333333 0.96037593499588 0.950548011791589 47 3.1 3.21603123006349 3.37083333333333 0.954076013863086 0.963920987775608 48 3.2 3.25774324508890 3.3375 0.976102844970456 0.98227507794669 49 3.3 3.33528285984052 3.3 1.01069177570925 0.989421329067662 50 3.5 3.50682312653337 3.275 1.07078568749111 0.998054328294534 51 3.6 3.47158177803101 3.2625 1.06408636874514 1.03699127089030 52 3.5 3.36747692996044 3.25416666666667 1.03482005530154 1.03935381675833 53 3.3 3.25600683399442 3.25 1.00184825661367 1.01351138626193 54 3.2 3.28343272508911 3.25833333333333 1.00770313813477 0.974589786947182 55 3.1 NA NA 0.990753628078384 NA 56 3.2 NA NA 0.977612294627524 NA 57 3 NA NA 0.9511440014692 NA 58 3 NA NA 0.96037593499588 NA 59 3.1 NA NA 0.954076013863086 NA 60 3.4 NA NA 0.976102844970456 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t12599175396vx5yy6yrlcglgk/1sin81259917471.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599175396vx5yy6yrlcglgk/1sin81259917471.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599175396vx5yy6yrlcglgk/263sr1259917471.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599175396vx5yy6yrlcglgk/263sr1259917471.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599175396vx5yy6yrlcglgk/3l54b1259917471.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599175396vx5yy6yrlcglgk/3l54b1259917471.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599175396vx5yy6yrlcglgk/46iuy1259917471.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599175396vx5yy6yrlcglgk/46iuy1259917471.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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