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: Thu, 03 Dec 2009 06:22:52 -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/03/t1259846635x5aqcaudgmfx3x0.htm/, Retrieved Thu, 03 Dec 2009 14:24:00 +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/03/t1259846635x5aqcaudgmfx3x0.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:

JSSHWWS9P7

Dataseries X:

» Textbox « » Textfile « » CSV «

11.1 10.9 10 9.2 9.2 9.5 9.6 9.5 9.1 8.9 9 10.1 10.3 10.2 9.6 9.2 9.3 9.4 9.4 9.2 9 9 9 9.8 10 9.8 9.3 9 9 9.1 9.1 9.1 9.2 8.8 8.3 8.4 8.1 7.7 7.9 7.9 8 7.9 7.6 7.1 6.8 6.5 6.9 8.2 8.7 8.3 7.9 7.5 7.8 8.3 8.4 8.2 7.7 7.2 7.3 8.1

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 11.1 NA NA 0.590364583333333 NA 2 10.9 NA NA 0.341406250000000 NA 3 10 NA NA 0.0445312500000002 NA 4 9.2 NA NA -0.198177083333334 NA 5 9.2 NA NA -0.0377604166666664 NA 6 9.5 NA NA 0.150781250000000 NA 7 9.6 9.65703125 9.64166666666667 0.0153645833333326 -0.0570312499999996 8 9.5 9.44661458333333 9.57916666666667 -0.132552083333335 0.0533854166666661 9 9.1 9.24973958333333 9.53333333333333 -0.283593750000000 -0.149739583333334 10 8.9 9.04765625 9.51666666666667 -0.469010416666666 -0.147656249999999 11 9 9.08411458333333 9.52083333333333 -0.436718749999999 -0.0841145833333332 12 10.1 9.93619791666667 9.52083333333333 0.415364583333333 0.163802083333334 13 10.3 10.0986979166667 9.50833333333333 0.590364583333333 0.201302083333335 14 10.2 9.82890625 9.4875 0.341406250000000 0.37109375 15 9.6 9.51536458333333 9.47083333333333 0.0445312500000002 0.084635416666666 16 9.2 9.27265625 9.47083333333333 -0.198177083333334 -0.0726562499999996 17 9.3 9.43723958333333 9.475 -0.0377604166666664 -0.137239583333333 18 9.4 9.61328125 9.4625 0.150781250000000 -0.213281250000000 19 9.4 9.45286458333333 9.4375 0.0153645833333326 -0.0528645833333314 20 9.2 9.27578125 9.40833333333333 -0.132552083333335 -0.0757812500000004 21 9 9.09557291666667 9.37916666666666 -0.283593750000000 -0.095572916666665 22 9 8.88932291666667 9.35833333333333 -0.469010416666666 0.110677083333334 23 9 8.90078125 9.3375 -0.436718749999999 0.0992187500000004 24 9.8 9.72786458333333 9.3125 0.415364583333333 0.0721354166666686 25 10 9.87786458333333 9.2875 0.590364583333333 0.122135416666667 26 9.8 9.61223958333333 9.27083333333333 0.341406250000000 0.187760416666668 27 9.3 9.31953125 9.275 0.0445312500000002 -0.0195312499999982 28 9 9.07682291666667 9.275 -0.198177083333334 -0.076822916666666 29 9 9.19973958333333 9.2375 -0.0377604166666664 -0.199739583333335 30 9.1 9.30078125 9.15 0.150781250000000 -0.200781250000000 31 9.1 9.02786458333333 9.0125 0.0153645833333326 0.0721354166666686 32 9.1 8.71328125 8.84583333333333 -0.132552083333335 0.386718750000002 33 9.2 8.41640625 8.7 -0.283593750000000 0.783593750000001 34 8.8 8.12682291666667 8.59583333333333 -0.469010416666666 0.673177083333336 35 8.3 8.07161458333333 8.50833333333333 -0.436718749999999 0.228385416666669 36 8.4 8.83203125 8.41666666666667 0.415364583333333 -0.432031249999999 37 8.1 8.89453125 8.30416666666667 0.590364583333333 -0.79453125 38 7.7 8.49973958333333 8.15833333333333 0.341406250000000 -0.799739583333333 39 7.9 8.01953125 7.975 0.0445312500000002 -0.119531250000000 40 7.9 7.58098958333333 7.77916666666667 -0.198177083333334 0.319010416666668 41 8 7.58723958333333 7.625 -0.0377604166666664 0.412760416666667 42 7.9 7.70911458333333 7.55833333333333 0.150781250000000 0.190885416666668 43 7.6 7.59036458333333 7.575 0.0153645833333326 0.00963541666666679 44 7.1 7.49244791666666 7.625 -0.132552083333335 -0.392447916666665 45 6.8 7.36640625 7.65 -0.283593750000000 -0.566406250000001 46 6.5 7.16432291666667 7.63333333333333 -0.469010416666666 -0.664322916666667 47 6.9 7.17161458333333 7.60833333333333 -0.436718749999999 -0.271614583333332 48 8.2 8.03203125 7.61666666666667 0.415364583333333 0.16796875 49 8.7 8.25703125 7.66666666666667 0.590364583333333 0.44296875 50 8.3 8.08723958333333 7.74583333333333 0.341406250000000 0.212760416666668 51 7.9 7.87369791666667 7.82916666666667 0.0445312500000002 0.0263020833333334 52 7.5 7.69765625 7.89583333333333 -0.198177083333334 -0.197656249999999 53 7.8 7.90390625 7.94166666666666 -0.0377604166666664 -0.103906249999999 54 8.3 8.10494791666667 7.95416666666667 0.150781250000000 0.195052083333335 55 8.4 NA NA 0.0153645833333326 NA 56 8.2 NA NA -0.132552083333335 NA 57 7.7 NA NA -0.283593750000000 NA 58 7.2 NA NA -0.469010416666666 NA 59 7.3 NA NA -0.436718749999999 NA 60 8.1 NA NA 0.415364583333333 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/03/t1259846635x5aqcaudgmfx3x0/17q2e1259846570.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t1259846635x5aqcaudgmfx3x0/17q2e1259846570.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t1259846635x5aqcaudgmfx3x0/2alb31259846570.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t1259846635x5aqcaudgmfx3x0/2alb31259846570.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t1259846635x5aqcaudgmfx3x0/3dfi81259846570.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t1259846635x5aqcaudgmfx3x0/3dfi81259846570.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t1259846635x5aqcaudgmfx3x0/4nhgd1259846570.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t1259846635x5aqcaudgmfx3x0/4nhgd1259846570.ps (open in new window)

Parameters (Session):

par1 = additive ; par2 = 12 ;

Parameters (R input):

par1 = additive ; 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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