*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: Wed, 16 Dec 2009 07:24:13 -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/16/t12609735169z33xriusdmzy9g.htm/, Retrieved Wed, 16 Dec 2009 15:25:22 +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/16/t12609735169z33xriusdmzy9g.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 «

19915 19843 19761 20858 21968 23061 22661 22269 21857 21568 21274 20987 19683 19381 19071 20772 22485 24181 23479 22782 22067 21489 20903 20330 19736 19483 19242 20334 21423 22523 21986 21462 20908 20575 20237 19904 19610 19251 18941 20450 21946 23409 22741 22069 21539 21189 20960 20704 19697 19598 19456 20316 21083 22158 21469 20892 20578 20233 19947 20049

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 19915 NA NA 0.935037164241845 NA 2 19843 NA NA 0.924230234259409 NA 3 19761 NA NA 0.913587492962456 NA 4 20858 NA NA 0.976177171134057 NA 5 21968 NA NA 1.03771212449783 NA 6 23061 NA NA 1.10244455450293 NA 7 22661 22929.2396766862 21325.5 1.07520291091352 0.988301414243624 8 22269 22327.4019818817 21296.5833333333 1.04840300589132 0.997384291198362 9 21857 21727.0900574691 21248.5833333333 1.02251946478639 1.00597916896314 10 21568 21314.375849904 21216.25 1.00462503269447 1.01189920605145 11 21274 20983.4826382474 21234.2083333333 0.988192369070226 1.01384504978325 12 20987 20703.1472006182 21302.4166666667 0.97186847504555 1.01371061107914 13 19683 19994.0555225107 21383.1666666667 0.935037164241845 0.984442599843712 14 19381 19814.2254059496 21438.625 0.924230234259409 0.9781356375496 15 19071 19613.5814895377 21468.75 0.913587492962456 0.97233643993948 16 20772 20962.6319431767 21474.2083333333 0.976177171134057 0.990906106461561 17 22485 22264.5892491580 21455.4583333333 1.03771212449783 1.00989961002089 18 24181 23606.2318288633 21412.625 1.10244455450293 1.02434815413589 19 23479 22995.8574570416 21387.4583333333 1.07520291091352 1.02100998163956 20 22782 22429.4465411218 21393.9166666667 1.04840300589132 1.01571833073241 21 22067 21887.3273785967 21405.2916666667 1.02251946478639 1.00820897948367 22 21489 21493.1153869709 21394.1666666667 1.00462503269447 0.999808525339543 23 20903 21079.7902195497 21331.6666666667 0.988192369070226 0.991613283732504 24 20330 20621.4292596748 21218.3333333333 0.97186847504555 0.985867649812 25 19736 19717.1676422496 21087.0416666667 0.935037164241845 1.00095512489887 26 19483 19380.9539740474 20969.8333333333 0.924230234259409 1.00526527363355 27 19242 19063.4114880466 20866.5416666667 0.913587492962456 1.00936812973194 28 20334 20285.1243123609 20780.1666666667 0.976177171134057 1.00240943495768 29 21423 21495.5148508895 20714.3333333333 1.03771212449783 0.996626512489116 30 22523 22786.2427562620 20668.8333333333 1.10244455450293 0.988447294313599 31 21986 22198.4600982353 20645.8333333333 1.07520291091352 0.990429061417093 32 21462 21629.5150476267 20630.9166666667 1.04840300589132 0.992255256428178 33 20908 21072.8054149389 20608.7083333333 1.02251946478639 0.992179237092843 34 20575 20696.2802985387 20601 1.00462503269447 0.994139995362003 35 20237 20384.0616170422 20627.625 0.988192369070226 0.992785460532592 36 19904 20104.3952309506 20686.3333333333 0.97186847504555 0.990032267638567 37 19610 19406.4236246666 20754.7083333333 0.935037164241845 1.01049015415054 38 19251 19234.5790106966 20811.4583333333 0.924230234259409 1.00085372231408 39 18941 19060.2139318213 20863.0416666667 0.913587492962456 0.993745404314575 40 20450 20416.6641861712 20914.9166666667 0.976177171134057 1.00163277475325 41 21946 21761.4718207973 20970.625 1.03771212449783 1.00847958174531 42 23409 23188.9106297942 21034.0833333333 1.10244455450293 1.00949114745921 43 22741 22655.64533598 21071.0416666667 1.07520291091352 1.00376747882279 44 22069 22109.9020416179 21089.125 1.04840300589132 0.998150057764125 45 21539 21600.7662985903 21125.0416666667 1.02251946478639 0.997140550583416 46 21189 21238.694097441 21140.9166666667 1.00462503269447 0.997660209370076 47 20960 20850.2413671511 21099.375 0.988192369070226 1.00526414207472 48 20704 20420.2119908206 21011.2916666667 0.97186847504555 1.01389740759337 49 19697 19548.0427951674 20906.1666666667 0.935037164241845 1.00762005722995 50 19598 19227.801322312 20804.125 0.924230234259409 1.01925330262584 51 19456 18925.0029828628 20715.0416666667 0.913587492962456 1.02805796213707 52 20316 20143.5786225465 20635.1666666667 0.976177171134057 1.00855961995058 53 21083 21328.2270088194 20553.125 1.03771212449783 0.98850223186775 54 22158 22582.0608377301 20483.625 1.10244455450293 0.981221340214372 55 21469 NA NA 1.07520291091352 NA 56 20892 NA NA 1.04840300589132 NA 57 20578 NA NA 1.02251946478639 NA 58 20233 NA NA 1.00462503269447 NA 59 19947 NA NA 0.988192369070226 NA 60 20049 NA NA 0.97186847504555 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/16/t12609735169z33xriusdmzy9g/1h9mt1260973451.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/16/t12609735169z33xriusdmzy9g/1h9mt1260973451.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/16/t12609735169z33xriusdmzy9g/2zjkz1260973451.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/16/t12609735169z33xriusdmzy9g/2zjkz1260973451.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/16/t12609735169z33xriusdmzy9g/3ejn91260973451.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/16/t12609735169z33xriusdmzy9g/3ejn91260973451.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/16/t12609735169z33xriusdmzy9g/49b1j1260973451.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/16/t12609735169z33xriusdmzy9g/49b1j1260973451.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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