WS 9 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, 11 Dec 2009 02:02:56 -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/11/t1260522271farxw4hbf39wgaz.htm/, Retrieved Fri, 11 Dec 2009 10:04:37 +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/11/t1260522271farxw4hbf39wgaz.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 «

108.8 128.4 121.1 119.5 128.7 108.7 105.5 119.8 111.3 110.6 120.1 97.5 107.7 127.3 117.2 119.8 116.2 111 112.4 130.6 109.1 118.8 123.9 101.6 112.8 128 129.6 125.8 119.5 115.7 113.6 129.7 112 116.8 127 112.1 114.2 121.1 131.6 125 120.4 117.7 117.5 120.6 127.5 112.3 124.5 115.2 104.7 130.9 129.2 113.5 125.6 107.6 107 121.6 110.7 106.3 118.6 104.6

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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 108.8 NA NA 0.931589383338221 NA 2 128.4 NA NA 1.07567516197286 NA 3 121.1 NA NA 1.07522779088427 NA 4 119.5 NA NA 1.02612324059806 NA 5 128.7 NA NA 1.02207725546480 NA 6 108.7 NA NA 0.958011024227935 NA 7 105.5 109.374607482350 114.954166666667 0.951462749493057 0.964574890172977 8 119.8 121.948216255011 114.8625 1.06168868216355 0.982384192889554 9 111.3 111.635304139402 114.654166666667 0.973669840224461 0.996996432786322 10 110.6 111.183928997099 114.504166666667 0.971003346286662 0.994748080928907 11 120.1 119.711506887963 113.995833333333 1.05013931989879 1.00324524452274 12 97.5 102.592191349491 113.570833333333 0.903332205447332 0.950364727738934 13 107.7 106.158491853821 113.954166666667 0.931589383338221 1.01452081806420 14 127.3 123.370977118604 114.691666666667 1.07567516197286 1.03184722187633 15 117.2 123.704957341235 115.05 1.07522779088427 0.947415548406103 16 119.8 118.312009640956 115.3 1.02612324059806 1.01257683276245 17 116.2 118.356546182824 115.8 1.02207725546480 0.981779240334603 18 111 111.253021901070 116.129166666667 0.958011024227935 0.997725707610038 19 112.4 110.857303600310 116.5125 0.951462749493057 1.01391605559208 20 130.6 123.956577345437 116.754166666667 1.06168868216355 1.05359475710635 21 109.1 114.211472258329 117.3 0.973669840224461 0.955245544451367 22 118.8 114.643128418245 118.066666666667 0.971003346286662 1.03625923017897 23 123.9 124.393378022512 118.454166666667 1.05013931989879 0.996033727595834 24 101.6 107.304574354575 118.7875 0.903332205447332 0.946837547337685 25 112.8 110.890189596693 119.033333333333 0.931589383338221 1.01722253709055 26 128 128.054646053027 119.045833333333 1.07567516197286 0.999573259895585 27 129.6 128.090990704884 119.129166666667 1.07522779088427 1.01178076058911 28 125.8 122.279686171268 119.166666666667 1.02612324059806 1.02878903225022 29 119.5 121.844384817097 119.2125 1.02207725546480 0.980759188692885 30 115.7 114.749762139502 119.779166666667 0.958011024227935 1.00828095712602 31 113.6 114.437182195277 120.275 0.951462749493057 0.992684351543637 32 129.7 127.451302590892 120.045833333333 1.06168868216355 1.01764358122197 33 112 116.686216435566 119.841666666667 0.973669840224461 0.959839160282018 34 116.8 116.415209525218 119.891666666667 0.971003346286662 1.00330532819853 35 127 125.907328875366 119.895833333333 1.05013931989879 1.00867837586894 36 112.1 108.414920190437 120.016666666667 0.903332205447332 1.03399052273515 37 114.2 112.035268213713 120.2625 0.931589383338221 1.01932187801932 38 121.1 129.130321215000 120.045833333333 1.07567516197286 0.937812272598394 39 131.6 129.363343590764 120.3125 1.07522779088427 1.01728972324890 40 125 123.925758869728 120.770833333333 1.02612324059806 1.00866842487042 41 120.4 123.139016007353 120.479166666667 1.02207725546480 0.977756716789184 42 117.7 115.444320132067 120.504166666667 0.958011024227935 1.01953911517996 43 117.5 114.401502342171 120.2375 0.951462749493057 1.02708441405394 44 120.6 127.668064030167 120.25 1.06168868216355 0.944637180144776 45 127.5 117.384013154394 120.558333333333 0.973669840224461 1.08617857384293 46 112.3 116.500172318018 119.979166666667 0.971003346286662 0.963947072056229 47 124.5 125.719178913884 119.716666666667 1.05013931989879 0.99030236337513 48 115.2 107.959490203524 119.5125 0.903332205447332 1.06706691355087 49 104.7 110.536961955511 118.654166666667 0.931589383338221 0.947194478188574 50 130.9 127.207551862974 118.258333333333 1.07567516197286 1.02902695699233 51 129.2 126.446788207990 117.6 1.07522779088427 1.02177367911853 52 113.5 119.697276015763 116.65 1.02612324059806 0.948225421479541 53 125.6 118.718531877467 116.154166666667 1.02207725546480 1.05796456554597 54 107.6 110.618339597519 115.466666666667 0.958011024227935 0.972713931446621 55 107 NA NA 0.951462749493057 NA 56 121.6 NA NA 1.06168868216355 NA 57 110.7 NA NA 0.973669840224461 NA 58 106.3 NA NA 0.971003346286662 NA 59 118.6 NA NA 1.05013931989879 NA 60 104.6 NA NA 0.903332205447332 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/11/t1260522271farxw4hbf39wgaz/1p3of1260522169.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260522271farxw4hbf39wgaz/1p3of1260522169.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260522271farxw4hbf39wgaz/2xl9n1260522169.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260522271farxw4hbf39wgaz/2xl9n1260522169.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260522271farxw4hbf39wgaz/3so2o1260522169.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260522271farxw4hbf39wgaz/3so2o1260522169.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260522271farxw4hbf39wgaz/4xdj11260522169.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260522271farxw4hbf39wgaz/4xdj11260522169.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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