*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: Tue, 07 Dec 2010 09:46:51 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/07/t129171510907oikse81eeez4o.htm/, Retrieved Tue, 07 Dec 2010 10:45:09 +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/2010/Dec/07/t129171510907oikse81eeez4o.htm/}, year = {2010}, } @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 = {2010}, 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 «

46 62 66 59 58 61 41 27 58 70 49 59 44 36 72 45 56 54 53 35 61 52 47 51 52 63 74 45 51 64 36 30 55 64 39 40 63 45 59 55 40 64 27 28 45 57 45 69 60 56 58 50 51 53 37 22 55 70 62 58 39 49 58 47 42 62 39 40 72 70 54 65

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 3 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 46 NA NA 1.02430936011645 NA 2 62 NA NA 0.984160598768847 NA 3 66 NA NA 1.26227639794752 NA 4 59 NA NA 0.954726884053607 NA 5 58 NA NA 0.940644204431537 NA 6 61 NA NA 1.16559081846246 NA 7 41 41.1814059469025 54.5833333333333 0.75446850589745 0.995594954986811 8 27 29.6042866915401 53.4166666666667 0.554214415442248 0.912030081363714 9 58 56.3979207400056 52.5833333333333 1.07254365908093 1.02840670788875 10 70 64.4289840654308 52.25 1.23309060412308 1.08646754275858 11 49 49.3588894006121 51.5833333333333 0.956876692742076 0.992728981446498 12 59 56.1805528595678 51.2083333333333 1.09709785893379 1.05018546448768 13 44 52.6665729326543 51.4166666666667 1.02430936011645 0.835444524865204 14 36 51.4223912856722 52.25 0.984160598768847 0.700084128721385 15 72 66.5324851418172 52.7083333333333 1.26227639794752 1.08217812466389 16 45 49.7253585444587 52.0833333333333 0.954726884053607 0.904970850230595 17 56 48.2080154771163 51.25 0.940644204431537 1.16163255105538 18 54 59.2508666051753 50.8333333333333 1.16559081846246 0.911379075007206 19 53 38.3521490497871 50.8333333333333 0.75446850589745 1.38193038234175 20 35 28.9807954741675 52.2916666666667 0.554214415442248 1.20769631845329 21 61 57.3810857608298 53.5 1.07254365908093 1.06306806835713 22 52 66.0731048709282 53.5833333333333 1.23309060412308 0.787007059855601 23 47 51.0732934751083 53.375 0.956876692742076 0.920246116943809 24 51 58.7861602745355 53.5833333333333 1.09709785893379 0.867551133835352 25 52 54.5871529828727 53.2916666666667 1.02430936011645 0.952605094028545 26 63 51.5454113605184 52.375 0.984160598768847 1.22222324620452 27 74 65.5331829934421 51.9166666666667 1.26227639794752 1.12919892823465 28 45 49.8049191181299 52.1666666666667 0.954726884053607 0.903525209894764 29 51 49.2270466985838 52.3333333333333 0.940644204431537 1.03601583723419 30 64 60.0764934349195 51.5416666666667 1.16559081846246 1.06530851487414 31 36 38.8865642414644 51.5416666666667 0.75446850589745 0.925769625119349 32 30 28.4034887914152 51.25 0.554214415442248 1.05620827850793 33 55 53.4931149966614 49.875 1.07254365908093 1.02816970003397 34 64 61.2435000047795 49.6666666666667 1.23309060412308 1.04500885800135 35 39 47.4850058773255 49.625 0.956876692742076 0.821311891605405 36 40 53.9406447309113 49.1666666666667 1.09709785893379 0.741555837894492 37 63 49.9777608623486 48.7916666666667 1.02430936011645 1.26056067564767 38 45 47.5677622738276 48.3333333333333 0.984160598768847 0.946018854974803 39 59 60.378887701823 47.8333333333333 1.26227639794752 0.977162750850387 40 55 44.9915044110262 47.125 0.954726884053607 1.22245301018476 41 40 44.2886646253182 47.0833333333333 0.940644204431537 0.903165637040532 42 64 56.5797209795321 48.5416666666667 1.16559081846246 1.13114732437709 43 27 37.440499605161 49.625 0.75446850589745 0.721144223093598 44 28 27.6876285048023 49.9583333333333 0.554214415442248 1.01128198809600 45 45 54.0293868262019 50.375 1.07254365908093 0.83288007958989 46 57 61.8086665316692 50.125 1.23309060412308 0.922200772132733 47 45 48.2026633968821 50.375 0.956876692742076 0.933558372687571 48 69 55.2663046437896 50.375 1.09709785893379 1.24850033749730 49 60 51.5569044591948 50.3333333333333 1.02430936011645 1.16376265467000 50 56 49.7001102378267 50.5 0.984160598768847 1.12675806415774 51 58 63.9553374960077 50.6666666666667 1.26227639794752 0.906882869684185 52 50 49.2877753892675 51.625 0.954726884053607 1.01445032982535 53 51 49.7365623093175 52.875 0.940644204431537 1.02540259382675 54 53 61.9220122308184 53.125 1.16559081846246 0.855915337544894 55 37 39.0751813679388 51.7916666666667 0.75446850589745 0.94689259792812 56 22 28.0571047817638 50.625 0.554214415442248 0.784115117048687 57 55 53.9846975070735 50.3333333333333 1.07254365908093 1.01880722759989 58 70 61.9114240820128 50.2083333333333 1.23309060412308 1.13064755072137 59 62 47.5647456017207 49.7083333333333 0.956876692742076 1.30348642078635 60 58 54.5349060711671 49.7083333333333 1.09709785893379 1.06353900975480 61 39 51.3861862325087 50.1666666666667 1.02430936011645 0.758958834258208 62 49 50.1921905372112 51 0.984160598768847 0.97624748941118 63 58 66.2169160423303 52.4583333333333 1.26227639794752 0.875909110036513 64 47 50.7596460021835 53.1666666666667 0.954726884053607 0.92593238333416 65 42 49.6973688007996 52.8333333333333 0.940644204431537 0.845115164312769 66 62 61.5334819579976 52.7916666666667 1.16559081846246 1.00758153166630 67 39 NA NA 0.75446850589745 NA 68 40 NA NA 0.554214415442248 NA 69 72 NA NA 1.07254365908093 NA 70 70 NA NA 1.23309060412308 NA 71 54 NA NA 0.956876692742076 NA 72 65 NA NA 1.09709785893379 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/07/t129171510907oikse81eeez4o/1d5l61291715207.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t129171510907oikse81eeez4o/1d5l61291715207.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t129171510907oikse81eeez4o/2d5l61291715207.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t129171510907oikse81eeez4o/2d5l61291715207.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t129171510907oikse81eeez4o/3d5l61291715207.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t129171510907oikse81eeez4o/3d5l61291715207.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t129171510907oikse81eeez4o/46w2r1291715207.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t129171510907oikse81eeez4o/46w2r1291715207.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = 1 ; par3 = 1 ; par4 = 12 ;

Parameters (R input):

par1 = multiplicative ; par2 = 12 ; par3 = 1 ; par4 = 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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