Thomas Van den Bosch opgave 9 oef 2

*Unverified author*

R Software Module: rwasp_decompose.wasp (opens new window with default values)

Title produced by software: Classical Decomposition

Date of computation: Sun, 07 Jun 2009 13:20:53 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Jun/07/t124440248007u9m35lblj0jf7.htm/, Retrieved Sun, 07 Jun 2009 21:21:25 +0200

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/Jun/07/t124440248007u9m35lblj0jf7.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 «

36.80 35.40 33.00 28.73 26.70 26.46 24.60 28.00 31.60 33.50 34.50 35.00 34.76 33.50 32.74 34.40 31.93 29.24 25.75 26.03 26.08 23.80 20.61 19.70 18.18 19.60 20.60 20.03 23.00 23.60 22.56 22.55 23.75 24.92 24.50 30.58 28.07 27.70 27.00 25.23 26.86 25.60 24.55 23.96 23.50 23.64 21.55 21.05 21.89 21.98 21.45 22.15 22.58 23.80 23.30 22.38 23.00 21.96 22.40 20.80 20.40 16.00 12.78 9.75 7.50 11.24 12.24 12.75 12.52 14.49 14.21 14.32 22.15 22.58 23.80 23.30 22.38 23.00 21.96 22.40 20.80 20.40 16.00 12.78 9.75 7.50 11.24 12.24 12.75 12.52 14.49 14.21 14.32

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 'George Udny Yule' @ 72.249.76.132 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 36.8 NA NA 1.02927662037037 NA 2 35.4 NA NA 0.404832175925924 NA 3 33 NA NA 0.0203877314814813 NA 4 28.73 NA NA -0.398640046296296 NA 5 26.7 NA NA -0.28086226851852 NA 6 26.46 NA NA 0.373582175925926 NA 7 24.6 30.7874016203704 31.1058333333333 -0.318431712962963 -6.18740162037037 8 28 30.9291377314815 30.9416666666667 -0.0125289351851842 -2.92913773148148 9 31.6 31.0989988425926 30.8516666666667 0.247332175925927 0.501001157407405 10 33.5 31.5542766203704 31.0770833333333 0.477193287037038 1.94572337962963 11 34.5 30.6388599537037 31.53125 -0.892390046296294 3.86114004629629 12 35 31.2152488425926 31.865 -0.649751157407407 3.78475115740741 13 34.76 33.0580266203704 32.02875 1.02927662037037 1.70197337962963 14 33.5 32.3994155092593 31.9945833333333 0.404832175925924 1.10058449074074 15 32.74 31.7028877314815 31.6825 0.0203877314814813 1.03711226851852 16 34.4 30.6496932870370 31.0483333333333 -0.398640046296296 3.75030671296296 17 31.93 29.7845543981481 30.0654166666667 -0.28086226851852 2.14544560185185 18 29.24 29.2227488425926 28.8491666666667 0.373582175925926 0.0172511574074079 19 25.75 27.2024016203704 27.5208333333333 -0.318431712962963 -1.45240162037037 20 26.03 26.2383043981481 26.2508333333333 -0.0125289351851842 -0.208304398148144 21 26.08 25.4131655092593 25.1658333333333 0.247332175925927 0.666834490740744 22 23.8 24.5384432870370 24.06125 0.477193287037038 -0.738443287037036 23 20.61 22.1980266203704 23.0904166666667 -0.892390046296294 -1.58802662037037 24 19.7 21.8335821759259 22.4833333333333 -0.649751157407407 -2.13358217592592 25 18.18 23.1446932870370 22.1154166666667 1.02927662037037 -4.96469328703703 26 19.6 22.2423321759259 21.8375 0.404832175925924 -2.64233217592593 27 20.6 21.6158043981481 21.5954166666667 0.0203877314814813 -1.01580439814815 28 20.03 21.1463599537037 21.545 -0.398640046296296 -1.1163599537037 29 23 21.4728877314815 21.75375 -0.28086226851852 1.52711226851852 30 23.6 22.7427488425926 22.3691666666667 0.373582175925926 0.857251157407411 31 22.56 22.9161516203704 23.2345833333333 -0.318431712962963 -0.356151620370373 32 22.55 23.9716377314815 23.9841666666667 -0.0125289351851842 -1.42163773148148 33 23.75 24.8356655092593 24.5883333333333 0.247332175925927 -1.08566550925926 34 24.92 25.5488599537037 25.0716666666667 0.477193287037038 -0.628859953703707 35 24.5 24.5567766203704 25.4491666666667 -0.892390046296294 -0.0567766203703641 36 30.58 25.0435821759259 25.6933333333333 -0.649751157407407 5.53641782407407 37 28.07 26.8888599537037 25.8595833333333 1.02927662037037 1.18114004629630 38 27.7 26.4060821759259 26.00125 0.404832175925924 1.29391782407407 39 27 26.0699710648148 26.0495833333333 0.0203877314814813 0.930028935185184 40 25.23 25.5871932870370 25.9858333333333 -0.398640046296296 -0.357193287037038 41 26.86 25.5287210648148 25.8095833333333 -0.28086226851852 1.33127893518519 42 25.6 25.6631655092593 25.2895833333333 0.373582175925926 -0.0631655092592567 43 24.55 24.3165682870370 24.635 -0.318431712962963 0.233431712962965 44 23.96 24.1266377314815 24.1391666666667 -0.0125289351851842 -0.166637731481480 45 23.5 23.9169155092593 23.6695833333333 0.247332175925927 -0.416915509259258 46 23.64 23.7871932870370 23.31 0.477193287037038 -0.147193287037037 47 21.55 22.1109432870370 23.0033333333333 -0.892390046296294 -0.560943287037034 48 21.05 22.1002488425926 22.75 -0.649751157407407 -1.05024884259259 49 21.89 23.652193287037 22.6229166666667 1.02927662037037 -1.76219328703703 50 21.98 22.9098321759259 22.505 0.404832175925924 -0.929832175925924 51 21.45 22.4387210648148 22.4183333333333 0.0203877314814813 -0.988721064814815 52 22.15 21.9288599537037 22.3275 -0.398640046296296 0.221140046296298 53 22.58 22.0120543981481 22.2929166666667 -0.28086226851852 0.567945601851854 54 23.8 22.6914988425926 22.3179166666667 0.373582175925926 1.10850115740741 55 23.3 21.9269849537037 22.2454166666667 -0.318431712962963 1.3730150462963 56 22.38 21.9216377314815 21.9341666666667 -0.0125289351851842 0.458362268518513 57 23 21.5710821759259 21.32375 0.247332175925927 1.42891782407407 58 21.96 20.9230266203704 20.4458333333333 0.477193287037038 1.03697337962963 59 22.4 18.4084432870370 19.3008333333333 -0.892390046296294 3.99155671296296 60 20.8 17.4994155092593 18.1491666666667 -0.649751157407407 3.30058449074074 61 20.4 18.1942766203704 17.165 1.02927662037037 2.20572337962963 62 16 16.7077488425926 16.3029166666667 0.404832175925924 -0.707748842592592 63 12.78 15.4853877314815 15.465 0.0203877314814813 -2.70538773148148 64 9.75 14.3184432870370 14.7170833333333 -0.398640046296296 -4.56844328703704 65 7.5 13.7837210648148 14.0645833333333 -0.28086226851852 -6.28372106481481 66 11.24 13.8269155092593 13.4533333333333 0.373582175925926 -2.58691550925926 67 12.24 12.9378182870370 13.25625 -0.318431712962963 -0.697818287037038 68 12.75 13.5908043981481 13.6033333333333 -0.0125289351851842 -0.840804398148148 69 12.52 14.5839988425926 14.3366666666667 0.247332175925927 -2.06399884259259 70 14.49 15.8376099537037 15.3604166666667 0.477193287037038 -1.34760995370370 71 14.21 15.6526099537037 16.545 -0.892390046296294 -1.44260995370370 72 14.32 17.0052488425926 17.655 -0.649751157407407 -2.68524884259259 73 22.15 19.5792766203704 18.55 1.02927662037037 2.57072337962963 74 22.58 19.7619155092593 19.3570833333333 0.404832175925924 2.81808449074074 75 23.8 20.1245543981481 20.1041666666667 0.0203877314814813 3.67544560185186 76 23.3 20.2967766203704 20.6954166666667 -0.398640046296296 3.00322337962963 77 22.38 20.7353877314815 21.01625 -0.28086226851852 1.64461226851851 78 23 21.4002488425926 21.0266666666667 0.373582175925926 1.59975115740741 79 21.96 20.1274016203704 20.4458333333333 -0.318431712962963 1.83259837962963 80 22.4 19.2883043981481 19.3008333333333 -0.0125289351851842 3.11169560185185 81 20.8 18.3964988425926 18.1491666666667 0.247332175925927 2.40350115740741 82 20.4 17.6421932870370 17.165 0.477193287037038 2.75780671296296 83 16 15.4105266203704 16.3029166666667 -0.892390046296294 0.589473379629627 84 12.78 14.8152488425926 15.465 -0.649751157407407 -2.03524884259259 85 9.75 NA 14.7170833333333 NA NA 86 7.5 NA 14.0645833333333 NA NA 87 11.24 NA 13.4533333333333 NA NA 88 12.24 NA NA NA NA 89 12.75 NA NA NA NA 90 12.52 NA NA NA NA 91 14.49 NA NA NA NA 92 14.21 NA NA NA NA 93 14.32 NA NA NA NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/07/t124440248007u9m35lblj0jf7/1393a1244402451.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/07/t124440248007u9m35lblj0jf7/1393a1244402451.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/07/t124440248007u9m35lblj0jf7/2fk571244402451.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/07/t124440248007u9m35lblj0jf7/2fk571244402451.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/07/t124440248007u9m35lblj0jf7/3dkoq1244402451.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/07/t124440248007u9m35lblj0jf7/3dkoq1244402451.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/07/t124440248007u9m35lblj0jf7/4vf701244402451.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/07/t124440248007u9m35lblj0jf7/4vf701244402451.ps (open in new window)

Parameters (Session):

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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