Opgave 9 oef 2 decomposition

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Fri, 08 Jan 2010 16:23:10 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Jan/09/t1262993284lkvcrcf1pgt4avt.htm/, Retrieved Sat, 09 Jan 2010 00:28:10 +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/Jan/09/t1262993284lkvcrcf1pgt4avt.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:

KDGP2W52

Dataseries X:

» Textbox « » Textfile « » CSV «

10 15 12 13 12 15 13 13 16 14 12 15 14 19 16 16 11 13 12 11 6 9 6 15 17 13 12 13 10 14 13 10 11 12 7 11 9 13 12 5 13 11 8 8 8 8 0 3 0 -1 -1 -4 1 -1 0 -1 6 0 -3 -3

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 10 NA NA 0.466145833333334 NA 2 15 NA NA 1.74739583333333 NA 3 12 NA NA 0.747395833333334 NA 4 13 NA NA -1.25260416666667 NA 5 12 NA NA 0.299479166666667 NA 6 15 NA NA 1.14322916666667 NA 7 13 13.7057291666667 13.5 0.205729166666666 -0.705729166666666 8 13 13.3098958333333 13.8333333333333 -0.523437500000001 -0.309895833333334 9 16 13.6953125 14.1666666666667 -0.471354166666667 2.3046875 10 14 14.7994791666667 14.4583333333333 0.341145833333334 -0.799479166666668 11 12 10.6744791666667 14.5416666666667 -3.8671875 1.32552083333333 12 15 15.5807291666667 14.4166666666667 1.1640625 -0.580729166666668 13 14 14.7578125 14.2916666666667 0.466145833333334 -0.7578125 14 19 15.9140625 14.1666666666667 1.74739583333333 3.0859375 15 16 14.4140625 13.6666666666667 0.747395833333334 1.58593750000000 16 16 11.7890625 13.0416666666667 -1.25260416666667 4.2109375 17 11 12.8828125 12.5833333333333 0.299479166666667 -1.8828125 18 13 13.4765625 12.3333333333333 1.14322916666667 -0.4765625 19 12 12.6640625 12.4583333333333 0.205729166666666 -0.664062500000002 20 11 11.8098958333333 12.3333333333333 -0.523437500000001 -0.809895833333334 21 6 11.4453125 11.9166666666667 -0.471354166666667 -5.4453125 22 9 11.9661458333333 11.625 0.341145833333334 -2.96614583333333 23 6 7.59114583333333 11.4583333333333 -3.8671875 -1.59114583333333 24 15 12.6223958333333 11.4583333333333 1.1640625 2.37760416666667 25 17 12.0078125 11.5416666666667 0.466145833333334 4.9921875 26 13 13.2890625 11.5416666666667 1.74739583333333 -0.2890625 27 12 12.4557291666667 11.7083333333333 0.747395833333334 -0.455729166666666 28 13 10.7890625 12.0416666666667 -1.25260416666667 2.2109375 29 10 12.5078125 12.2083333333333 0.299479166666667 -2.5078125 30 14 13.2265625 12.0833333333333 1.14322916666667 0.7734375 31 13 11.7890625 11.5833333333333 0.205729166666666 1.2109375 32 10 10.7265625 11.25 -0.523437500000001 -0.7265625 33 11 10.7786458333333 11.25 -0.471354166666667 0.221354166666668 34 12 11.2578125 10.9166666666667 0.341145833333334 0.7421875 35 7 6.84114583333333 10.7083333333333 -3.8671875 0.158854166666668 36 11 11.8723958333333 10.7083333333333 1.1640625 -0.872395833333334 37 9 10.8411458333333 10.375 0.466145833333334 -1.84114583333333 38 13 11.8307291666667 10.0833333333333 1.74739583333333 1.16927083333333 39 12 10.6223958333333 9.875 0.747395833333334 1.37760416666667 40 5 8.33072916666667 9.58333333333333 -1.25260416666667 -3.33072916666667 41 13 9.42447916666666 9.125 0.299479166666667 3.57552083333334 42 11 9.64322916666667 8.5 1.14322916666667 1.35677083333333 43 8 7.99739583333333 7.79166666666667 0.205729166666666 0.00260416666666785 44 8 6.30989583333333 6.83333333333333 -0.523437500000001 1.69010416666667 45 8 5.23697916666667 5.70833333333333 -0.471354166666667 2.76302083333333 46 8 5.1328125 4.79166666666667 0.341145833333334 2.8671875 47 0 0.0494791666666656 3.91666666666667 -3.8671875 -0.0494791666666656 48 3 4.08072916666667 2.91666666666667 1.1640625 -1.08072916666667 49 0 2.54947916666667 2.08333333333333 0.466145833333334 -2.54947916666667 50 -1 3.12239583333333 1.375 1.74739583333333 -4.12239583333333 51 -1 1.6640625 0.916666666666667 0.747395833333334 -2.6640625 52 -4 -0.752604166666667 0.5 -1.25260416666667 -3.24739583333333 53 1 0.341145833333334 0.0416666666666667 0.299479166666667 0.658854166666666 54 -1 0.809895833333334 -0.333333333333333 1.14322916666667 -1.80989583333333 55 0 NA NA 0.205729166666666 NA 56 -1 NA NA -0.523437500000001 NA 57 6 NA NA -0.471354166666667 NA 58 0 NA NA 0.341145833333334 NA 59 -3 NA NA -3.8671875 NA 60 -3 NA NA 1.1640625 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jan/09/t1262993284lkvcrcf1pgt4avt/1l6ds1262992988.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/09/t1262993284lkvcrcf1pgt4avt/1l6ds1262992988.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/09/t1262993284lkvcrcf1pgt4avt/2fufu1262992988.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/09/t1262993284lkvcrcf1pgt4avt/2fufu1262992988.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/09/t1262993284lkvcrcf1pgt4avt/31mw01262992988.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/09/t1262993284lkvcrcf1pgt4avt/31mw01262992988.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/09/t1262993284lkvcrcf1pgt4avt/4ym901262992988.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/09/t1262993284lkvcrcf1pgt4avt/4ym901262992988.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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