opdracht 9 deel 2

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Wed, 15 Dec 2010 20:07:32 +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/15/t12924435508yvpcth4o3wvgqk.htm/, Retrieved Wed, 15 Dec 2010 21:05:51 +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/15/t12924435508yvpcth4o3wvgqk.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:

KDGP2W92

Dataseries X:

» Textbox « » Textfile « » CSV «

110,04 111,73 110,99 115,83 125,33 123,03 123,46 130,34 131,21 132,97 133,91 133,14 135,31 133,09 135,39 131,85 130,25 127,65 118,3 119,73 122,51 123,28 133,52 153,2 163,63 168,45 166,26 162,31 161,56 156,59 157,97 158,68 163,55 162,89 164,95 159,82 159,05 166,76 164,55 163,22 160,68 155,24 157,6 156,56 154,82 151,11 149,65 148,99 148,53 146,7 145,11 142,7 143,59 140,96 140,77 139,81 140,58 139,59 138,05 136,06 135,98 134,75 132,22 135,37 138,84 138,83 136,55 135,63 139,14 136,09 135,97 134,51 134,54 134,08 132,86 134,48 129,08 133,13 134,78 134,13 132,43 127,84 128,12 128,94

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 'RServer@AstonUniversity' @ vre.aston.ac.uk Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 110.04 NA NA 2.57600115740739 NA 2 111.73 NA NA 3.60273726851851 NA 3 110.99 NA NA 2.32794560185187 NA 4 115.83 NA NA 1.27843171296296 NA 5 125.33 NA NA 0.365931712962961 NA 6 123.03 NA NA -1.49802662037037 NA 7 123.46 121.376556712963 124.55125 -3.17469328703704 2.08344328703706 8 130.34 124.010792824074 126.494166666667 -2.48337384259259 6.32920717592594 9 131.21 127.453709490741 128.400833333333 -0.947123842592572 3.75629050925926 10 132.97 127.876487268518 130.085 -2.20851273148149 5.0935127314815 11 133.91 130.28009837963 130.9575 -0.677401620370366 3.62990162037039 12 133.14 132.193084490741 131.355 0.838084490740731 0.94691550925927 13 135.31 133.908501157407 131.3325 2.57600115740739 1.40149884259259 14 133.09 134.278153935185 130.675416666667 3.60273726851851 -1.18815393518517 15 135.39 132.198778935185 129.870833333333 2.32794560185187 3.19122106481481 16 131.85 130.383015046296 129.104583333333 1.27843171296296 1.46698495370373 17 130.25 129.050515046296 128.684583333333 0.365931712962961 1.19948495370372 18 127.65 128.006140046296 129.504166666667 -1.49802662037037 -0.356140046296275 19 118.3 128.345306712963 131.52 -3.17469328703704 -10.045306712963 20 119.73 131.689959490741 134.173333333333 -2.48337384259259 -11.9599594907407 21 122.51 135.985792824074 136.932916666667 -0.947123842592572 -13.4757928240741 22 123.28 137.279820601852 139.488333333333 -2.20851273148149 -13.9998206018519 23 133.52 141.384681712963 142.062083333333 -0.677401620370366 -7.86468171296295 24 153.2 145.410584490741 144.5725 0.838084490740731 7.78941550925927 25 163.63 150.007251157407 147.43125 2.57600115740739 13.6227488425926 26 168.45 154.309820601852 150.707083333333 3.60273726851851 14.1401793981481 27 166.26 156.367945601852 154.04 2.32794560185187 9.89205439814813 28 162.31 158.67884837963 157.400416666667 1.27843171296296 3.63115162037036 29 161.56 160.72634837963 160.360416666667 0.365931712962961 0.833651620370404 30 156.59 160.447806712963 161.945833333333 -1.49802662037037 -3.85780671296294 31 157.97 158.856140046296 162.030833333333 -3.17469328703704 -0.886140046296305 32 158.68 159.286209490741 161.769583333333 -2.48337384259259 -0.606209490740724 33 163.55 160.680792824074 161.627916666667 -0.947123842592572 2.86920717592594 34 162.89 159.386070601852 161.594583333333 -2.20851273148149 3.50392939814813 35 164.95 160.918431712963 161.595833333333 -0.677401620370366 4.03156828703703 36 159.82 162.341001157407 161.502916666667 0.838084490740731 -2.5210011574074 37 159.05 164.007251157407 161.43125 2.57600115740739 -4.9572511574074 38 166.76 164.930237268519 161.3275 3.60273726851851 1.82976273148148 39 164.55 163.203362268519 160.875416666667 2.32794560185187 1.34663773148151 40 163.22 161.299265046296 160.020833333333 1.27843171296296 1.92073495370371 41 160.68 159.258431712963 158.8925 0.365931712962961 1.42156828703705 42 155.24 156.30572337963 157.80375 -1.49802662037037 -1.06572337962965 43 157.6 153.73947337963 156.914166666667 -3.17469328703704 3.86052662037034 44 156.56 153.156626157407 155.64 -2.48337384259259 3.40337384259257 45 154.82 153.047042824074 153.994166666667 -0.947123842592572 1.77295717592591 46 151.11 150.120653935185 152.329166666667 -2.20851273148149 0.989346064814782 47 149.65 150.084681712963 150.762083333333 -0.677401620370366 -0.434681712962941 48 148.99 150.293084490741 149.455 0.838084490740731 -1.30308449074073 49 148.53 150.734751157407 148.15875 2.57600115740739 -2.2047511574074 50 146.7 150.362320601852 146.759583333333 3.60273726851851 -3.66232060185186 51 145.11 147.796278935185 145.468333333333 2.32794560185187 -2.68627893518516 52 142.7 145.673431712963 144.395 1.27843171296296 -2.97343171296296 53 143.59 143.79759837963 143.431666666667 0.365931712962961 -0.207598379629644 54 140.96 140.911556712963 142.409583333333 -1.49802662037037 0.0484432870370597 55 140.77 138.17322337963 141.347916666667 -3.17469328703704 2.59677662037038 56 139.81 137.843709490741 140.327083333333 -2.48337384259259 1.96629050925924 57 140.58 138.344959490741 139.292083333333 -0.947123842592572 2.23504050925925 58 139.59 136.241070601852 138.449583333333 -2.20851273148149 3.34892939814816 59 138.05 137.26884837963 137.94625 -0.677401620370366 0.781151620370395 60 136.06 138.497667824074 137.659583333333 0.838084490740731 -2.43766782407405 61 135.98 139.971001157407 137.395 2.57600115740739 -3.99100115740742 62 134.75 140.647737268519 137.045 3.60273726851851 -5.89773726851851 63 132.22 139.138778935185 136.810833333333 2.32794560185187 -6.9187789351852 64 135.37 137.883431712963 136.605 1.27843171296296 -2.51343171296298 65 138.84 136.738431712963 136.3725 0.365931712962961 2.10156828703703 66 138.83 134.72322337963 136.22125 -1.49802662037037 4.1067766203704 67 136.55 132.92197337963 136.096666666667 -3.17469328703704 3.62802662037041 68 135.63 133.525376157407 136.00875 -2.48337384259259 2.10462384259259 69 139.14 135.060376157407 136.0075 -0.947123842592572 4.07962384259258 70 136.09 133.788570601852 135.997083333333 -2.20851273148149 2.30142939814817 71 135.97 134.875931712963 135.553333333333 -0.677401620370366 1.09406828703706 72 134.51 135.747251157407 134.909166666667 0.838084490740731 -1.2372511574074 73 134.54 137.173917824074 134.597916666667 2.57600115740739 -2.63391782407408 74 134.08 138.064403935185 134.461666666667 3.60273726851851 -3.98440393518518 75 132.86 136.447528935185 134.119583333333 2.32794560185187 -3.58752893518516 76 134.48 134.774681712963 133.49625 1.27843171296296 -0.294681712962927 77 129.08 133.19134837963 132.825416666667 0.365931712962961 -4.11134837962962 78 133.13 130.76822337963 132.26625 -1.49802662037037 2.36177662037036 79 134.78 NA NA -3.17469328703704 NA 80 134.13 NA NA -2.48337384259259 NA 81 132.43 NA NA -0.947123842592572 NA 82 127.84 NA NA -2.20851273148149 NA 83 128.12 NA NA -0.677401620370366 NA 84 128.94 NA NA 0.838084490740731 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/15/t12924435508yvpcth4o3wvgqk/1c3zl1292443647.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/15/t12924435508yvpcth4o3wvgqk/1c3zl1292443647.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/15/t12924435508yvpcth4o3wvgqk/2c3zl1292443647.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/15/t12924435508yvpcth4o3wvgqk/2c3zl1292443647.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/15/t12924435508yvpcth4o3wvgqk/35ug61292443647.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/15/t12924435508yvpcth4o3wvgqk/35ug61292443647.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/15/t12924435508yvpcth4o3wvgqk/45ug61292443647.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/15/t12924435508yvpcth4o3wvgqk/45ug61292443647.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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