Opgave 9 - Classical Decomposition: goudkoers - Talia Vereycken

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Mon, 01 Jun 2009 03:54:23 -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/01/t12438501034thylqdgnrbseoi.htm/, Retrieved Mon, 01 Jun 2009 11:55:07 +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/01/t12438501034thylqdgnrbseoi.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 «

10.812 10.738 10.171 9.721 9.897 9.828 9.924 10.371 10.846 10.413 10.709 10.662 10.570 10.297 10.635 10.872 10.296 10.383 10.431 10.574 10.653 10.805 10.872 10.625 10.407 10.463 10.556 10.646 10.702 11.353 11.346 11.451 11.964 12.574 13.031 13.812 14.544 14.931 14.886 16.005 17.064 15.168 16.050 15.839 15.137 14.954 15.648 15.305 15.579 16.348 15.928 16.171 15.937 15.713 15.594 15.683 16.438 17.032 17.696 17.745 19.394 20.148 20.108 18.584 18.441 18.391 19.178 18.079 18.483 19.644 19.195 19.650 20.830

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 10.812 NA NA 1.01520968589506 NA 2 10.738 NA NA 1.02456102948526 NA 3 10.171 NA NA 1.01743275298979 NA 4 9.721 NA NA 1.01566286173115 NA 5 9.897 NA NA 1.00506080507181 NA 6 9.828 NA NA 0.980121548860336 NA 7 9.924 10.2091028289092 10.3309166666667 0.988208806470142 0.972073664680717 8 10.371 10.0172304245211 10.3024583333333 0.97231457778486 1.03531610639732 9 10.846 10.1028585586721 10.3034166666667 0.980534795933916 1.07355754185928 10 10.413 10.3413879426258 10.3707083333333 0.997172768747794 1.0069248013682 11 10.709 10.4648516984953 10.4352916666667 1.00283269819118 1.02333031642865 12 10.662 10.4843400347384 10.4750416666667 1.00088766883871 1.01694526929430 13 10.57 10.6792867887552 10.5192916666667 1.01520968589506 0.989766471215078 14 10.297 10.8079662299113 10.548875 1.02456102948526 0.952723184080905 15 10.635 10.7331948625089 10.5492916666667 1.01743275298979 0.990851292297704 16 10.872 10.7229453012984 10.5575833333333 1.01566286173115 1.01390053707385 17 10.296 10.6342552357300 10.5807083333333 1.00506080507181 0.968191920521758 18 10.383 10.3755258778376 10.5859583333333 0.980121548860336 1.00072036080391 19 10.431 10.4529021765387 10.577625 0.988208806470142 0.997904679851698 20 10.574 10.2849005251638 10.57775 0.97231457778486 1.02810911725678 21 10.653 10.3754063763252 10.581375 0.980534795933916 1.02675496396056 22 10.805 10.5387866019725 10.5686666666667 0.997172768747794 1.02526034619371 23 10.872 10.6061257548529 10.5761666666667 1.00283269819118 1.02506798913123 24 10.625 10.6429390265965 10.6335 1.00088766883871 0.998314466844955 25 10.407 10.8749684557114 10.7120416666667 1.01520968589506 0.956968293046803 26 10.463 11.0516409947572 10.7867083333333 1.02456102948526 0.946737231598777 27 10.556 11.0675063079288 10.877875 1.01743275298979 0.953783057023213 28 10.646 11.1785970526425 11.0062083333333 1.01566286173115 0.9523556444396 29 10.702 11.2264035600515 11.169875 1.00506080507181 0.953288374389323 30 11.353 11.1661572605850 11.392625 0.980121548860336 1.01673294895053 31 11.346 11.5598607412530 11.6977916666667 0.988208806470142 0.981499713012126 32 11.451 11.7225486546335 12.0563333333333 0.97231457778486 0.9768353570001 33 11.964 12.1811020586540 12.4229166666667 0.980534795933916 0.982177141476308 34 12.574 12.7903611649397 12.826625 0.997172768747794 0.983084045700543 35 13.031 13.3527173764155 13.315 1.00283269819118 0.975906224377685 36 13.812 13.7512373858279 13.7390416666667 1.00088766883871 1.00441870156606 37 14.544 14.3083653130049 14.094 1.01520968589506 1.01646831638978 38 14.931 14.8283010195686 14.4728333333333 1.02456102948526 1.00692587642346 39 14.886 15.0456683721188 14.787875 1.01743275298979 0.989387751466414 40 16.005 15.2544944360555 15.01925 1.01566286173115 1.04919897982135 41 17.064 15.3045215316975 15.2274583333333 1.00506080507181 1.11496461778687 42 15.168 15.0926058621152 15.3987083333333 0.980121548860336 1.00499543541874 43 16.05 15.32123051088 15.5040416666667 0.988208806470142 1.04756598946817 44 15.839 15.1741438664476 15.6062083333333 0.97231457778486 1.04381506722251 45 15.137 15.4028942643939 15.7086666666667 0.980534795933916 0.98273738299895 46 14.954 15.7144456626965 15.759 0.997172768747794 0.951608495837581 47 15.648 15.7634853981714 15.7189583333333 1.00283269819118 0.992673866517822 48 15.305 15.7086400368535 15.6947083333333 1.00088766883871 0.974304584234755 49 15.579 15.9371846532164 15.6984166666667 1.01520968589506 0.97752522412143 50 16.348 16.0578596350367 15.6729166666667 1.02456102948526 1.01806843325061 51 15.928 15.9946787724701 15.720625 1.01743275298979 0.995831190271553 52 16.171 16.1098518427768 15.8614166666667 1.01566286173115 1.00379569953963 53 15.937 16.1144749079847 16.0333333333333 1.00506080507181 0.988986615511948 54 15.713 15.8978982296976 16.2203333333333 0.980121548860336 0.988369643142374 55 15.594 16.2866281640675 16.4809583333333 0.988208806470142 0.957472586892136 56 15.683 16.3331833562745 16.79825 0.97231457778486 0.960192490215035 57 16.438 16.7972964554449 17.13075 0.980534795933916 0.978609864010082 58 17.032 17.3562490775744 17.4054583333333 0.997172768747794 0.981318021185044 59 17.696 17.6602180927127 17.6103333333333 1.00283269819118 1.00202613054377 60 17.745 17.8420738066361 17.82625 1.00088766883871 0.99455927558152 61 19.394 18.3622667903982 18.0871666666667 1.01520968589506 1.05618768212982 62 20.148 18.7866925569849 18.3363333333333 1.02456102948526 1.07246126154915 63 20.108 18.8442535554062 18.521375 1.01743275298979 1.06706269584402 64 18.584 19.0085536501575 18.7154166666667 1.01566286173115 0.977665126028462 65 18.441 18.9822902826565 18.8867083333333 1.00506080507181 0.971484458693004 66 18.391 18.6502837308868 19.0285416666667 0.980121548860336 0.98609759858734 67 19.178 18.9417393502181 19.16775 0.988208806470142 1.01247301767877 68 18.079 NA NA 0.97231457778486 NA 69 18.483 NA NA 0.980534795933916 NA 70 19.644 NA NA 0.997172768747794 NA 71 19.195 NA NA 1.00283269819118 NA 72 19.65 NA NA 1.00088766883871 NA 73 20.83 NA NA NA NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438501034thylqdgnrbseoi/1jkke1243850060.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438501034thylqdgnrbseoi/1jkke1243850060.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438501034thylqdgnrbseoi/2v9uc1243850060.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438501034thylqdgnrbseoi/2v9uc1243850060.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438501034thylqdgnrbseoi/3arzo1243850060.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438501034thylqdgnrbseoi/3arzo1243850060.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438501034thylqdgnrbseoi/4f3f71243850060.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438501034thylqdgnrbseoi/4f3f71243850060.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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