WErkloosheid V Claudine Kuypers

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Sun, 30 May 2010 07:45:50 +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/May/30/t1275205634imyd4ez9z81pekt.htm/, Retrieved Sun, 30 May 2010 09:47:16 +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/2010/May/30/t1275205634imyd4ez9z81pekt.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 «

43657 42811 45419 50846 54500 51035 38675 36214 38763 39486 40540 40719 40471 39947 42683 47090 51520 48823 36122 33812 36928 37737 40123 41713 42025 42169 46352 50939 56139 52713 38532 37860 40880 41988 44576 46728 46913 49357 54709 60819 63695 60109 45544 43596 44431 45575 47980 49211 51374 52954 57529 62960 64530 61008 44964 43480 45429 47616 49364 51010 53188 55317 60106 65845 67028 63617 47605 45844 47925 50156 52258 53476 54327 55214 59347 64718 66208 62744 45587 43684 45676 47088 48907 50964 51798

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 43657 NA NA 0.975980189395886 NA 2 42811 NA NA 0.995022987322727 NA 3 45419 NA NA 1.07968564766037 NA 4 50846 NA NA 1.18409664287106 NA 5 54500 NA NA 1.24142341825675 NA 6 51035 NA NA 1.17042490499207 NA 7 38675 37493.0452313273 43422.6666666667 0.86344409750654 1.03152464040678 8 36214 36197.9420516869 43170.5833333333 0.8384862852603 1.00044361495165 9 38763 37111.0149739746 42937.25 0.864308146748442 1.04451468188579 10 39486 38182.9027601615 42666.75 0.894910035570123 1.03412776781335 11 40540 39517.5648402619 42386.0833333333 0.932324049133938 1.02587292926249 12 40719 40478.4729396346 42169.75 0.959893595281797 1.00594209817955 13 40471 40963.0678583408 41971.2083333333 0.975980189395886 0.987987524273267 14 39947 41556.8863097869 41764.75 0.995022987322727 0.961260660921853 15 42683 44902.1916494094 41588.2083333333 1.07968564766037 0.950577208641918 16 47090 49067.6327718535 41438.875 1.18409664287106 0.959695777845065 17 51520 51331.1513877165 41348.625 1.24142341825675 1.00367902544903 18 48823 48423.5994526018 41372.6666666667 1.17042490499207 1.00824805573962 19 36122 35814.6538131241 41478.8333333333 0.86344409750654 1.00858157637037 20 33812 34911.3547208121 41636.1666666667 0.8384862852603 0.968510109974143 21 36928 36198.6296865632 41881.625 0.864308146748442 1.02014911392371 22 37737 37760.6170871269 42194.875 0.894910035570123 0.999374557701946 23 40123 39668.2517147031 42547.7083333333 0.932324049133938 1.01146378440289 24 41713 41181.5949981785 42902.25 0.959893595281797 1.01290394414896 25 42025 42127.9408802261 43164.75 0.975980189395886 0.997556470169792 26 42169 43217.6625942108 43433.8333333333 0.995022987322727 0.97573532367872 27 46352 47254.7816887593 43767.1666666667 1.07968564766037 0.98089544260927 28 50939 52229.2694830395 44108.9583333333 1.18409664287106 0.975296045764942 29 56139 55208.1167229323 44471.625 1.24142341825675 1.01686134815537 30 52713 52512.4300904873 44866.125 1.17042490499207 1.00381947491607 31 38532 39095.6694299742 45278.75 0.86344409750654 0.985582305196645 32 37860 38387.5092379299 45781.9166666667 0.8384862852603 0.986258310361832 33 40880 40129.5031379751 46429.625 0.864308146748442 1.01870187277038 34 41988 42230.3571235363 47189.5 0.894910035570123 0.994261068576158 35 44576 44673.2391383018 47916 0.932324049133938 0.997823324653027 36 46728 46592.2752213832 48539 0.959893595281797 1.00291303178417 37 46913 47959.0158534542 49139.3333333333 0.975980189395886 0.978189380352373 38 49357 49423.2892918135 49670.5 0.995022987322727 0.998658743827791 39 54709 54046.3193208569 50057.4583333333 1.07968564766037 1.01226134707174 40 60819 59625.0384396919 50354.875 1.18409664287106 1.02002449963224 41 63695 62873.3373449344 50646.1666666667 1.24142341825675 1.01306853890319 42 60109 59564.6302846995 50891.4583333333 1.17042490499207 1.00913914369482 43 45544 44191.7524702952 51180.7916666667 0.86344409750654 1.03059954525709 44 43596 43195.9136515408 51516.5416666667 0.8384862852603 1.00926213418442 45 44431 44757.2610455424 51783.9166666667 0.864308146748442 0.992710433169481 46 45575 46526.9320680629 51990.625 0.894910035570123 0.979540192620688 47 47980 48587.7181990968 52114.625 0.932324049133938 0.987492349473862 48 49211 50093.8470702717 52186.875 0.959893595281797 0.982376137551718 49 51374 50946.3285498301 52200.1666666667 0.975980189395886 1.00839454897621 50 52954 51911.5101087785 52171.1666666667 0.995022987322727 1.02008205673534 51 57529 56368.1383192486 52207.9166666667 1.07968564766037 1.02059428810965 52 62960 61969.1550936956 52334.5416666667 1.18409664287106 1.01598932412111 53 64530 65146.487075714 52477.25 1.24142341825675 0.990536909918143 54 61008 61575.9079485196 52609.875 1.17042490499207 0.990777108004735 55 44964 45555.670352819 52760.4166666667 0.86344409750654 0.987012146935022 56 43480 44384.8173301828 52934.4583333333 0.8384862852603 0.97961426035728 57 45429 45929.5870080884 53140.2916666667 0.864308146748442 0.989100990435638 58 47616 47759.4469145519 53367.875 0.894910035570123 0.996996470356775 59 49364 49965.2658285275 53592.1666666667 0.932324049133938 0.987966323834021 60 51010 51647.0348985706 53804.9583333333 0.959893595281797 0.987665605589523 61 53188 52726.0690910347 54023.7083333333 0.975980189395886 1.00876095860982 62 55317 53962.335404233 54232.25 0.995022987322727 1.02510389117927 63 60106 58772.4183089803 54434.75 1.07968564766037 1.02269060435813 64 65845 64704.4676760879 54644.5833333333 1.18409664287106 1.01762679402018 65 67028 68118.1443831661 54871 1.24142341825675 0.983996270112203 66 63617 64483.779857268 55094.3333333333 1.17042490499207 0.98655817231579 67 47605 47700.5734215374 55244.5416666667 0.86344409750654 0.997996388414604 68 45844 46357.9851809716 55287.7083333333 0.8384862852603 0.988912693703898 69 47925 47754.5736599477 55251.7916666667 0.864308146748442 1.00356879617994 70 50156 49375.0578321011 55173.2083333333 0.894910035570123 1.01581653171029 71 52258 51363.6742085577 55092.0833333333 0.932324049133938 1.01741163974779 72 53476 52814.8254483639 55021.5416666667 0.959893595281797 1.01251873022439 73 54327 53582.369709706 54901.0833333333 0.975980189395886 1.01389692718572 74 55214 54454.6230272109 54727 0.995022987322727 1.01394513322422 75 59347 58889.6091886534 54543.2916666667 1.07968564766037 1.00776691877647 76 64718 64322.201809881 54321.75 1.18409664287106 1.00615336818365 77 66208 67104.2635322807 54054.2916666667 1.24142341825675 0.986643717029253 78 62744 62980.5641376232 53810 1.17042490499207 0.996243854896151 79 45587 46280.5676495131 53599.9583333333 0.86344409750654 0.985013847393456 80 43684 NA NA 0.8384862852603 NA 81 45676 NA NA 0.864308146748442 NA 82 47088 NA NA 0.894910035570123 NA 83 48907 NA NA 0.932324049133938 NA 84 50964 NA NA 0.959893595281797 NA 85 51798 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/30/t1275205634imyd4ez9z81pekt/1i7ak1275205545.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/30/t1275205634imyd4ez9z81pekt/1i7ak1275205545.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/30/t1275205634imyd4ez9z81pekt/2i7ak1275205545.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/30/t1275205634imyd4ez9z81pekt/2i7ak1275205545.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/30/t1275205634imyd4ez9z81pekt/3byrn1275205545.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/30/t1275205634imyd4ez9z81pekt/3byrn1275205545.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/30/t1275205634imyd4ez9z81pekt/4byrn1275205545.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/30/t1275205634imyd4ez9z81pekt/4byrn1275205545.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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