Eigen cijfers Classical Decomposition 2 Filip Bosschaerts

*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 13:34:02 -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/t1243884875tkjdtz3wu2icc2g.htm/, Retrieved Mon, 01 Jun 2009 21:34:39 +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/t1243884875tkjdtz3wu2icc2g.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:

Filip Bosschaerts

Dataseries X:

» Textbox « » Textfile « » CSV «

528222 516141 501866 506174 517945 533590 528379 477580 469357 490243 492622 507561 516922 514258 509846 527070 541657 564591 555362 498662 511038 525919 531673 548854 560576 557274 565742 587625 619916 625809 619567 572942 572775 574205 579799 590072 593408 597141 595404 612117 628232 628884 620735 569028 567456 573100 584428 589379 590865 595454 594167 611324 612613 610763 593530 542722 536662 543599 555332 560854 562325 554788 547344 565464 577992 579714 569323 506971 500857 509127 509933 517009 519164 512238 509239 518585 522975 525192 516847 455626

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 528222 NA NA 1.00041753967733 NA 2 516141 NA NA 0.99718580421853 NA 3 501866 NA NA 0.993794120017889 NA 4 506174 NA NA 1.02524652617749 NA 5 517945 NA NA 1.05180812949859 NA 6 533590 NA NA 1.06231473857156 NA 7 528379 527555.231730297 505335.833333333 1.04396956821842 1.00156148251435 8 477580 478917.460144371 504786.541666667 0.948752434173694 0.997207326406585 9 469357 478984.659602032 505040.583333333 0.948408257492242 0.979899858149881 10 490243 486982.345611273 506243.75 0.961952311729821 1.00669563161398 11 492622 495205.402294754 508102.416666667 0.97461729378002 0.994783170210214 12 507561 506060.860639912 510382.125 0.991533276444413 1.00296434574725 13 516922 513012.238563647 512798.125 1.00041753967733 1.00762118550485 14 514258 513352.08299987 514800.833333333 0.99718580421853 1.00176470892031 15 509846 514204.936995087 517415.958333333 0.993794120017889 0.991522957713008 16 527070 533783.497016944 520639.166666667 1.02524652617749 0.987422808958196 17 541657 550887.444122581 523752.791666667 1.05180812949859 0.98324441004953 18 564591 559946.585595324 527100.458333333 1.06231473857156 1.00829438829373 19 555362 553971.92468196 530639.916666667 1.04396956821842 1.00250928838828 20 498662 506872.094835136 534251.166666667 0.948752434173694 0.98380243276599 21 511038 510596.924606742 538372.5 0.948408257492242 1.00086384263595 22 525919 522556.183807315 543224.625 0.961952311729821 1.00643531986969 23 531673 535073.219141282 549008.541666667 0.97461729378002 0.993645319893344 24 548854 550122.575064662 554820.083333333 0.991533276444413 0.997694013803173 25 560576 560279.883110193 560046.041666667 1.00041753967733 1.00052851601268 26 557274 564223.932296163 565816.25 0.99718580421853 0.987682315658112 27 565742 567937.066211508 571483.625 0.993794120017889 0.996135018574944 28 587625 590611.630404805 576067.916666667 1.02524652617749 0.994943156803807 29 619916 610138.206450867 580085.083333333 1.05180812949859 1.01602553887915 30 625809 620187.577317301 583807.75 1.06231473857156 1.00906406849846 31 619567 612698.605795343 586893.166666667 1.04396956821842 1.01121006990989 32 572942 559690.210192074 589922.291666667 0.948752434173694 1.02367700839966 33 572775 562234.750934379 592819.333333333 0.948408257492242 1.01874706081064 34 574205 572434.493366857 595075.75 0.961952311729821 1.00309294190629 35 579799 581303.418899713 596442.75 0.97461729378002 0.99741199027771 36 590072 591863.440600348 596917.375 0.991533276444413 0.996973219703296 37 593408 597343.477172352 597094.166666667 1.00041753967733 0.993411701436867 38 597141 595299.732105927 596979.75 0.99718580421853 1.00309300978107 39 595404 592892.64444016 596595.041666667 0.993794120017889 1.00423576777919 40 612117 611382.569683293 596327.375 1.02524652617749 1.00120126145743 41 628232 627376.421361235 596474.208333333 1.05180812949859 1.00136374050671 42 628884 633817.562307429 596638.208333333 1.06231473857156 0.992216116117912 43 620735 622731.370839582 596503.375 1.04396956821842 0.996794170114008 44 569028 565766.811407551 596327.125 0.948752434173694 1.00576419211360 45 567456 565446.021777237 596205.291666667 0.948408257492242 1.00355467744993 46 573100 573439.693451268 596120.708333333 0.961952311729821 0.999407621315462 47 584428 580323.075729332 595436.875 0.97461729378002 1.00707351549912 48 589379 589001.545053437 594031.041666667 0.991533276444413 1.00064083863571 49 590865 592389.701304319 592142.458333333 1.00041753967733 0.997426185328743 50 595454 588252.703126332 589912.833333333 0.99718580421853 1.01224184238406 51 594167 583887.503245884 587533.666666667 0.993794120017889 1.0176052693318 52 611324 599791.13245833 585021.375 1.02524652617749 1.01922813945982 53 612613 612762.204781933 582579.833333333 1.05180812949859 0.99975650459384 54 610763 616332.658446595 580178.958333333 1.06231473857156 0.990963226805743 55 593530 603206.921478565 577801.25 1.04396956821842 0.983957542372284 56 542722 545454.535699460 574917.666666667 0.948752434173694 0.994990351128062 57 536662 541799.358693183 571272.291666667 0.948408257492242 0.990517968301819 58 543599 545821.842174774 567410.5 0.961952311729821 0.99592753165407 59 555332 549739.828704838 564057.125 0.97461729378002 1.01017239611024 60 560854 556568.326325395 561320.875 0.991533276444413 1.00770017529186 61 562325 559251.954088175 559018.541666667 1.00041753967733 1.00549492208183 62 554788 554954.134609556 556520.291666667 0.99718580421853 0.99970063362142 63 547344 550103.59636014 553538.791666667 0.993794120017889 0.994983496966027 64 565464 564511.587839063 550610.583333333 1.02524652617749 1.00168714368572 65 577992 575636.314108328 547282.625 1.05180812949859 1.00409231633574 66 579714 577436.181346254 543564.125 1.06231473857156 1.00394471064912 67 569323 563679.754198091 539938.875 1.04396956821842 1.01001143958051 68 506971 508880.050299361 536367.583333333 0.948752434173694 0.996248525957662 69 500857 505508.200584156 533006.958333333 0.948408257492242 0.99079896116664 70 509127 509321.002600995 529465.958333333 0.961952311729821 0.999619095619453 71 509933 511888.779302519 525220.291666667 0.97461729378002 0.996179288584556 72 517009 516247.914835988 520656.166666667 0.991533276444413 1.00147426293093 73 519164 NA 516197.916666667 NA NA 74 512238 NA 511872.041666667 NA NA 75 509239 NA NA NA NA 76 518585 NA NA NA NA 77 522975 NA NA NA NA 78 525192 NA NA NA NA 79 516847 NA NA NA NA 80 455626 NA NA NA NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243884875tkjdtz3wu2icc2g/1o4i61243884840.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243884875tkjdtz3wu2icc2g/1o4i61243884840.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243884875tkjdtz3wu2icc2g/2cbni1243884840.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243884875tkjdtz3wu2icc2g/2cbni1243884840.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243884875tkjdtz3wu2icc2g/30s9z1243884840.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243884875tkjdtz3wu2icc2g/30s9z1243884840.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243884875tkjdtz3wu2icc2g/4a6581243884840.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243884875tkjdtz3wu2icc2g/4a6581243884840.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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