decompositie niet-werkende werkzoekende -HannesFrançois

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Thu, 14 Jan 2010 11:52:42 -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/14/t12634952278nvev6xg3300u0n.htm/, Retrieved Thu, 14 Jan 2010 19:53:53 +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/14/t12634952278nvev6xg3300u0n.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 «

492865 480961 461935 456608 441977 439148 488180 520564 501492 485025 464196 460170 467037 460070 447988 442867 436087 431328 484015 509673 512927 502831 470984 471067 476049 474605 470439 461251 454724 455626 516847 525192 522975 518585 509239 512238 519164 517009 509933 509127 500857 506971 569323 579714 577992 565464 547344 554788 562325 560854 555332 543599 536662 542722 593530 610763 612613 611324 594167 595454 590865 589379 584428 573100 567456 569028 620735 628884 628232 612117 595404 597141 593408 590072 579799 574205 572775 572942 619567 625809 619916 587625 565742 557274 560576 548854 531673 525919 511038 498662 555362 564591 541657 527070 509846 514258 516922 507561 492622 490243 469357 477580 528379 533590 517945 506174 501866 516141

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 492865 NA NA 1.00043707930492 NA 2 480961 NA NA 0.990879206650221 NA 3 461935 NA NA 0.972587540115597 NA 4 456608 NA NA 0.960199229979201 NA 5 441977 NA NA 0.942551498912698 NA 6 439148 NA NA 0.942851863426956 NA 7 488180 493019.281124421 473350.583333333 1.04155207257289 0.990184397832507 8 520564 504329.211647849 471403.958333333 1.06984509300882 1.03219085465842 9 501492 497017.679469886 469952.375 1.05759158993480 1.00900233676775 10 485025 483676.227716955 468798.708333333 1.03173541035664 1.0027885850198 11 464196 465471.621530716 467980.75 0.994638393845722 0.99725950740773 12 460170 465133.693376809 467409.5 0.99513102189153 0.989328458790475 13 467037 467114.201752897 466910.125 1.00043707930492 0.999834726170588 14 460070 462029.922681317 466282.791666667 0.990879206650221 0.995758017857495 15 447988 453522.878662893 466305.458333333 0.972587540115597 0.987795811582404 16 442867 448916.024763591 467523.833333333 0.960199229979201 0.9865252643481 17 436087 441631.169534254 468548.583333333 0.942551498912698 0.987446154355225 18 431328 442466.668868757 469285.458333333 0.942851863426956 0.97482597073982 19 484015 489649.252597605 470115 1.04155207257289 0.988493288680182 20 509673 503999.877666718 471096.125 1.06984509300882 1.01125619783788 21 512927 499857.136623596 472637.208333333 1.05759158993480 1.02614719770670 22 502831 489391.998901355 474338.666666667 1.03173541035664 1.02746060648481 23 470984 473329.720718028 475881.208333333 0.994638393845722 0.995044214180192 24 471067 475344.401082098 477670.166666667 0.99513102189153 0.991001469519026 25 476049 480260.403508625 480050.583333333 1.00043707930492 0.991230999936997 26 474605 477668.391187007 482065.208333333 0.990879206650221 0.993586782706316 27 470439 469886.704549818 483130.5 0.972587540115597 1.00117538003275 28 461251 464933.828268297 484205.583333333 0.960199229979201 0.992078811984893 29 454724 458509.792682096 486455.958333333 0.942551498912698 0.991743267553893 30 455626 461776.196460752 489765.375 0.942851863426956 0.98668143462593 31 516847 513773.985488560 493277.291666667 1.04155207257289 1.00598125751447 32 525192 531542.460086804 496840.583333333 1.06984509300882 0.988052769884522 33 522975 529063.365639655 500253 1.05759158993480 0.988492180644008 34 518585 519884.681020593 503893.416666667 1.03173541035664 0.997500059016854 35 509239 505087.778654726 507810.458333333 0.994638393845722 1.00821881170107 36 512238 509379.747901454 511872.041666667 0.99513102189153 1.00561124016085 37 519164 516423.536093286 516197.916666667 1.00043707930492 1.00530662085513 38 517009 515907.369364212 520656.166666667 0.990879206650221 1.00213532641944 39 509933 510822.71149088 525220.291666667 0.972587540115597 0.998258277341892 40 509127 508392.805491866 529465.958333333 0.960199229979201 1.00144414810792 41 500857 502386.507507981 533006.958333333 0.942551498912698 0.996955516350214 42 506971 505715.175427646 536367.583333333 0.942851863426956 1.00248326455952 43 569323 562374.454318926 539938.875 1.04155207257289 1.01235572780327 44 579714 581529.411866881 543564.125 1.06984509300882 0.99687821143723 45 577992 578801.501517442 547282.625 1.05759158993480 0.998601417730742 46 565464 568084.436142126 550610.583333333 1.03173541035664 0.995387241798206 47 547344 550570.934674635 553538.791666667 0.994638393845722 0.994138930206074 48 554788 553810.606549623 556520.291666667 0.99513102189153 1.00176485144708 49 562325 559262.877102297 559018.541666667 1.00043707930492 1.00547528366905 50 560854 556201.183296208 561320.875 0.990879206650221 1.00836534844500 51 555332 548594.931688426 564057.125 0.972587540115597 1.01228058795738 52 543599 544827.125182114 567410.5 0.960199229979201 0.997745844277296 53 536662 538453.554797709 571272.291666667 0.942551498912698 0.996672777472178 54 542722 542062.193333744 574917.666666667 0.942851863426956 1.00121721579990 55 593530 601810.089472708 577801.25 1.04155207257289 0.986241358166723 56 610763 620701.611639883 580178.958333333 1.06984509300882 0.983988100798344 57 612613 616131.532198952 582579.833333333 1.05759158993480 0.994289316460733 58 611324 603587.268403031 585021.375 1.03173541035664 1.01281791714633 59 594167 584383.542543621 587533.666666667 0.994638393845722 1.01674150064835 60 595454 587040.560661928 589912.833333333 0.99513102189153 1.01433195574865 61 590865 592401.271547437 592142.458333333 1.00043707930492 0.997406704507193 62 589379 588613.007292271 594031.041666667 0.990879206650221 1.00130135198889 63 584428 579114.485550368 595436.875 0.972587540115597 1.00917524009882 64 573100 572394.645116323 596120.708333333 0.960199229979201 1.00123228770516 65 567456 561954.191320099 596205.291666667 0.942551498912698 1.00979049318411 66 569028 562248.14101829 596327.125 0.942851863426956 1.01205848181095 67 620735 621289.326527975 596503.375 1.04155207257289 0.999107780378149 68 628884 638310.459486989 596638.208333333 1.06984509300882 0.985232171356608 69 628232 630826.106346352 596474.208333333 1.05759158993480 0.995887763172363 70 612117 615252.068952523 596327.375 1.03173541035664 0.994904415424623 71 595404 593396.334019655 596595.041666667 0.994638393845722 1.00338334746146 72 597141 594073.068666051 596979.75 0.99513102189153 1.00516423230704 73 593408 597355.144170007 597094.166666667 1.00043707930492 0.993392299022566 74 590072 591473.014975733 596917.375 0.990879206650221 0.997631312096647 75 579799 580092.787042282 596442.75 0.972587540115597 0.999493551637179 76 574205 571391.276929296 595075.75 0.960199229979201 1.00492433676241 77 572775 558762.75121776 592819.333333333 0.942551498912698 1.02507727788172 78 572942 556209.331975017 589922.291666667 0.942851863426956 1.03008340037296 79 619567 611279.794120534 586893.166666667 1.04155207257289 1.01355714021496 80 625809 624583.856598018 583807.75 1.06984509300882 1.00196153549126 81 619916 613493.105579962 580085.083333333 1.05759158993480 1.01046938321168 82 587625 594349.668395378 576067.916666667 1.03173541035664 0.988685669811958 83 565742 568419.554879131 571483.625 0.994638393845722 0.995289474374786 84 557274 563061.303065334 565816.25 0.99513102189153 0.989721717628564 85 560576 560290.826201283 560046.041666667 1.00043707930492 1.00050897459923 86 548854 549759.684006943 554820.083333333 0.990879206650221 0.998352581985748 87 531673 533958.867042034 549008.541666667 0.972587540115597 0.995719020353202 88 525919 521603.86663074 543224.625 0.960199229979201 1.00827281706543 89 511038 507443.806848376 538372.5 0.942551498912698 1.00708293825467 90 498662 503719.708029692 534251.166666667 0.942851863426956 0.989959281026595 91 555362 552689.104994073 530639.916666667 1.04155207257289 1.00483616373432 92 564591 563915.838870615 527100.458333333 1.06984509300882 1.00119727286032 93 541657 553916.547671541 523752.791666667 1.05759158993480 0.977867518630603 94 527070 537161.864268573 520639.166666667 1.03173541035664 0.981212619621994 95 509846 514641.777746812 517415.958333333 0.994638393845722 0.990681328344915 96 514258 512294.279345612 514800.833333333 0.99513102189153 1.00383318872289 97 516922 513022.258448041 512798.125 1.00043707930492 1.00760150556383 98 507561 505727.035108454 510382.125 0.990879206650221 1.00362639282504 99 492622 494174.079552623 508102.416666667 0.972587540115597 0.996859245320943 100 490243 486094.858931783 506243.75 0.960199229979201 1.00853360407335 101 469357 476026.758832577 505040.583333333 0.942551498912698 0.985988689272566 102 477580 475938.931443265 504786.541666667 0.942851863426956 1.00344806538889 103 528379 NA NA 1.04155207257289 NA 104 533590 NA NA 1.06984509300882 NA 105 517945 NA NA 1.05759158993480 NA 106 506174 NA NA 1.03173541035664 NA 107 501866 NA NA 0.994638393845722 NA 108 516141 NA NA 0.99513102189153 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jan/14/t12634952278nvev6xg3300u0n/1gam81263495158.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/14/t12634952278nvev6xg3300u0n/1gam81263495158.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/14/t12634952278nvev6xg3300u0n/2cge31263495158.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/14/t12634952278nvev6xg3300u0n/2cge31263495158.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/14/t12634952278nvev6xg3300u0n/3rcs21263495158.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/14/t12634952278nvev6xg3300u0n/3rcs21263495158.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/14/t12634952278nvev6xg3300u0n/4fmj21263495158.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/14/t12634952278nvev6xg3300u0n/4fmj21263495158.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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