Robin Bosmans-Datareeks werkloosheid

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Tue, 02 Jun 2009 05:52:28 -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/02/t1243943814545sib15zx5qi6z.htm/, Retrieved Tue, 02 Jun 2009 13:56:58 +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/02/t1243943814545sib15zx5qi6z.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 «

467 460 448 443 436 431 484 510 513 503 471 471 476 475 470 461 455 456 517 525 523 519 509 512 519 517 510 509 501 507 569 580 578 565 547 555 562 561 555 544 537 543 594 611 613 611 594 595 591 589 584 573 567 569 621 629 628 612 595 597 593 590 580 574 573 573 620 626 620 588 566 557 561 549 532 526 511 499 555 565 542 527 510 514 517 508 493 490 469 478 528 534 518 506 502 516 528

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 1 467 NA NA 0.999132198884102 NA 2 460 NA NA 0.990447201199623 NA 3 448 NA NA 0.973009564451151 NA 4 443 NA NA 0.960724070363275 NA 5 436 NA NA 0.943109726810955 NA 6 431 NA NA 0.945472181629265 NA 7 484 490.833970450881 470.125 1.04404992385191 0.98607681851237 8 510 497.701461910019 471.125 1.05641063817462 1.02471067302632 9 513 498.521569531802 472.666666666667 1.0547000765835 1.02904273626876 10 503 490.27544410345 474.333333333333 1.03360950970509 1.02595389193889 11 471 477.635754143836 475.875 1.00370003497523 0.986107082465528 12 471 475.623075966733 477.708333333333 0.995634873371269 0.99027995864722 13 476 479.708346989230 480.125 0.999132198884102 0.992269580021894 14 475 477.519356878368 482.125 0.990447201199623 0.994724073815902 15 470 470.125787890648 483.166666666667 0.973009564451151 0.999732437798802 16 461 465.230631073416 484.25 0.960724070363275 0.990906378920806 17 455 458.822882093529 486.5 0.943109726810955 0.991668065733587 18 456 463.084395627167 489.791666666667 0.945472181629265 0.984701718101357 19 517 515.021127020118 493.291666666667 1.04404992385191 1.00384231418104 20 525 524.860018733092 496.833333333333 1.05641063817462 1.00026670209563 21 523 527.613713310897 500.25 1.0547000765835 0.991255509107327 22 519 520.853058765559 503.916666666667 1.03360950970509 0.996442261911736 23 509 509.712334428252 507.833333333333 1.00370003497523 0.998602477554224 24 512 509.640600806918 511.875 0.995634873371269 1.00462953538110 25 519 515.718736657344 516.166666666667 0.999132198884102 1.00636250558575 26 517 515.651574124553 520.625 0.990447201199623 1.00261499419979 27 510 511.032731662782 525.208333333333 0.973009564451151 0.997979128148168 28 509 508.623334918157 529.416666666667 0.960724070363275 1.00074055800429 29 501 502.598891913004 532.916666666667 0.943109726810955 0.99681875161539 30 507 507.048852072928 536.291666666667 0.945472181629265 0.999903654109997 31 569 563.656452639553 539.875 1.04404992385191 1.00948014936301 32 580 574.159181847907 543.5 1.05641063817462 1.01017282025046 33 578 577.140671073797 547.208333333333 1.0547000765835 1.00148894189800 34 565 569.045102155559 550.541666666667 1.03360950970509 0.992891420837758 35 547 555.547969358788 553.5 1.00370003497523 0.984613445048402 36 555 554.070807031111 556.5 0.995634873371269 1.00167702928416 37 562 558.5565296845 559.041666666667 0.999132198884102 1.00616494505479 38 561 556.012297573438 561.375 0.990447201199623 1.00897048940883 39 555 548.899020546006 564.125 0.973009564451151 1.0111149395893 40 544 545.210909931159 567.5 0.960724070363275 0.997779006419164 41 537 538.869320156609 571.375 0.943109726810955 0.996531032503269 42 543 543.646504436827 575 0.945472181629265 0.998810799974706 43 594 603.330349745925 577.875 1.04404992385191 0.984535255437002 44 611 612.982272800825 580.25 1.05641063817462 0.996766182500242 45 613 614.494632119463 582.625 1.0547000765835 0.997567705165613 46 611 604.704630240385 585.041666666667 1.03360950970509 1.01041065248188 47 594 589.673770547945 587.5 1.00370003497523 1.00733664895428 48 595 587.258636143487 589.833333333333 0.995634873371269 1.01318220521600 49 591 591.527892247675 592.041666666667 0.999132198884102 0.999107578434435 50 589 588.243100245809 593.916666666667 0.990447201199623 1.00128671250691 51 584 579.224485304733 595.291666666667 0.973009564451151 1.00824466992751 52 573 572.551515766914 595.958333333333 0.960724070363275 1.00078330808798 53 567 562.132693417946 596.041666666667 0.943109726810955 1.00865864348231 54 569 563.65899894798 596.166666666667 0.945472181629265 1.00947558907422 55 621 622.601771257025 596.333333333333 1.04404992385191 0.99742729408914 56 629 630.104928561238 596.458333333333 1.05641063817462 0.998246437202513 57 628 628.952812335961 596.333333333333 1.0547000765835 0.998485081364972 58 612 616.246603098758 596.208333333333 1.03360950970509 0.993108922503744 59 595 598.707070862722 596.5 1.00370003497523 0.99380820597729 60 597 594.311049829866 596.916666666667 0.995634873371269 1.00452448287964 61 593 596.523553242096 597.041666666667 0.999132198884102 0.994093186726752 62 590 591.173173216025 596.875 0.990447201199623 0.99801551682455 63 580 580.319121064741 596.416666666667 0.973009564451151 0.999450093830864 64 574 571.710882205346 595.083333333333 0.960724070363275 1.0040039779999 65 573 559.146179283045 592.875 0.943109726810955 1.02477674216556 66 573 557.828587161266 590 0.945472181629265 1.02719726666562 67 620 612.857305301074 587 1.04404992385191 1.01165474350578 68 626 616.899795584055 583.958333333333 1.05641063817462 1.01475151147899 69 620 611.989719437577 580.25 1.0547000765835 1.01308891360101 70 588 595.61747996756 576.25 1.03360950970509 0.98721078506968 71 566 573.781853327504 571.666666666667 1.00370003497523 0.986437609899345 72 557 563.529338328138 566 0.995634873371269 0.988413489974614 73 561 559.722183916531 560.208333333333 0.999132198884102 1.00228294700511 74 549 549.656928032407 554.958333333333 0.990447201199623 0.998804839893934 75 532 534.344419144424 549.166666666667 0.973009564451151 0.995612531804528 76 526 522.033441733645 543.375 0.960724070363275 1.00759828384401 77 511 507.864587887699 538.5 0.943109726810955 1.00617371674867 78 499 505.236697058138 534.375 0.945472181629265 0.987655890606416 79 555 554.129497084404 530.75 1.04404992385191 1.00157093769629 80 565 556.948491867646 527.208333333333 1.05641063817462 1.01445646814727 81 542 552.531002620182 523.875 1.0547000765835 0.980940431269482 82 527 538.252152178928 520.75 1.03360950970509 0.979095016836668 83 510 519.41476809968 517.5 1.00370003497523 0.98187427720986 84 514 512.627505427032 514.875 0.995634873371269 1.00267737208487 85 517 512.429926502684 512.875 0.999132198884102 1.00891843598696 86 508 505.582027579024 510.458333333333 0.990447201199623 1.00478255216578 87 493 494.451027001927 508.166666666667 0.973009564451151 0.997065377716525 88 490 486.406590791006 506.291666666667 0.960724070363275 1.00738766553954 89 469 476.349004516766 505.083333333333 0.943109726810955 0.984572226566903 90 478 477.305873025841 504.833333333333 0.945472181629265 1.00145426028337 91 528 527.636730266661 505.375 1.04404992385191 1.00068848454344 92 534 NA NA 1.05641063817462 NA 93 518 NA NA 1.0547000765835 NA 94 506 NA NA 1.03360950970509 NA 95 502 NA NA 1.00370003497523 NA 96 516 NA NA 0.995634873371269 NA 97 528 NA NA NA NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t1243943814545sib15zx5qi6z/15v0i1243943546.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t1243943814545sib15zx5qi6z/15v0i1243943546.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t1243943814545sib15zx5qi6z/22h9p1243943546.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t1243943814545sib15zx5qi6z/22h9p1243943546.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t1243943814545sib15zx5qi6z/3g8zq1243943546.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t1243943814545sib15zx5qi6z/3g8zq1243943546.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t1243943814545sib15zx5qi6z/4cw981243943546.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t1243943814545sib15zx5qi6z/4cw981243943546.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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