Werkloosheid cijfers 2004-2010

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Wed, 26 May 2010 13:02:37 +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/26/t1274879063y6os1qlz90a4qy3.htm/, Retrieved Wed, 26 May 2010 15:04:23 +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/26/t1274879063y6os1qlz90a4qy3.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 «

580 575 558 564 581 597 587 536 524 537 536 533 528 516 502 506 518 534 528 478 469 490 493 508 517 514 510 527 542 565 555 499 511 526 532 549 561 557 566 588 620 626 620 573 573 574 580 590 593 597 595 612 628 629 621 569 567 573 584 589 591 595 594 611 613 611 594 543 537 544 555 561 562

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 580 NA NA 0.99948551451067 NA 2 575 NA NA 0.994839120116967 NA 3 558 NA NA 0.989801169879415 NA 4 564 NA NA 1.01675939712162 NA 5 581 NA NA 1.04390985124311 NA 6 597 NA NA 1.05992158038544 NA 7 587 580.320000294003 556.833333333333 1.04217898885484 1.01151089003069 8 536 536.275538691632 552.208333333333 0.971147131109875 0.999486199403567 9 524 521.28065884185 547.416666666667 0.952255732394915 1.0052166546217 10 537 522.528377393316 542.666666666667 0.962890130331664 1.02769538121332 11 536 524.424391996947 537.625 0.975446439427012 1.02207297787766 12 533 527.777912394454 532.375 0.991364944624473 1.00989447925521 13 528 527.020382755522 527.291666666667 0.99948551451067 1.00185878435926 14 516 519.720537001105 522.416666666667 0.994839120116967 0.992841273845798 15 502 512.428313989655 517.708333333333 0.989801169879415 0.979649223696359 16 506 522.063585447071 513.458333333333 1.01675939712162 0.969230595860627 17 518 532.089550427375 509.708333333333 1.04390985124311 0.973520339920116 18 534 537.247751057871 506.875 1.05992158038544 0.99395483545259 19 528 526.691206492514 505.375 1.04217898885484 1.00248493517900 20 478 490.267443355302 504.833333333333 0.971147131109875 0.974978058360666 21 469 480.968499503798 505.083333333333 0.952255732394915 0.975115834995128 22 490 487.503248902502 506.291666666667 0.962890130331664 1.00512150658097 23 493 495.689365635493 508.166666666667 0.975446439427012 0.994574493983656 24 508 506.050497358101 510.458333333333 0.991364944624473 1.00385238756226 25 517 512.611133254659 512.875 0.99948551451067 1.00856178584627 26 514 512.217791970223 514.875 0.994839120116967 1.00347939501071 27 510 512.222105412597 517.5 0.989801169879415 0.995661832261598 28 527 529.477456051083 520.75 1.01675939712162 0.99532094138708 29 542 546.878273319986 523.875 1.04390985124311 0.99107978217827 30 565 558.799489859041 527.208333333333 1.05992158038544 1.01109612706075 31 555 553.136498334706 530.75 1.04217898885484 1.00336897252469 32 499 518.956748186839 534.375 0.971147131109875 0.961544486594374 33 511 512.789711894661 538.5 0.952255732394915 0.99650985218083 34 526 523.210424568968 543.375 0.962890130331664 1.00533165109111 35 532 535.682669652 549.166666666667 0.975446439427012 0.993125277593182 36 549 550.16623739389 554.958333333333 0.991364944624473 0.997880209081143 37 561 559.920114274831 560.208333333333 0.99948551451067 1.00192864249317 38 557 563.078941986203 566 0.994839120116967 0.98920410348723 39 566 565.836335447732 571.666666666667 0.989801169879415 1.00028924362402 40 588 585.907602591333 576.25 1.01675939712162 1.00357120713132 41 620 605.728691183816 580.25 1.04390985124311 1.0235605627138 42 626 618.950039545915 583.958333333333 1.05992158038544 1.01139019307480 43 620 611.75906645779 587 1.04217898885484 1.01347088093018 44 573 572.976807354826 590 0.971147131109875 1.00004047745891 45 573 564.568617343635 592.875 0.952255732394915 1.01493420356242 46 574 572.999868391534 595.083333333333 0.962890130331664 1.00174543078216 47 580 581.772513914927 596.416666666667 0.975446439427012 0.996953252564307 48 590 591.720951322732 596.875 0.991364944624473 0.9970916167175 49 593 596.734497392641 597.041666666667 0.99948551451067 0.993741777274553 50 597 593.836051449819 596.916666666667 0.994839120116967 1.00532798327494 51 595 590.416397833071 596.5 0.989801169879415 1.00776333818598 52 612 606.200425558885 596.208333333333 1.01675939712162 1.00956709067924 53 628 622.51824129131 596.333333333333 1.04390985124311 1.00880578004802 54 629 632.199059300733 596.458333333333 1.05992158038544 0.994939791109035 55 621 621.486070353769 596.333333333333 1.04217898885484 0.9992178901878 56 569 578.96554799667 596.166666666667 0.971147131109875 0.982787321229816 57 567 567.584093829552 596.041666666667 0.952255732394915 0.99897091226498 58 573 573.842397255575 595.958333333333 0.962890130331664 0.998532005896387 59 584 580.675136670571 595.291666666667 0.975446439427012 1.00572585791858 60 589 588.788163361551 593.916666666667 0.991364944624473 1.00035978413227 61 591 591.737069820087 592.041666666667 0.99948551451067 0.998754396407324 62 595 586.789274348991 589.833333333333 0.994839120116967 1.0139926307619 63 594 581.508187304156 587.5 0.989801169879415 1.02148174861261 64 611 594.846612291027 585.041666666667 1.01675939712162 1.02715555132231 65 613 608.207977080519 582.625 1.04390985124311 1.00787892152037 66 611 615.019497018652 580.25 1.05992158038544 0.993464439683397 67 594 602.24918318449 577.875 1.04217898885484 0.986302707558902 68 543 NA NA 0.971147131109875 NA 69 537 NA NA 0.952255732394915 NA 70 544 NA NA 0.962890130331664 NA 71 555 NA NA 0.975446439427012 NA 72 561 NA NA 0.991364944624473 NA 73 562 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/26/t1274879063y6os1qlz90a4qy3/1krex1274878954.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/26/t1274879063y6os1qlz90a4qy3/1krex1274878954.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/26/t1274879063y6os1qlz90a4qy3/2krex1274878954.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/26/t1274879063y6os1qlz90a4qy3/2krex1274878954.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/26/t1274879063y6os1qlz90a4qy3/3krex1274878954.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/26/t1274879063y6os1qlz90a4qy3/3krex1274878954.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/26/t1274879063y6os1qlz90a4qy3/4il371274878954.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/26/t1274879063y6os1qlz90a4qy3/4il371274878954.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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