Paper Decomposition - Werkloosheid

*The author of this computation has been verified*

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

Title produced by software: Classical Decomposition

Date of computation: Sat, 18 Dec 2010 18:53:33 +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/Dec/18/t1292698337anq327zkk3ltwdq.htm/, Retrieved Sat, 18 Dec 2010 19:52:18 +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/Dec/18/t1292698337anq327zkk3ltwdq.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:

Dataseries X:

» Textbox « » Textfile « » CSV «

631.923 654.294 671.833 586.840 600.969 625.568 558.110 630.577 628.654 603.184 656.255 600.730 670.326 678.423 641.502 625.311 628.177 589.767 582.471 636.248 599.885 621.694 637.406 595.994 696.308 674.201 648.861 649.605 672.392 598.396 613.177 638.104 615.632 634.465 638.686 604.243 706.669 677.185 644.328 664.825 605.707 600.136 612.166 599.659 634.210 618.234 613.576 627.200 668.973 651.479 619.661 644.260 579.936 601.752 595.376 588.902 634.341 594.305 606.200 610.926 633.685 639.696 659.451 593.248 606.677 599.434 569.578 629.873 613.438 604.172 658.328 612.633 707.372 739.770 777.535 685.030 730.234 714.154 630.872 719.492 677.023 679.272 718.317 645.672

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 1 631.923 NA NA 45.4615902777777 NA 2 654.294 NA NA 40.5756666666667 NA 3 671.833 NA NA 28.0529722222221 NA 4 586.84 NA NA 5.67885416666671 NA 5 600.969 NA NA -1.80652083333332 NA 6 625.568 NA NA -22.4636041666667 NA 7 558.11 583.804583333333 622.344875 -38.5402916666666 -25.6945833333334 8 630.577 617.373381944445 624.950375 -7.57699305555553 13.2036180555555 9 628.654 616.253506944444 624.691958333333 -8.43845138888887 12.4004930555556 10 603.184 606.825756944444 625.031125 -18.2053680555556 -3.64175694444441 11 656.255 630.382333333333 627.76775 2.61458333333334 25.8726666666665 12 600.73 602.057270833333 627.409708333333 -25.3524375 -1.32727083333339 13 670.326 672.394631944444 626.933041666667 45.4615902777777 -2.06863194444441 14 678.423 668.760041666667 628.184375 40.5756666666667 9.66295833333322 15 641.502 655.274930555555 627.221958333333 28.0529722222221 -13.7729305555555 16 625.311 632.473354166667 626.7945 5.67885416666671 -7.16235416666655 17 628.177 624.973854166667 626.780375 -1.80652083333332 3.20314583333345 18 589.767 603.3340625 625.797666666667 -22.4636041666667 -13.5670624999998 19 582.471 588.142625 626.682916666667 -38.5402916666666 -5.67162499999995 20 636.248 620.012590277778 627.589583333333 -7.57699305555553 16.2354097222224 21 599.885 619.281840277778 627.720291666667 -8.43845138888887 -19.3968402777776 22 621.694 610.833798611111 629.039166666667 -18.2053680555556 10.860201388889 23 637.406 634.508291666667 631.893708333333 2.61458333333334 2.8977083333333 24 595.994 608.743104166667 634.095541666667 -25.3524375 -12.7491041666666 25 696.308 681.196090277778 635.7345 45.4615902777777 15.1119097222222 26 674.201 677.666916666667 637.09125 40.5756666666667 -3.46591666666666 27 648.861 665.877680555556 637.824708333333 28.0529722222221 -17.0166805555556 28 649.605 644.6918125 639.012958333333 5.67885416666671 4.91318749999994 29 672.392 637.791895833333 639.598416666667 -1.80652083333332 34.6001041666667 30 598.396 617.531854166667 639.995458333333 -22.4636041666667 -19.1358541666666 31 613.177 602.230583333333 640.770875 -38.5402916666666 10.9464166666667 32 638.104 633.749923611111 641.326916666667 -7.57699305555553 4.35407638888887 33 615.632 632.823923611111 641.262375 -8.43845138888887 -17.1919236111112 34 634.465 623.502298611111 641.707666666667 -18.2053680555556 10.9627013888889 35 638.686 642.177875 639.563291666667 2.61458333333334 -3.49187500000005 36 604.243 611.5048125 636.85725 -25.3524375 -7.26181250000002 37 706.669 682.349215277778 636.887625 45.4615902777777 24.3197847222222 38 677.185 675.819291666667 635.243625 40.5756666666667 1.36570833333315 39 644.328 662.468805555555 634.415833333333 28.0529722222221 -18.1408055555555 40 664.825 640.192479166667 634.513625 5.67885416666671 24.6325208333334 41 605.707 630.9845625 632.791083333333 -1.80652083333332 -25.2775624999998 42 600.136 610.237770833333 632.701375 -22.4636041666667 -10.1017708333333 43 612.166 593.546958333333 632.08725 -38.5402916666666 18.6190416666667 44 599.659 621.868506944444 629.4455 -7.57699305555553 -22.2095069444445 45 634.21 618.908173611111 627.346625 -8.43845138888887 15.301826388889 46 618.234 607.256590277778 625.461958333333 -18.2053680555556 10.9774097222222 47 613.576 626.145875 623.531291666667 2.61458333333334 -12.569875 48 627.2 597.172395833333 622.524833333333 -25.3524375 30.0276041666667 49 668.973 667.354173611111 621.892583333333 45.4615902777777 1.61882638888892 50 651.479 661.320458333333 620.744791666667 40.5756666666667 -9.8414583333332 51 619.661 648.355013888889 620.302041666667 28.0529722222221 -28.6940138888889 52 644.26 624.9893125 619.310458333333 5.67885416666671 19.2706874999999 53 579.936 616.1995625 618.006083333333 -1.80652083333332 -36.2635624999999 54 601.752 594.5570625 617.020666666667 -22.4636041666667 7.19493750000004 55 595.376 576.331958333333 614.87225 -38.5402916666666 19.0440416666667 56 588.902 605.333965277778 612.910958333333 -7.57699305555553 -16.4319652777779 57 634.341 605.639465277778 614.077916666667 -8.43845138888887 28.7015347222223 58 594.305 595.404965277778 613.610333333333 -18.2053680555556 -1.09996527777776 59 606.2 615.213625 612.599041666667 2.61458333333334 -9.01362499999993 60 610.926 588.264229166667 613.616666666667 -25.3524375 22.6617708333333 61 633.685 657.906756944444 612.445166666667 45.4615902777777 -24.2217569444445 62 639.696 653.653041666667 613.077375 40.5756666666667 -13.9570416666667 63 659.451 641.966513888889 613.913541666667 28.0529722222221 17.4844861111112 64 593.248 619.1325625 613.453708333333 5.67885416666671 -25.8845624999999 65 606.677 614.2303125 616.036833333333 -1.80652083333332 -7.55331249999995 66 599.434 595.816354166667 618.279958333333 -22.4636041666667 3.61764583333331 67 569.578 582.881083333333 621.421375 -38.5402916666666 -13.3030833333335 68 629.873 621.084423611111 628.661416666667 -7.57699305555553 8.78857638888883 69 613.438 629.312881944444 637.751333333333 -8.43845138888887 -15.8748819444444 70 604.172 628.290381944444 646.49575 -18.2053680555556 -24.1183819444443 71 658.328 658.082791666666 655.468208333333 2.61458333333334 0.245208333333494 72 612.633 640.043979166667 665.396416666667 -25.3524375 -27.4109791666666 73 707.372 718.191923611111 672.730333333333 45.4615902777777 -10.819923611111 74 739.77 719.594041666667 679.018375 40.5756666666667 20.1759583333334 75 777.535 713.454847222222 685.401875 28.0529722222221 64.0801527777778 76 685.03 696.859270833333 691.180416666667 5.67885416666671 -11.8292708333333 77 730.234 695.002604166667 696.809125 -1.80652083333332 35.2313958333333 78 714.154 678.2216875 700.685291666667 -22.4636041666667 35.9323125 79 630.872 NA NA -38.5402916666666 NA 80 719.492 NA NA -7.57699305555553 NA 81 677.023 NA NA -8.43845138888887 NA 82 679.272 NA NA -18.2053680555556 NA 83 718.317 NA NA 2.61458333333334 NA 84 645.672 NA NA -25.3524375 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292698337anq327zkk3ltwdq/1bwf01292698409.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292698337anq327zkk3ltwdq/1bwf01292698409.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292698337anq327zkk3ltwdq/2bwf01292698409.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292698337anq327zkk3ltwdq/2bwf01292698409.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292698337anq327zkk3ltwdq/34oxl1292698409.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292698337anq327zkk3ltwdq/34oxl1292698409.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292698337anq327zkk3ltwdq/4wfwo1292698409.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292698337anq327zkk3ltwdq/4wfwo1292698409.ps (open in new window)

Parameters (Session):

par1 = additive ; par2 = 12 ;

Parameters (R input):

par1 = additive ; 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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