Classical Decomposition - Verkoop vrouwenkleding VS 2000-2008 - Amelie Barratt

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Fri, 05 Jun 2009 06:08:11 -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/05/t1244204146o8yzemp1kqy7rje.htm/, Retrieved Fri, 05 Jun 2009 14:15:46 +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/05/t1244204146o8yzemp1kqy7rje.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 «

461683 731993 522673 572709 382812 942567 802385 272643 992660 672651 122826 493878 801948 742156 612673 662804 572750 202510 652313 492663 292397 382618 872790 913865 221989 842162 142783 112683 862759 392482 602262 542540 982375 112533 622742 883970 562056 322091 832619 82723 872811 562534 112447 342593 422622 682787 602944 84298 942258 722391 662901 3007 452988 12759 942577 52626 882755 22968 433067 904437 812315 32428 53073 403145 243133 313018 32720 922852 192947 693108 33335 944749 962536 292541 953227 193417 73378 433219 962953 632986 333186 733212 463421 495024 752596 22640 123421 243440 623653 413304 852981 613232 493203 253344 803665 574904 132570 132785 393406 333452 583642 893230 272991 373159 133072 653091 373307

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 461683 NA NA 1.46290700644046 NA 2 731993 NA NA 0.801189422137117 NA 3 522673 NA NA 0.91959859576183 NA 4 572709 NA NA 0.499704797449402 NA 5 382812 NA NA 1.04383763885848 NA 6 942567 NA NA 0.659504091026777 NA 7 802385 678215.931793363 595134.375 1.13960134094651 1.18308191003167 8 272643 646709.923142663 609735.541666667 1.06064002989711 0.421584686183724 9 992660 650329.829650468 613909 1.05932610476548 1.52639469195735 10 672651 523571.288003282 621412.958333333 0.842549678087711 1.28473622487065 11 122826 708807.913714954 633081 1.11961646884831 0.173285311328217 12 493878 849051.917995406 610159.375 1.39152482578082 0.581681743521687 13 801948 838349.093452173 573070.666666667 1.46290700644046 0.956580028848985 14 742156 461473.222841218 575985.166666667 0.801189422137117 1.60823199107992 15 612673 511273.867595292 555975.041666667 0.91959859576183 1.19832645247766 16 662804 257204.409662341 514712.708333333 0.499704797449402 2.5769542632264 17 572750 557280.385202032 533876.5 1.04383763885848 1.02775912307116 18 202510 384243.213803093 582624.458333333 0.659504091026777 0.527035983265997 19 652313 656363.601246822 575958.958333333 1.13960134094651 0.993828723532006 20 492663 589674.403274957 555960.916666667 1.06064002989711 0.835483102647543 21 292397 572617.754882048 540549.083333333 1.05932610476548 0.510632088347016 22 382618 419630.708665777 498048.625 0.842549678087711 0.911796949314173 23 872790 545489.039547876 487210.625 1.11961646884831 1.60001381645249 24 913865 705795.074963483 507209.833333333 1.39152482578082 1.29480217759706 25 221989 750529.627720839 513039.875 1.46290700644046 0.295776464780108 26 842162 411036.312361238 513032.625 0.801189422137117 2.048874940421 27 142783 500132.815657813 543859.916666667 0.91959859576183 0.285490164871907 28 112683 280512.015603575 561355.458333333 0.499704797449402 0.401704717559215 29 862759 563341.689411473 539683.25 1.04383763885848 1.53150213487898 30 392482 348230.663160531 528018.958333333 0.659504091026777 1.12707478553969 31 602262 616459.130758682 540942.791666667 1.13960134094651 0.976969875129906 32 542540 565790.601728392 533442.625 1.06064002989711 0.958905995155512 33 982375 572582.885397766 540516.166666667 1.05932610476548 1.71569047041557 34 112533 478577.485200279 568011 0.842549678087711 0.235140606234132 35 622742 635025.748226086 567181.5 1.11961646884831 0.980656298960476 36 883970 799689.60410787 574685.833333333 1.39152482578082 1.10539138618183 37 562056 821220.951539558 561362.375 1.46290700644046 0.684415076047808 38 322091 426731.346077763 532622.291666667 0.801189422137117 0.754786361396815 39 832619 460689.584271437 500968.125 0.91959859576183 1.80733193982832 40 82723 250554.817101649 501405.666666667 0.499704797449402 0.330159287923168 41 872811 547327.219342576 524341.333333333 1.04383763885848 1.59467859290532 42 562534 323286.762033030 490196.75 0.659504091026777 1.74004650379877 43 112447 538711.016357337 472718.833333333 1.13960134094651 0.208733433298516 44 342593 535877.503545208 505239.75 1.06064002989711 0.639312152000235 45 422622 545391.220168892 514847.333333333 1.05932610476548 0.774896962714446 46 682787 425027.765947478 504454.25 0.842549678087711 1.60645269486788 47 602944 541491.448945853 483640.125 1.11961646884831 1.11348757431679 48 84298 616779.753680094 443240.208333333 1.39152482578082 0.136674395514810 49 942258 665508.093548239 454921.666666667 1.46290700644046 1.41584754435703 50 722391 382510.630643898 477428.458333333 0.801189422137117 1.88855143394045 51 662901 445562.723803669 484518.708333333 0.91959859576183 1.48778379470563 52 3007 237958.654167176 476198.458333333 0.499704797449402 0.0126366490452894 53 452988 460987.71130762 441627.791666667 1.04383763885848 0.982646584472006 54 12759 309124.10403359 468722.041666667 0.659504091026777 0.0412746849356451 55 942577 566929.112511016 497480.208333333 1.13960134094651 1.66260115982611 56 52626 491413.042858519 463317.458333333 1.06064002989711 0.107091174653969 57 882755 433433.339362789 409159.5 1.05932610476548 2.03665689699316 58 22968 337375.778261603 400422.416666667 0.842549678087711 0.0680783905659951 59 433067 457196.364718616 408350.875 1.11961646884831 0.947223192088444 60 904437 573472.022289731 412117.708333333 1.39152482578082 1.57712488987485 61 812315 565732.16694435 386717.791666667 1.46290700644046 1.43586496837806 62 32428 308511.206619362 385066.5 0.801189422137117 0.105111254645635 63 53073 361019.618485346 392583.916666667 0.91959859576183 0.147008631338837 64 403145 195766.5584783 391764.416666667 0.499704797449402 2.05931494701475 65 243133 420699.362359123 403031.416666667 1.04383763885848 0.577925763035631 66 313018 255924.244754116 388055.583333333 0.659504091026777 1.22308849753855 67 32720 451275.815724054 395994.458333333 1.13960134094651 0.0725055472948447 68 922852 438141.601876914 413091.708333333 1.06064002989711 2.10628709085528 69 192947 488811.377032913 461436.166666667 1.05932610476548 0.39472690093915 70 693108 413021.152184835 490203.916666667 0.842549678087711 1.67814165529668 71 33335 531137.235841944 474392.125 1.11961646884831 0.0627615571842902 72 944749 657255.268208385 472327.375 1.39152482578082 1.43741563696446 73 962536 754999.661987935 516095.458333333 1.46290700644046 1.27488268996786 74 292541 434867.524808244 542777.416666667 0.801189422137117 0.67271291441916 75 953227 493404.151049232 536542.958333333 0.91959859576183 1.93193956308018 76 193417 271868.017912129 544057.25 0.499704797449402 0.711437121164119 77 73378 588357.519386127 563648.5 1.04383763885848 0.124716685998269 78 433219 371188.824949287 562830.208333333 0.659504091026777 1.16711218356099 79 962953 610078.930201225 535344.166666667 1.13960134094651 1.57840723934259 80 632986 546601.679080832 515350.791666667 1.06064002989711 1.15803888686261 81 333186 497385.032861832 469529.666666667 1.05932610476548 0.669875404338022 82 733212 368226.823018119 437038.708333333 0.842549678087711 1.99119660537038 83 463421 517320.048999887 462051.125 1.11961646884831 0.895811018528882 84 495024 673705.990659168 484149.458333333 1.39152482578082 0.734777494728314 85 752596 700348.44299579 478737.5 1.46290700644046 1.07460223197001 86 22640 379228.791856361 473332.25 0.801189422137117 0.0597001084468693 87 123421 440650.074838677 479176.541666667 0.91959859576183 0.280088458047317 88 243440 232787.188397339 465849.416666667 0.499704797449402 1.04576201841692 89 623653 480198.455719937 460031.75 1.04383763885848 1.29874011998849 90 413304 314937.55015798 477536.916666667 0.659504091026777 1.31233636571021 91 852981 518553.747838675 455030.833333333 1.13960134094651 1.64492302592588 92 613232 460090.575042274 433785.791666667 1.06064002989711 1.33285060217470 93 493203 476299.014330712 449624.541666667 1.05932610476548 1.03549028060249 94 253344 391469.152694191 464624.416666667 0.842549678087711 0.647162102700614 95 803665 522533.729689824 466707.791666667 1.11961646884831 1.53801554681849 96 574904 674941.838645065 485037.583333333 1.39152482578082 0.851783023488529 97 132570 NA 480868.25 NA NA 98 132785 NA 446698.958333333 NA NA 99 393406 NA 421690.458333333 NA NA 100 333452 NA 423341.125 NA NA 101 583642 NA 422065.666666667 NA NA 102 893230 NA NA NA NA 103 272991 NA NA NA NA 104 373159 NA NA NA NA 105 133072 NA NA NA NA 106 653091 NA NA NA NA 107 373307 NA NA NA NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t1244204146o8yzemp1kqy7rje/1gmx01244203687.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t1244204146o8yzemp1kqy7rje/1gmx01244203687.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t1244204146o8yzemp1kqy7rje/20ui31244203687.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t1244204146o8yzemp1kqy7rje/20ui31244203687.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t1244204146o8yzemp1kqy7rje/38pa01244203687.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t1244204146o8yzemp1kqy7rje/38pa01244203687.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t1244204146o8yzemp1kqy7rje/4216d1244203687.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t1244204146o8yzemp1kqy7rje/4216d1244203687.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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