MULTIPLICATIEF MODEL - YANNICK DE ROEY

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Thu, 04 Jun 2009 02:29:36 -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/04/t1244104302i8x3vki45c7qlnc.htm/, Retrieved Thu, 04 Jun 2009 10:31:42 +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/04/t1244104302i8x3vki45c7qlnc.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 «

509 501 507 569 580 578 565 547 555 562 561 555 544 537 543 594 611 613 611 594 595 591 589 584 573 567 569 621 629 628 612 595 597 593 590 580 574 573 573 620 626 620 588 566 557 561 549 532 526 511 499 555 565 542 527 510 514 517 508 493 490 469 478 528 534 518 506 502 516 528 533 536

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 1 509 NA NA 0.964282792278105 NA 2 501 NA NA 0.947199384523121 NA 3 507 NA NA 0.950306185176819 NA 4 569 NA NA 1.04339036178599 NA 5 580 NA NA 1.06115357635289 NA 6 578 NA NA 1.04512323603582 NA 7 565 564.910990486354 550.541666666667 1.02610033842977 1.00015756378464 8 547 550.551007441081 553.5 0.994672100164555 0.993550084564215 9 555 555.338743956228 556.5 0.997913286534103 0.999390022828562 10 562 559.84506857718 559.041666666667 1.00143710560128 1.0038491567467 11 561 557.438781654084 561.375 0.992988255006163 1.00638853711496 12 555 550.266354427088 564.125 0.97543337811139 1.00860246230726 13 544 547.230484617824 567.5 0.964282792278105 0.994096665466142 14 537 541.206048331898 571.375 0.947199384523121 0.992228378923587 15 543 546.42605647667 575 0.950306185176819 0.993730063864887 16 594 602.94920531708 577.875 1.04339036178599 0.985157613214907 17 611 615.734362678762 580.25 1.06115357635289 0.992311030590911 18 613 608.914925395372 582.625 1.04512323603582 1.00670877725977 19 611 600.311452162181 585.041666666667 1.02610033842977 1.01780500405135 20 594 584.369858846676 587.5 0.994672100164555 1.01647953091272 21 595 588.602520174032 589.833333333333 0.997913286534103 1.01086893040158 22 591 592.892493062022 592.041666666667 1.00143710560128 0.996808033354837 23 589 589.75227445241 593.916666666667 0.992988255006163 0.998724422973852 24 584 580.667361378227 595.291666666667 0.97543337811139 1.00573932485866 25 573 574.672365748072 595.958333333333 0.964282792278105 0.997089879646648 26 567 564.570299816802 596.041666666667 0.947199384523121 1.00430362734984 27 569 566.54087072958 596.166666666667 0.950306185176819 1.00434060347183 28 621 622.208452411714 596.333333333333 1.04339036178599 0.99805780135736 29 629 632.933893562148 596.458333333333 1.06115357635289 0.993784669138181 30 628 623.241823089363 596.333333333333 1.04512323603582 1.00763455970758 31 612 611.76957260798 596.208333333333 1.02610033842977 1.00037665716364 32 595 593.321907748157 596.5 0.994672100164555 1.00282829983173 33 597 595.671072620315 596.916666666667 0.997913286534103 1.00223097518206 34 593 597.899678590028 597.041666666667 1.00143710560128 0.991805182766476 35 590 592.689864706803 596.875 0.992988255006163 0.995461598270904 36 580 581.764723928602 596.416666666667 0.97543337811139 0.996966602036842 37 574 573.828618304829 595.083333333333 0.964282792278105 1.00029866355512 38 573 561.570835099145 592.875 0.947199384523121 1.02035213402569 39 573 560.680649254323 590 0.950306185176819 1.02197213469390 40 620 612.470142368378 587 1.04339036178599 1.01229424442227 41 626 619.669473857737 583.958333333333 1.06115357635289 1.01021597223896 42 620 606.432757709787 580.25 1.04512323603582 1.02237221211705 43 588 591.290320020153 576.25 1.02610033842977 0.99443535618841 44 566 568.620883927404 571.666666666667 0.994672100164555 0.995390806068709 45 557 564.818920178303 566 0.997913286534103 0.98615676653354 46 561 561.013411867048 560.208333333333 1.00143710560128 0.999976093500148 47 549 551.067107017795 554.958333333333 0.992988255006163 0.996248901465047 48 532 535.675496812839 549.166666666667 0.97543337811139 0.993138575808102 49 526 523.967162254115 543.375 0.964282792278105 1.00387970447831 50 511 510.066868565701 538.5 0.947199384523121 1.00182942961366 51 499 507.819867703862 534.375 0.950306185176819 0.982631897125763 52 555 553.779434517915 530.75 1.04339036178599 1.00220406430070 53 565 559.449008399711 527.208333333333 1.06115357635289 1.00992224763463 54 542 547.513935278267 523.875 1.04512323603582 0.989929141665654 55 527 534.341751237301 520.75 1.02610033842977 0.986260195426802 56 510 514.742811835157 517.5 0.994672100164555 0.990786055237473 57 514 513.800603404246 514.875 0.997913286534103 1.0003880816691 58 517 513.612055535254 512.875 1.00143710560128 1.00659631024668 59 508 506.879129670021 510.458333333333 0.992988255006163 1.00221131679008 60 493 495.682728310272 508.166666666667 0.97543337811139 0.994587811603973 61 490 488.208342040469 506.291666666667 0.964282792278105 1.00366986346862 62 469 478.41462246622 505.083333333333 0.947199384523121 0.98032120670207 63 478 479.746239150097 504.833333333333 0.950306185176819 0.99636007745847 64 528 527.303404087596 505.375 1.04339036178599 1.00132105331960 65 534 537.872219013869 506.875 1.06115357635289 0.992800857012158 66 518 532.70802276776 509.708333333333 1.04512323603582 0.972390085864781 67 506 NA NA 1.02610033842977 NA 68 502 NA NA 0.994672100164555 NA 69 516 NA NA 0.997913286534103 NA 70 528 NA NA 1.00143710560128 NA 71 533 NA NA 0.992988255006163 NA 72 536 NA NA 0.97543337811139 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/04/t1244104302i8x3vki45c7qlnc/1q4y21244104171.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/04/t1244104302i8x3vki45c7qlnc/1q4y21244104171.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/04/t1244104302i8x3vki45c7qlnc/2djoq1244104171.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/04/t1244104302i8x3vki45c7qlnc/2djoq1244104171.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/04/t1244104302i8x3vki45c7qlnc/32kvj1244104171.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/04/t1244104302i8x3vki45c7qlnc/32kvj1244104171.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/04/t1244104302i8x3vki45c7qlnc/4ylgj1244104171.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/04/t1244104302i8x3vki45c7qlnc/4ylgj1244104171.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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