*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: Wed, 16 Dec 2009 13:36:18 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/16/t1260995834z49ns9wgp79srex.htm/, Retrieved Wed, 16 Dec 2009 21:37:20 +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/2009/Dec/16/t1260995834z49ns9wgp79srex.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 «

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

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 1 519 NA NA 1.00038941125041 NA 2 517 NA NA 0.9919493997656 NA 3 510 NA NA 0.974412888622656 NA 4 509 NA NA 0.963273968430398 NA 5 501 NA NA 0.946208433180536 NA 6 507 NA NA 0.949311983527773 NA 7 569 563.701301874927 539.875 1.04413299722144 1.00939983304535 8 580 577.615334963959 543.5 1.06276970554546 1.0041284655924 9 578 575.676085573907 547.208333333333 1.05202360875457 1.00403684378130 10 565 564.319985769068 550.541666666667 1.02502684163003 1.00120501532478 11 547 549.975026006866 553.5 0.99363148330057 0.9945906161804 12 555 554.757753635811 556.5 0.99686927877055 1.00043667053340 13 562 559.259363781113 559.041666666667 1.00038941125041 1.00490047444241 14 561 556.855594293413 561.375 0.9919493997656 1.00744251426951 15 555 549.690670794256 564.125 0.974412888622656 1.00965875807583 16 544 546.657977084251 567.5 0.963273968430398 0.99513776950914 17 537 540.639843508528 571.375 0.946208433180536 0.993267526335263 18 543 545.854390528469 575 0.949311983527773 0.994770783970968 19 594 603.378355769341 577.875 1.04413299722144 0.98445692378643 20 611 616.672121642755 580.25 1.06276970554546 0.990802046267885 21 613 612.935255050631 582.625 1.05202360875457 1.00010563097625 22 611 599.683411805304 585.041666666667 1.02502684163003 1.01887093751789 23 594 583.758496439085 587.5 0.99363148330057 1.01754407622910 24 595 587.98672959483 589.833333333333 0.99686927877055 1.01192759981165 25 591 592.272214352377 592.041666666667 1.00038941125041 0.99785197697689 26 589 589.135281010786 593.916666666667 0.9919493997656 0.999770373604932 27 584 580.059872489662 595.291666666667 0.974412888622656 1.00679262210197 28 573 574.071148769166 595.958333333333 0.963273968430398 0.998134118442526 29 567 563.979651526982 596.041666666667 0.946208433180536 1.00535542100649 30 569 565.948160846474 596.166666666667 0.949311983527773 1.00539243585307 31 621 622.651310676387 596.333333333333 1.04413299722144 0.997347936721448 32 629 633.897847286804 596.458333333333 1.06276970554546 0.992273443887264 33 628 627.356745353975 596.333333333333 1.05202360875457 1.00102534108510 34 612 611.129544870173 596.208333333333 1.02502684163003 1.00142433815732 35 595 592.70117978879 596.5 0.99363148330057 1.00387854839774 36 597 595.047886986121 596.916666666667 0.99686927877055 1.00328059817802 37 593 597.274161408629 597.041666666667 1.00038941125041 0.992843886970519 38 590 592.069797985092 596.875 0.9919493997656 0.996504131789637 39 580 581.156086989362 596.416666666667 0.974412888622656 0.99801071172574 40 574 573.22828404679 595.083333333333 0.963273968430398 1.00134626286715 41 573 560.98332482191 592.875 0.946208433180536 1.02142073506713 42 573 560.094070281386 590 0.949311983527773 1.02304243234021 43 620 612.906069368987 587 1.04413299722144 1.01157425417294 44 626 620.613225967486 583.958333333333 1.06276970554546 1.00867976028728 45 620 610.436698979839 580.25 1.05202360875457 1.01566632713292 46 588 590.671717489307 576.25 1.02502684163003 0.995476814937638 47 566 568.025997953493 571.666666666667 0.99363148330057 0.996433265447722 48 557 564.228011784132 566 0.99686927877055 0.98718955522737 49 561 560.426484760905 560.208333333333 1.00038941125041 1.00102335498890 50 549 550.490585644918 554.958333333333 0.9919493997656 0.997292259515807 51 532 535.115078001942 549.166666666667 0.974412888622656 0.99417867645671 52 526 523.418992595868 543.375 0.963273968430398 1.00493105416625 53 511 509.533241267718 538.5 0.946208433180536 1.00287863207635 54 499 507.288591197653 534.375 0.949311983527773 0.983660994271358 55 555 554.173588275281 530.75 1.04413299722144 1.00149125065179 56 565 560.301045177781 527.208333333333 1.06276970554546 1.00838648234313 57 542 551.1288680363 523.875 1.05202360875457 0.983436055402384 58 527 533.78272777884 520.75 1.02502684163003 0.98729309244032 59 510 514.204292608045 517.5 0.99363148330057 0.991823692122987 60 514 513.263069906987 514.875 0.99686927877055 1.00143577462752 61 517 513.074719295053 512.875 1.00038941125041 1.00765050499923 62 508 506.348837355348 510.458333333333 0.9919493997656 1.00326091919807 63 493 495.164149568413 508.166666666667 0.974412888622656 0.995629430017704 64 490 487.69758293324 506.291666666667 0.963273968430398 1.00472099339290 65 469 477.914109458935 505.083333333333 0.946208433180536 0.98134788389272 66 478 479.244333017604 504.833333333333 0.949311983527773 0.997403551942349 67 528 NA NA 1.04413299722144 NA 68 534 NA NA 1.06276970554546 NA 69 518 NA NA 1.05202360875457 NA 70 506 NA NA 1.02502684163003 NA 71 502 NA NA 0.99363148330057 NA 72 516 NA NA 0.99686927877055 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/16/t1260995834z49ns9wgp79srex/12ark1260995776.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/16/t1260995834z49ns9wgp79srex/12ark1260995776.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/16/t1260995834z49ns9wgp79srex/2v22f1260995776.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/16/t1260995834z49ns9wgp79srex/2v22f1260995776.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/16/t1260995834z49ns9wgp79srex/3rp781260995776.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/16/t1260995834z49ns9wgp79srex/3rp781260995776.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/16/t1260995834z49ns9wgp79srex/4kpns1260995776.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/16/t1260995834z49ns9wgp79srex/4kpns1260995776.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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