workshop 9,8

*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: Fri, 04 Dec 2009 12:05:12 -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/04/t12599535596n9ccrl3srtruv9.htm/, Retrieved Fri, 04 Dec 2009 20:06:04 +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/04/t12599535596n9ccrl3srtruv9.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 «

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

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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 611 NA NA 1.01413185152240 NA 2 594 NA NA 0.988651500949887 NA 3 595 NA NA 0.9955518230391 NA 4 591 NA NA 1.00402060596939 NA 5 589 NA NA 0.996516403003817 NA 6 584 NA NA 0.980027776551986 NA 7 573 581.310157476894 595.958333333333 0.975420805386665 0.985704434422816 8 567 568.757880346414 596.041666666667 0.954225035184477 0.9969092641928 9 569 568.528119988323 596.166666666667 0.95363956386076 1.00083000294108 10 621 613.247235016046 596.333333333333 1.02836316660041 1.01264215236739 11 629 633.932829159202 596.458333333333 1.06282835486000 0.992218687955088 12 628 624.136249761407 596.333333333333 1.04662311307111 1.00619055573213 13 612 604.633860976416 596.208333333333 1.01413185152240 1.01218280929832 14 595 589.730620316608 596.5 0.988651500949887 1.00893523161569 15 597 594.261475702422 596.916666666667 0.9955518230391 1.00460828172370 16 593 599.442135955641 597.041666666667 1.00402060596939 0.989253114572317 17 590 594.795728042903 596.875 0.996516403003817 0.991937184790007 18 580 584.504899731881 596.416666666667 0.980027776551986 0.992292793894547 19 574 580.456664272181 595.083333333333 0.975420805386665 0.988876578270874 20 573 565.736167734997 592.875 0.954225035184477 1.01283961089864 21 573 562.647342677849 590 0.95363956386076 1.01839990441060 22 620 603.649178794442 587 1.02836316660041 1.02708662875714 23 626 620.647474723452 583.958333333333 1.06282835486000 1.00862409901681 24 620 607.303061359513 580.25 1.04662311307111 1.02090708815474 25 588 584.393479439782 576.25 1.01413185152240 1.00617139083016 26 566 565.179108043019 571.666666666667 0.988651500949887 1.00145244568545 27 557 563.48233184013 566 0.9955518230391 0.988495944817007 28 561 562.460710302435 560.208333333333 1.00402060596939 0.997403000288412 29 549 553.025082150326 554.958333333333 0.996516403003817 0.992721700551672 30 532 538.198587289799 549.166666666667 0.980027776551986 0.988482713563012 31 526 530.019280126979 543.375 0.975420805386665 0.99241672845181 32 511 513.850181446841 538.5 0.954225035184477 0.994453283175233 33 499 509.601141938094 534.375 0.95363956386076 0.979197177820725 34 555 545.803750673169 530.75 1.02836316660041 1.01684900353926 35 565 560.331965585147 527.208333333333 1.06282835486000 1.00833083725641 36 542 548.299683360129 523.875 1.04662311307111 0.988510510672698 37 527 528.109161680289 520.75 1.01413185152240 0.997899749217075 38 510 511.627151741567 517.5 0.988651500949887 0.996819653265024 39 514 512.584744887256 514.875 0.9955518230391 1.00276101684036 40 517 514.937068286551 512.875 1.00402060596939 1.00400618219293 41 508 508.680102216657 510.458333333333 0.996516403003817 0.9986630060549 42 493 498.017448451168 508.166666666667 0.980027776551986 0.989925155299735 43 490 493.847425260557 506.291666666667 0.975420805386665 0.992209283548402 44 469 481.963161521093 505.083333333333 0.954225035184477 0.97310341836048 45 478 481.429039822374 504.833333333333 0.95363956386076 0.992877372283901 46 528 519.709035320683 505.375 1.02836316660041 1.01595308935547 47 534 538.72112236966 506.875 1.06282835486000 0.991236426095763 48 518 533.472522591622 509.708333333333 1.04662311307111 0.970996589446715 49 506 520.714450262938 513.458333333333 1.01413185152240 0.97174180540696 50 502 511.833120804265 517.708333333333 0.988651500949887 0.980788424186357 51 516 520.09286488601 522.416666666667 0.9955518230391 0.992130511371452 52 528 529.411698689276 527.291666666667 1.00402060596939 0.997333457698855 53 533 530.520420049157 532.375 0.996516403003817 1.00467386335594 54 536 526.887433368762 537.625 0.980027776551986 1.01729509199522 55 537 529.328357056497 542.666666666667 0.975420805386665 1.01449316448143 56 524 NA NA 0.954225035184477 NA 57 536 NA NA 0.95363956386076 NA 58 587 NA NA 1.02836316660041 NA 59 597 NA NA 1.06282835486000 NA 60 581 NA NA 1.04662311307111 NA 61 564 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t12599535596n9ccrl3srtruv9/1ws621259953506.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599535596n9ccrl3srtruv9/1ws621259953506.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599535596n9ccrl3srtruv9/2yyh81259953506.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599535596n9ccrl3srtruv9/2yyh81259953506.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599535596n9ccrl3srtruv9/3cbgu1259953506.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599535596n9ccrl3srtruv9/3cbgu1259953506.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599535596n9ccrl3srtruv9/4f4xx1259953506.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599535596n9ccrl3srtruv9/4f4xx1259953506.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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