Ad hoc techniek 1

*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 06:39:53 -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/t1259934063y4e0plt36nuqbrm.htm/, Retrieved Fri, 04 Dec 2009 14:41:08 +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/t1259934063y4e0plt36nuqbrm.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 «

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

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 1 562 NA NA 0.99884113513149 NA 2 561 NA NA 0.9897846687593 NA 3 555 NA NA 0.97174729402293 NA 4 544 NA NA 0.964130906071395 NA 5 537 NA NA 0.947491984032662 NA 6 543 NA NA 0.950243475305589 NA 7 594 604.199808745448 577.875 1.04555450356123 0.983118483988556 8 611 612.201946500066 580.25 1.05506582766061 0.998036682981919 9 613 612.243826200002 582.625 1.05083686110277 1.00123508603540 10 611 600.442639619416 585.041666666667 1.02632457452218 1.01758262935370 11 594 589.242605030293 587.5 1.00296613622178 1.00807374573579 12 595 588.07128505649 589.833333333333 0.997012633608064 1.01178210043506 13 591 591.355570378473 592.041666666667 0.99884113513149 0.99939871982901 14 589 587.849611187294 593.916666666667 0.9897846687593 1.00195694407347 15 584 578.473066237733 595.291666666667 0.97174729402293 1.00955434934631 16 573 574.581847897465 595.958333333333 0.964130906071395 0.997246958108312 17 567 564.744701316135 596.041666666667 0.947491984032662 1.00399348356631 18 569 566.503485194682 596.166666666667 0.950243475305589 1.00440688340066 19 621 623.499002290349 596.333333333333 1.04555450356123 0.995991970666883 20 629 629.302805123398 596.458333333333 1.05506582766061 0.999518824449958 21 628 626.649048170952 596.333333333333 1.05083686110277 1.00215583480577 22 612 611.903264034914 596.208333333333 1.02632457452218 1.00015809029102 23 595 598.269300256289 596.5 1.00296613622178 0.994535403613576 24 597 595.13345787788 596.916666666667 0.997012633608064 1.00313634210514 25 593 596.349776054131 597.041666666667 0.99884113513149 0.994382866920325 26 590 590.777724165707 596.875 0.9897846687593 0.998683558749942 27 580 579.566281943509 596.416666666667 0.97174729402293 1.00074834936055 28 574 573.738233354653 595.083333333333 0.964130906071395 1.00045624751869 29 573 561.744310033364 592.875 0.947491984032662 1.02003703422642 30 573 560.643650430297 590 0.950243475305589 1.02203957818878 31 620 613.740493590444 587 1.04555450356123 1.01019894641942 32 626 616.114482277641 583.958333333333 1.05506582766061 1.01604493646345 33 620 609.748088654883 580.25 1.05083686110277 1.01681335544279 34 588 591.419536068409 576.25 1.02632457452218 0.99421808739843 35 566 573.362307873449 571.666666666667 1.00296613622178 0.987159414261543 36 557 564.309150622164 566 0.997012633608064 0.987047612795033 37 561 559.559127576787 560.208333333333 0.99884113513149 1.00257501370669 38 549 549.289250133547 554.958333333333 0.9897846687593 0.999473410168729 39 532 533.651222300926 549.166666666667 0.97174729402293 0.996905802456882 40 526 523.884631086544 543.375 0.964130906071395 1.00403785258802 41 511 510.224433401588 538.5 0.947491984032662 1.00152004989891 42 499 507.786357116424 534.375 0.950243475305589 0.982696744421573 43 555 554.928052765125 530.75 1.04555450356123 1.00012965146476 44 565 556.239496557901 527.208333333333 1.05506582766061 1.01574951706290 45 542 550.507160610214 523.875 1.05083686110277 0.984546684913628 46 527 534.458522182428 520.75 1.02632457452218 0.986044712783377 47 510 519.034975494769 517.5 1.00296613622178 0.98259274245217 48 514 513.336879728952 514.875 0.997012633608064 1.00129178381144 49 517 512.280647180563 512.875 0.99884113513149 1.00921243627963 50 508 505.243832373758 510.458333333333 0.9897846687593 1.00545512374351 51 493 493.809583245985 508.166666666667 0.97174729402293 0.998360535571903 52 490 488.13144331973 506.291666666667 0.964130906071395 1.00382797852063 53 469 478.56240960183 505.083333333333 0.947491984032662 0.980018469044014 54 478 479.714581116771 504.833333333333 0.950243475305589 0.996425830724637 55 528 528.397107237258 505.375 1.04555450356123 0.999248468184592 56 534 NA NA 1.05506582766061 NA 57 518 NA NA 1.05083686110277 NA 58 506 NA NA 1.02632457452218 NA 59 502 NA NA 1.00296613622178 NA 60 516 NA NA 0.997012633608064 NA 61 528 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259934063y4e0plt36nuqbrm/1uig31259933988.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259934063y4e0plt36nuqbrm/1uig31259933988.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259934063y4e0plt36nuqbrm/2lmcg1259933988.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259934063y4e0plt36nuqbrm/2lmcg1259933988.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259934063y4e0plt36nuqbrm/3rs291259933988.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259934063y4e0plt36nuqbrm/3rs291259933988.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259934063y4e0plt36nuqbrm/4k3ex1259933988.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259934063y4e0plt36nuqbrm/4k3ex1259933988.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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