*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Sat, 06 Jun 2009 09:18:22 -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/06/t1244301521gu3n2cz8y7x2ffa.htm/, Retrieved Sat, 06 Jun 2009 17:18:46 +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/06/t1244301521gu3n2cz8y7x2ffa.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 «

3851,3 3851,8 3854,1 3858,4 3861,6 3856,3 3855,8 3860,4 3855,1 3839,5 3833 3833,6 3826,8 3818,2 3811,4 3806,8 3810,3 3818,2 3858,9 3867,8 3872,3 3873,3 3876,7 3882,6 3883,5 3882,2 3888,1 3893,7 3901,9 3914,3 3930,3 3948,3 3971,5 3990,1 3993 3998 4015,8 4041,2 4060,7 4076,7 4103 4125,3 4139,7 4146,7 4158 4155,1 4144,8 4148,2 4142,5 4142,1 4145,4 4146,3 4143,5 4149,2 4158,9 4166,1 4179,1 4194,4 4211,7 4226,3 4235,8 4243,6 4258,7 4278,2 4298 4315,1 4334,3 4356 4374 4395,5 4417,8 4432,8 4446,3

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 'George Udny Yule' @ 72.249.76.132 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 3851.3 NA NA 0.999161804058414 NA 2 3851.8 NA NA 0.998270904092836 NA 3 3854.1 NA NA 0.997989468309282 NA 4 3858.4 NA NA 0.997597499163265 NA 5 3861.6 NA NA 0.997964223363847 NA 6 3856.3 NA NA 0.99877111874649 NA 7 3855.8 3854.87928625939 3849.8875 1.00129660574741 1.00023884372823 8 3860.4 3855.41164410464 3847.46666666667 1.00206498928420 1.00129385818061 9 3855.1 3856.58113623656 3844.2875 1.00319789720112 0.999615945786114 10 3839.5 3850.975409707 3840.35833333333 1.00276460565711 0.997020129061828 11 3833 3839.233682183 3836.07083333333 1.00082450220214 0.998376321240375 12 3833.6 3832.71520315581 3832.34583333333 1.00009638217388 1.00023085379354 13 3826.8 3827.67646564483 3830.8875 0.999161804058414 0.99977101887981 14 3818.2 3824.70027162348 3831.325 0.998270904092836 0.9983004494047 15 3811.4 3824.64493887508 3832.35 0.997989468309282 0.996536949419683 16 3806.8 3825.26267060406 3834.475 0.997597499163265 0.9951734894584 17 3810.3 3829.8914581877 3837.70416666667 0.997964223363847 0.994884591795463 18 3818.2 3836.84583740589 3841.56666666667 0.99877111874649 0.995140321452557 19 3858.9 3850.95754122021 3845.97083333333 1.00129660574741 1.00206246334704 20 3867.8 3858.95227373344 3851 1.00206498928420 1.00229277939683 21 3872.3 3869.19634979387 3856.8625 1.00319789720112 1.00080214337179 22 3873.3 3874.36071594809 3863.67916666667 1.00276460565711 0.999726221685108 23 3876.7 3874.30841088309 3871.11666666667 1.00082450220214 1.00061729445962 24 3882.6 3879.31136042859 3878.9375 1.00009638217388 1.00084773797869 25 3883.5 3882.65950708733 3885.91666666667 0.999161804058414 1.00021647350512 26 3882.2 3885.51576699324 3892.24583333333 0.998270904092836 0.999146634014097 27 3888.1 3891.89279588132 3899.73333333333 0.997989468309282 0.999025462395744 28 3893.7 3899.34259822943 3908.73333333333 0.997597499163265 0.998552936017474 29 3901.9 3910.4687528558 3918.44583333333 0.997964223363847 0.99780876580345 30 3914.3 3923.27283154808 3928.1 0.99877111874649 0.997712921855465 31 3930.3 3943.52741242156 3938.42083333333 1.00129660574741 0.99664579168896 32 3948.3 3958.71619395826 3950.55833333333 1.00206498928420 0.997368794970915 33 3971.5 3977.05266371670 3964.375 1.00319789720112 0.998603824443322 34 3990.1 3990.19256245906 3979.19166666667 1.00276460565711 0.999976802508247 35 3993 3998.48988109591 3995.19583333333 1.00082450220214 0.998627011382005 36 3998 4012.7533872884 4012.36666666667 1.00009638217388 0.99632337553184 37 4015.8 4026.50550147827 4029.88333333333 0.999161804058414 0.997341242555278 38 4041.2 4039.87756500070 4046.875 0.998270904092836 1.00032734531629 39 4060.7 4054.74388566214 4062.9125 0.997989468309282 1.00146892492000 40 4076.7 4067.76199602566 4077.55833333333 0.997597499163265 1.00219727800768 41 4103 4082.43046309418 4090.75833333333 0.997964223363847 1.00503855168919 42 4125.3 4098.29914701575 4103.34166666667 0.99877111874649 1.00658830700631 43 4139.7 4120.21454264407 4114.87916666667 1.00129660574741 1.00472923367321 44 4146.7 4132.87926436665 4124.3625 1.00206498928420 1.00334409372965 45 4158 4145.30985103352 4132.09583333333 1.00319789720112 1.00306132699907 46 4155.1 4149.9663896271 4138.525 1.00276460565711 1.00123702456621 47 4144.8 4146.52850537998 4143.1125 1.00082450220214 0.99958314397749 48 4148.2 4146.19541414821 4145.79583333333 1.00009638217388 1.00048347597051 49 4142.5 4144.11517216431 4147.59166666667 0.999161804058414 0.99961024920949 50 4142.1 4142.02563526199 4149.2 0.998270904092836 1.00001795371264 51 4145.4 4142.54200913665 4150.8875 0.997989468309282 1.00068991234296 52 4146.3 4143.42560968095 4153.40416666667 0.997597499163265 1.00069372316287 53 4143.5 4149.36475519205 4157.82916666667 0.997964223363847 0.998586589625626 54 4149.2 4158.75393052421 4163.87083333333 0.99877111874649 0.997702693959822 55 4158.9 4176.42065878003 4171.0125 1.00129660574741 0.995804862533855 56 4166.1 4187.75902361311 4179.12916666667 1.00206498928420 0.994828015773835 57 4179.1 4201.47221331183 4188.07916666667 1.00319789720112 0.99467514904872 58 4194.4 4209.90246574439 4198.29583333333 1.00276460565711 0.996317618787957 59 4211.7 4213.70050988611 4210.22916666667 1.00082450220214 0.999525236812294 60 4226.3 4223.9862444083 4223.57916666667 1.00009638217388 1.0005477658917 61 4235.8 4234.24789323875 4237.8 0.999161804058414 1.00036656020157 62 4243.6 4245.66695241733 4253.02083333333 0.998270904092836 0.999513161903537 63 4258.7 4260.47109797519 4269.05416666667 0.997989468309282 0.999584295272879 64 4278.2 4275.25811919538 4285.55416666667 0.997597499163265 1.00068811770485 65 4298 4293.76186194427 4302.52083333333 0.997964223363847 1.00098704543754 66 4315.1 4314.40408628819 4319.7125 0.99877111874649 1.00016130007711 67 4334.3 4342.71099257953 4337.0875 1.00129660574741 0.998063193108199 68 4356 NA NA 1.00206498928420 NA 69 4374 NA NA 1.00319789720112 NA 70 4395.5 NA NA 1.00276460565711 NA 71 4417.8 NA NA 1.00082450220214 NA 72 4432.8 NA NA 1.00009638217388 NA 73 4446.3 NA NA NA NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t1244301521gu3n2cz8y7x2ffa/15hrk1244301500.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t1244301521gu3n2cz8y7x2ffa/15hrk1244301500.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t1244301521gu3n2cz8y7x2ffa/2bidj1244301500.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t1244301521gu3n2cz8y7x2ffa/2bidj1244301500.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t1244301521gu3n2cz8y7x2ffa/3qcz81244301500.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t1244301521gu3n2cz8y7x2ffa/3qcz81244301500.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t1244301521gu3n2cz8y7x2ffa/45ecb1244301500.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t1244301521gu3n2cz8y7x2ffa/45ecb1244301500.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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