Opgave 9 Oefening 2 Anke Winckelmans

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Thu, 28 May 2009 14:20:25 -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/May/28/t1243542082n8dcrffv9xd3p9x.htm/, Retrieved Thu, 28 May 2009 22:21:27 +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/May/28/t1243542082n8dcrffv9xd3p9x.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 «

3779,7 3795,5 3813,1 3826,9 3833,3 3844,8 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

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 1 seconds R Server 'George Udny Yule' @ 72.249.76.132 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 3779.7 NA NA 1.00159506626097 NA 2 3795.5 NA NA 1.00229277370746 NA 3 3813.1 NA NA 1.00295040237925 NA 4 3826.9 NA NA 1.00201952762084 NA 5 3833.3 NA NA 1.00057170934675 NA 6 3844.8 NA NA 1.00018478332918 NA 7 3851.3 3834.99353371675 3838.7375 0.999024688121226 1.00425201923807 8 3851.8 3837.43810865427 3844.6125 0.998133910414709 1.00374257276315 9 3854.1 3840.80084701114 3849.06666666667 0.99785251325286 1.00346259895230 10 3858.4 3841.56156153952 3851.34166666667 0.997460597897145 1.00438322754711 11 3861.6 3846.81701778664 3851.85416666667 0.998692279442038 1.00384291276268 12 3856.3 3848.37766057999 3851.375 0.999221748227578 1.00205861797327 13 3855.8 3856.02832565977 3849.8875 1.00159506626097 0.99994078734893 14 3860.4 3856.28803708035 3847.46666666667 1.00229277370746 1.00106630077425 15 3855.1 3855.62969498652 3844.2875 1.00295040237925 0.999862617774936 16 3839.5 3848.11404306141 3840.35833333333 1.00201952762084 0.997761489663503 17 3833 3838.26395088353 3836.07083333333 1.00057170934675 0.99862855943445 18 3833.6 3833.05398695499 3832.34583333333 1.00018478332918 1.00014244856630 19 3826.8 3827.151189915 3830.8875 0.999024688121226 0.999908237250744 20 3818.2 3824.17540431963 3831.325 0.998133910414709 0.99843746594027 21 3811.4 3824.12007916459 3832.35 0.99785251325286 0.996673723915235 22 3806.8 3824.73772612166 3834.475 0.997460597897145 0.995310076819347 23 3810.3 3832.68552203254 3837.70416666667 0.998692279442038 0.99415931155743 24 3818.2 3838.57696059945 3841.56666666667 0.999221748227578 0.994691532615182 25 3858.9 3852.10541165024 3845.97083333333 1.00159506626097 1.00176386355607 26 3867.8 3859.82947154745 3851 1.00229277370746 1.00206499497227 27 3872.3 3868.24179629644 3856.8625 1.00295040237925 1.00104910807475 28 3873.3 3871.48197346180 3863.67916666667 1.00201952762084 1.00046959447329 29 3876.7 3873.32982024734 3871.11666666667 1.00057170934675 1.00087009883203 30 3882.6 3879.65426298494 3878.9375 1.00018478332918 1.00075927822826 31 3883.5 3882.12668598174 3885.91666666667 0.999024688121226 1.00035375301461 32 3882.2 3884.98255392036 3892.24583333333 0.998133910414709 0.999283766688335 33 3888.1 3891.35870768262 3899.73333333333 0.99785251325286 0.999162578439201 34 3893.7 3898.80748768717 3908.73333333333 0.997460597897145 0.998689987206781 35 3901.9 3913.32160116182 3918.44583333333 0.998692279442038 0.997081353815022 36 3914.3 3925.04294921275 3928.1 0.999221748227578 0.997262972825583 37 3930.3 3944.70287552607 3938.42083333333 1.00159506626097 0.996348805986015 38 3948.3 3959.6160696098 3950.55833333333 1.00229277370746 0.9971421295876 39 3971.5 3976.07150143224 3964.375 1.00295040237925 0.998850246674238 40 3990.1 3987.22775414611 3979.19166666667 1.00201952762084 1.00072036162241 41 3993 3997.47992413333 3995.19583333333 1.00057170934675 0.998879312912547 42 3998 4013.10808513723 4012.36666666667 1.00018478332918 0.996235315666382 43 4015.8 4025.95294024826 4029.88333333333 0.999024688121226 0.997478127439902 44 4041.2 4039.32316870952 4046.875 0.998133910414709 1.00046464004292 45 4060.7 4054.18744925146 4062.9125 0.99785251325286 1.00160637632820 46 4076.7 4067.20377312715 4077.55833333333 0.997460597897145 1.00233482938219 47 4103 4085.40876456318 4090.75833333333 0.998692279442038 1.00430586911876 48 4125.3 4100.14823374173 4103.34166666667 0.999221748227578 1.00613435535118 49 4139.7 4121.44267159337 4114.87916666667 1.00159506626097 1.00442983922413 50 4146.7 4133.81872990005 4124.3625 1.00229277370746 1.00311607037986 51 4158 4144.28717871129 4132.09583333333 1.00295040237925 1.00330884919345 52 4155.1 4146.88286554703 4138.525 1.00201952762084 1.00198152075171 53 4144.8 4145.48115614087 4143.1125 1.00057170934675 0.999835687073415 54 4148.2 4146.56190728953 4145.79583333333 1.00018478332918 1.00039504841531 55 4142.5 4143.54647124586 4147.59166666667 0.999024688121226 0.99974744551482 56 4142.1 4141.45722109271 4149.2 0.998133910414709 1.00015520597533 57 4145.4 4141.97352410488 4150.8875 0.99785251325286 1.00082725683184 58 4146.3 4142.85700339183 4153.40416666667 0.997460597897145 1.00083106817478 59 4143.5 4152.39188798892 4157.82916666667 0.998692279442038 0.997858610596307 60 4149.2 4160.63029347715 4163.87083333333 0.999221748227578 0.997252749542521 61 4158.9 4177.66554131282 4171.0125 1.00159506626097 0.99550812741536 62 4166.1 4188.7109641401 4179.12916666667 1.00229277370746 0.994601927816536 63 4179.1 4200.43568540448 4188.07916666667 1.00295040237925 0.994920601813135 64 4194.4 4206.7744077292 4198.29583333333 1.00201952762084 0.997058457019596 65 4211.7 4212.63619403319 4210.22916666667 1.00057170934675 0.999777765278066 66 4226.3 4224.35961368615 4223.57916666667 1.00018478332918 1.00045933265425 67 4235.8 4233.66682332013 4237.8 0.999024688121226 1.00050386031043 68 4243.6 4245.08431545022 4253.02083333333 0.998133910414709 0.999650344883653 69 4258.7 4259.88642942092 4269.05416666667 0.99785251325286 0.999721488016035 70 4278.2 4274.67142140394 4285.55416666667 0.997460597897145 1.00082546194741 71 4298 NA NA 0.998692279442038 NA 72 4315.1 NA NA 0.999221748227578 NA 73 4334.3 NA NA NA NA 74 4356 NA NA NA NA 75 4374 NA NA NA NA 76 4395.5 NA NA NA NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/28/t1243542082n8dcrffv9xd3p9x/1ouit1243542023.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/28/t1243542082n8dcrffv9xd3p9x/1ouit1243542023.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/28/t1243542082n8dcrffv9xd3p9x/2ra4r1243542023.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/28/t1243542082n8dcrffv9xd3p9x/2ra4r1243542023.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/28/t1243542082n8dcrffv9xd3p9x/3yg511243542023.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/28/t1243542082n8dcrffv9xd3p9x/3yg511243542023.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/28/t1243542082n8dcrffv9xd3p9x/4je8g1243542023.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/28/t1243542082n8dcrffv9xd3p9x/4je8g1243542023.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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