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: Sun, 07 Jun 2009 09:45:48 -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/07/t12443896843zgb4a0vcyrijy8.htm/, Retrieved Sun, 07 Jun 2009 17:48:08 +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/07/t12443896843zgb4a0vcyrijy8.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 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 3779.7 NA NA -0.152083333333394 NA 2 3795.5 NA NA 0.343055555555465 NA 3 3813.1 3811.11805555556 3810.5 0.618055555555606 1.98194444444425 4 3826.9 3822.55347222222 3823.3625 -0.809027777777677 4.34652777777774 5 3833.3 3834.14791666667 3834.3 -0.152083333333394 -0.847916666666151 6 3844.8 3842.53055555556 3842.1875 0.343055555555465 2.26944444444462 7 3851.3 3848.51805555556 3847.9 0.618055555555606 2.78194444444443 8 3851.8 3851.39097222222 3852.2 -0.809027777777677 0.409027777777737 9 3854.1 3855.03541666667 3855.1875 -0.152083333333394 -0.935416666666242 10 3858.4 3857.38055555556 3857.0375 0.343055555555465 1.01944444444462 11 3861.6 3858.43055555556 3857.8125 0.618055555555606 3.16944444444425 12 3856.3 3857.46597222222 3858.275 -0.809027777777677 -1.16597222222208 13 3855.8 3857.56041666667 3857.7125 -0.152083333333394 -1.76041666666606 14 3860.4 3855.14305555556 3854.8 0.343055555555465 5.2569444444448 15 3855.1 3850.46805555556 3849.85 0.618055555555606 4.63194444444434 16 3839.5 3842.84097222222 3843.65 -0.809027777777677 -3.34097222222226 17 3833 3836.61041666667 3836.7625 -0.152083333333394 -3.61041666666642 18 3833.6 3830.90555555556 3830.5625 0.343055555555465 2.69444444444434 19 3826.8 3825.81805555556 3825.2 0.618055555555606 0.981944444444252 20 3818.2 3818.34097222222 3819.15 -0.809027777777677 -0.140972222221990 21 3811.4 3813.58541666667 3813.7375 -0.152083333333394 -2.18541666666624 22 3806.8 3812.01805555556 3811.675 0.343055555555465 -5.21805555555557 23 3810.3 3818.23055555556 3817.6125 0.618055555555606 -7.93055555555566 24 3818.2 3830.36597222222 3831.175 -0.809027777777677 -12.1659722222221 25 3858.9 3846.39791666667 3846.55 -0.152083333333394 12.5020833333338 26 3867.8 3861.53055555556 3861.1875 0.343055555555465 6.26944444444462 27 3872.3 3870.91805555556 3870.3 0.618055555555606 1.38194444444434 28 3873.3 3873.56597222222 3874.375 -0.809027777777677 -0.265972222221535 29 3876.7 3877.47291666667 3877.625 -0.152083333333394 -0.772916666666333 30 3882.6 3880.48055555556 3880.1375 0.343055555555465 2.11944444444407 31 3883.5 3883.29305555556 3882.675 0.618055555555606 0.206944444444161 32 3882.2 3884.67847222222 3885.4875 -0.809027777777677 -2.47847222222208 33 3888.1 3889.02291666667 3889.175 -0.152083333333394 -0.922916666666879 34 3893.7 3895.83055555556 3895.4875 0.343055555555465 -2.13055555555593 35 3901.9 3905.39305555556 3904.775 0.618055555555606 -3.4930555555552 36 3914.3 3916.06597222222 3916.875 -0.809027777777677 -1.76597222222199 37 3930.3 3932.24791666667 3932.4 -0.152083333333394 -1.94791666666697 38 3948.3 3950.91805555556 3950.575 0.343055555555465 -2.61805555555566 39 3971.5 3968.50555555556 3967.8875 0.618055555555606 2.99444444444407 40 3990.1 3981.12847222222 3981.9375 -0.809027777777677 8.97152777777774 41 3993 3993.53541666667 3993.6875 -0.152083333333394 -0.535416666666606 42 3998 4005.95555555556 4005.6125 0.343055555555465 -7.9555555555553 43 4015.8 4021.08055555556 4020.4625 0.618055555555606 -5.28055555555511 44 4041.2 4037.95347222222 4038.7625 -0.809027777777677 3.24652777777783 45 4060.7 4059.34791666667 4059.5 -0.152083333333394 1.35208333333367 46 4076.7 4081.25555555556 4080.9125 0.343055555555465 -4.55555555555566 47 4103 4101.91805555555 4101.3 0.618055555555606 1.08194444444507 48 4125.3 4119.11597222222 4119.925 -0.809027777777677 6.18402777777828 49 4139.7 4135.39791666667 4135.55 -0.152083333333394 4.30208333333303 50 4146.7 4146.49305555556 4146.15 0.343055555555465 0.206944444444161 51 4158 4151.13055555556 4150.5125 0.618055555555606 6.86944444444453 52 4155.1 4150.52847222222 4151.3375 -0.809027777777677 4.5715277777781 53 4144.8 4149.43541666667 4149.5875 -0.152083333333394 -4.63541666666606 54 4148.2 4146.36805555555 4146.025 0.343055555555465 1.83194444444507 55 4142.5 4145.09305555556 4144.475 0.618055555555606 -2.59305555555602 56 4142.1 4143.50347222222 4144.3125 -0.809027777777677 -1.40347222222226 57 4145.4 4144.04791666667 4144.2 -0.152083333333394 1.35208333333230 58 4146.3 4145.55555555556 4145.2125 0.343055555555465 0.744444444444525 59 4143.5 4148.40555555556 4147.7875 0.618055555555606 -4.90555555555602 60 4149.2 4151.14097222222 4151.95 -0.809027777777677 -1.94097222222263 61 4158.9 4158.72291666667 4158.875 -0.152083333333394 0.17708333333303 62 4166.1 4169.31805555556 4168.975 0.343055555555465 -3.21805555555511 63 4179.1 4181.84305555556 4181.225 0.618055555555606 -2.74305555555566 64 4194.4 4194.54097222222 4195.35 -0.809027777777677 -0.140972222223354 65 4211.7 4209.81041666667 4209.9625 -0.152083333333394 1.88958333333267 66 4226.3 4223.54305555556 4223.2 0.343055555555465 2.75694444444434 67 4235.8 4235.84305555556 4235.225 0.618055555555606 -0.043055555554929 68 4243.6 4246.77847222222 4247.5875 -0.809027777777677 -3.17847222222281 69 4258.7 4261.69791666667 4261.85 -0.152083333333394 -2.99791666666715 70 4278.2 4278.90555555556 4278.5625 0.343055555555465 -0.705555555555293 71 4298 4297.56805555556 4296.95 0.618055555555606 0.431944444444525 72 4315.1 4315.31597222222 4316.125 -0.809027777777677 -0.215972222222263 73 4334.3 4335.19791666667 4335.35 -0.152083333333394 -0.897916666666788 74 4356 4355.24305555555 4354.9 0.343055555555465 0.756944444445253 75 4374 NA NA 0.618055555555606 NA 76 4395.5 NA NA -0.809027777777677 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/07/t12443896843zgb4a0vcyrijy8/11c4w1244389547.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/07/t12443896843zgb4a0vcyrijy8/11c4w1244389547.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/07/t12443896843zgb4a0vcyrijy8/235e41244389547.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/07/t12443896843zgb4a0vcyrijy8/235e41244389547.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/07/t12443896843zgb4a0vcyrijy8/3munu1244389547.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/07/t12443896843zgb4a0vcyrijy8/3munu1244389547.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/07/t12443896843zgb4a0vcyrijy8/4vd2j1244389547.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/07/t12443896843zgb4a0vcyrijy8/4vd2j1244389547.ps (open in new window)

Parameters (Session):

par1 = additive ; par2 = 4 ;

Parameters (R input):

par1 = additive ; par2 = 4 ;

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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