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Classical Decomposition eigen reeks - Wouters Danai

R Software Module: rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Mon, 19 May 2008 07:39:49 -0600
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/May/19/t12112044442a2js6uzdwua45z.htm/, Retrieved Mon, 19 May 2008 15:40:50 +0200
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
4356,5 4340,3 4321,5 4302,8 4285,7 4270,5 4253 4236,9 4231,6 4223,7 4209,2 4192,5 4178,5 4167 4159 4148,1 4142,7 4147,6 4146,1 4141 4142,4 4149 4145,3 4154,7 4157,9 4146,7 4140,1 4125,8 4102,7 4076,7 4060,5 4041,7 4016,2 3997,2 3993 3991,3 3971,8 3947,3 3929,8 3914,7 3902,8 3893,3 3887,1 3881,8 3883,4 3883,2 3876,4 3872,4 3872,6 3868,7 3858,7 3818,2 3810,3 3806,8 3811,4 3818,2 3826,8 3833,6 3833 3839,5 3855,1 3860,4 3855,8 3856,3 3861,6 3858,4 3854,1 3851,8 3851,3 3844,8 3833,3 3826,9 3813,1 3795,5 3779,7 3765,5 3747,7 3735,4 3735,8 3705,8 3674,3 3665,8 3652,5 3649,7 3649,5 3647,3 3646,8 3640,6 3629,5 3618,4 3611,9 3611,4 3607,7 3606,9 3603,9 3596,9 3604,2 3612,2 3623,5 3639 3647,5 3660,6 3679,4 3691,4 3697,4 3707,1 3722,7
 
Text written by user:
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
14356.5NANA10.9116898148149NA
24340.3NANA7.62488425925908NA
34321.5NANA4.76655092592627NA
44302.8NANA-3.23900462962951NA
54285.7NANA-5.63206018518526NA
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473876.43870.043634259263871.02916666667-0.9855324074074166.35636574074033
483872.43869.990856481483863.570833333336.420023148148072.40914351851825
493872.63867.724189814813856.812510.91168981481494.8758101851854
503868.73858.633217592593851.008333333337.6248842592590810.0667824074071
513858.73850.7665509259338464.766550925926277.9334490740739
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543806.83828.257523148153834.52083333333-6.26331018518524-21.4575231481485
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623860.43855.091550925933847.466666666677.624884259259085.30844907407436
633855.83854.654050925933849.88754.766550925926271.14594907407445
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863647.33653.033217592593645.408333333337.62488425925908-5.73321759259215
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963596.93623.470023148153617.056.42002314814807-26.5700231481474
973604.2NA3621.62083333333NANA
983612.2NA3627.76666666667NANA
993623.5NA3634.8375NANA
1003639NA3642.75NANA
1013647.5NA3651.875NANA
1023660.6NANANANA
1033679.4NANANANA
1043691.4NANANANA
1053697.4NANANANA
1063707.1NANANANA
1073722.7NANANANA
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t12112044442a2js6uzdwua45z/1cax81211204386.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t12112044442a2js6uzdwua45z/1cax81211204386.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t12112044442a2js6uzdwua45z/26x8i1211204386.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t12112044442a2js6uzdwua45z/26x8i1211204386.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t12112044442a2js6uzdwua45z/33af21211204386.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t12112044442a2js6uzdwua45z/33af21211204386.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t12112044442a2js6uzdwua45z/4eyrc1211204386.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t12112044442a2js6uzdwua45z/4eyrc1211204386.ps (open in new window)


 
Parameters (Session):
par1 = additive ; par2 = 12 ;
 
Parameters (R input):
par1 = additive ; 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')
 





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