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Paper Statistiek

*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: Mon, 27 Dec 2010 13:16:37 +0000
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/27/t1293457386kqhn0a317nm4zmz.htm/, Retrieved Mon, 27 Dec 2010 14:43:10 +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/2010/Dec/27/t1293457386kqhn0a317nm4zmz.htm/},
    year = {2010},
}
@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 = {2010},
    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:
Classical Decomposition - Handelsbalans Belgiƫ (1995-2009)
 
Dataseries X:
» Textbox « » Textfile « » CSV «
2540.9 2370.3 1807.5 1834.8 786.8 1561.4 1347.2 1549.8 1553.8 1822.5 3078.7 1589.1 1791.5 2558.1 2111.8 2083.1 2052.1 2243.5 2622 1952.6 808.9 1709.8 1582.1 865.6 1116.1 1119.4 2350 1975.6 2536.5 2785 2819.7 1829.5 758.3 2921.6 2482 1892.7 1855.1 2151.3 1642.2 1640.5 1366.1 1532.8 824.4 -518.7 -978.5 1162.5 1243.4 1199.5 883.1 1437.2 534.5 -1901.9 -2521.1 -1721.1 -3094.5 -3694.8 -2492.1 -464.6 -626.1 -1711.4
 
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'George Udny Yule' @ 72.249.76.132


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
12540.9NANA-435.086830357143NA
22370.3NANA425.647098214286NA
31807.52212.050669642861919.1125292.938169642857-404.550669642857
41834.81315.23906251598.7375-283.4984375519.5609375
5786.81005.000669642861440.0875-435.086830357143-218.200669642857
61561.41772.572098214291346.925425.647098214286-211.172098214286
71347.21700.113169642861407.175292.938169642857-352.913169642857
81549.81252.18906251535.6875-283.4984375297.6109375
91553.81349.675669642861784.7625-435.086830357143204.124330357143
101822.52431.759598214292006.1125425.647098214286-609.259598214286
113078.72333.675669642862040.7375292.938169642857745.024330357143
121589.11878.90156252162.4-283.4984375-289.8015625
131791.51698.400669642862133.4875-435.08683035714393.0993303571427
142558.12500.022098214292074.375425.64709821428658.077901785714
152111.82461.638169642862168.7292.938169642857-349.838169642857
162083.11878.45156252161.95-283.4984375204.6484375
172052.11751.313169642862186.4-435.086830357143300.786830357143
182243.52659.509598214292233.8625425.647098214286-416.009598214285
1926222355.088169642862062.15292.938169642857266.911830357143
201952.61556.53906251840.0375-283.4984375396.0609375
21808.91208.250669642861643.3375-435.086830357143-399.350669642857
221709.81803.122098214291377.475425.647098214286-93.3220982142857
231582.11572.938169642861280292.9381696428579.16183035714266
24865.6961.10156251244.6-283.4984375-95.5015624999999
251116.1831.7006696428571266.7875-435.086830357143284.399330357143
261119.41927.172098214291501.525425.647098214286-807.772098214286
2723502110.763169642861817.825292.938169642857239.236830357143
281975.61920.07656252203.575-283.498437555.5234374999995
292536.52035.400669642862470.4875-435.086830357143501.099330357143
3027852936.584598214292510.9375425.647098214286-151.584598214286
312819.72563.338169642862270.4292.938169642857256.361830357142
321829.51781.70156252065.2-283.498437547.7984375000001
33758.31604.975669642862040.0625-435.086830357143-846.675669642857
342921.62431.397098214292005.75425.647098214286490.202901785714
3524822443.688169642862150.75292.93816964285738.3118303571428
361892.71908.06406252191.5625-283.4984375-15.3640624999998
371855.11555.213169642861990.3-435.086830357143299.886830357143
382151.32279.447098214291853.8425.647098214286-128.147098214286
391642.22054.088169642861761.15292.938169642857-411.888169642857
401640.51339.21406251622.7125-283.4984375301.2859375
411366.11008.088169642861443.175-435.086830357143358.011830357143
421532.81496.697098214291071.05425.64709821428636.1029017857143
43824.4801.013169642857508.075292.93816964285723.3868303571427
44-518.7-114.7859375168.7125-283.4984375-403.9140625
45-978.5-260.286830357143174.8-435.086830357143-718.213169642857
461162.5867.597098214286441.95425.647098214286294.902901785714
471243.41182.36316964286889.425292.93816964285761.0368303571429
481199.5872.96406251156.4625-283.4984375326.5359375
49883.1667.1006696428571102.1875-435.086830357143215.999330357143
501437.21051.54709821429625.9425.647098214286385.652901785714
51534.5105.638169642857-187.3292.938169642857428.861830357143
52-1901.9-1291.1109375-1007.6125-283.4984375-610.7890625
53-2521.1-2291.11183035714-1856.025-435.086830357143-229.988169642857
54-1721.1-2108.11540178571-2533.7625425.647098214286387.015401785714
55-3094.5-2461.31183035714-2754.25292.938169642857-633.188169642857
56-3694.8-2877.0609375-2593.5625-283.4984375-817.7390625
57-2492.1-2563.03683035714-2127.95-435.08683035714370.9368303571428
58-464.6-1145.82790178571-1571.475425.647098214286681.227901785714
59-626.1NANA292.938169642857NA
60-1711.4NANA-283.4984375NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/27/t1293457386kqhn0a317nm4zmz/1cgfk1293455794.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/27/t1293457386kqhn0a317nm4zmz/1cgfk1293455794.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/27/t1293457386kqhn0a317nm4zmz/2cgfk1293455794.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/27/t1293457386kqhn0a317nm4zmz/2cgfk1293455794.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/27/t1293457386kqhn0a317nm4zmz/3npfn1293455794.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/27/t1293457386kqhn0a317nm4zmz/3npfn1293455794.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/27/t1293457386kqhn0a317nm4zmz/4yheq1293455794.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/27/t1293457386kqhn0a317nm4zmz/4yheq1293455794.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')
 





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