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Klassieke decompositie Nieuwbouw

*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: Sun, 19 Dec 2010 00:01:25 +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/19/t1292716787ll3gipb6dder47i.htm/, Retrieved Sun, 19 Dec 2010 00:59:52 +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/19/t1292716787ll3gipb6dder47i.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:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
4143 4429 5219 4929 5761 5592 4163 4962 5208 4755 4491 5732 5731 5040 6102 4904 5369 5578 4619 4731 5011 5299 4146 4625 4736 4219 5116 4205 4121 5103 4300 4578 3809 5657 4248 3830 4736 4839 4411 4570 4104 4801 3953 3828 4440 4026 4109 4785 3224 3552 3940 3913 3681 4309 3830 4143 4087 3818 3380 3430 3458 3970 5260 5024 5634 6549 4676
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
14143NANA45.5468750000002NA
24429NANA-136.703125NA
35219NANA363.255208333333NA
44929NANA-109.557291666667NA
55761NANA-167.473958333333NA
65592NANA497.078125NA
741634787.317708333335014.83333333333-227.515625000000-624.317708333333
849625058.2656255106.45833333333-48.1927083333335-96.265625
952085157.182291666675168.70833333333-11.526041666667150.817708333334
1047555563.7031255204.45833333333359.244791666667-808.703125
1144914813.067708333335187.08333333333-374.015625-322.067708333334
1257324980.026041666675170.16666666667-190.140625751.973958333333
1357315234.130208333335188.5833333333345.5468750000002496.869791666667
1450405061.255208333335197.95833333333-136.703125-21.2552083333330
1561025543.380208333335180.125363.255208333333558.619791666666
1649045085.026041666675194.58333333333-109.557291666667-181.026041666666
1753695035.401041666675202.875-167.473958333333333.598958333334
1855785639.4531255142.375497.078125-61.4531249999991
1946194827.276041666675054.79166666667-227.515625000000-208.276041666667
2047314930.932291666674979.125-48.1927083333335-199.932291666666
2150114892.307291666674903.83333333333-11.5260416666671118.692708333333
2252995192.869791666674833.625359.244791666667106.130208333333
2341464378.4843754752.5-374.015625-232.484374999999
2446254490.567708333334680.70833333333-190.140625134.432291666666
2547364693.1718754647.62545.546875000000242.8281250000009
2642194491.255208333334627.95833333333-136.703125-272.255208333333
2751164934.755208333334571.5363.255208333333181.244791666666
2842054426.776041666674536.33333333333-109.557291666667-221.776041666666
2941214388.026041666674555.5-167.473958333333-267.026041666666
3051035023.7031254526.625497.07812579.296875
3143004265.9843754493.5-227.51562500000034.0156250000009
3245784471.1406254519.33333333333-48.1927083333335106.859375000001
3338094504.2656254515.79166666667-11.5260416666671-695.265625
3456574860.869791666674501.625359.244791666667796.130208333334
3542484142.1093754516.125-374.015625105.890625000001
3638304312.692708333334502.83333333333-190.140625-482.692708333333
3747364521.338541666674475.7916666666745.5468750000002214.661458333334
3848394293.380208333334430.08333333333-136.703125545.619791666667
3944114788.380208333334425.125363.255208333333-377.380208333333
4045704273.901041666674383.45833333333-109.557291666667296.098958333334
4141044142.2343754309.70833333333-167.473958333333-38.2343749999991
4248014840.786458333334343.70833333333497.078125-39.786458333333
4339534092.9843754320.5-227.515625000000-139.984375
4438284155.682291666674203.875-48.1927083333335-327.682291666667
4544404119.098958333334130.625-11.5260416666671320.901041666667
4640264442.869791666674083.625359.244791666667-416.869791666667
4741093664.6093754038.625-374.015625444.390625000000
4847853810.3593754000.5-190.140625974.640625
4932244020.4218753974.87545.5468750000002-796.421875
5035523846.1718753982.875-136.703125-294.171875000000
5139404344.5468753981.29166666667363.255208333333-404.546875000000
5239133848.3593753957.91666666667-109.55729166666764.640625
5336813751.401041666673918.875-167.473958333333-70.4010416666665
5443094329.119791666673832.04166666667497.078125-20.1197916666670
5538303557.817708333333785.33333333333-227.515625000000272.182291666666
5641433764.307291666673812.5-48.1927083333335378.692708333334
5740873873.3906253884.91666666667-11.5260416666671213.609375000000
5838184345.4531253986.20833333333359.244791666667-527.453125
5933803739.8593754113.875-374.015625-359.859375000000
6034304098.442708333334288.58333333333-190.140625-668.442708333333
613458NA4417.16666666667NANA
623970NANANANA
635260NANANANA
645024NANANANA
655634NANANANA
666549NANANANA
674676NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292716787ll3gipb6dder47i/110v61292716882.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292716787ll3gipb6dder47i/110v61292716882.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/19/t1292716787ll3gipb6dder47i/210v61292716882.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292716787ll3gipb6dder47i/210v61292716882.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/19/t1292716787ll3gipb6dder47i/3usc91292716882.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292716787ll3gipb6dder47i/3usc91292716882.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/19/t1292716787ll3gipb6dder47i/4mjtc1292716882.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292716787ll3gipb6dder47i/4mjtc1292716882.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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