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CD huwelijken

*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 08:21:31 +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/t1292746822m0kn7o8tqjupz7v.htm/, Retrieved Sun, 19 Dec 2010 09:20:23 +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/t1292746822m0kn7o8tqjupz7v.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 «
3111 3995 5245 5588 10681 10516 7496 9935 10249 6271 3616 3724 2886 3318 4166 6401 9209 9820 7470 8207 9564 5309 3385 3706 2733 3045 3449 5542 10072 9418 7516 7840 10081 4956 3641 3970 2931 3170 3889 4850 8037 12370 6712 7297 10613 5184 3506 3810 2692 3073 3713 4555 7807 10869 9682 7704 9826 5456 3677 3431 2765 3483 3445 6081 8767 9407 6551 12480 9530 5960 3252 3717 2642 2989 3607 5366 8898 9435 7328 8594 11349 5797 3621 3851
 
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'RServer@AstonUniversity' @ vre.aston.ac.uk


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
13111NANA-3319.28472222222NA
23995NANA-2903.97222222222NA
35245NANA-2370.46527777778NA
45588NANA-620.479166666667NA
510681NANA2715.27777777778NA
610516NANA4135.86111111111NA
774968085.916666666676692.8751393.04166666667-589.916666666666
899359397.909722222226655.291666666672742.61805555556537.090277777779
91024910409.77083333336582.1253827.64583333333-160.770833333333
1062715957.104166666676571.04166666667-613.9375313.895833333334
1136163933.736111111116543.58333333333-2609.84722222222-317.736111111111
1237244076.791666666676453.25-2376.45833333333-352.791666666667
1328863103.881944444446423.16666666667-3319.28472222222-217.881944444443
1433183446.111111111116350.08333333333-2903.97222222222-128.111111111109
1541663879.076388888896249.54166666667-2370.46527777778286.923611111112
1664015560.43756180.91666666667-620.479166666667840.5625
1792098846.486111111116131.208333333332715.27777777778362.513888888888
18982010256.69444444446120.833333333334135.86111111111-436.694444444445
1974707506.756113.708333333331393.04166666667-36.7500000000009
2082078838.576388888896095.958333333332742.61805555556-631.57638888889
2195649882.354166666676054.708333333333827.64583333333-318.354166666667
2253095375.104166666675989.04166666667-613.9375-66.104166666667
2333853379.361111111115989.20833333333-2609.847222222225.63888888889051
2437063631.958333333336008.41666666667-2376.4583333333374.041666666666
2527332674.298611111115993.58333333333-3319.2847222222258.7013888888887
2630453076.236111111115980.20833333333-2903.97222222222-31.2361111111095
2734493615.993055555555986.45833333333-2370.46527777778-166.993055555555
2855425372.81255993.29166666667-620.479166666667169.187500000001
29100728704.527777777785989.252715.277777777781367.47222222222
30941810146.77777777786010.916666666674135.86111111111-728.777777777777
3175167423.208333333336030.166666666671393.0416666666792.791666666667
3278408786.243055555556043.6252742.61805555556-946.243055555555
33100819894.81256067.166666666663827.64583333333186.187500000002
3449565442.729166666676056.66666666667-613.9375-486.729166666666
3536413333.194444444445943.04166666667-2609.84722222222307.805555555557
3639703604.791666666675981.25-2376.45833333333365.208333333335
3729312751.465277777786070.75-3319.28472222222179.534722222224
3831703110.652777777786014.625-2903.9722222222259.3472222222226
3938893643.701388888896014.16666666667-2370.46527777778245.298611111111
4048505425.354166666676045.83333333333-620.479166666667-575.354166666667
4180378764.986111111116049.708333333332715.27777777778-727.98611111111
421237010173.27777777786037.416666666674135.861111111112196.72222222222
4367127413.833333333336020.791666666671393.04166666667-701.833333333333
4472978749.409722222226006.791666666672742.61805555556-1452.40972222222
45106139823.06255995.416666666673827.64583333333789.9375
4651845361.854166666675975.79166666667-613.9375-177.854166666666
4735063344.069444444445953.91666666667-2609.84722222222161.930555555557
4838103505.333333333335881.79166666667-2376.45833333333304.666666666668
4926922623.715277777785943-3319.2847222222268.2847222222226
5030733179.736111111116083.70833333333-2903.97222222222-106.736111111112
5137133697.409722222226067.875-2370.4652777777815.5902777777792
5245555425.93756046.41666666667-620.479166666667-870.9375
5378078780.152777777786064.8752715.27777777778-973.152777777776
541086910192.06944444446056.208333333334135.86111111111676.930555555557
5596827436.56043.458333333331393.041666666672245.5
5677048806.201388888896063.583333333332742.61805555556-1102.20138888889
5798269897.145833333336069.53827.64583333333-71.1458333333321
5854565507.979166666676121.91666666667-613.9375-51.979166666667
5936773615.652777777786225.5-2609.8472222222261.3472222222226
6034313828.1256204.58333333333-2376.45833333333-397.124999999998
6127652693.923611111116013.20833333333-3319.2847222222271.0763888888896
6234833177.777777777786081.75-2903.97222222222305.222222222223
6334453897.951388888896268.41666666667-2370.46527777778-452.95138888889
6460815656.604166666676277.08333333333-620.479166666667424.395833333333
6587678995.652777777786280.3752715.27777777778-228.652777777777
66940710410.44444444446274.583333333334135.86111111111-1003.44444444444
6765517674.416666666676281.3751393.04166666667-1123.41666666667
68124808998.284722222226255.666666666672742.618055555563481.71527777778
69953010069.47916666676241.833333333333827.64583333333-539.479166666667
7059605604.854166666676218.79166666667-613.9375355.145833333333
7132523584.611111111116194.45833333333-2609.84722222222-332.611111111111
7237173824.6256201.08333333333-2376.45833333333-107.624999999999
7326422915.340277777786234.625-3319.28472222222-273.340277777777
7429893201.111111111116105.08333333333-2903.97222222222-212.111111111111
7536073648.493055555556018.95833333333-2370.46527777778-41.4930555555547
7653665467.479166666676087.95833333333-620.479166666667-101.479166666667
7788988811.819444444456096.541666666672715.2777777777886.1805555555557
78943510253.36111111116117.54135.86111111111-818.361111111111
797328NANA1393.04166666667NA
808594NANA2742.61805555556NA
8111349NANA3827.64583333333NA
825797NANA-613.9375NA
833621NANA-2609.84722222222NA
843851NANA-2376.45833333333NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292746822m0kn7o8tqjupz7v/10ogx1292746886.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292746822m0kn7o8tqjupz7v/10ogx1292746886.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/19/t1292746822m0kn7o8tqjupz7v/20ogx1292746886.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292746822m0kn7o8tqjupz7v/20ogx1292746886.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/19/t1292746822m0kn7o8tqjupz7v/3l8iv1292746887.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292746822m0kn7o8tqjupz7v/3l8iv1292746887.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/19/t1292746822m0kn7o8tqjupz7v/4l8iv1292746887.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292746822m0kn7o8tqjupz7v/4l8iv1292746887.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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