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Tom Knaepen - Goudwaarde te Brussel EUR/kg - Additief decompositiemodel

R Software Module: rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Sun, 25 May 2008 09:33:47 -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/25/t1211729815gfcu3lt7ktjkwq1.htm/, Retrieved Sun, 25 May 2008 17:36:55 +0200
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
10236 10893 10756 10940 10997 10827 10166 10186 10457 10368 10244 10511 10812 10738 10171 9721 9897 9828 9924 10371 10846 10413 10709 10662 10570 10297 10635 10872 10296 10383 10431 10574 10653 10805 10872 10625 10407 10463 10556 10646 10702 11353 11346 11451 11964 12574 13031 13812 14544 14931 14886 16005 17064 15168 16050 15839 15137 14954 15648 15305 15579 16348 15928 16171 15937 15713 15594 15683 16438 17032 17696 17745
 
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 time13 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
110236NANA207.032986111110NA
210893NANA110.855902777778NA
310756NANA-42.0295138888891NA
410940NANA110.449652777778NA
510997NANA185.137152777778NA
610827NANA-227.842013888889NA
71016610499.730902777810572.4166666667-72.6857638888886-333.730902777779
81018610530.178819444410589.9583333333-59.7795138888891-344.178819444443
91045710472.189236111110559.125-86.935763888889-15.1892361111095
101036810306.366319444410483.9583333333-177.59201388889061.6336805555566
111024410458.137152777810387.333333333370.8038194444449-214.137152777779
121051110282.460069444410299.875-17.4149305555551228.539930555555
131081210455.199652777810248.1666666667207.032986111110356.800347222221
141073810356.647569444410245.7916666667110.855902777778381.352430555558
151017110227.678819444410269.7083333333-42.0295138888891-56.6788194444453
16972110398.241319444410287.7916666667110.449652777778-677.241319444443
17989710494.178819444410309.0416666667185.137152777778-597.178819444442
18982810106.866319444410334.7083333333-227.842013888889-278.866319444445
19992410258.230902777810330.9166666667-72.6857638888886-334.230902777777
201037110242.678819444410302.4583333333-59.7795138888891128.321180555555
211084610216.480902777810303.4166666667-86.935763888889629.519097222223
221041310193.116319444410370.7083333333-177.592013888890219.883680555557
231070910506.095486111110435.291666666770.8038194444449202.904513888889
241066210457.626736111110475.0416666667-17.4149305555551204.373263888889
251057010726.324652777810519.2916666667207.032986111110-156.324652777777
261029710659.730902777810548.875110.855902777778-362.730902777777
271063510507.262152777810549.2916666667-42.0295138888891127.737847222224
281087210668.032986111110557.5833333333110.449652777778203.967013888889
291029610765.845486111110580.7083333333185.137152777778-469.845486111111
301038310358.116319444410585.9583333333-227.84201388888924.8836805555547
311043110504.939236111110577.625-72.6857638888886-73.9392361111113
321057410517.970486111110577.75-59.779513888889156.0295138888905
331065310494.439236111110581.375-86.935763888889158.560763888889
341080510391.074652777810568.6666666667-177.592013888890413.925347222221
351087210646.970486111110576.166666666770.8038194444449225.029513888889
361062510616.085069444410633.5-17.41493055555518.91493055555475
371040710919.074652777810712.0416666667207.032986111110-512.074652777777
381046310897.564236111110786.7083333333110.855902777778-434.564236111111
391055610835.845486111110877.875-42.0295138888891-279.845486111111
401064611116.657986111111006.2083333333110.449652777778-470.657986111113
411070211355.012152777811169.875185.137152777778-653.012152777776
421135311164.782986111111392.625-227.842013888889188.217013888891
431134611625.105902777811697.7916666667-72.6857638888886-279.105902777776
441145111996.553819444412056.3333333333-59.7795138888891-545.553819444445
451196412335.980902777812422.9166666667-86.935763888889-371.980902777779
461257412649.032986111112826.625-177.592013888890-75.0329861111131
471303113385.80381944441331570.8038194444449-354.803819444445
481381213721.626736111113739.0416666667-17.414930555555190.3732638888887
491454414301.032986111114094207.032986111110242.967013888889
501493114583.689236111114472.8333333333110.855902777778347.310763888891
511488614745.845486111114787.875-42.0295138888891140.154513888889
521600515129.699652777815019.25110.449652777778875.300347222223
531706415412.595486111115227.4583333333185.1371527777781651.40451388889
541516815170.866319444415398.7083333333-227.842013888889-2.86631944444343
551605015431.355902777815504.0416666667-72.6857638888886618.644097222223
561583915546.428819444415606.2083333333-59.7795138888891292.571180555557
571513715621.730902777815708.6666666667-86.935763888889-484.730902777776
581495415581.407986111115759-177.592013888890-627.40798611111
591564815789.762152777815718.958333333370.8038194444449-141.762152777776
601530515677.293402777815694.7083333333-17.4149305555551-372.293402777776
611557915905.449652777815698.4166666667207.032986111110-326.449652777776
621634815783.772569444415672.9166666667110.855902777778564.227430555558
631592815678.595486111115720.625-42.0295138888891249.404513888889
641617115971.866319444415861.4166666667110.449652777778199.133680555555
651593716218.470486111116033.3333333333185.137152777778-281.470486111111
661571315992.491319444416220.3333333333-227.842013888889-279.491319444447
6715594NANA-72.6857638888886NA
6815683NANA-59.7795138888891NA
6916438NANA-86.935763888889NA
7017032NANA-177.592013888890NA
7117696NANA70.8038194444449NA
7217745NANA-17.4149305555551NA
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211729815gfcu3lt7ktjkwq1/10yx21211729612.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211729815gfcu3lt7ktjkwq1/10yx21211729612.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211729815gfcu3lt7ktjkwq1/2moe81211729612.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211729815gfcu3lt7ktjkwq1/2moe81211729612.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211729815gfcu3lt7ktjkwq1/3sfp31211729612.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211729815gfcu3lt7ktjkwq1/3sfp31211729612.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211729815gfcu3lt7ktjkwq1/4au9q1211729612.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211729815gfcu3lt7ktjkwq1/4au9q1211729612.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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