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Laurane Potié -Goudkoers te Brussel (EUR/kg)- Classical Decomposition

R Software Module: rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Sat, 17 May 2008 03:16:03 -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/17/t121101584519rttjhqpbg5wjl.htm/, Retrieved Sat, 17 May 2008 11:17:29 +0200
 
User-defined keywords:
periode: januari 2000 tot en met januari 2008
 
Dataseries X:
» Textbox « » Textfile « » CSV «
9026 9787 9536 9490 9736 9694 9647 9753 10070 10137 9984 9732 9103 9155 9308 9394 9948 10177 10002 9728 10002 10063 10018 9960 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 19394
 
Text written by user:
bron: belgostat
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
19026NANA26.5781249999999NA
29787NANA74.0156249999999NA
39536NANA-30.9357638888894NA
49490NANA111.425347222223NA
59736NANA259.640625NA
69694NANA-13.0607638888891NA
796479652.973958333339719.20833333333-66.2343749999998-5.97395833333394
897539573.258680555559696.08333333333-122.824652777778179.741319444445
9100709593.168402777789660.25-67.0815972222221476.831597222221
10101379506.300347222229646.75-140.449652777778630.699652777777
1199849646.647569444459651.58333333333-4.93576388888881337.352430555555
1297329654.404513888899680.54166666666-26.137152777777277.5954861111131
1391039742.036458333339715.4583333333326.5781249999999-639.036458333332
1491559803.223958333339729.2083333333374.0156249999999-648.223958333334
1593089694.397569444459725.33333333333-30.9357638888894-386.397569444445
1693949830.842013888899719.41666666667111.425347222223-436.842013888889
1799489977.3906259717.75259.640625-29.390625
18101779715.605902777789728.66666666667-13.0607638888891461.394097222223
19100029719.1406259785.375-66.2343749999998282.859375000002
2097289782.175347222229905-122.824652777778-54.1753472222226
21100029970.6684027777810037.75-67.081597222222131.3315972222226
221006310022.050347222210162.5-140.44965277777840.9496527777774
231001810265.689236111110270.625-4.93576388888881-247.689236111111
24996010315.279513888910341.4166666667-26.1371527777772-355.279513888887
251023610401.911458333310375.333333333326.5781249999999-165.911458333332
261089310475.26562510401.2574.0156249999999417.734375
271075610408.355902777810439.2916666667-30.9357638888894347.644097222221
281094010582.383680555610470.9583333333111.425347222223357.616319444445
291099710752.723958333310493.0833333333259.640625244.276041666666
301082710512.397569444410525.4583333333-13.0607638888891314.602430555555
311016610506.182291666710572.4166666667-66.2343749999998-340.182291666668
321018610467.133680555610589.9583333333-122.824652777778-281.133680555555
331045710492.043402777810559.125-67.0815972222221-35.0434027777756
341036810343.508680555610483.9583333333-140.44965277777824.4913194444453
351024410382.397569444410387.3333333333-4.93576388888881-138.397569444445
361051110273.737847222210299.875-26.1371527777772237.262152777777
371081210274.744791666710248.166666666726.5781249999999537.255208333332
381073810319.807291666710245.791666666774.0156249999999418.192708333336
391017110238.772569444410269.7083333333-30.9357638888894-67.7725694444453
40972110399.217013888910287.7916666667111.425347222223-678.217013888887
41989710568.682291666710309.0416666667259.640625-671.682291666664
42982810321.647569444410334.7083333333-13.0607638888891-493.647569444445
43992410264.682291666710330.9166666667-66.2343749999998-340.682291666666
441037110179.633680555610302.4583333333-122.824652777778191.366319444443
451084610236.335069444410303.4166666667-67.0815972222221609.664930555557
461041310230.258680555610370.7083333333-140.449652777778182.741319444445
471070910430.355902777810435.2916666667-4.93576388888881278.644097222223
481066210448.904513888910475.0416666667-26.1371527777772213.095486111111
491057010545.869791666710519.291666666726.578124999999924.1302083333339
501029710622.89062510548.87574.0156249999999-325.890625
511063510518.355902777810549.2916666667-30.9357638888894116.644097222224
521087210669.008680555610557.5833333333111.425347222223202.991319444445
531029610840.348958333310580.7083333333259.640625-544.348958333334
541038310572.897569444410585.9583333333-13.0607638888891-189.897569444445
551043110511.39062510577.625-66.2343749999998-80.390625
561057410454.925347222210577.75-122.824652777778119.074652777779
571065310514.293402777810581.375-67.0815972222221138.706597222223
581080510428.217013888910568.6666666667-140.449652777778376.782986111109
591087210571.230902777810576.1666666667-4.93576388888881300.769097222223
601062510607.362847222210633.5-26.137152777777217.6371527777774
611040710738.619791666710712.041666666726.5781249999999-331.619791666666
621046310860.723958333310786.708333333374.0156249999999-397.723958333334
631055610846.939236111110877.875-30.9357638888894-290.939236111111
641064611117.633680555611006.2083333333111.425347222223-471.633680555557
651070211429.51562511169.875259.640625-727.515624999998
661135311379.564236111111392.625-13.0607638888891-26.5642361111095
671134611631.557291666711697.7916666667-66.2343749999998-285.557291666664
681145111933.508680555612056.3333333333-122.824652777778-482.508680555557
691196412355.835069444412422.9166666667-67.0815972222221-391.835069444445
701257412686.175347222212826.625-140.449652777778-112.175347222224
711303113310.064236111113315-4.93576388888881-279.064236111111
721381213712.904513888913739.0416666667-26.137152777777299.0954861111113
731454414120.5781251409426.5781249999999423.421875
741493114546.848958333314472.833333333374.0156249999999384.151041666668
751488614756.939236111114787.875-30.9357638888894129.060763888889
761600515130.675347222215019.25111.425347222223874.32465277778
771706415487.098958333315227.4583333333259.6406251576.90104166667
781516815385.647569444415398.7083333333-13.0607638888891-217.647569444443
791605015437.807291666715504.0416666667-66.2343749999998612.192708333334
801583915483.383680555615606.2083333333-122.824652777778355.616319444445
811513715641.585069444415708.6666666667-67.0815972222221-504.585069444442
821495415618.550347222215759-140.449652777778-664.550347222221
831564815714.022569444415718.9583333333-4.93576388888881-66.0225694444416
841530515668.571180555615694.7083333333-26.1371527777772-363.571180555553
851557915724.994791666715698.416666666726.5781249999999-145.994791666664
861634815746.932291666715672.916666666774.0156249999999601.067708333336
871592815689.689236111115720.625-30.9357638888894238.310763888889
881617115972.842013888915861.4166666667111.425347222223198.157986111111
891593716292.973958333316033.3333333333259.640625-355.973958333334
901571316207.272569444416220.3333333333-13.0607638888891-494.272569444447
911559416414.723958333316480.9583333333-66.2343749999998-820.723958333336
9215683NANA-122.824652777778NA
9316438NANA-67.0815972222221NA
9417032NANA-140.449652777778NA
9517696NANA-4.93576388888881NA
9617745NANA-26.1371527777772NA
9719394NANANANA
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/17/t121101584519rttjhqpbg5wjl/1sbf31211015759.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/17/t121101584519rttjhqpbg5wjl/1sbf31211015759.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/17/t121101584519rttjhqpbg5wjl/220be1211015759.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/17/t121101584519rttjhqpbg5wjl/220be1211015759.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/17/t121101584519rttjhqpbg5wjl/3gyuz1211015759.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/17/t121101584519rttjhqpbg5wjl/3gyuz1211015759.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/17/t121101584519rttjhqpbg5wjl/4knuw1211015759.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/17/t121101584519rttjhqpbg5wjl/4knuw1211015759.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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