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opgave 9 - classical decomposition eigen reeks

*Unverified author*
R Software Module: /rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Mon, 16 May 2011 17:13:56 +0000
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2011/May/16/t1305565912jvl8ouqny67d4ts.htm/, Retrieved Mon, 16 May 2011 19:11:56 +0200
 
Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
KDGP2W92
 
Dataseries X:
» Textbox « » Textfile « » CSV «
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 20148 20108 18584 18441 18391 19178 18079 18483 19644 19195 19650 20830 23595 22937 21814 21928 21777 21383 21467 22052 22680 24320 24977 25204 25739 26434 27525 30695 32436 30160 30236 31293 31077 32226 33865 32810
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Herman Ole Andreas Wold' @ www.yougetit.org


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
110407NANA249.868055555555NA
210463NANA978.543055555556NA
310556NANA567.326388888889NA
410646NANA213.259722222223NA
510702NANA692.309722222222NA
611353NANA249.243055555556NA
71134611403.709722222211697.7916666667-294.081944444445-57.7097222222183
81145111265.143055555612056.3333333333-791.190277777777185.856944444444
91196411603.118055555612422.9166666667-819.79861111111360.881944444442
101257412283.876388888912826.625-542.748611111111290.123611111108
111303113113.718055555613315-201.281944444446-82.7180555555551
121381213437.593055555613739.0416666667-301.448611111111374.406944444445
131454414343.868055555614094249.868055555555200.131944444445
141493115451.376388888914472.8333333333978.543055555556-520.376388888888
151488615355.201388888914787.875567.326388888889-469.201388888889
161600515232.509722222215019.25213.259722222223772.490277777779
171706415919.768055555615227.4583333333692.3097222222221144.23194444444
181516815647.951388888915398.7083333333249.243055555556-479.951388888889
191605015209.959722222215504.0416666667-294.081944444445840.04027777778
201583914815.018055555615606.2083333333-791.1902777777771023.98194444444
211513714888.868055555615708.6666666667-819.79861111111248.131944444445
221495415216.251388888915759-542.748611111111-262.251388888888
231564815517.676388888915718.9583333333-201.281944444446130.323611111115
241530515393.259722222215694.7083333333-301.448611111111-88.2597222222194
251557915948.284722222215698.4166666667249.868055555555-369.284722222219
261634816651.459722222215672.9166666667978.543055555556-303.45972222222
271592816287.951388888915720.625567.326388888889-359.951388888889
281617116074.676388888915861.4166666667213.25972222222396.323611111111
291593716725.643055555616033.3333333333692.309722222222-788.643055555556
301571316469.576388888916220.3333333333249.243055555556-756.576388888892
311559416186.876388888916480.9583333333-294.081944444445-592.87638888889
321568316007.059722222216798.25-791.190277777777-324.059722222224
331643816310.951388888917130.75-819.79861111111127.048611111109
341703216862.709722222217405.4583333333-542.748611111111169.290277777778
351769617409.051388888917610.3333333333-201.281944444446286.948611111111
361774517524.801388888917826.25-301.448611111111220.198611111115
371939418337.034722222218087.1666666667249.8680555555551056.96527777778
382014819314.876388888918336.3333333333978.543055555556833.123611111114
392010819088.701388888918521.375567.3263888888891019.29861111111
401858418928.676388888918715.4166666667213.259722222223-344.676388888885
411844119579.018055555618886.7083333333692.309722222222-1138.01805555555
421839119277.784722222219028.5416666667249.243055555556-886.784722222223
431917818873.668055555619167.75-294.081944444445304.331944444446
441807918580.018055555619371.2083333333-791.190277777777-501.01805555556
451848318812.909722222219632.7083333333-819.79861111111-329.909722222226
461964419342.418055555619885.1666666667-542.748611111111301.581944444446
471919519963.759722222220165.0416666667-201.281944444446-768.759722222221
481965020149.968055555620451.4166666667-301.448611111111-499.968055555557
492083020934.243055555620684.375249.868055555555-104.243055555555
502359521895.959722222220917.4166666667978.5430555555561699.04027777777
512293721774.618055555621207.2916666667567.3263888888891162.38194444445
522181421695.759722222221482.5213.259722222223118.240277777779
532192822514.851388888921822.5416666667692.309722222222-586.851388888885
542177722507.284722222222258.0416666667249.243055555556-730.284722222219
552138322368.168055555622662.25-294.081944444445-985.168055555554
562146722142.643055555622933.8333333333-791.190277777777-675.643055555553
572205222349.076388888923168.875-819.79861111111-297.076388888883
582268023009.793055555623552.5416666667-542.748611111111-329.793055555558
592432023954.509722222224155.7916666667-201.281944444446365.490277777779
602497724663.759722222224965.2083333333-301.448611111111313.240277777775
612520426024.909722222225775.0416666667249.868055555555-820.909722222223
622573927484.668055555626506.125978.543055555556-1745.66805555556
632643427823.868055555627256.5416666667567.326388888889-1389.86805555555
642752528204.718055555627991.4583333333213.259722222223-679.718055555553
653069529363.059722222228670.75692.3097222222221331.94027777778
663243629619.743055555629370.5249.2430555555562816.25694444445
673016029763.668055555630057.75-294.081944444445396.331944444446
6830236NANA-791.190277777777NA
6931293NANA-819.79861111111NA
7031077NANA-542.748611111111NA
7132226NANA-201.281944444446NA
7233865NANA-301.448611111111NA
7332810NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2011/May/16/t1305565912jvl8ouqny67d4ts/1uysc1305566035.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/16/t1305565912jvl8ouqny67d4ts/1uysc1305566035.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/May/16/t1305565912jvl8ouqny67d4ts/2jsyo1305566035.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/16/t1305565912jvl8ouqny67d4ts/2jsyo1305566035.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/May/16/t1305565912jvl8ouqny67d4ts/30ux71305566035.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/16/t1305565912jvl8ouqny67d4ts/30ux71305566035.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/May/16/t1305565912jvl8ouqny67d4ts/478n81305566035.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/16/t1305565912jvl8ouqny67d4ts/478n81305566035.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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