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classical decomposition - goudkoers

*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: Mon, 20 Dec 2010 14:39:40 +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/20/t129285611691w6s05j820qnf7.htm/, Retrieved Mon, 20 Dec 2010 15:41:57 +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/20/t129285611691w6s05j820qnf7.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 «
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 20148 20108 18584 18441 18391 19178 18079 18483 19644 19195 19650 20830 23595 22937 21814 21928 21777 21383 21467 22052 22680 24320 24977 25204
 
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
110570NANA312.677083333333NA
210297NANA1076.83541666667NA
310635NANA677.06875NA
410872NANA244.11875NA
510296NANA203.49375NA
610383NANA-362.172916666667NA
71043110202.3437510577.625-375.28125228.65625
81057410009.502083333310577.75-568.247916666667564.497916666669
91065310023.0937510581.375-558.28125629.90625
101080510265.6187510568.6666666667-303.047916666666539.381249999999
111087210392.6437510576.1666666667-183.522916666667479.356250000001
121062510469.860416666710633.5-163.639583333332155.139583333332
131040711024.7187510712.0416666667312.677083333333-617.71875
141046311863.5437510786.70833333331076.83541666667-1400.54375
151055611554.9437510877.875677.06875-998.94375
161064611250.327083333311006.2083333333244.11875-604.327083333334
171070211373.3687511169.875203.49375-671.368749999998
181135311030.452083333311392.625-362.172916666667322.547916666668
191134611322.510416666711697.7916666667-375.2812523.4895833333358
201145111488.085416666712056.3333333333-568.247916666667-37.0854166666668
211196411864.635416666712422.9166666667-558.2812599.364583333332
221257412523.577083333312826.625-303.04791666666650.4229166666646
231303113131.477083333313315-183.522916666667-100.477083333333
241381213575.402083333313739.0416666667-163.639583333332236.597916666666
251454414406.677083333314094312.677083333333137.322916666666
261493115549.6687514472.83333333331076.83541666667-618.668749999999
271488615464.9437514787.875677.06875-578.94375
281600515263.3687515019.25244.11875741.631250000002
291706415430.952083333315227.4583333333203.493751633.04791666666
301516815036.535416666715398.7083333333-362.172916666667131.464583333334
311605015128.760416666715504.0416666667-375.28125921.239583333334
321583915037.960416666715606.2083333333-568.247916666667801.039583333335
331513715150.385416666715708.6666666667-558.28125-13.3854166666642
341495415455.952083333315759-303.047916666666-501.952083333332
351564815535.435416666715718.9583333333-183.522916666667112.564583333336
361530515531.0687515694.7083333333-163.639583333332-226.068749999999
371557916011.0937515698.4166666667312.677083333333-432.093749999998
381634816749.752083333315672.91666666671076.83541666667-401.752083333331
391592816397.6937515720.625677.06875-469.69375
401617116105.535416666715861.4166666667244.1187565.4645833333343
411593716236.827083333316033.3333333333203.49375-299.827083333334
421571315858.160416666716220.3333333333-362.172916666667-145.160416666669
431559416105.677083333316480.9583333333-375.28125-511.677083333336
441568316230.002083333316798.25-568.247916666667-547.002083333333
451643816572.4687517130.75-558.28125-134.46875
461703217102.410416666717405.4583333333-303.047916666666-70.4104166666657
471769617426.810416666717610.3333333333-183.522916666667269.189583333333
481774517662.610416666717826.25-163.63958333333282.3895833333372
491939418399.8437518087.1666666667312.677083333333994.15625
502014819413.1687518336.33333333331076.83541666667734.831250000003
512010819198.4437518521.375677.06875909.556250000001
521858418959.535416666718715.4166666667244.11875-375.535416666666
531844119090.202083333318886.7083333333203.49375-649.202083333334
541839118666.3687519028.5416666667-362.172916666667-275.368750000001
551917818792.4687519167.75-375.28125385.53125
561807918802.960416666719371.2083333333-568.247916666667-723.960416666669
571848319074.427083333319632.7083333333-558.28125-591.427083333336
581964419582.1187519885.1666666667-303.04791666666661.8812500000022
591919519981.5187520165.0416666667-183.522916666667-786.51875
601965020287.777083333320451.4166666667-163.639583333332-637.777083333334
612083020997.052083333320684.375312.677083333333-167.052083333332
622359521994.252083333320917.41666666671076.835416666671600.74791666666
632293721884.360416666721207.2916666667677.068751052.63958333334
642181421726.6187521482.5244.1187587.3812499999985
652192822026.035416666721822.5416666667203.49375-98.0354166666657
662177721895.8687522258.0416666667-362.172916666667-118.868749999998
672138322286.9687522662.25-375.28125-903.96875
6821467NANA-568.247916666667NA
6922052NANA-558.28125NA
7022680NANA-303.047916666666NA
7124320NANA-183.522916666667NA
7224977NANA-163.639583333332NA
7325204NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/20/t129285611691w6s05j820qnf7/1bo1o1292855976.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/20/t129285611691w6s05j820qnf7/1bo1o1292855976.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/20/t129285611691w6s05j820qnf7/2bo1o1292855976.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/20/t129285611691w6s05j820qnf7/2bo1o1292855976.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/20/t129285611691w6s05j820qnf7/34xir1292855976.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/20/t129285611691w6s05j820qnf7/34xir1292855976.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/20/t129285611691w6s05j820qnf7/4w6zu1292855976.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/20/t129285611691w6s05j820qnf7/4w6zu1292855976.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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