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Classical Decomposition Eigen Reeks Dimitri Troquay

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 08:36:15 -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/t1211726251qwkchuqorzxbp4x.htm/, Retrieved Sun, 25 May 2008 16:37:35 +0200
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
8,16 8,43 8,43 8,43 8,43 8,43 8,43 8,43 8,43 8,43 8,43 8,43 8,43 8,43 8,68 8,68 8,68 8,68 8,68 8,68 8,68 8,68 8,68 8,68 8,68 8,68 8,92 8,92 8,92 8,92 8,92 8,92 8,92 8,92 8,92 8,92 8,92 8,92 9,3 9,3 9,3 9,3 9,3 9,3 9,3 9,3 9,3 9,3 9,3 9,3 9,6 9,6 9,6 9,6 9,6 9,6 9,6 9,6 9,6 9,6 9,6 9,6 9,9 9,9 9,9 9,9 9,9 9,9 9,9 9,9 9,9 9,9 9,9 9,9 10,2 10,2 10,2 10,2 10,2 10,2 10,2 10,2 10,2 10,2
 
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 time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
18.16NANA-0.109694444444444NA
28.43NANA-0.134194444444444NA
38.43NANA0.135305555555555NA
48.43NANA0.110805555555556NA
58.43NANA0.0863055555555551NA
68.43NANA0.0618055555555553NA
78.438.456055555555558.418750.0373055555555552-0.0260555555555531
88.438.442805555555558.430.0128055555555558-0.0128055555555537
98.438.428305555555558.44041666666666-0.01211111111111090.00169444444444622
108.438.423805555555558.46125-0.03744444444444430.00619444444444639
118.438.419305555555558.48208333333333-0.06277777777777770.0106944444444448
128.438.414805555555558.50291666666666-0.08811111111111070.0151944444444467
138.438.414055555555558.52375-0.1096944444444440.0159444444444468
148.438.410388888888898.54458333333333-0.1341944444444440.0196111111111126
158.688.700722222222228.565416666666670.135305555555555-0.0207222222222221
168.688.697055555555568.586250.110805555555556-0.0170555555555563
178.688.693388888888898.607083333333330.0863055555555551-0.0133888888888887
188.688.689722222222228.627916666666670.0618055555555553-0.00972222222222285
198.688.686055555555568.648750.0373055555555552-0.00605555555555704
208.688.682388888888898.669583333333330.0128055555555558-0.00238888888888944
218.688.677888888888898.69-0.01211111111111090.00211111111111073
228.688.672555555555568.71-0.03744444444444430.00744444444444348
238.688.667222222222228.73-0.06277777777777770.0127777777777762
248.688.661888888888898.75-0.08811111111111070.0181111111111107
258.688.660305555555568.77-0.1096944444444440.0196944444444433
268.688.655805555555568.79-0.1341944444444440.0241944444444435
278.928.945305555555568.810.135305555555555-0.0253055555555566
288.928.940805555555568.830.110805555555556-0.0208055555555564
298.928.936305555555558.850.0863055555555551-0.0163055555555545
308.928.931805555555558.870.0618055555555553-0.0118055555555543
318.928.927305555555558.890.0373055555555552-0.00730555555555412
328.928.922805555555558.910.0128055555555558-0.00280555555555395
338.928.923722222222228.93583333333333-0.0121111111111109-0.00372222222222085
348.928.930055555555558.9675-0.0374444444444443-0.0100555555555548
358.928.936388888888898.99916666666667-0.0627777777777777-0.0163888888888888
368.928.942722222222229.03083333333333-0.0881111111111107-0.022722222222221
378.928.952805555555559.0625-0.109694444444444-0.0328055555555533
388.928.959972222222229.09416666666667-0.134194444444444-0.0399722222222216
399.39.261138888888899.125833333333330.1353055555555550.0388611111111103
409.39.268305555555569.15750.1108055555555560.0316944444444438
419.39.275472222222229.189166666666670.08630555555555510.0245277777777773
429.39.282638888888899.220833333333330.06180555555555530.0173611111111107
439.39.289805555555569.25250.03730555555555520.0101944444444442
449.39.296972222222229.284166666666670.01280555555555580.00302777777777763
459.39.300388888888899.3125-0.0121111111111109-0.000388888888890548
469.39.300055555555569.3375-0.0374444444444443-5.55555555550313e-05
479.39.299722222222229.3625-0.06277777777777770.000277777777776933
489.39.299388888888899.3875-0.08811111111111070.000611111111110674
499.39.302805555555569.4125-0.109694444444444-0.00280555555555573
509.39.303305555555569.4375-0.134194444444444-0.00330555555555456
519.69.597805555555569.46250.1353055555555550.00219444444444328
529.69.598305555555569.48750.1108055555555560.00169444444444444
539.69.598805555555559.51250.08630555555555510.00119444444444561
549.69.599305555555559.53750.06180555555555530.000694444444446773
559.69.599805555555559.56250.03730555555555520.000194444444446162
569.69.600305555555559.58750.0128055555555558-0.000305555555554449
579.69.600388888888899.6125-0.0121111111111109-0.000388888888890548
589.69.600055555555559.6375-0.0374444444444443-5.55555555550313e-05
599.69.599722222222229.6625-0.06277777777777770.000277777777776933
609.69.599388888888899.6875-0.08811111111111070.000611111111110674
619.69.602805555555569.7125-0.109694444444444-0.00280555555555573
629.69.603305555555569.7375-0.134194444444444-0.00330555555555634
639.99.897805555555569.76250.1353055555555550.00219444444444328
649.99.898305555555569.78750.1108055555555560.00169444444444267
659.99.898805555555569.81250.08630555555555510.00119444444444383
669.99.899305555555569.83750.06180555555555530.000694444444443221
679.99.899805555555569.86250.03730555555555520.000194444444442610
689.99.900305555555569.88750.0128055555555558-0.000305555555556225
699.99.900388888888899.9125-0.0121111111111109-0.000388888888888772
709.99.900055555555569.9375-0.0374444444444443-5.55555555550313e-05
719.99.899722222222229.9625-0.06277777777777770.000277777777776933
729.99.899388888888899.9875-0.08811111111111070.00061111111111245
739.99.9028055555555510.0125-0.109694444444444-0.00280555555555395
749.99.9033055555555510.0375-0.134194444444444-0.00330555555555279
7510.210.197805555555610.06250.1353055555555550.00219444444444505
7610.210.198305555555610.08750.1108055555555560.00169444444444622
7710.210.198805555555610.11250.08630555555555510.00119444444444916
7810.210.199305555555610.13750.06180555555555530.00069444444444855
7910.2NANA0.0373055555555552NA
8010.2NANA0.0128055555555558NA
8110.2NANA-0.0121111111111109NA
8210.2NANA-0.0374444444444443NA
8310.2NANA-0.0627777777777777NA
8410.2NANA-0.0881111111111107NA
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211726251qwkchuqorzxbp4x/1ikct1211726169.png (open in new window)
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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211726251qwkchuqorzxbp4x/2zbt11211726169.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211726251qwkchuqorzxbp4x/2zbt11211726169.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211726251qwkchuqorzxbp4x/3ty8f1211726169.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211726251qwkchuqorzxbp4x/3ty8f1211726169.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211726251qwkchuqorzxbp4x/4vdr01211726169.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211726251qwkchuqorzxbp4x/4vdr01211726169.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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