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paper

*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: Tue, 28 Dec 2010 18:23:04 +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/28/t1293561475fbxe39m4hbpbsqw.htm/, Retrieved Tue, 28 Dec 2010 19:38:00 +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/28/t1293561475fbxe39m4hbpbsqw.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 «
9782 9938 10111 10259 10419 10622 11173 11542 11538 11837 12060 12423 12791 12891 13098 13418 13614 13653 13980 14087 14332 14232 14226 14186 14310 14152 14127 14163 13964 13811 14440 14724 14790 14961 15117 15452 16080 16284 16524 16782 16663 16678 17448 17745 17789 17864 18079 18483 19037 19344 19590 19862 20207 20593 21253 21507 21528 21818 22205 22621 23006 23178 23358 23519 23725 23789 24472 24773 24477 24669 24827
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
R Framework
error message
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
19782NANA158.710937500001NA
29938NANA63.1588541666667NA
310111NANA22.2942708333341NA
410259NANA35.7630208333337NA
510419NANA-118.132812500001NA
610622NANA-258.289062500001NA
71117311226.283854166711100.7083333333125.575520833334-53.2838541666679
81154211496.638020833311349.125147.51302083333345.3619791666679
91153811624.106770833311596.62527.4817708333323-86.1067708333303
101183711777.096354166711852.7083333333-75.611979166666859.9036458333321
111206012019.304687512117.4583333333-98.15364583333240.6953124999982
121242312346.565104166712376.875-30.309895833333876.434895833334
131279112778.835937512620.125158.71093750000112.1640625000018
141289112906.283854166712843.12563.1588541666667-15.2838541666642
151309813087.877604166713065.583333333322.294270833334110.1223958333339
161341813317.554687513281.791666666735.7630208333337100.4453125
171361413353.700520833313471.8333333333-118.132812500001260.299479166668
181365313377.252604166713635.5416666667-258.289062500001275.747395833336
191398013897.867187513772.2916666667125.57552083333482.1328125
201408714035.638020833313888.125147.51302083333351.3619791666697
211433214011.023437513983.541666666727.4817708333323320.976562500002
221423213981.846354166714057.4583333333-75.6119791666668250.153645833332
231422614004.929687514103.0833333333-98.153645833332221.0703125
241418614093.940104166714124.25-30.309895833333892.059895833334
251431014308.710937514150158.7109375000011.2890625
261415214258.867187514195.708333333363.1588541666667-106.8671875
271412714263.627604166714241.333333333322.2942708333341-136.627604166666
281416314326.554687514290.791666666735.7630208333337-163.5546875
291396414240.158854166714358.2916666667-118.132812500001-276.158854166666
301381114189.877604166714448.1666666667-258.289062500001-378.877604166666
311444014700.242187514574.6666666667125.575520833334-260.2421875
321472414884.763020833314737.25147.513020833333-160.763020833330
331479014953.440104166714925.958333333327.4817708333323-163.440104166664
341496115059.346354166715134.9583333333-75.6119791666668-98.346354166666
351511715258.388020833315356.5416666667-98.153645833332-141.388020833334
361545215558.148437515588.4583333333-30.3098958333338-106.1484375
371608015991.960937515833.25158.71093750000188.0390625000018
381628416147.617187516084.458333333363.1588541666667136.382812500002
391652416357.585937516335.291666666722.2942708333341166.414062499998
401678216616.971354166716581.208333333335.7630208333337165.028645833336
411666316707.450520833316825.5833333333-118.132812500001-44.4505208333321
421667816817.002604166717075.2916666667-258.289062500001-139.002604166668
431744817450.367187517324.7916666667125.575520833334-2.36718749999636
441774517723.013020833317575.5147.51302083333321.9869791666679
451778917858.231770833317830.7527.4817708333323-69.2317708333321
461786418011.221354166718086.8333333333-75.6119791666668-147.221354166664
471807918264.679687518362.8333333333-98.153645833332-185.6796875
481848318643.315104166718673.625-30.3098958333338-160.315104166668
491903719154.002604166718995.2916666667158.710937500001-117.002604166664
501934419373.742187519310.583333333363.1588541666667-29.7421875
511959019645.419270833319623.12522.2942708333341-55.4192708333321
521986219979.429687519943.666666666735.7630208333337-117.429687499996
532020720162.200520833320280.3333333333-118.13281250000144.7994791666642
542059320366.377604166720624.6666666667-258.289062500001226.622395833332
552125321088.033854166720962.4583333333125.575520833334164.966145833336
562150721435.096354166721287.5833333333147.51302083333371.9036458333321
572152821631.815104166721604.333333333327.4817708333323-103.815104166668
582181821838.096354166721913.7083333333-75.6119791666668-20.0963541666642
592220522114.513020833322212.6666666667-98.15364583333290.4869791666715
602262122462.106770833322492.4166666667-30.3098958333338158.893229166668
6123006NA22759.7083333333NANA
6223178NA23029.9166666667NANA
6323358NA23288.875NANA
6423519NA23530.5416666667NANA
6523725NA23758.5833333333NANA
6623789NANANANA
6724472NANANANA
6824773NANANANA
6924477NANANANA
7024669NANANANA
7124827NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293561475fbxe39m4hbpbsqw/1ot7o1293560581.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293561475fbxe39m4hbpbsqw/1ot7o1293560581.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t1293561475fbxe39m4hbpbsqw/2zl6r1293560581.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293561475fbxe39m4hbpbsqw/2zl6r1293560581.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t1293561475fbxe39m4hbpbsqw/3zl6r1293560581.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293561475fbxe39m4hbpbsqw/3zl6r1293560581.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t1293561475fbxe39m4hbpbsqw/4suou1293560581.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293561475fbxe39m4hbpbsqw/4suou1293560581.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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Software written by Ed van Stee & Patrick Wessa


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