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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 17:24:09 +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/t1293558460g69y3zzd1rjqxqj.htm/, Retrieved Tue, 28 Dec 2010 18:47:41 +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/t1293558460g69y3zzd1rjqxqj.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 «
26548 26752 26967 27034 27056 27476 28497 29085 28720 29067 29249 29672 29761 30066 30315 30571 30757 30742 31310 31381 31470 31226 31081 31061 31114 30828 30418 30195 29877 29192 29876 29409 28458 28340 28164 28438 28053 27599 27226 27119 26625 26541 27023 26631 26154 26029 26008 26632 27010 27041 27244 26976 26715 27017 27714 27655 27103 27088 26968 27770 27616 27481 27279 26918 26503 26547 27467 27305 26259 26048 25743
 
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
126548NANA171.072916666667NA
226752NANA93.1250000000008NA
326967NANA42.114583333333NA
427034NANA-5.9270833333331NA
527056NANA-183.302083333332NA
627476NANA-260.229166666665NA
72849728533.802083333328144.125389.677083333333-36.8020833333321
82908528643.2812528416.0833333333227.197916666667441.718749999996
92872028506.666666666728693.6666666667-187.000000000001213.333333333332
102906728737.7187528980.5416666667-242.822916666667329.28125
112924929006.166666666729282.125-275.958333333335242.833333333339
122967229804.4687529572.4166666667232.052083333333-132.468749999993
132976129996.7812529825.7083333333171.072916666667-235.78125
143006630131.708333333330038.583333333393.1250000000008-65.7083333333321
153031530290.947916666730248.833333333342.11458333333324.0520833333358
163057130447.447916666730453.375-5.9270833333331123.552083333336
173075730436.364583333330619.6666666667-183.302083333332320.635416666672
183074230493.645833333330753.875-260.229166666665248.354166666672
193131031257.802083333330868.125389.67708333333352.1979166666715
203138131183.447916666730956.25227.197916666667197.552083333336
213147030805.291666666730992.2916666667-187.000000000001664.70833333334
223122630738.0937530980.9166666667-242.822916666667487.906250000004
233108130652.62530928.5833333333-275.958333333335428.375000000004
243106131059.385416666730827.3333333333232.0520833333331.61458333333212
253111430874.072916666730703171.072916666667239.927083333336
263082830654.208333333330561.083333333393.1250000000008173.791666666668
273041830395.5312530353.416666666742.11458333333322.4687500000036
283019530101.739583333330107.6666666667-5.927083333333193.2604166666679
292987729682.572916666729865.875-183.302083333332194.427083333332
302919229374.812529635.0416666667-260.229166666665-182.812499999996
312987629787.885416666729398.2083333333389.67708333333388.1145833333358
322940929363.322916666729136.125227.19791666666745.6770833333321
332845828681.583333333328868.5833333333-187.000000000001-223.583333333336
342834028364.5937528607.4166666667-242.822916666667-24.5937499999964
352816428067.791666666728343.75-275.95833333333596.208333333332
362843828329.8437528097.7916666667232.052083333333108.15625
372805328039.5312527868.4583333333171.07291666666713.46875
382759927726.958333333327633.833333333393.1250000000008-127.958333333328
392722627464.197916666727422.083333333342.114583333333-238.197916666664
402711927223.864583333327229.7916666667-5.9270833333331-104.864583333332
412662526860.364583333327043.6666666667-183.302083333332-235.364583333332
422654126618.354166666726878.5833333333-260.229166666665-77.3541666666715
432702327149.552083333326759.875389.677083333333-126.552083333328
442663126920.364583333326693.1666666667227.197916666667-289.364583333332
452615426483.666666666726670.6666666667-187.000000000001-329.666666666668
462602926422.635416666726665.4583333333-242.822916666667-393.635416666664
472600826387.291666666726663.25-275.958333333335-379.291666666661
482663226918.885416666726686.8333333333232.052083333333-286.885416666661
492701026906.5312526735.4583333333171.072916666667103.468750000004
502704126900.041666666726806.916666666793.1250000000008140.958333333339
512724426931.239583333326889.12542.114583333333312.760416666672
522697626966.864583333326972.7916666667-5.92708333333319.13541666666788
532671526873.614583333327056.9166666667-183.302083333332-158.614583333328
542701726884.104166666727144.3333333333-260.229166666665132.895833333336
552771427606.677083333327217389.677083333333107.322916666668
562765527487.7812527260.5833333333227.197916666667167.218750000004
572710327093.37527280.375-187.0000000000019.62500000000364
582708827036.5937527279.4166666667-242.82291666666751.4062500000036
592696826992.208333333327268.1666666667-275.958333333335-24.2083333333285
602777027471.802083333327239.75232.052083333333298.197916666675
6127616NA27209.875NANA
6227481NA27185NANA
6327279NA27135.25NANA
6426918NA27056.75NANA
6526503NA26962.375NANA
6626547NANANANA
6727467NANANANA
6827305NANANANA
6926259NANANANA
7026048NANANANA
7125743NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293558460g69y3zzd1rjqxqj/181x01293557045.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293558460g69y3zzd1rjqxqj/181x01293557045.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t1293558460g69y3zzd1rjqxqj/281x01293557045.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293558460g69y3zzd1rjqxqj/281x01293557045.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t1293558460g69y3zzd1rjqxqj/31aw31293557045.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293558460g69y3zzd1rjqxqj/31aw31293557045.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t1293558460g69y3zzd1rjqxqj/41aw31293557045.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293558460g69y3zzd1rjqxqj/41aw31293557045.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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