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Paper: analyse (18t/m24) CD

*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 19:02:44 +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/t1293562850w8d85nzmf8jmomd.htm/, Retrieved Tue, 28 Dec 2010 20:00:55 +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/t1293562850w8d85nzmf8jmomd.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 «
49915 47469 45652 43492 41087 42931 67256 72316 65624 59450 52851 51214 44092 43752 40320 40551 38329 39530 59648 61031 55560 43877 38510 36085 35994 32617 30001 27894 26083 28817 48742 49915 40264 34276 30426 30793 29855 28081 26820 25782 22654 27373 43675 45096 38145 34017 31537 33814 36531 36935 36497 35110 33137 37407 53963 56602 49694 43957 41723 45599 42503 42153 39098 37449 34748 36548 53639 55289 47774 42156 38019
 
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


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
149915NANA-3120.28645833333NA
247469NANA-4089.88020833333NA
345652NANA-5697.00520833334NA
443492NANA-6444.93229166667NA
541087NANA-8451.13020833334NA
642931NANA-5045.72395833333NA
76725666283.35937553028.791666666713254.5677083333972.640625
87231667573.067708333352631.291666666714941.77604166674742.93229166666
96562459980.161458333352254.257725.911458333345643.83854166667
105945052796.494791666751909.5416666667886.9531250000026653.50520833334
115285149145.901041666751672.0833333333-2526.182291666673705.09895833333
125121449981.39062551415.4583333333-1434.067708333331232.609375
134409247836.463541666750956.75-3120.28645833333-3744.46354166666
144375246079.661458333350169.5416666667-4089.88020833333-2327.66145833333
154032043582.994791666749280-5697.00520833334-3262.99479166666
164055141766.85937548211.7916666667-6444.93229166667-1215.859375
173832938514.244791666746965.375-8451.13020833334-185.244791666664
183953040691.73437545737.4583333333-5045.72395833333-1161.734375
195964858024.23437544769.666666666713254.56770833331623.765625
206103158910.067708333343968.291666666714941.77604166672120.93229166667
215556050800.286458333343074.3757725.911458333344759.71354166667
224387743003.994791666742117.0416666667886.953125000002873.005208333336
233851038553.23437541079.4166666667-2526.18229166667-43.2343749999927
243608538688.723958333340122.7916666667-1434.06770833333-2603.72395833334
253599436101.713541666739222-3120.28645833333-107.713541666664
263261734214.536458333338304.4166666667-4089.88020833333-1597.53645833334
273000131506.911458333337203.9166666667-5697.00520833334-1505.91145833333
282789429721.60937536166.5416666667-6444.93229166667-1827.60937499999
292608326978.536458333335429.6666666667-8451.13020833334-895.536458333328
302881729826.60937534872.3333333333-5045.72395833333-1009.60937499999
314874247650.60937534396.041666666713254.56770833331091.390625
324991548893.026041666733951.2514941.77604166671021.97395833334
334026441355.619791666733629.70833333337725.91145833334-1091.61979166666
343427634296.119791666733409.1666666667886.953125000002-20.1197916666570
353042630652.10937533178.2916666667-2526.18229166667-226.109375
363079331541.182291666732975.25-1434.06770833333-748.18229166666
372985529583.67187532703.9583333333-3120.28645833333271.328125000007
382808128202.161458333332292.0416666667-4089.88020833333-121.161458333332
392682026305.95312532002.9583333333-5697.00520833334514.046875000004
402578225458.942708333331903.875-6444.93229166667323.057291666672
412265423488.244791666731939.375-8451.13020833334-834.24479166666
422737327065.817708333332111.5416666667-5045.72395833333307.182291666664
434367545770.151041666732515.583333333313254.5677083333-2095.15104166666
444509648104.442708333333162.666666666714941.7760416667-3008.44270833333
453814541660.70312533934.79166666677725.91145833334-3515.70312499999
463401735613.619791666734726.6666666667886.953125000002-1596.61979166666
473153733025.942708333335552.125-2526.18229166667-1488.94270833333
483381434972.932291666736407-1434.06770833333-1158.93229166666
493653134133.463541666737253.75-3120.286458333332397.53645833334
503693534071.95312538161.8333333333-4089.880208333332863.046875
513649733425.45312539122.4583333333-5697.005208333343071.546875
523511033572.901041666740017.8333333333-6444.932291666671537.09895833334
533313732405.286458333340856.4166666667-8451.13020833334731.713541666672
543740736726.151041666741771.875-5045.72395833333680.848958333336
555396355766.317708333342511.7513254.5677083333-1803.31770833333
565660257919.77604166674297814941.7760416667-1317.77604166666
574969451029.70312543303.79166666677725.91145833334-1335.703125
584395744396.57812543509.625886.953125000002-439.578124999993
594172341148.026041666743674.2083333333-2526.18229166667574.973958333328
604559942271.473958333343705.5416666667-1434.067708333333327.52604166666
6142503NA43656.25NANA
6242153NA43588.0416666667NANA
6339098NA43453.3333333333NANA
6437449NA43298.2916666667NANA
6534748NA43068.9166666667NANA
6636548NANANANA
6753639NANANANA
6855289NANANANA
6947774NANANANA
7042156NANANANA
7138019NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293562850w8d85nzmf8jmomd/1wzq11293562961.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293562850w8d85nzmf8jmomd/1wzq11293562961.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t1293562850w8d85nzmf8jmomd/2wzq11293562961.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293562850w8d85nzmf8jmomd/2wzq11293562961.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t1293562850w8d85nzmf8jmomd/36rp31293562961.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293562850w8d85nzmf8jmomd/36rp31293562961.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t1293562850w8d85nzmf8jmomd/46rp31293562961.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293562850w8d85nzmf8jmomd/46rp31293562961.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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