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s

*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: Thu, 03 Dec 2009 10:11:08 -0700
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/03/t1259860380iwb4vtrwylfe3yo.htm/, Retrieved Thu, 03 Dec 2009 18:13:05 +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/2009/Dec/03/t1259860380iwb4vtrwylfe3yo.htm/},
    year = {2009},
}
@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 = {2009},
    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 «
13132.1 17665.9 16913 17318.8 16224.2 15469.6 16557.5 19414.8 17335 16525.2 18160.4 15553.8 15262.2 18581 17564.1 18948.6 17187.8 17564.8 17668.4 20811.7 17257.8 18984.2 20532.6 17082.3 16894.9 20274.9 20078.6 19900.9 17012.2 19642.9 19024 21691 18835.9 19873.4 21468.2 19406.8 18385.3 20739.3 22268.3 21569 17514.8 21124.7 21251 21393 22145.2 20310.5 23466.9 21264.6 18388.1 22635.4 22014.3 18422.7 16120.2 16037.7 16410.7 17749.8 16349.8 15662.3 17782.3 16398.9
 
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
113132.1NANA-2010.92994791667NA
217665.9NANA1332.96796875NA
316913NANA1284.24921875000NA
417318.8NANA532.475260416667NA
516224.2NANA-2206.14765625000NA
615469.6NANA-577.237239583333NA
716557.516455.3117187516777.9458333333-322.634114583333102.188281250001
819414.818678.079427083316904.82916666671773.25026041667736.720572916664
91733516704.2835937516970.0875-265.80390625630.716406250001
1016525.216764.5335937517065.125-300.591406249999-239.333593749998
1118160.418845.876302083317173.18333333331672.69296875000-685.476302083334
1215553.816388.341927083317300.6333333333-912.29140625-834.541927083334
1315262.215423.290885416717434.2208333333-2010.92994791667-161.090885416666
141858118871.6804687517538.71251332.96796875-290.680468749997
1517564.118877.9492187517593.71284.24921875000-1313.84921875
1618948.618225.416927083317692.9416666667532.475260416667723.183072916665
1717187.815688.094010416717894.2416666667-2206.147656250001499.70598958333
1817564.817479.5335937518056.7708333333-577.23723958333385.2664062500007
1917668.417865.853385416718188.4875-322.634114583333-197.453385416666
2020811.720100.3460937518327.09583333331773.25026041667711.353906250002
2117257.818236.641927083318502.4458333333-265.80390625-978.841927083336
2218984.218346.304427083318646.8958333333-300.591406249999637.895572916666
2320532.620351.951302083318679.25833333331672.69296875000180.648697916669
2417082.317846.237760416718758.5291666667-912.29140625-763.937760416666
2516894.916890.670052083318901.6-2010.929947916674.22994791666861
2620274.920327.688802083318994.72083333331332.96796875-52.7888020833343
2720078.620381.3617187519097.11251284.24921875000-302.76171875
2819900.919732.391927083319199.9166666667532.475260416667168.508072916666
2917012.217069.8023437519275.95-2206.14765625000-57.6023437499971
3019642.918834.550260416719411.7875-577.237239583333808.349739583336
311902419248.107552083319570.7416666667-322.634114583333-224.107552083336
322169121425.441927083319652.19166666671773.25026041667265.558072916665
3318835.919496.975260416719762.7791666667-265.80390625-661.075260416666
3419873.419622.929427083319923.5208333333-300.591406249999250.470572916664
3521468.221686.659635416720013.96666666671672.69296875000-218.459635416668
3619406.819184.3585937520096.65-912.29140625222.44140625
3718385.318240.253385416720251.1833333333-2010.92994791667145.046614583331
3820739.321664.526302083320331.55833333331332.96796875-925.226302083334
3922268.321741.278385416720457.02916666671284.24921875000527.021614583333
402156921145.604427083320613.1291666667532.475260416667423.395572916666
4117514.818508.473177083320714.6208333333-2206.14765625000-993.673177083336
4221124.720298.0710937520875.3083333333-577.237239583333826.62890625
432125120630.1992187520952.8333333333-322.634114583333620.80078125
442139322805.204427083321031.95416666671773.25026041667-1412.20442708334
4522145.220834.5710937521100.375-265.803906251310.62890625
4620310.520658.104427083320958.6958333333-300.591406249999-347.604427083334
4723466.922442.184635416720769.49166666671672.692968750001024.71536458334
4821264.619587.1335937520499.425-912.291406251677.46640624999
4918388.118074.857552083320085.7875-2010.92994791667313.242447916666
5022635.421065.276302083319732.30833333331332.967968751570.12369791667
5122014.320623.282552083319339.03333333331284.249218750001391.01744791667
5218422.719436.3585937518903.8833333333532.475260416667-1013.65859375000
5316120.216267.2023437518473.35-2206.14765625000-147.002343749995
5416037.717456.516927083318033.7541666667-577.237239583333-1418.81692708333
5516410.7NANA-322.634114583333NA
5617749.8NANA1773.25026041667NA
5716349.8NANA-265.80390625NA
5815662.3NANA-300.591406249999NA
5917782.3NANA1672.69296875000NA
6016398.9NANA-912.29140625NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2009/Dec/03/t1259860380iwb4vtrwylfe3yo/1pad11259860265.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/03/t1259860380iwb4vtrwylfe3yo/1pad11259860265.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/03/t1259860380iwb4vtrwylfe3yo/2rvwa1259860265.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/03/t1259860380iwb4vtrwylfe3yo/2rvwa1259860265.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/03/t1259860380iwb4vtrwylfe3yo/3vxv31259860265.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/03/t1259860380iwb4vtrwylfe3yo/3vxv31259860265.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/03/t1259860380iwb4vtrwylfe3yo/415qi1259860265.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/03/t1259860380iwb4vtrwylfe3yo/415qi1259860265.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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