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*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: Wed, 08 Dec 2010 17:14:43 +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/08/t1291828372ly44tjkel8mporm.htm/, Retrieved Wed, 08 Dec 2010 18:12:53 +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/08/t1291828372ly44tjkel8mporm.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 «
16198,1 17535,2 16571,8 16198,9 16554,2 19554,2 15903,8 18003,8 18329,6 16260,7 14851,9 18174,1 18406,6 18466,5 16016,5 17428,5 17167,2 19630 17183,6 18344,7 19301,4 18147,5 16192,9 18374,4 20515,2 18957,2 16471,5 18746,8 19009,5 19211,2 20547,7 19325,8 20605,5 20056,9 16141,4 20359,8 19711,6 15638,6 14384,5 13855,6 14308,3 15290,6 14423,8 13779,7 15686,3 14733,8 12522,5 16189,4 16059,1 16007,1 15806,8 15160 15692,1 18908,9 16969,9 16997,5 19858,9 17681,2 16006,9 19539,9
 
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'RServer@AstonUniversity' @ vre.aston.ac.uk


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
116198.1NANA1537.8921875NA
217535.2NANA131.494270833335NA
316571.8NANA-1471.47864583333NA
416198.9NANA-874.305729166667NA
516554.2NANA-654.583854166667NA
619554.2NANA1035.0578125NA
715903.816891.707812517103.3791666667-211.671354166669-987.907812499998
818003.817388.673437517234.2041666667154.469270833332615.126562500001
918329.618545.426562517249.87083333331295.55572916667-215.826562499995
1016260.717411.338020833317277.9666666667133.371354166667-1150.63802083333
1114851.915135.365104166717354.7416666667-2219.3765625-283.465104166666
1218174.118527.017187517383.44166666671143.57552083333-352.917187499999
1318406.618977.817187517439.9251537.8921875-571.217187499999
1418466.517638.948437517507.4541666667131.494270833335827.551562499997
1516016.516090.671354166717562.15-1471.47864583333-74.171354166665
1617428.516806.952604166717681.2583333333-874.305729166667621.547395833331
1717167.217161.166145833317815.75-654.5838541666676.03385416666788
181963018915.028645833317879.97083333331035.0578125714.971354166668
1917183.617764.503645833317976.175-211.671354166669-580.903645833336
2018344.718238.948437518084.4791666667154.469270833332105.751562500001
2119301.419419.439062518123.88333333331295.55572916667-118.0390625
2218147.518331.142187518197.7708333333133.371354166667-183.642187500001
2316192.916110.085937518329.4625-2219.376562582.8140624999978
2418374.419532.350520833318388.7751143.57552083333-1157.95052083333
2520515.220049.388020833318511.49583333331537.8921875465.811979166665
2618957.218824.040104166718692.5458333333131.494270833335133.159895833331
2716471.517316.283854166718787.7625-1471.47864583333-844.783854166668
2818746.818047.352604166718921.6583333333-874.305729166667699.447395833333
2919009.518344.486979166718999.0708333333-654.583854166667665.013020833336
3019211.220114.707812519079.651035.0578125-903.507812499996
3120547.718917.220312519128.8916666667-211.6713541666691630.4796875
3219325.819111.602604166718957.1333333333154.469270833332214.197395833333
3320605.520027.455729166718731.91295.55572916667578.044270833332
3420056.918574.513020833318441.1416666667133.3713541666671482.38697916667
3516141.415822.081770833318041.4583333333-2219.3765625319.318229166667
3620359.818825.792187517682.21666666671143.575520833331534.0078125
3719711.618801.588020833317263.69583333331537.8921875910.011979166666
3815638.616908.940104166716777.4458333333131.494270833335-1270.34010416667
3914384.514869.913020833316341.3916666667-1471.47864583333-485.413020833332
4013855.615040.323437515914.6291666667-874.305729166667-1184.7234375
4114308.314887.461979166715542.0458333333-654.583854166667-579.161979166667
4215290.616252.549479166715217.49166666671035.0578125-961.94947916667
4314423.814679.866145833314891.5375-211.671354166669-256.066145833334
4413779.714909.173437514754.7041666667154.469270833332-1129.4734375
4515686.316124.876562514829.32083333331295.55572916667-438.576562499999
4614733.815076.304687514942.9333333333133.371354166667-342.504687500003
4712522.512835.565104166715054.9416666667-2219.3765625-313.065104166666
4816189.416406.938020833315263.36251143.57552083333-217.538020833332
4916059.117058.104687515520.21251537.8921875-999.004687499999
5016007.115891.869270833315760.375131.494270833335115.230729166669
5115806.814596.829687516068.3083333333-1471.478645833331209.9703125
521516015490.669270833316364.975-874.305729166667-330.669270833332
5315692.115978.382812516632.9666666667-654.583854166667-286.2828125
5418908.917952.811979166716917.75416666671035.0578125956.088020833336
5516969.9NANA-211.671354166669NA
5616997.5NANA154.469270833332NA
5719858.9NANA1295.55572916667NA
5817681.2NANA133.371354166667NA
5916006.9NANA-2219.3765625NA
6019539.9NANA1143.57552083333NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/08/t1291828372ly44tjkel8mporm/1cyfy1291828479.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/08/t1291828372ly44tjkel8mporm/1cyfy1291828479.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/08/t1291828372ly44tjkel8mporm/2cyfy1291828479.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/08/t1291828372ly44tjkel8mporm/2cyfy1291828479.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/08/t1291828372ly44tjkel8mporm/35pwj1291828479.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/08/t1291828372ly44tjkel8mporm/35pwj1291828479.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/08/t1291828372ly44tjkel8mporm/45pwj1291828479.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/08/t1291828372ly44tjkel8mporm/45pwj1291828479.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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