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WS9

*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: Sun, 06 Dec 2009 08:00:53 -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/06/t1260111746wvxq5igyp86j9d9.htm/, Retrieved Sun, 06 Dec 2009 16:02:31 +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/06/t1260111746wvxq5igyp86j9d9.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 «
10414,9 12476,8 12384,6 12266,7 12919,9 11497,3 12142 13919,4 12656,8 12034,1 13199,7 10881,3 11301,2 13643,9 12517 13981,1 14275,7 13435 13565,7 16216,3 12970 14079,9 14235 12213,4 12581 14130,4 14210,8 14378,5 13142,8 13714,7 13621,9 15379,8 13306,3 14391,2 14909,9 14025,4 12951,2 14344,3 16093,4 15413,6 14705,7 15972,8 16241,4 16626,4 17136,2 15622,9 18003,9 16136,1 14423,7 16789,4 16782,2 14133,8 12607 12004,5 12175,4 13268 12299,3 11800,6 13873,3 12269,6
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
110414.9NANA0.899427736488814NA
212476.8NANA1.03514025597007NA
312384.6NANA1.04534944959334NA
412266.7NANA1.02007093876694NA
512919.9NANA0.964273893705422NA
611497.3NANA0.966611702500658NA
71214212193.736919143412269.72083333330.993807201058440.995757090751875
813919.413704.421473910812355.27916666671.109195615011591.01568680053357
912656.812276.353584871212409.4250.9892765849240571.03099018063455
1012034.112333.528430168812486.3750.9877589316490020.975722403214609
1113199.713347.85306047812614.31.058152498392930.98890060747547
1210881.311870.847252283212751.52916666670.9309351919387330.916640553849865
1311301.211595.051364872512891.58750.8994277364888140.97465717437331
1413643.913505.073802792313046.61251.035140255970071.01027955857442
151251713751.955304198513155.36666666671.045349449593340.91019783900683
1613981.113519.671698179613253.65833333331.020070938766941.03413014103608
1714275.712903.949405837013382.03750.9642738937054221.10630470959089
181343513030.582240156813480.67916666670.9666117025006581.03103604676980
1913565.713505.351240510413589.50833333330.993807201058441.00446850721724
2016216.315155.055229113313663.10416666671.109195615011591.07002579369345
211297013606.460685216213753.950.9892765849240570.953223641331815
2214079.913671.653686198113841.08333333330.9877589316490021.02986078518168
231423514613.548944524513810.43751.058152498392930.974096029242348
2412213.412823.527538746913774.88750.9309351919387330.952421240029047
251258112402.104125208313788.88333333330.8994277364888141.01442463899558
2614130.414239.773225635913756.37083333331.035140255970070.992319173634102
2714210.814358.427854248213735.52916666671.045349449593340.9897183831164
2814378.514038.739045666813762.51251.020070938766941.02420167176183
2913142.813310.455136960113803.60416666670.9642738937054220.9874042521285
3013714.713442.886434309713907.2250.9666117025006581.02021988112587
3113621.913911.462271496213998.150.993807201058440.9791853461667
3215379.815553.681646554914022.48751.109195615011590.988820547410817
3313306.313958.535977819214109.84166666670.9892765849240570.95327332473437
3414391.214057.204806856214231.41250.9877589316490021.02375971594160
3514909.915173.549700486514339.66251.058152498392930.982624388775817
3614025.413497.509102124114498.87083333330.9309351919387331.03911024574104
3712951.213223.480272247814702.10416666670.8994277364888140.979409333500562
3814344.315385.488026365514863.19166666671.035140255970070.932326616836508
3916093.415758.342414652815074.71251.045349449593341.02126223536275
4015413.615592.409092502715285.61251.020070938766940.98853229854079
4114705.714913.31539896415465.850.9642738937054220.986078521548708
4215972.815159.093429453315682.71250.9666117025006581.05367778583419
4316241.415733.968029747215832.01250.993807201058441.03225073098492
4416626.417741.856539365815995.24583333331.109195615011590.937128533482907
4517136.215952.901085083016125.8250.9892765849240571.07417452842001
4615622.915904.104110266916101.20.9877589316490020.98231877078286
4718003.916888.567998131815960.42916666671.058152498392931.06604064962711
4816136.114622.792530966515707.63750.9309351919387331.10348963550079
4914423.713826.790164575515372.8750.8994277364888141.04317052825129
5016789.415592.861124311615063.5251.035140255970071.07673632607571
5116782.215389.691220008314722.05416666671.045349449593341.09048321763476
5214133.814649.498019662314361.25416666671.020070938766940.96479756378204
531260713528.650230066114029.88333333330.9642738937054220.931874191852646
5412004.513239.362312799413696.67083333330.9666117025006580.90672796139089
5512175.4NANA0.99380720105844NA
5613268NANA1.10919561501159NA
5712299.3NANA0.989276584924057NA
5811800.6NANA0.987758931649002NA
5913873.3NANA1.05815249839293NA
6012269.6NANA0.930935191938733NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2009/Dec/06/t1260111746wvxq5igyp86j9d9/1xj9u1260111652.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/06/t1260111746wvxq5igyp86j9d9/1xj9u1260111652.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/06/t1260111746wvxq5igyp86j9d9/2a5an1260111652.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/06/t1260111746wvxq5igyp86j9d9/2a5an1260111652.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/06/t1260111746wvxq5igyp86j9d9/30v9w1260111652.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/06/t1260111746wvxq5igyp86j9d9/30v9w1260111652.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/06/t1260111746wvxq5igyp86j9d9/4oigu1260111652.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/06/t1260111746wvxq5igyp86j9d9/4oigu1260111652.ps (open in new window)


 
Parameters (Session):
par1 = multiplicative ; par2 = 12 ;
 
Parameters (R input):
par1 = multiplicative ; 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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