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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: Tue, 28 Dec 2010 11:28:46 +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/t1293535748akzjyp3s7i8jahp.htm/, Retrieved Tue, 28 Dec 2010 12:29:08 +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/t1293535748akzjyp3s7i8jahp.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 «
11100 8962 9173 8738 8459 8078 8411 8291 7810 8616 8312 9692 9911 8915 9452 9112 8472 8230 8384 8625 8221 8649 8625 10443 10357 8586 8892 8329 8101 7922 8120 7838 7735 8406 8209 9451 10041 9411 10405 8467 8464 8102 7627 7513 7510 8291 8064 9383 9706 8579 9474 8318 8213 8059 9111 7708 7680 8014 8007 8718 9486 9113 9025 8476 7952 7759 7835 7600 7651 8319 8812 8630
 
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'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
111100NANA1289.76388888889NA
28962NANA320.922222222222NA
39173NANA856.805555555556NA
48738NANA-48.5944444444443NA
58459NANA-350.286111111111NA
68078NANA-571.602777777777NA
784118437.788888888898753.95833333333-316.169444444444-26.7888888888901
882918062.880555555568702.45833333333-639.577777777778228.119444444445
978107868.722222222228712.125-843.402777777779-58.7222222222226
1086168503.347222222228739.33333333333-235.986111111111112.652777777777
1183128374.080555555568755.45833333333-381.377777777778-62.0805555555562
1296929681.838888888898762.33333333333919.50555555555610.1611111111106
13991110057.30555555568767.541666666671289.76388888889-146.305555555557
1489159101.255555555568780.33333333333320.922222222222-186.255555555555
1594529668.180555555568811.375856.805555555556-216.180555555557
1691128781.280555555568829.875-48.5944444444443330.719444444445
1784728494.005555555568844.29166666667-350.286111111111-22.0055555555573
1882308317.022222222228888.625-571.602777777777-87.0222222222219
1983848622.330555555568938.5-316.169444444444-238.330555555556
2086258303.797222222228943.375-639.577777777778321.202777777778
2182218062.930555555568906.33333333333-843.402777777779158.069444444445
2286498614.388888888898850.375-235.98611111111134.6111111111131
2386258420.913888888898802.29166666667-381.377777777778204.086111111112
24104439693.505555555558774919.505555555556749.494444444446
251035710039.93055555568750.166666666671289.76388888889317.069444444445
2685869027.297222222228706.375320.922222222222-441.297222222222
2788929510.138888888898653.33333333333856.805555555556-618.138888888888
2883298574.363888888898622.95833333333-48.5944444444443-245.363888888887
2981018245.213888888898595.5-350.286111111111-144.213888888889
3079227965.230555555558536.83333333333-571.602777777777-43.230555555554
3181208166.163888888898482.33333333333-316.169444444444-46.1638888888883
3278387863.963888888898503.54166666667-639.577777777778-25.9638888888894
3377357757.555555555568600.95833333333-843.402777777779-22.5555555555547
3484068433.763888888898669.75-235.986111111111-27.7638888888887
3582098309.247222222228690.625-381.377777777778-100.247222222220
3694519632.755555555568713.25919.505555555556-181.755555555555
37100419989.972222222228700.208333333331289.7638888888951.0277777777792
3894118987.047222222228666.125320.922222222222423.952777777778
39104059500.013888888898643.20833333333856.805555555556904.986111111113
4084678580.447222222228629.04166666667-48.5944444444443-113.447222222221
4184648267.922222222228618.20833333333-350.286111111111196.077777777778
4281028037.730555555558609.33333333333-571.60277777777764.2694444444478
4376278276.372222222228592.54166666666-316.169444444444-649.37222222222
4475137904.338888888898543.91666666666-639.577777777778-391.338888888886
4575107627.055555555558470.45833333333-843.402777777779-117.055555555553
4682918189.472222222228425.45833333333-235.986111111111101.527777777779
4780648027.413888888898408.79166666667-381.37777777777836.5861111111117
4893839316.047222222228396.54166666667919.50555555555666.9527777777785
4997069746.347222222228456.583333333331289.76388888889-40.3472222222208
5085798847.463888888898526.54166666667320.922222222222-268.463888888888
5194749398.555555555568541.75856.80555555555675.4444444444434
5283188488.697222222228537.29166666667-48.5944444444443-170.697222222221
5382138173.088888888898523.375-350.28611111111139.9111111111106
5480597921.688888888898493.29166666667-571.602777777777137.311111111112
5591118140.247222222228456.41666666667-316.169444444444970.752777777778
5677087829.922222222228469.5-639.577777777778-121.922222222222
5776807629.638888888898473.04166666667-843.40277777777950.3611111111131
5880148224.930555555558460.91666666667-235.986111111111-210.930555555555
5980078075.247222222228456.625-381.377777777778-68.2472222222204
6087189352.755555555558433.25919.505555555556-634.755555555554
6194869657.347222222228367.583333333331289.76388888889-171.347222222221
6291138630.838888888898309.91666666667320.922222222222482.161111111112
6390259161.013888888898304.20833333333856.805555555556-136.013888888888
6484768267.113888888898315.70833333333-48.5944444444443208.886111111113
6579528011.672222222228361.95833333333-350.286111111111-59.6722222222215
6677597820.230555555558391.83333333333-571.602777777777-61.230555555554
677835NANA-316.169444444444NA
687600NANA-639.577777777778NA
697651NANA-843.402777777779NA
708319NANA-235.986111111111NA
718812NANA-381.377777777778NA
728630NANA919.505555555556NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293535748akzjyp3s7i8jahp/153771293535722.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293535748akzjyp3s7i8jahp/153771293535722.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t1293535748akzjyp3s7i8jahp/253771293535722.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293535748akzjyp3s7i8jahp/253771293535722.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t1293535748akzjyp3s7i8jahp/3xd7s1293535722.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293535748akzjyp3s7i8jahp/3xd7s1293535722.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t1293535748akzjyp3s7i8jahp/4xd7s1293535722.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293535748akzjyp3s7i8jahp/4xd7s1293535722.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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