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Paper

*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, 26 Dec 2010 13:04:45 +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/26/t1293368785ah9i7v40bg0zoww.htm/, Retrieved Sun, 26 Dec 2010 14:06:29 +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/26/t1293368785ah9i7v40bg0zoww.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:
Klassieke decompositie
 
Dataseries X:
» Textbox « » Textfile « » CSV «
548604 563668 586111 604378 600991 544686 537034 551531 563250 574761 580112 575093 557560 564478 580523 596594 586570 536214 523597 536535 536322 532638 528222 516141 501866 506174 517945 533590 528379 477580 469357 490243 492622 507561 516922 514258 509846 527070 541657 564591 555362 498662 511038 525919 531673 548854 560576 557274 565742 587625 619916 625809 619567 572942 572775 574205 579799 590072 593408 597141 595404
 
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'George Udny Yule' @ 72.249.76.132


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1548604NANA-8940.15277777778NA
2563668NANA3034.60763888886NA
3586111NANA21299.5347222222NA
4604378NANA36103.4097222222NA
5600991NANA28128.9201388889NA
6544686NANA-23359.2465277778NA
7537034543417.211805556569558.083333333-26140.8715277778-6383.2118055555
8551531552554.097222222569965-17410.9027777778-1023.09722222213
9563250557206.076388889569765.916666667-12559.84027777776043.92361111112
10574761566022.763888889569208.75-3185.986111111088738.23611111112
11580112571633.305555556568283.5416666673349.763888888898478.6944444445
12575093567010.430555556567329.666666667-319.2361111111088082.56944444438
13557560557476.638888889566416.791666667-8940.1527777777883.361111111124
14564478568266.690972222565232.0833333333034.60763888886-3788.69097222225
15580523584784.784722222563485.2521299.5347222222-4261.78472222225
16596594596711.534722222560608.12536103.4097222222-117.534722222248
17586570584819.836805556556690.91666666728128.92013888891750.16319444450
18536214528713.253472222552072.5-23359.24652777787500.74652777775
19523597521154.711805556547295.583333333-26140.87152777782442.28819444450
20536535525134.763888889542545.666666667-17410.902777777811400.2361111111
21536322524949.076388889537508.916666667-12559.840277777711372.9236111111
22532638529090.347222222532276.333333333-3185.986111111083547.65277777787
23528222530576.305555556527226.5416666673349.76388888889-2354.30555555550
24516141522039.597222222522358.833333333-319.236111111108-5898.59722222213
25501866508715.597222222517655.75-8940.15277777778-6849.59722222219
26506174516501.524305556513466.9166666673034.60763888886-10327.5243055555
27517945531016.784722222509717.2521299.5347222222-13071.7847222221
28533590542954.951388889506851.54166666736103.4097222222-9364.95138888882
29528379533464.753472222505335.83333333328128.9201388889-5085.75347222213
30477580481427.295138889504786.541666667-23359.2465277778-3847.29513888882
31469357478899.711805555505040.583333333-26140.8715277778-9542.71180555545
32490243488832.847222222506243.75-17410.90277777781410.15277777793
33492622495542.576388889508102.416666667-12559.8402777777-2920.57638888876
34507561507196.138888889510382.125-3185.98611111108364.861111111182
35516922516147.888888889512798.1253349.76388888889774.11111111124
36514258514481.597222222514800.833333333-319.236111111108-223.597222222132
37509846508475.805555556517415.958333333-8940.152777777781370.19444444450
38527070523673.774305556520639.1666666673034.607638888863396.22569444450
39541657545052.326388889523752.79166666721299.5347222222-3395.32638888882
40564591563203.868055556527100.45833333336103.40972222221387.13194444450
41555362558768.836805556530639.91666666728128.9201388889-3406.83680555550
42498662510891.920138889534251.166666667-23359.2465277778-12229.9201388889
43511038512231.628472222538372.5-26140.8715277778-1193.62847222225
44525919525813.722222222543224.625-17410.9027777778105.277777777752
45531673536448.701388889549008.541666667-12559.8402777777-4775.70138888888
46548854551634.097222222554820.083333333-3185.98611111108-2780.09722222213
47560576563395.805555556560046.0416666673349.76388888889-2819.80555555550
48557274565497.013888889565816.25-319.236111111108-8223.01388888888
49565742562543.472222222571483.625-8940.152777777783198.52777777775
50587625579102.524305556576067.9166666673034.607638888868522.4756944445
51619916601384.618055556580085.08333333321299.534722222218531.3819444444
52625809619911.159722222583807.7536103.40972222225897.84027777775
53619567615022.086805556586893.16666666728128.92013888894544.91319444438
54572942566563.045138889589922.291666667-23359.24652777786378.95486111112
55572775566678.461805556592819.333333333-26140.87152777786096.53819444438
56574205NANA-17410.9027777778NA
57579799NANA-12559.8402777777NA
58590072NANA-3185.98611111108NA
59593408NANA3349.76388888889NA
60597141NANA-319.236111111108NA
61595404NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/26/t1293368785ah9i7v40bg0zoww/15gqg1293368682.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/26/t1293368785ah9i7v40bg0zoww/15gqg1293368682.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/26/t1293368785ah9i7v40bg0zoww/25gqg1293368682.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/26/t1293368785ah9i7v40bg0zoww/25gqg1293368682.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/26/t1293368785ah9i7v40bg0zoww/3yp711293368682.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/26/t1293368785ah9i7v40bg0zoww/3yp711293368682.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/26/t1293368785ah9i7v40bg0zoww/4yp711293368682.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/26/t1293368785ah9i7v40bg0zoww/4yp711293368682.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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