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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 14:45:45 +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/08/t12918220378pf99nr0gdwt3bv.htm/, Retrieved Wed, 08 Dec 2010 16:27:19 +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/t12918220378pf99nr0gdwt3bv.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 «
186448 190530 194207 190855 200779 204428 207617 212071 214239 215883 223484 221529 225247 226699 231406 232324 237192 236727 240698 240688 245283 243556 247826 245798 250479 249216 251896 247616 249994 246552 248771 247551 249745 245742 249019 245841 248771 244723 246878 246014 248496 244351 248016 246509 249426 247840 251035 250161 254278 250801 253985 249174 251287 247947 249992 243805 255812 250417 253033 248705 253950 251484 251093 245996 252721 248019 250464 245571 252690 250183 253639 254436 265280 268705 270643 271480
 
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
1186448NANA1908.125NA
2190530NANA-982.354166666667NA
3194207193450.736111111192301.3751149.36111111111756.263888888876
4190855193754.868055556195830-2075.13194444444-2899.86805555556
5200779201151.625199243.51908.125-372.625
6204428202589.395833333203571.75-982.3541666666671838.60416666666
7207617209055.611111111207906.251149.36111111111-1438.61111111112
8212071208945.493055556211020.625-2075.131944444443125.50694444444
9214239216344214435.8751908.125-2105
10215883216619.145833333217601.5-982.354166666667-736.145833333343
11223484221309.111111111220159.751149.361111111112174.88888888888
12221529220812.618055556222887.75-2075.13194444444716.381944444438
13225247227138.1252252301908.125-1891.125
14226699226587.270833333227569.625-982.354166666667111.729166666657
15231406231561.486111111230412.1251149.36111111111-155.486111111124
16232324231083.618055556233158.75-2075.131944444441240.38194444444
17237192237481.875235573.751908.125-289.875
18236727236798.395833333237780.75-982.354166666667-71.395833333343
19240698240986.986111111239837.6251149.36111111111-288.986111111124
20240688239627.493055556241702.625-2075.131944444441060.50694444444
21245283245355.375243447.251908.125-72.375
22243556243994.645833333244977-982.354166666667-438.645833333343
23247826247414.611111111246265.251149.36111111111411.388888888876
24245798245547.118055556247622.25-2075.13194444444250.881944444438
25250479250746.625248838.51908.125-267.625
26249216248592.145833333249574.5-982.354166666667623.854166666657
27251896250890.486111111249741.1251149.361111111111005.51388888888
28247616247272.368055556249347.5-2075.13194444444343.631944444438
29249994250532248623.8751908.125-538
30246552247242.770833333248225.125-982.354166666667-690.770833333343
31248771249335.236111111248185.8751149.36111111111-564.236111111124
32247551245978.368055556248053.5-2075.131944444441572.63194444444
33249745249891.375247983.251908.125-146.375
34245742246818.145833333247800.5-982.354166666667-1076.14583333334
35249019248614.3611111112474651149.36111111111404.638888888876
36245841245140.743055556247215.875-2075.13194444444700.256944444438
37248771248729246820.8751908.12542
38244723245592.520833333246574.875-982.354166666667-869.520833333343
39246878247711.486111111246562.1251149.36111111111-833.486111111124
40246014244406.118055556246481.25-2075.131944444441607.88194444444
41248496248485.1252465771908.12510.875
42244351245798.770833333246781.125-982.354166666667-1447.77083333334
43248016248108.611111111246959.251149.36111111111-92.611111111124
44246509245436.493055556247511.625-2075.131944444441072.50694444444
45249426250233.25248325.1251908.125-807.25
46247840248176.645833333249159-982.354166666667-336.645833333343
47251035251371.3611111112502221149.36111111111-336.361111111124
48250161249123.493055556251198.625-2075.131944444441037.50694444444
49254278253845.625251937.51908.125432.375
50250801251200.520833333252182.875-982.354166666667-399.520833333343
51253985252834.986111111251685.6251149.361111111111150.01388888888
52249174248879.868055556250955-2075.13194444444294.131944444438
53251287252007.25250099.1251908.125-720.25
54247947247946.520833333248928.875-982.3541666666670.479166666656965
55249992249972.736111111248823.3751149.3611111111119.263888888876
56243805247622.618055556249697.75-2075.13194444444-3817.61805555556
57255812252294.75250386.6251908.1253517.25
58250417250396.895833333251379.25-982.35416666666720.104166666657
59253033252908.3611111112517591149.36111111111124.638888888876
60248705249584.493055556251659.625-2075.13194444444-879.493055555562
61253950253458.625251550.51908.125491.375
62251484249987.020833333250969.375-982.3541666666671496.97916666666
63251093251626.486111111250477.1251149.36111111111-533.486111111124
64245996247815.243055556249890.375-2075.13194444444-1819.24305555556
65252721251286.75249378.6251908.1251434.25
66248019248264.520833333249246.875-982.354166666667-245.520833333343
67250464250339.236111111249189.8751149.36111111111124.763888888876
68245571247381.368055556249456.5-2075.13194444444-1810.36805555556
69252690252032250123.8751908.125658
70250183250646.520833333251628.875-982.354166666667-463.520833333343
71253639255460.111111111254310.751149.36111111111-1821.11111111112
72254436256124.618055556258199.75-2075.13194444444-1688.61805555556
73265280264548.625262640.51908.125731.375
74268705265914.145833333266896.5-982.3541666666672790.85416666669
75270643NANA1149.36111111111NA
76271480NANA-2075.13194444444NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/08/t12918220378pf99nr0gdwt3bv/1li201291819541.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/08/t12918220378pf99nr0gdwt3bv/1li201291819541.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/08/t12918220378pf99nr0gdwt3bv/2li201291819541.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/08/t12918220378pf99nr0gdwt3bv/2li201291819541.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/08/t12918220378pf99nr0gdwt3bv/3va2l1291819541.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/08/t12918220378pf99nr0gdwt3bv/3va2l1291819541.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/08/t12918220378pf99nr0gdwt3bv/4va2l1291819541.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/08/t12918220378pf99nr0gdwt3bv/4va2l1291819541.ps (open in new window)


 
Parameters (Session):
par1 = additive ; par2 = 4 ;
 
Parameters (R input):
par1 = additive ; par2 = 4 ;
 
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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