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Paper CD

*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 22:56:31 +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/t12935768974olduo0qa8ilm9g.htm/, Retrieved Tue, 28 Dec 2010 23:55:02 +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/t12935768974olduo0qa8ilm9g.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 «
1203 1319 1328 1260 1286 1274 1389 1255 1244 1336 1214 1239 1174 1061 1116 1123 1086 1074 965 1035 1016 941 1003 998 891 828 833 887 842 793 778 699 686 727 641 619 627 593 535 536 504 487 477 435 433 393 389 377 339 370 350 341 367 396 408 405 391 396 368 356
 
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'Gwilym Jenkins' @ 72.249.127.135


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
11203NANA-11.6996527777778NA
21319NANA-37.3767361111112NA
31328NANA-24.1371527777778NA
41260NANA7.78993055555548NA
51286NANA4.39409722222224NA
61274NANA10.1545138888889NA
713891294.560763888891277.7083333333316.852430555555694.4392361111111
812551255.237847222221265.75-10.5121527777778-0.237847222222172
912441244.477430555561246.16666666667-1.68923611111110-0.477430555555429
1013361254.196180555561231.62522.571180555555681.8038194444443
1112141221.800347222221217.583333333334.2170138888889-7.80034722222217
1212391220.352430555561200.9166666666719.435763888888918.6475694444446
1311741163.217013888891174.91666666667-11.699652777777810.7829861111113
1410611110.706597222221148.08333333333-37.3767361111112-49.7065972222224
1511161105.279513888891129.41666666667-24.137152777777810.7204861111113
1611231111.248263888891103.458333333337.7899305555554811.7517361111111
1710861082.602430555561078.208333333334.394097222222243.39756944444457
1810741069.529513888891059.37510.15451388888894.47048611111109
199651054.394097222221037.5416666666716.8524305555556-89.394097222222
2010351005.529513888891016.04166666667-10.512152777777829.4704861111112
211016992.852430555556994.541666666667-1.6892361111111023.1475694444445
22941995.487847222222972.91666666666722.5711805555556-54.4878472222221
231003957.133680555555952.9166666666674.217013888888945.8663194444446
24998950.477430555555931.04166666666719.435763888888947.5225694444446
25891899.842013888889911.541666666667-11.6996527777778-8.84201388888891
26828852.373263888889889.75-37.3767361111112-24.3732638888887
27833837.862847222222862-24.1371527777778-4.86284722222206
28887847.123263888889839.3333333333337.7899305555554839.8767361111113
29842819.727430555555815.3333333333334.3940972222222422.2725694444446
30793794.612847222222784.45833333333310.1545138888889-1.61284722222217
31778774.519097222222757.66666666666716.85243055555563.48090277777794
32699726.362847222222736.875-10.5121527777778-27.3628472222221
33686712.977430555556714.666666666667-1.68923611111110-26.9774305555555
34727710.196180555556687.62522.571180555555616.8038194444445
35641663.133680555556658.9166666666674.2170138888889-22.1336805555557
36619651.519097222222632.08333333333319.4357638888889-32.5190972222222
37627595.092013888889606.791666666667-11.699652777777831.9079861111111
38593545.873263888889583.25-37.376736111111247.1267361111112
39535537.571180555556561.708333333333-24.1371527777778-2.57118055555554
40536545.039930555555537.257.78993055555548-9.03993055555543
41504517.227430555556512.8333333333334.39409722222224-13.2274305555556
42487502.404513888889492.2510.1545138888889-15.4045138888889
43477487.019097222222470.16666666666716.8524305555556-10.0190972222223
44435438.362847222222448.875-10.5121527777778-3.36284722222217
45433430.185763888889431.875-1.689236111111102.81423611111109
46393438.612847222222416.04166666666722.5711805555556-45.6128472222222
47389406.425347222222402.2083333333334.2170138888889-17.4253472222222
48377412.144097222222392.70833333333319.4357638888889-35.1440972222222
49339374.342013888889386.041666666667-11.6996527777778-35.3420138888889
50370344.539930555556381.916666666667-37.376736111111225.4600694444445
51350354.779513888889378.916666666667-24.1371527777778-4.77951388888891
52341385.081597222222377.2916666666677.78993055555548-44.0815972222222
53367380.935763888889376.5416666666674.39409722222224-13.9357638888889
54396384.946180555555374.79166666666710.154513888888911.0538194444445
55408NANA16.8524305555556NA
56405NANA-10.5121527777778NA
57391NANA-1.68923611111110NA
58396NANA22.5711805555556NA
59368NANA4.2170138888889NA
60356NANA19.4357638888889NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/28/t12935768974olduo0qa8ilm9g/1u05e1293576988.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t12935768974olduo0qa8ilm9g/1u05e1293576988.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t12935768974olduo0qa8ilm9g/2u05e1293576988.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t12935768974olduo0qa8ilm9g/2u05e1293576988.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t12935768974olduo0qa8ilm9g/35amz1293576988.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t12935768974olduo0qa8ilm9g/35amz1293576988.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t12935768974olduo0qa8ilm9g/45amz1293576988.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t12935768974olduo0qa8ilm9g/45amz1293576988.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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