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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: Mon, 29 Nov 2010 10:22:51 +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/Nov/29/t1291026372sb6ha7dawpykmpe.htm/, Retrieved Mon, 29 Nov 2010 11:26:29 +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/Nov/29/t1291026372sb6ha7dawpykmpe.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 «
142970 142524 146142 146522 148128 148798 150181 152388 155694 160662 155520 158262 154338 158196 160371 154856 150636 145899 141242 140834 141119 139104 134437 129425 123155 119273 120472 121523 121983 123658 124794 124827 120382 117395 115790 114283 117271 117448 118764 120550 123554 125412 124182 119828 115361 114226 115214 115864 114276 113469 114883 114172 111225 112149 115618 118002 121382 120663
 
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
1142970NANA-2919.26851851852NA
2142524NANA-2054.94907407407NA
3146142NANA520.787037037037NA
4146522NANA833.24537037036NA
5148128NANA1785.99537037038NA
6148798NANA2533.32870370371NA
7150181153217.856481481151122.9166666672094.93981481482-3036.85648148149
8152388153945.814814815152249.5833333331696.23148148148-1557.81481481480
9155694153569.009259259153495.45833333373.55092592591812124.99074074076
10160662153660.300925926154435.583333333-775.282407407417001.69907407407
11155520153463.148148148154887.333333333-1424.185185185192056.85185185185
12158262152506.648148148154871.041666667-2364.393518518535755.35185185185
13154338151458.523148148154377.791666667-2919.268518518522879.47685185185
14158196151468.967592593153523.916666667-2054.949074074076727.03240740742
15160371152955.995370370152435.208333333520.7870370370377415.00462962964
16154856151762.912037037150929.666666667833.245370370363093.08796296295
17150636150938.953703704149152.9583333331785.99537037038-302.953703703708
18145899149606.287037037147072.9583333332533.32870370371-3707.28703703702
19141242146667.064814815144572.1252094.93981481482-5425.06481481483
20140834143347.273148148141651.0416666671696.23148148148-2513.27314814818
21141119138440.342592593138366.79166666773.55092592591812678.65740740739
22139104134540.175925926135315.458333333-775.282407407414563.82407407407
23134437131308.523148148132732.708333333-1424.185185185193128.47685185185
24129425128247.731481481130612.125-2364.393518518531177.26851851851
25123155126080.814814815129000.083333333-2919.26851851852-2925.81481481482
26119273125592.842592593127647.791666667-2054.94907407407-6319.8425925926
27120472126637.578703704126116.791666667520.787037037037-6165.57870370371
28121523125181.453703704124348.208333333833.24537037036-3658.45370370371
29121983124452.703703704122666.7083333331785.99537037038-2469.70370370372
30123658123792.162037037121258.8333333332533.32870370371-134.162037037036
31124794122477.689814815120382.752094.939814814822316.31018518520
32124827121757.773148148120061.5416666671696.231481481483069.22685185188
33120382119987.884259259119914.33333333373.5509259259181394.115740740759
34117395119027.342592593119802.625-775.28240740741-1632.34259259255
35115790118403.356481481119827.541666667-1424.18518518519-2613.35648148146
36114283117601.689814815119966.083333333-2364.39351851853-3318.6898148148
37117271117094.398148148120013.666666667-2919.26851851852176.601851851854
38117448117724.925925926119779.875-2054.94907407407-276.925925925912
39118764119883.162037037119362.375520.787037037037-1119.16203703704
40120550119854.370370370119021.125833.24537037036695.62962962965
41123554120651.078703704118865.0833333331785.995370370382902.92129629632
42125412121440.287037037118906.9583333332533.328703703713971.71296296298
43124182120942.981481481118848.0416666672094.939814814823239.01851851853
44119828120253.689814815118557.4583333331696.23148148148-425.689814814803
45115361118303.509259259118229.95833333373.5509259259181-2942.50925925924
46114226117027.217592593117802.5-775.28240740741-2801.2175925926
47115214115598.856481481117023.041666667-1424.18518518519-384.856481481489
48115864113592.314814815115956.708333333-2364.393518518532271.68518518518
49114276NA115047.25NANA
50113469NA114614.333333333NANA
51114883NA114789.125NANA
52114172NA115308.208333333NANA
53111225NANANANA
54112149NANANANA
55115618NANANANA
56118002NANANANA
57121382NANANANA
58120663NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Nov/29/t1291026372sb6ha7dawpykmpe/1ykuj1291026168.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/29/t1291026372sb6ha7dawpykmpe/1ykuj1291026168.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/29/t1291026372sb6ha7dawpykmpe/2rbtm1291026168.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/29/t1291026372sb6ha7dawpykmpe/2rbtm1291026168.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/29/t1291026372sb6ha7dawpykmpe/3rbtm1291026168.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/29/t1291026372sb6ha7dawpykmpe/3rbtm1291026168.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/29/t1291026372sb6ha7dawpykmpe/42lt71291026168.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/29/t1291026372sb6ha7dawpykmpe/42lt71291026168.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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,m$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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This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa


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