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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: Sun, 19 Dec 2010 12:38:53 +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/19/t1292762501449vqnt280m34vh.htm/, Retrieved Sun, 19 Dec 2010 13:41:46 +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/19/t1292762501449vqnt280m34vh.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 «
19876 45335 48674 156392 100837 101605 532850 294189 80763 105995 25045 90474 48481 50730 68694 207716 99132 104012 422632 364974 82687 66834 28408 97073 40284 24421 116346 72120 108751 91738 402216 390070 106045 110070 70668 167841 28607 95371 30605 131063 81214 85451 455196 454570 63114 74287 42350 113375
 
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
119876NANA-95585.2060185185NA
245335NANA-79017.525462963NA
348674NANA-65958.9143518519NA
4156392NANA-188.733796296302NA
5100837NANA-40589.3587962963NA
6101605NANA-43779.775462963NA
7532850450784.905092593134694.791666667316090.11342592682065.0949074074
8294189348563.696759259136111.458333333212452.238425926-54374.6967592593
98076389266.0023148148137170.416666667-47904.4143518519-8503.0023148148
1010599597309.4189814815140143.083333333-42833.66435185188685.58101851854
112504547075.2106481481142210.541666667-95135.3310185185-22030.2106481481
1290474124690.363425926142239.791666667-17549.4282407407-34216.3634259259
134848142162.4606481481137747.666666667-95585.20601851856318.53935185188
145073057087.099537037136104.625-79017.525462963-6357.09953703702
156869473175.2523148148139134.166666667-65958.9143518519-4481.2523148148
16207716137393.891203704137582.625-188.73379629630270322.1087962963
179913295501.6828703704136091.041666667-40589.35879629633630.31712962964
1810401292726.349537037136506.125-43779.77546296311285.6504629630
19422632452529.655092593136439.541666667316090.113425926-29897.6550925926
20364974347454.030092593135001.791666667212452.23842592617519.9699074074
218268787986.6689814815135891.083333333-47904.4143518519-5299.66898148149
226683489393.0856481481132226.75-42833.6643518518-22559.0856481481
232840831842.3773148148126977.708333333-95135.3310185185-3434.37731481482
2497073109317.655092593126867.083333333-17549.4282407407-12244.6550925926
254028429919.7939814815125505-95585.206018518510364.2060185185
262442146682.474537037125700-79017.525462963-22261.474537037
2711634661760.0023148148127718.916666667-65958.914351851954585.9976851852
2872120130304.932870370130493.666666667-188.733796296302-58184.9328703703
2910875193466.6412037037134056-40589.358796296315284.3587962963
309173894985.724537037138765.5-43779.775462963-3247.72453703702
31402216457317.738425926141227.625316090.113425926-55101.738425926
32390070356149.571759259143697.333333333212452.23842592633920.4282407408
3310604595176.6273148148143081.041666667-47904.414351851910868.3726851852
3411007099130.7939814815141964.458333333-42833.664351851810939.2060185185
357066848137.7106481481143273.041666667-95135.331018518522530.2893518519
36167841124314.280092593141863.708333333-17549.428240740743526.7199074074
372860748224.0439814815143809.25-95585.2060185185-19617.0439814815
389537169686.724537037148704.25-79017.52546296325684.275462963
393060583644.0439814815149602.958333333-65958.9143518519-53039.0439814815
40131063146134.474537037146323.208333333-188.733796296302-15071.4745370371
4181214103062.974537037143652.333333333-40589.3587962963-21848.9745370371
428545196423.224537037140203-43779.775462963-10972.2245370370
43455196NANA316090.113425926NA
44454570NANA212452.238425926NA
4563114NANA-47904.4143518519NA
4674287NANA-42833.6643518518NA
4742350NANA-95135.3310185185NA
48113375NANA-17549.4282407407NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292762501449vqnt280m34vh/14ob51292762330.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292762501449vqnt280m34vh/14ob51292762330.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/19/t1292762501449vqnt280m34vh/24ob51292762330.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292762501449vqnt280m34vh/24ob51292762330.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/19/t1292762501449vqnt280m34vh/3xxbq1292762330.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292762501449vqnt280m34vh/3xxbq1292762330.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/19/t1292762501449vqnt280m34vh/4p6sb1292762330.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292762501449vqnt280m34vh/4p6sb1292762330.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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Software written by Ed van Stee & Patrick Wessa


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