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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: Tue, 28 Dec 2010 20:23:35 +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/t1293567711rbrq73mn4nm0ah0.htm/, Retrieved Tue, 28 Dec 2010 21:21:52 +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/t1293567711rbrq73mn4nm0ah0.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 «
235243 230354 227184 221678 217142 219452 256446 265845 248624 241114 229245 231805 219277 219313 212610 214771 211142 211457 240048 240636 230580 208795 197922 194596 194581 185686 178106 172608 167302 168053 202300 202388 182516 173476 166444 171297 169701 164182 161914 159612 151001 158114 186530 187069 174330 169362 166827 178037 186413 189226 191563 188906 186005 195309 223532 226899 214126 206903 204442 220375 214320 212588 205816 202196 195722 198563 229139 229527 211868 203555 195770
 
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
R Framework
error message
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1235243NANA-2518.29427083334NA
2230354NANA-4661.00260416667NA
3227184NANA-7449.46093750002NA
4221678NANA-8807.7421875NA
5217142NANA-13304.7630208333NA
6219452NANA-8556.58593750002NA
7256446256162.4453125234679.08333333321483.3619791667283.554687500058
8265845256304.341145833233553.79166666722750.54947916679540.65885416669
9248624241517.872395833232486.59031.372395833347106.12760416669
10241114230070.591145833231591.458333333-1520.8671874999911043.4088541667
11229245224099.1640625231053.666666667-6954.502604166665145.8359375
12231805230978.4765625230470.541666667507.934895833342826.5234375
13219277226935.872395833229454.166666667-2518.29427083334-7658.87239583331
14219313223059.5390625227720.541666667-4661.00260416667-3746.5390625
15212610218468.872395833225918.333333333-7449.46093750002-5858.87239583331
16214771215012.1328125223819.875-8807.7421875-241.1328125
17211142207863.361979167221168.125-13304.76302083333278.63802083331
18211457209756.0390625218312.625-8556.585937500021700.96093750003
19240048237216.611979167215733.2521483.36197916672831.38802083334
20240636236053.674479167213303.12522750.54947916674582.32552083334
21230580219495.705729167210464.3333333339031.3723958333411084.2942708333
22208795205749.0078125207269.875-1520.867187499993045.99218750003
23197922196731.9140625203686.416666667-6954.502604166661190.08593750003
24194596200559.184895833200051.25507.934895833342-5963.18489583334
25194581194151.622395833196669.916666667-2518.29427083334429.377604166686
26185686188842.4140625193503.416666667-4661.00260416667-3156.4140625
27178106182457.622395833189907.083333333-7449.46093750002-4351.62239583331
28172608177625.049479167186432.791666667-8807.7421875-5017.04947916666
29167302170344.8203125183649.583333333-13304.7630208333-3042.8203125
30168053172810.622395833181367.208333333-8556.58593750002-4757.62239583331
31202300200843.111979167179359.7521483.36197916671456.88802083337
32202388200177.6328125177427.08333333322750.54947916672210.3671875
33182516184887.7890625175856.4166666679031.37239583334-2371.78906250003
34173476173119.3828125174640.25-1520.86718749999356.6171875
35166444166465.0390625173419.541666667-6954.50260416666-21.0390625
36171297172834.143229167172326.208333333507.934895833342-1537.14322916666
37169701168736.705729167171255-2518.29427083334964.294270833372
38164182165298.622395833169959.625-4661.00260416667-1116.62239583334
39161914161530.7890625168980.25-7449.46093750002383.2109375
40159612159660.0078125168467.75-8807.7421875-48.0078125
41151001155007.528645833168312.291666667-13304.7630208333-4006.52864583331
42158114160052.497395833168609.083333333-8556.58593750002-1938.49739583331
43186530191069.611979167169586.2521483.3619791667-4539.61197916669
44187069194076.6328125171326.08333333322750.5494791667-7007.63281250003
45174330182636.330729167173604.9583333339031.37239583334-8306.33072916666
46169362174540.049479167176060.916666667-1520.86718749999-5178.04947916663
47166827171785.497395833178740-6954.50260416666-4958.49739583334
48178037182256.2265625181748.291666667507.934895833342-4219.2265625
49186413182321.5390625184839.833333333-2518.294270833344091.46093749997
50189226183380.1640625188041.166666667-4661.002604166675845.8359375
51191563183909.455729167191358.916666667-7449.460937500027653.54427083337
52188906185773.549479167194581.291666667-8807.74218753132.45052083334
53186005184408.028645833197712.791666667-13304.76302083331596.97135416669
54195309192487.580729167201044.166666667-8556.585937500022821.41927083334
55223532225454.403645833203971.04166666721483.3619791667-1922.40364583331
56226899228857.799479167206107.2522750.5494791667-1958.79947916666
57214126216705.9140625207674.5416666679031.37239583334-2579.91406249997
58206903207301.299479167208822.166666667-1520.86718749999-398.299479166657
59204442202826.2890625209780.791666667-6954.502604166661615.7109375
60220375210829.184895833210321.25507.9348958333429545.81510416669
61214320NA210690.458333333NANA
62212588NA211033.583333333NANA
63205816NA211049NANA
64202196NA210815.416666667NANA
65195722NA210314.583333333NANA
66198563NANANANA
67229139NANANANA
68229527NANANANA
69211868NANANANA
70203555NANANANA
71195770NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293567711rbrq73mn4nm0ah0/1ojn71293567811.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293567711rbrq73mn4nm0ah0/1ojn71293567811.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t1293567711rbrq73mn4nm0ah0/2ojn71293567811.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293567711rbrq73mn4nm0ah0/2ojn71293567811.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t1293567711rbrq73mn4nm0ah0/3bqx31293567811.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293567711rbrq73mn4nm0ah0/3bqx31293567811.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t1293567711rbrq73mn4nm0ah0/4bqx31293567811.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293567711rbrq73mn4nm0ah0/4bqx31293567811.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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