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WS9(2)

*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: Thu, 03 Dec 2009 11:26:24 -0700
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/03/t12598648443q7cdb03p0m93te.htm/, Retrieved Thu, 03 Dec 2009 19:27:29 +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/2009/Dec/03/t12598648443q7cdb03p0m93te.htm/},
    year = {2009},
}
@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 = {2009},
    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 «
10.9 10 9.2 9.2 9.5 9.6 9.5 9.1 8.9 9 10.1 10.3 10.2 9.6 9.2 9.3 9.4 9.4 9.2 9 9 9 9.8 10 9.8 9.3 9 9 9.1 9.1 9.1 9.2 8.8 8.3 8.4 8.1 7.7 7.9 7.9 8 7.9 7.6 7.1 6.8 6.5 6.9 8.2 8.7 8.3 7.9 7.5 7.8 8.3 8.4 8.2 7.7 7.2 7.3 8.1 8.5
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
110.9NANA1.03660757891921NA
210NANA1.00372519784928NA
39.2NANA0.976527009841275NA
49.2NANA0.996523399948899NA
59.5NANA1.01888012923296NA
69.6NANA1.01641461885775NA
79.59.407746816101949.579166666666670.9821049307805421.00980608701545
89.19.198343499943289.533333333333330.9648612062877560.989308564097016
98.98.97226731524259.516666666666670.9427951644738190.991945479029616
1099.022297543053029.520833333333330.9476373787014120.997528618076868
1110.19.96513430322679.520833333333331.046666185021621.01353375606083
1210.310.14783721081289.508333333333331.067257200085481.01499460289184
1310.29.834814404995979.48751.036607578919211.03713192541981
149.69.506114061297569.470833333333331.003725197849281.00987637409956
159.29.248524555705079.470833333333330.9765270098412750.994753265192431
169.39.442059214515819.4750.9965233999488990.984954636346972
179.49.64115322286699.46251.018880129232960.974987097778415
189.49.592412965469979.43751.016414618857750.979941129915632
199.29.23997055709369.408333333333330.9821049307805420.99567416834863
2099.049594063973919.379166666666660.9648612062877560.99451974711536
2198.822991414200829.358333333333330.9427951644738191.02006219631068
2298.848564023624439.33750.9476373787014121.01711418666026
239.89.747078848013859.31251.046666185021621.00542943714844
24109.91215124579399.28751.067257200085481.00886273342968
259.89.610216096230159.270833333333331.036607578919211.01974814113122
269.39.309551210052089.2751.003725197849280.998974041837617
2799.057288016277839.2750.9765270098412750.99367492607336
2899.205384907027959.23750.9965233999488990.977688612795414
299.19.322753182481599.151.018880129232960.976106502218661
309.19.160436752455439.01251.016414618857750.99340241583577
319.18.687536533529548.845833333333330.9821049307805421.04747760943261
329.28.394292494703488.70.9648612062877561.09598277708394
338.88.104110101289538.595833333333330.9427951644738191.08586876165462
348.38.062814697117848.508333333333330.9476373787014121.02941718392300
358.48.809440390598658.416666666666671.046666185021620.95352254258561
368.18.862681665709858.304166666666671.067257200085480.913944594370268
377.78.456990164682538.158333333333331.036607578919210.910489411724302
387.98.004708452848027.9751.003725197849280.986919142219257
397.97.596566364056927.779166666666670.9765270098412751.03994352466646
4087.598490924610357.6250.9965233999488991.05284063366967
417.97.701035643452467.558333333333331.018880129232961.02583605189734
427.67.699340737847427.5751.016414618857750.98709750078223
437.17.488550097201637.6250.9821049307805420.948114108584674
446.87.381188228101347.650.9648612062877560.921260885085052
456.57.196669755483487.633333333333330.9427951644738190.903195536386444
466.97.209941056286577.608333333333330.9476373787014120.957011984721245
478.27.972107442581357.616666666666671.046666185021621.02858623758649
488.78.182305200655357.666666666666671.067257200085481.06327004268958
498.38.029389538378367.745833333333331.036607578919211.03370249510603
507.97.8583318614957.829166666666671.003725197849281.00530241522494
517.57.710494515205077.895833333333330.9765270098412750.97270025744912
527.87.91405666792757.941666666666660.9965233999488990.985588090569312
538.38.104342361273847.954166666666671.018880129232961.02414232148695
548.48.072026098095267.941666666666671.016414618857751.04063092684774
558.2NANA0.982104930780542NA
567.7NANA0.964861206287756NA
577.2NANA0.942795164473819NA
587.3NANA0.947637378701412NA
598.1NANA1.04666618502162NA
608.5NANA1.06725720008548NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2009/Dec/03/t12598648443q7cdb03p0m93te/13v3b1259864782.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/03/t12598648443q7cdb03p0m93te/13v3b1259864782.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/03/t12598648443q7cdb03p0m93te/2ft8a1259864782.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/03/t12598648443q7cdb03p0m93te/2ft8a1259864782.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/03/t12598648443q7cdb03p0m93te/3034i1259864782.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/03/t12598648443q7cdb03p0m93te/3034i1259864782.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/03/t12598648443q7cdb03p0m93te/4eitr1259864782.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/03/t12598648443q7cdb03p0m93te/4eitr1259864782.ps (open in new window)


 
Parameters (Session):
par1 = multiplicative ; par2 = 12 ;
 
Parameters (R input):
par1 = multiplicative ; 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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