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workshop8

*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 00:13:34 +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/t1292717629fico72uq3et6iq7.htm/, Retrieved Sun, 19 Dec 2010 01:13:51 +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/t1292717629fico72uq3et6iq7.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 «
40,7819 39,5915 38,8859 39,9068 41,47 41,5613 41,6005 41,4113 41,84 42,2892 43,1521 43,5998 43,116 42,4185 42,3687 42,2975 42,8528 43,535 44,7265 45,7293 45,7585 46,1685 46,5075 46,527 46,601 46,4607 46,7135 46,4113 45,55 44,6081 44,4395 44,9847 45,7558 45,3942 45,697 45,5664 46,0205 45,9195 45,8005 45,535 45,4977 45,5782 45,7697 45,2445 45,0615 45,2865 44,791 44,7625 44,7644 44,9973 44,7265 45,1465 44,7465 45,1795 45,6515 45,492 45,2775 45,2115 45,411 45,4005 44,7692 44,8913 45,032 44,879 44,833 44,8257 44,7815 44,479 44,6317 44,5043 44,3217 44,1005 44,047 43,6835 43,7864 44,1807 43,9595 43,937 43,991 43,865 43,671 43,93 43,863 43,7095 43,9435 43,736 43,6295 43,598 43,8726 43,8935 43,5957 43,7155 43,528 43,3415 43,3374 43,332 43,3869 43,5016 43,4875 43,6023 43,3886 43,3105 43,4455 43,5185 43,5755 43,6217 43,644 43,5789 43,5215 43,5033 43,632 43,263 43,3717 43,2745 43,2647 43,324 43,4455 43,409 etc...
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
140.7819NANA-0.0243586391752579NA
239.5915NANA0.00192136082474247NA
338.885940.144930020618640.127220.0177100206185563-1.25903002061856
439.906840.291730226804140.28310.00863022680412419-0.384930226804123
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44140.795540.904081360824740.92844-0.0243586391752579-0.108581360824751
44241.45641.108161360824841.106240.001921360824742470.347838639175251
44341.355741.258650020618641.240940.01771002061855630.0970499793814312
44441.37441.320470226804141.311840.008630226804124190.0535297731958764
44541.223541.291237030927841.29514-0.0039029690721651-0.0677370309278373
44641.1541.338101360824741.36246-0.0243586391752579-0.188101360824746
44741.372541.449581360824841.447660.00192136082474247-0.0770813608247494
44841.692341.581570020618641.563860.01771002061855630.110729979381439
44941.841.670490226804141.661860.008630226804124190.129509773195871
45041.804541.655457030927841.65936-0.00390296907216510.149042969072156
45141.6441.611441360824741.6358-0.02435863917525790.0285586391752517
45241.3641.596321360824841.59440.00192136082474247-0.236321360824753
45341.574541.566210020618641.54850.01771002061855630.00828997938143772
45441.59341.565130226804141.55650.008630226804124190.0278697731958744
45541.57541.681697030927841.6856-0.0039029690721651-0.106697030927833
45641.6841.810101360824841.83446-0.0243586391752579-0.130101360824753
45742.005542.030781360824742.028860.00192136082474247-0.0252813608247422
45842.318842.203070020618642.185360.01771002061855630.115729979381442
45942.56542.315990226804142.307360.008630226804124190.249009773195873
46042.357542.441357030927842.44526-0.0039029690721651-0.083857030927831
46142.2942.557701360824742.58206-0.0243586391752579-0.267701360824745
46242.69542.561121360824742.55920.001921360824742470.133878639175258
46343.002842.599510020618642.58180.01771002061855630.403289979381441
46442.450742.589930226804142.58130.00863022680412419-0.139230226804131
46542.470542.501837030927842.50574-0.0039029690721651-0.0313370309278369
46642.287542.390821360824842.41518-0.0243586391752579-0.103321360824751
46742.317242.477421360824742.47550.00192136082474247-0.16022136082475
46842.5542.578970020618642.561260.0177100206185563-0.0289700206185586
46942.752342.743490226804142.734860.008630226804124190.00880977319587828
47042.899342.905217030927842.90912-0.0039029690721651-0.00591703092784002
47143.155543.060761360824743.08512-0.02435863917525790.0947386391752545
47243.188543.198581360824843.196660.00192136082474247-0.0100813608247563
47343.4343.197510020618643.17980.01771002061855630.232489979381434
47443.3143.097670226804143.089040.008630226804124190.212329773195876
47542.81542.903437030927842.90734-0.0039029690721651-0.0884370309278424
47642.701742.581381360824842.60574-0.02435863917525790.120318639175252
47742.2842.379661360824742.377740.00192136082474247-0.0996613608247472
47841.92242.271690020618642.253980.0177100206185563-0.349690020618567
47942.1742.186570226804142.177940.00863022680412419-0.0165702268041272
48042.196242.181497030927842.1854-0.00390296907216510.014702969072161
48142.321542.254841360824742.2792-0.02435863917525790.0666586391752517
48242.317342.339721360824742.33780.00192136082474247-0.0224213608247439
48342.39142.398770020618642.381060.0177100206185563-0.00777002061855825
48442.46342.386190226804142.377560.008630226804124190.0768097731958761
48542.412542.272797030927842.2767-0.00390296907216510.139702969072161
48642.30442.104341360824842.1287-0.02435863917525790.199658639175247
48741.81341.945821360824841.94390.00192136082474247-0.132821360824749
48841.65141.710610020618641.69290.0177100206185563-0.0596100206185568
48941.53941.431630226804141.4230.008630226804124190.107369773195877
49041.1575NANA-0.0039029690721651NA
49140.9545NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292717629fico72uq3et6iq7/1z5qs1292717603.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292717629fico72uq3et6iq7/1z5qs1292717603.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/19/t1292717629fico72uq3et6iq7/2z5qs1292717603.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292717629fico72uq3et6iq7/2z5qs1292717603.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/19/t1292717629fico72uq3et6iq7/3sepv1292717603.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292717629fico72uq3et6iq7/3sepv1292717603.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/19/t1292717629fico72uq3et6iq7/4sepv1292717603.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292717629fico72uq3et6iq7/4sepv1292717603.ps (open in new window)


 
Parameters (Session):
par1 = additive ; par2 = 5 ;
 
Parameters (R input):
par1 = additive ; par2 = 5 ;
 
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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