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*Unverified author*
R Software Module: /rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Tue, 14 Dec 2010 21:37:16 +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/14/t1292362513mrsg1p4geux825p.htm/, Retrieved Tue, 14 Dec 2010 22:35:18 +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/14/t1292362513mrsg1p4geux825p.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:
KDGP2W92
 
Dataseries X:
» Textbox « » Textfile « » CSV «
6715 7703 9856 8326 9269 7035 10342 11682 10304 11385 9777 8882 7897 6930 9545 9110 7459 7320 10017 12307 11072 10749 9589 9080 7384 8062 8511 8684 8306 7643 10577 13747 11783 11611 9946 8693 7303 7609 9423 8584 7586 6843 11811 13414 12103 11501 8213 7982 7687 7180 7862 8043 8340 6692 10065 12684 11587 9843 8110 7940 6475 6121 9669 7778 7826 7403 10741 14023 11519 10236 8075 8157
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
16715NANA0.800776848677603NA
27703NANA0.779707070744588NA
39856NANA0.976046082316398NA
48326NANA0.914378409649798NA
59269NANA0.859805822503413NA
67035NANA0.781935322507343NA
71034210668.50952983559322.251.144413583612910.969395019152172
81168212946.34556876979339.291666666671.386223498616830.90233957821892
91030411497.39192339719294.1251.237060177628030.896203249280514
101138511153.7668761519313.833333333331.197548471930751.02073139293806
1197779210.283071645049271.083333333330.9934419463721471.06153089149884
1288828550.701107072469207.541666666670.9286627654401921.03874523138851
1378977371.851571819939205.8750.8007768486776031.07123697799173
1469307187.632168275149218.3750.7797070707445880.964156183532558
1595459054.210145434549276.416666666670.9760460823163981.05420570615019
1691108487.184200168629281.916666666670.9143784096497981.07338308974359
1774597951.125994085529247.583333333330.8598058225034130.938106125541013
1873207231.3378625479192480.7819353225073431.01226082076890
191001710568.51639296739234.8751.144413583612910.94781515470475
201230712837.35374619099260.666666666671.386223498616830.958686676656528
211107211461.05328067939264.751.237060177628030.966054317072663
221074911022.13633994469203.916666666671.197548471930750.975219292202478
2395899160.983515056329221.458333333330.9934419463721471.04672167396003
2490808608.897307040049270.208333333330.9286627654401921.05472276833581
2573847452.8301306424593070.8007768486776030.990764564677322
2680627321.709296648599390.333333333330.7797070707445881.10110899973730
2785119252.876191772699479.958333333330.9760460823163980.919822098945586
2886848728.199109312159545.50.9143784096497980.994936056251857
2983068250.947449440989596.291666666670.8598058225034131.00667227017217
3076437502.70200009649595.041666666670.7819353225073431.01869966312161
311057710958.37995378489575.541666666671.144413583612910.965197414636726
321374713242.99739747379553.291666666671.386223498616831.03805804587883
331178311841.65546199619572.416666666671.237060177628030.995046683955264
341161111503.95000848489606.251.197548471930751.00930549867100
3599469509.309097503069572.083333333330.9934419463721471.04592246376886
3686938830.422070879429508.750.9286627654401920.984437655439754
3773037628.867574543419526.833333333330.8007768486776030.95728493497111
3876097457.410814752769564.3750.7797070707445881.02032732123961
3994239334.742056926989563.833333333330.9760460823163981.00945478113212
4085848752.96352457359572.583333333330.9143784096497980.980696420806606
4175868164.536964279389495.791666666660.8598058225034130.929140260273113
4268437345.467838995549393.958333333330.7819353225073430.931594848686418
431181110734.98088548379380.333333333331.144413583612911.10023484214782
441341413000.63932246559378.458333333331.386223498616831.03179541153951
451210311499.14442531549295.541666666671.237060177628031.05251308726545
461150111026.97643168539207.958333333331.197548471930751.04298762868057
4782139156.388846054359216.833333333330.9934419463721470.89696933344406
4879828582.662583916369241.958333333330.9286627654401920.930014424073716
4976877337.451533028829162.916666666670.8007768486776031.04763894730994
5071807063.951134178289059.750.7797070707445881.01642832228273
5178628792.060435159069007.833333333330.9760460823163980.894215873284971
5280438153.740873449668917.250.9143784096497980.986418396761877
5383407604.015218492378843.8750.8598058225034131.09678896745469
5466926910.614057766158837.833333333330.7819353225073430.96836546565345
551006510054.34090662998785.583333333331.144413583612911.00106014839452
561268412047.61066716648690.958333333331.386223498616831.05282286674220
571158710789.79350179388722.1251.237060177628031.07388524146209
58984310522.10995506058786.3751.197548471930750.93545876654388
5981108696.508011712918753.916666666670.9934419463721470.932558216364204
6079408137.059233632648762.1250.9286627654401920.97578249979819
6164757062.78507393248819.916666666670.8007768486776030.916777154085883
6261216942.414294525968903.8750.7797070707445880.881681752243792
6396698742.282084960928956.833333333330.9760460823163981.10600411952313
6477788202.31722646238970.3750.9143784096497980.948268615472055
6578267725.606091891398985.291666666670.8598058225034131.01299495559500
6674037031.846613393228992.8750.7819353225073431.05278178080561
6710741NANA1.14441358361291NA
6814023NANA1.38622349861683NA
6911519NANA1.23706017762803NA
7010236NANA1.19754847193075NA
718075NANA0.993441946372147NA
728157NANA0.928662765440192NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/14/t1292362513mrsg1p4geux825p/1lzta1292362632.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/14/t1292362513mrsg1p4geux825p/1lzta1292362632.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/14/t1292362513mrsg1p4geux825p/2lzta1292362632.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/14/t1292362513mrsg1p4geux825p/2lzta1292362632.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/14/t1292362513mrsg1p4geux825p/3vqsv1292362632.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/14/t1292362513mrsg1p4geux825p/3vqsv1292362632.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/14/t1292362513mrsg1p4geux825p/4vqsv1292362632.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/14/t1292362513mrsg1p4geux825p/4vqsv1292362632.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')
 





Copyright

Creative Commons License

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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