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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, 12 Jan 2010 02:15:15 -0700
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Jan/12/t1263287929eyz2pbi0exveq4w.htm/, Retrieved Tue, 12 Jan 2010 10:18:54 +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/Jan/12/t1263287929eyz2pbi0exveq4w.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:
KDGP2W51
 
Dataseries X:
» Textbox « » Textfile « » CSV «
43129 37863 35953 29133 24693 22205 21725 27192 21790 13253 37702 30364 32609 30212 29965 28352 25814 22414 20506 28806 22228 13971 36845 35338 35022 34777 26887 23970 22780 17351 21382 24561 17409 11514 31514 27071 29462 26105 22397 23843 21705 18089 20764 25316 17704 15548 28029 29383 36438 32034 22679 24319 18004 17537 20366 22782 19169 13807 29743 25591 29096 26482 22405 27044 17970 18730 19684 19785 18479 10698
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
143129NANA1.33683193550557NA
237863NANA1.23266852849010NA
335953NANA1.0198499134512NA
429133NANA1.01014867576483NA
524693NANA0.888102797837222NA
622205NANA0.762742006310812NA
72172524009.115136332928311.83333333330.8480240348146380.904864668132798
82719228504.232930232527554.70833333331.034459613413890.953963576797721
92179021174.124166231526986.41666666670.7846215534197071.02908624833469
101325315115.428210608126704.3750.5660281587046360.876786275277265
113770234798.49613108726718.54166666671.302410010442321.08343762494722
123036432506.604765347826773.95833333331.214112771845070.934087094582336
133260935736.024195942826731.8751.336831935505570.91249658387298
143021232971.828689562726748.33333333331.232668528490100.91629737265873
152996527366.482602563926833.83333333331.01984991345121.09495255328109
162835227154.8167019103268821.010148675764831.04408732753499
172581423868.835816089426876.20833333330.8881027978372221.08149388595649
182241420630.455101193327047.750.7627420063108121.0864520385061
192050623198.156818706627355.54166666670.8480240348146380.883949537898817
202880628598.972189827727646.29166666671.034459613413891.00723899477220
212222821740.490157541627708.250.7846215534197071.02242405019048
221397115507.709309097027397.41666666670.5660281587046360.90090675041248
233684535280.225033699427088.41666666671.302410010442321.04435274902034
243533832478.781347659726751.04166666671.214112771845071.08803343394368
253502235528.42533662526576.58333333331.336831935505570.98574591100431
263477732587.082025107726436.20833333331.232668528490101.06720202726973
272688726575.801463414526058.54166666671.01984991345121.01170984577883
282397026016.757950076725755.3751.010148675764830.921329246557
292278022585.231238948725430.8750.8881027978372221.00862372224533
301735118965.039711330524864.29166666670.7627420063108120.91489394507483
312138220596.949094917124288.16666666670.8480240348146381.03811491213894
322456124511.692949777723695.16666666671.034459613413891.00201157261244
331740918161.438941617623146.750.7846215534197070.958569420405705
341151412992.822615465722954.3750.5660281587046360.886181574301996
353151429830.778748757422904.29166666671.302410010442321.05642565571014
362707127791.344875726622890.251.214112771845070.974080244085065
372946230607.101371383822895.251.336831935505570.962587069010905
382610528229.290609763122900.95833333331.232668528490100.92474870732216
392239723400.158807913022944.70833333331.01984991345120.957130256416303
402384323359.772306118123125.08333333331.010148675764831.02068631866567
412170520557.766560052823147.95833333330.8881027978372221.05580535398123
421808917618.641165607323099.08333333330.7627420063108121.02669665781666
432076419916.763150326223486.08333333330.8480240348146381.04253888261256
442531624851.642240235924023.79166666671.034459613413891.01868519413225
451770419052.638256043524282.58333333330.7846215534197070.929215143964868
461554813762.502988771024314.16666666670.5660281587046361.12973635774581
472802931492.002717076624179.79166666671.302410010442320.890035487797073
482938329141.842982275624002.58333333331.214112771845071.00827528368302
493643832034.5036705199239631.336831935505571.13746104434053
503203429387.844942977723840.83333333331.232668528490101.0900425009781
512267924268.645996709523796.29166666671.01984991345120.934497952752492
522431924026.175805425823784.79166666671.010148675764831.01218771547106
531800421122.340909494523783.66666666670.8881027978372220.852367646045669
541753718074.760885381223697.08333333330.7627420063108120.970247966831135
552036619702.283738187623233.16666666670.8480240348146381.03368727557841
562278223478.009181073922695.91666666671.034459613413890.970354846711835
571916917617.238509191622453.16666666670.7846215534197071.08808199366767
581380712766.930211129422555.29166666670.5660281587046361.08146592576843
592974329522.270377533922667.41666666671.302410010442321.00747671570118
602559127579.431609007522715.70833333331.214112771845070.927901646516961
6129096NA22737NANA
6226482NA22583.7083333333NANA
6322405NA22430.0833333333NANA
6427044NA22271.7916666667NANA
6517970NANANANA
6618730NANANANA
6719684NANANANA
6819785NANANANA
6918479NANANANA
7010698NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Jan/12/t1263287929eyz2pbi0exveq4w/1glta1263287712.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Jan/12/t1263287929eyz2pbi0exveq4w/1glta1263287712.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Jan/12/t1263287929eyz2pbi0exveq4w/2j4951263287712.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Jan/12/t1263287929eyz2pbi0exveq4w/2j4951263287712.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Jan/12/t1263287929eyz2pbi0exveq4w/3wc4n1263287712.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Jan/12/t1263287929eyz2pbi0exveq4w/3wc4n1263287712.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Jan/12/t1263287929eyz2pbi0exveq4w/4iw631263287712.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Jan/12/t1263287929eyz2pbi0exveq4w/4iw631263287712.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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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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