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R Software Module: rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Wed, 30 Apr 2008 12:37:13 -0600
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Apr/30/t1209580751u160bsbj3efhp1l.htm/, Retrieved Wed, 30 Apr 2008 20:39:11 +0200
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
56421 53152 53536 52408 41454 38271 35306 26414 31917 38030 27534 18387 50556 43901 48572 43899 37532 40357 35489 29027 34485 42598 30306 26451 47460 50104 61465 53726 39477 43895 31481 29896 33842 39120 33702 25094 51442 45594 52518 48564 41745 49585 32747 33379 35645 37034 35681 20972 58552 54955 65540 51570 51145 46641 35704 33253 35193 41668 34865 21210 56126 49231 59723 48103 47472 50497 40059 34149 36860 46356 36577
 
Text written by user:
 
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'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
156421NANA1.27290607861445NA
253152NANA1.18369742267573NA
353536NANA1.37107436141492NA
452408NANA1.23211188309341NA
541454NANA0.999602689108356NA
638271NANA1.12129732166393NA
73530639158.960021967839158.1250.8350219677860331.07976371078956
82641438529.057470339338528.29166666670.765803672637930.895234840812961
93191737936.8551472019379360.8551472018891690.983851717179406
103803037375.595535195737374.6250.9705351956924231.04842698905968
112753436857.473703042636856.66666666670.807036375906450.92567842766349
121838736780.752432496236780.16666666670.5857658295172060.853440305658104
135055636875.981239411936874.70833333331.272906078614451.07707947723675
144390136992.39203075636991.20833333331.183697422675731.00261729759688
154857237208.454407694737207.08333333331.371074361414920.952136797108823
164389937505.648778549837504.41666666671.232111883093410.949996633357874
173753237811.249602689137810.250.9996026891083560.993035428207678
184035738262.871297321738261.751.121297321663930.940661263882834
193548938469.585021967838468.750.8350219677860331.10481048301531
202902738598.97413700638598.20833333330.765803672637930.982013735257662
213448539394.730147201939393.8750.8551472018891691.02367158605017
224259840341.512201862440340.54166666670.9705351956924231.08801828608190
233030640831.848703042640831.04166666670.807036375906450.919697591078992
242645141060.085765829541059.50.5857658295172061.09977642391545
254746041041.189572745341039.91666666671.272906078614450.908499940532697
265010440910.308697422740909.1251.183697422675731.03469298855082
276146540919.912741028140918.54166666671.371074361414921.09558664360505
285372640748.065445216440746.83333333331.232111883093411.07013975641955
293947740744.416269355840743.41666666670.9996026891083560.96930238323718
304389540829.496297321740828.3751.121297321663930.958809158065132
313148140938.585021967840937.750.8350219677860330.920930059591863
322989640916.515803672640915.750.765803672637930.954124666706601
333384240355.896813868640355.04166666670.8551472018891690.980657472732529
343912039768.137201862439767.16666666670.9705351956924231.01359137728657
353370239647.390369709239646.58333333330.807036375906451.05331143096379
362509439978.752432496239978.16666666670.5857658295172061.07157601881627
375144240269.2729060786402681.272906078614451.00360178415761
384559440467.058697422740465.8751.183697422675730.951870915779354
395251840687.496074361440686.1251.371074361414920.941457761213188
404856440675.565445216440674.33333333331.232111883093410.969044812149636
414174540670.874602689140669.8750.9996026891083561.0268433904822
424958540581.70463065540580.58333333331.121297321663931.08971077997909
433274740705.918355301140705.08333333330.8350219677860330.963440659319884
443337941392.140803672641391.3750.765803672637931.05304279996280
453564542324.8551472019423240.8551472018891690.984852143144363
463703442992.80386852942991.83333333330.9705351956924230.887571538558598
473568143509.557036375943508.750.807036375906451.01617217988428
482097243778.335765829543777.750.5857658295172060.817828759195339
495855243779.564572745343778.29166666671.272906078614451.05071894621025
505495543897.433697422743896.251.183697422675731.05764297619096
516554043873.537741028143872.16666666671.371074361414921.08957302906259
525157044047.648778549844046.41666666671.232111883093410.950246770931844
535114544206.499602689144205.50.9996026891083561.15744259294215
544664144182.537963988344181.41666666671.121297321663930.941472049509816
553570444091.085021967844090.250.8350219677860330.969787119023152
563325343751.432470339343750.66666666670.765803672637930.992495879511314
573519343270.646813868643269.79166666670.8551472018891690.951109689952694
584166842883.92886852942882.95833333330.9705351956924231.00116723495115
593486542586.265369709242585.45833333330.807036375906451.01446071457417
602121042593.669099162942593.08333333330.5857658295172060.850114756599057
6156126NA42935.2083333333NANA
6249231NA43154NANA
6359723NA43260.7916666667NANA
6448103NA43525.5833333333NANA
6547472NA43792.25NANA
6650497NANANANA
6740059NANANANA
6834149NANANANA
6936860NANANANA
7046356NANANANA
7136577NANANANA
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/30/t1209580751u160bsbj3efhp1l/1frvn1209580628.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/30/t1209580751u160bsbj3efhp1l/1frvn1209580628.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/30/t1209580751u160bsbj3efhp1l/24zja1209580628.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/30/t1209580751u160bsbj3efhp1l/24zja1209580628.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/30/t1209580751u160bsbj3efhp1l/3xe0o1209580628.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/30/t1209580751u160bsbj3efhp1l/3xe0o1209580628.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/30/t1209580751u160bsbj3efhp1l/49x211209580628.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/30/t1209580751u160bsbj3efhp1l/49x211209580628.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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,m$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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