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*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: Wed, 29 Dec 2010 19:48:26 +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/29/t1293652149iug8ovyv0ttgihk.htm/, Retrieved Wed, 29 Dec 2010 20:49:10 +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/29/t1293652149iug8ovyv0ttgihk.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 «
60178 53200 59909 55970 47682 50173 43090 36031 42143 48478 36046 31060 54874 60051 71622 66526 50140 55973 40393 38483 42879 47875 40578 31027 62027 56493 65566 62653 53470 59600 42542 42018 44038 44988 43309 26843 69770 64886 79354 63025 54003 55926 45629 40361 43039 44570 43269 25563 68707 60223 74283 61232 61531 65305 51699 44599 35221 55066 45335 28702 69517 69240 71525 77740 62107 65450 51493 43067 49172 54483 38158 27898 58648 56000 62381 59849 48345 55376 45400 38389 44098 48290 41267 31238
 
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'RServer@AstonUniversity' @ vre.aston.ac.uk


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
160178NANA11907.8628472222NA
253200NANA9100.44618055555NA
359909NANA18710.1614583333NA
455970NANA13080.2239583333NA
547682NANA2807.10590277778NA
650173NANA7441.94618055556NA
74309040781.348958333346775.6666666667-5994.317708333332308.65104166667
83603135789.154513888946840.125-11050.9704861111241.845486111124
94214338514.876736111147613.625-9098.748263888893628.1232638889
104847845893.314236111148541.5-2648.185763888892584.68576388888
113604638276.522569444449083.75-10807.2274305556-2230.52256944445
123106025979.536458333349427.8333333333-23448.2968755080.46354166666
135487461464.987847222249557.12511907.8628472222-6590.98784722222
146005158647.362847222249546.91666666679100.446180555551403.63715277779
157162268389.911458333349679.7518710.16145833333232.08854166666
166652662765.51562549685.291666666713080.22395833333760.484375
175014052656.1059027778498492807.10590277778-2516.10590277778
185597357478.404513888950036.45833333337441.94618055556-1505.40451388889
194039344338.807291666750333.125-5994.31770833333-3945.80729166666
203848339431.946180555650482.9166666667-11050.9704861111-948.946180555555
214287940983.585069444450082.3333333333-9098.748263888891895.41493055555
224787547020.439236111149668.625-2648.18576388889854.560763888898
234057838838.772569444449646-10807.22743055561739.22743055556
243102726487.57812549935.875-23448.2968754539.421875
256202762084.404513888950176.541666666711907.8628472222-57.4045138888832
265649359513.821180555550413.3759100.44618055555-3020.82118055555
276556669319.119791666750608.958333333318710.1614583333-3753.11979166666
286265363617.182291666750536.958333333313080.2239583333-964.182291666657
295347053337.564236111150530.45833333332807.10590277778132.435763888891
305960057911.862847222250469.91666666677441.946180555561688.13715277777
314254244623.89062550618.2083333333-5994.31770833333-2081.89062500001
324201840239.571180555651290.5416666667-11050.97048611111778.42881944445
334403843116.001736111152214.75-9098.74826388889921.998263888898
344498850156.564236111152804.75-2648.18576388889-5168.5642361111
354330942035.230902777852842.4583333333-10807.22743055561273.76909722223
362684329263.286458333352711.5833333333-23448.296875-2420.28645833333
376977064594.987847222252687.12511907.86284722225175.01215277778
386488661847.154513888952746.70833333339100.446180555553038.84548611112
397935471346.20312552636.041666666718710.16145833338007.79687500001
406302565657.22395833335257713080.2239583333-2632.22395833333
415400355365.022569444452557.91666666672807.10590277778-1362.02256944444
425592659944.862847222252502.91666666677441.94618055556-4018.86284722223
434562946410.973958333352405.2916666667-5994.31770833333-781.973958333343
444036141115.737847222252166.7083333333-11050.9704861111-754.737847222226
454303942662.376736111151761.125-9098.74826388889376.623263888891
464457048826.939236111151475.125-2648.18576388889-4256.9392361111
474326940906.855902777851714.0833333333-10807.22743055562362.14409722223
482556328970.244791666752418.5416666667-23448.296875-3407.24479166666
496870764970.112847222253062.2511907.86284722223736.88715277778
506022362592.196180555653491.759100.44618055555-2369.19618055555
517428372052.744791666753342.583333333318710.16145833332230.25520833334
526123266534.39062553454.166666666713080.2239583333-5302.39062500001
536153156784.689236111153977.58333333332807.105902777784746.31076388888
546530561636.404513888954194.45833333337441.946180555563668.5954861111
555169948364.682291666754359-5994.317708333333334.31770833334
564459943717.487847222254768.4583333333-11050.9704861111881.512152777788
573522145930.501736111155029.25-9098.74826388889-10709.5017361111
585506652953.980902777855602.1666666667-2648.185763888892112.01909722222
594533545506.772569444456314-10807.2274305556-171.772569444445
602870232895.744791666756344.0416666667-23448.296875-4193.74479166666
616951768249.362847222256341.511907.86284722221267.63715277777
626924065369.529513888956269.08333333339100.446180555553870.47048611112
637152575496.70312556786.541666666718710.1614583333-3971.70312499999
647774070423.76562557343.541666666713080.22395833337316.23437500001
656210759827.314236111157020.20833333332807.105902777782279.68576388888
666545064129.612847222256687.66666666677441.946180555561320.38715277778
675149350206.973958333356201.2916666667-5994.317708333331286.02604166666
684306744145.779513888955196.75-11050.9704861111-1078.77951388888
694917245165.335069444454264.0833333333-9098.748263888894006.66493055555
705448350489.439236111153137.625-2648.185763888893993.5607638889
713815841011.522569444451818.75-10807.2274305556-2853.52256944444
722789827377.286458333350825.5833333333-23448.296875520.713541666672
735864862059.821180555550151.958333333311907.8628472222-3411.82118055554
745600058803.612847222249703.16666666679100.44618055555-2803.61284722222
756238168006.994791666749296.833333333318710.1614583333-5625.99479166666
765984961907.598958333348827.37513080.2239583333-2058.59895833334
774834551505.980902777848698.8752807.10590277778-3160.98090277777
785537656409.529513888948967.58333333337441.94618055556-1033.52951388888
7945400NANA-5994.31770833333NA
8038389NANA-11050.9704861111NA
8144098NANA-9098.74826388889NA
8248290NANA-2648.18576388889NA
8341267NANA-10807.2274305556NA
8431238NANA-23448.296875NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293652149iug8ovyv0ttgihk/1ne8o1293652102.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293652149iug8ovyv0ttgihk/1ne8o1293652102.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t1293652149iug8ovyv0ttgihk/2ne8o1293652102.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293652149iug8ovyv0ttgihk/2ne8o1293652102.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t1293652149iug8ovyv0ttgihk/3y68a1293652102.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293652149iug8ovyv0ttgihk/3y68a1293652102.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t1293652149iug8ovyv0ttgihk/4y68a1293652102.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293652149iug8ovyv0ttgihk/4y68a1293652102.ps (open in new window)


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