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CD

*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: Thu, 09 Dec 2010 16:00:21 +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/09/t1291910355p3kaeyo7fys2l6u.htm/, Retrieved Thu, 09 Dec 2010 16:59:15 +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/09/t1291910355p3kaeyo7fys2l6u.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 «
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 31956 29506 34506 27165 26736 23691 18157 17328 18205 20995 17382 9367 31124 26551 30651 25859 25100 25778 20418 18688 20424 24776 19814 12738 31566 30111 30019 31934 25826 26835 20205 17789 20520 22518 15572 11509 25447 24090 27786 26195 20516 22759 19028 16971 20036 22485 18730 14538
 
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'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
131514NANA6639.09548611111NA
227071NANA4559.94965277778NA
329462NANA8450.10243055555NA
426105NANA5336.984375NA
522397NANA948.352430555557NA
623843NANA2142.24131944445NA
72170519387.387152777823147.9583333333-3760.571180555552317.61284722222
81808918018.345486111123099.0833333333-5080.7378472222270.6545138888905
92076420404.685763888923486.0833333333-3081.39756944445359.314236111109
102531623654.907986111124023.7916666667-368.8836805555561661.09201388889
111770419250.803819444424282.5833333333-5031.77951388889-1546.80381944444
121554813560.810763888924314.1666666667-10753.35590277781987.18923611111
132802930818.887152777824179.79166666676639.09548611111-2789.88715277778
142938328562.532986111124002.58333333334559.94965277778820.46701388889
153643832413.1024305556239638450.102430555554024.89756944445
163203429177.817708333323840.83333333335336.9843752856.18229166667
172267924744.644097222223796.2916666667948.352430555557-2065.64409722223
182431925927.032986111123784.79166666672142.24131944445-1608.03298611111
191800420023.095486111123783.6666666667-3760.57118055555-2019.09548611111
201753718616.345486111123697.0833333333-5080.73784722222-1079.34548611111
212036620151.769097222223233.1666666667-3081.39756944445214.230902777781
222278222327.032986111122695.9166666667-368.883680555556454.967013888891
231916917421.387152777822453.1666666667-5031.779513888891747.61284722223
241380711801.935763888922555.2916666667-10753.35590277782005.06423611111
252974329306.512152777822667.41666666676639.09548611111436.487847222223
262559127275.657986111122715.70833333334559.94965277778-1684.65798611111
272909631187.1024305556227378450.10243055555-2091.10243055555
282648227920.692708333322583.70833333335336.984375-1438.69270833333
292240523378.435763888922430.0833333333948.352430555557-973.435763888887
302704424414.032986111122271.79166666672142.241319444452629.96701388889
311797018473.887152777822234.4583333333-3760.57118055555-503.887152777777
321873017409.053819444422489.7916666667-5080.737847222221320.94618055555
331968419796.935763888922878.3333333333-3081.39756944445-112.935763888887
341978522763.324652777823132.2083333333-368.883680555556-2978.32465277778
351847918309.345486111123341.125-5031.77951388889169.654513888891
361069812628.519097222223381.875-10753.3559027778-1930.51909722222
373195629889.053819444423249.95833333336639.095486111112066.94618055556
382950627759.282986111123199.33333333334559.949652777781746.71701388889
393450631529.394097222223079.29166666678450.102430555552976.60590277778
402716528405.067708333323068.08333333335336.984375-1240.06770833333
412673624021.144097222223072.7916666667948.3524305555572714.85590277778
422369125113.866319444422971.6252142.24131944445-1422.86631944444
431815719120.928819444422881.5-3760.57118055555-963.928819444442
441732817642.970486111122723.7083333333-5080.73784722222-314.970486111109
451820519358.560763888922439.9583333333-3081.39756944445-1153.56076388889
462099521856.032986111122224.9166666667-368.883680555556-861.032986111113
471738217070.553819444422102.3333333333-5031.77951388889311.446180555558
48936711367.769097222222121.125-10753.3559027778-2000.76909722222
493112428941.387152777822302.29166666676639.095486111112182.61284722223
502655127013.116319444422453.16666666674559.94965277778-462.116319444442
513065131052.394097222222602.29166666678450.10243055555-401.394097222219
522585928189.276041666722852.29166666675336.984375-2330.27604166666
532510024059.519097222223111.1666666667948.3524305555571040.48090277778
542577825495.199652777823352.95833333332142.24131944445282.800347222223
552041819751.262152777823511.8333333333-3760.57118055555666.737847222226
561868818597.845486111123678.5833333333-5080.7378472222290.1545138888869
572042420719.185763888923800.5833333333-3081.39756944445-295.185763888891
582477623658.491319444424027.375-368.8836805555561117.50868055555
591981419278.970486111124310.75-5031.77951388889535.029513888894
601273813631.685763888924385.0416666667-10753.3559027778-893.685763888887
613156631059.303819444424420.20833333336639.09548611111506.696180555558
623011128933.824652777824373.8754559.949652777781177.17534722222
633001932790.519097222224340.41666666678450.10243055555-2771.51909722222
643193429587.317708333324250.33333333335336.9843752346.68229166667
652582624927.852430555623979.5948.352430555557898.147569444445
662683525893.782986111123751.54166666672142.24131944445941.21701388889
672020519684.803819444423445.375-3760.57118055555520.196180555558
681778917858.803819444422939.5416666667-5080.73784722222-69.8038194444416
692052019514.227430555622595.625-3081.397569444451005.77256944444
702251821894.574652777822263.4583333333-368.883680555556623.425347222223
711557216771.303819444421803.0833333333-5031.77951388889-1199.30381944444
721150910658.644097222221412-10753.3559027778850.355902777777
732544727832.220486111121193.1256639.09548611111-2385.22048611111
742409025669.9496527778211104559.94965277778-1579.94965277777
752778629505.852430555621055.758450.10243055555-1719.85243055555
762619526371.192708333321034.20833333335336.984375-176.192708333332
772051622112.769097222221164.4166666667948.352430555557-1596.76909722222
782275923564.449652777821422.20833333332142.24131944445-805.449652777777
7919028NANA-3760.57118055555NA
8016971NANA-5080.73784722222NA
8120036NANA-3081.39756944445NA
8222485NANA-368.883680555556NA
8318730NANA-5031.77951388889NA
8414538NANA-10753.3559027778NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/09/t1291910355p3kaeyo7fys2l6u/1g02p1291910418.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/09/t1291910355p3kaeyo7fys2l6u/1g02p1291910418.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/09/t1291910355p3kaeyo7fys2l6u/2g02p1291910418.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/09/t1291910355p3kaeyo7fys2l6u/2g02p1291910418.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/09/t1291910355p3kaeyo7fys2l6u/39s1s1291910418.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/09/t1291910355p3kaeyo7fys2l6u/39s1s1291910418.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/09/t1291910355p3kaeyo7fys2l6u/49s1s1291910418.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/09/t1291910355p3kaeyo7fys2l6u/49s1s1291910418.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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Software written by Ed van Stee & Patrick Wessa


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