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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 13:33:53 +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/t1293629506ilkoaru05c9wx31.htm/, Retrieved Wed, 29 Dec 2010 14:31:46 +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/t1293629506ilkoaru05c9wx31.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 «
3106.54 3125.67 3039.71 3051.67 3112.83 3228.01 3223.98 3328.8 3264.26 3394.14 3549.25 3744.63 3839.25 3912.28 3911.06 3675.8 3703.32 3795.91 3906.01 4070.78 4144.38 4140.3 4388.53 4433.57 4305.23 4471.65 4614.76 4697.86 4639.4 4384.47 4350.83 4325.29 4441.82 4162.5 4127.47 3722.23 3757.12 3719.52 3925.43 3751.41 3168.22 2994.38 3136 2672.2 2100.18 1881.46 1908.64 1900.09 1696.58 1748.74 1953.35 2071.37 2030.98 2169.14 2229.85 2480.93 2525.93 2475.14 2529.66 2453.37 2386.53 2517.3 2457.46 2589.73 2679.07 2506.13 2592.31
 
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
13106.54NANA-84.5178298611111NA
23125.67NANA-1.82782986111098NA
33039.71NANA152.797586805556NA
43051.67NANA118.021440972222NA
53112.83NANA-25.4149131944442NA
63228.01NANA-50.848559027778NA
73223.983342.085815972223294.6537547.432065972222-118.105815972222
83328.83416.681857638893357.9587558.7231076388888-87.8818576388894
93264.263431.213732638893427.040416666674.1733159722223-166.953732638889
103394.143381.752795138893489.35208333333-107.59928819444512.3872048611111
113549.253528.069461805563539.96125-11.891788194444521.1805381944446
123744.633489.180190972223588.2275-99.0473090277778255.449809027778
133839.253555.790086805563640.30791666667-84.5178298611111283.459913194445
143912.283697.813836805563699.64166666667-1.82782986111098214.466163194445
153911.063920.026753472223767.22916666667152.797586805556-8.96675347222208
163675.83953.012274305563834.99083333333118.021440972222-277.212274305555
173703.323875.635920138893901.05083333333-25.4149131944442-172.315920138888
183795.913913.878107638893964.72666666667-50.848559027778-117.968107638889
193906.014060.280399305564012.8483333333347.432065972222-154.270399305555
204070.784114.294357638894055.5712558.7231076388888-43.5143576388891
214144.384112.372482638894108.199166666674.173315972222332.0075173611112
224140.34072.506545138894180.10583333333-107.59928819444567.793454861112
234388.534249.803211805564261.695-11.8917881944445138.726788194444
244433.574226.174357638894325.22166666667-99.0473090277778207.395642361111
254305.234283.761336805564368.27916666667-84.517829861111121.4686631944442
264471.654395.590086805564397.41791666667-1.8278298611109876.0599131944446
274614.764573.213420138894420.41583333333152.79758680555641.5465798611121
284697.864551.755607638894433.73416666667118.021440972222146.104392361111
294639.44398.366753472224423.78166666667-25.4149131944442241.033246527778
304384.474332.416440972224383.265-50.84855902777852.0535590277777
314350.834378.219982638894330.7879166666747.432065972222-27.3899826388888
324325.294335.334357638894276.6112558.7231076388888-10.0443576388889
334441.824220.723732638894216.550416666674.1733159722223221.096267361111
344162.54040.793628472224148.39291666667-107.599288194445121.706371527778
354127.474035.766545138894047.65833333333-11.891788194444591.7034548611118
363722.233829.391440972223928.43875-99.0473090277778-107.161440972222
373757.123735.382586805563819.90041666667-84.517829861111121.7374131944448
383719.523698.575920138893700.40375-1.8278298611109820.9440798611108
393925.433686.754253472223533.95666666667152.797586805556238.675746527777
403751.413459.366440972223341.345118.021440972222292.043559027778
413168.223128.435503472223153.85041666667-25.414913194444239.7844965277773
422994.382934.628107638892985.47666666667-50.84855902777859.7518923611115
4331362871.130399305562823.6983333333347.432065972222264.869600694445
442672.22714.449774305552655.7266666666758.7231076388888-42.2497743055551
452100.182495.614149305562491.440833333334.1733159722223-395.434149305555
461881.462231.669878472222339.26916666667-107.599288194445-350.209878472222
471908.642209.990711805562221.8825-11.8917881944445-301.350711805555
481900.092041.065190972222140.1125-99.0473090277778-140.975190972222
491696.581983.453420138892067.97125-84.5178298611111-286.873420138889
501748.742020.417586805562022.24541666667-1.82782986111098-271.677586805555
511953.352184.813003472222032.01541666667152.797586805556-231.463003472222
522071.372192.513107638892074.49166666667118.021440972222-121.143107638889
532030.982099.689253472222125.10416666667-25.4149131944442-68.7092534722224
542169.142123.184774305562174.03333333333-50.84855902777845.9552256944439
552229.852273.266649305562225.8345833333347.432065972222-43.4166493055554
562480.932345.328940972222286.6058333333358.7231076388888135.601059027777
572525.932343.807065972222339.633754.1733159722223182.122934027778
582475.142274.637378472222382.23666666667-107.599288194445200.502621527778
592529.662418.946961805562430.83875-11.8917881944445110.713038194444
602453.372372.836440972222471.88375-99.047309027777880.5335590277782
612386.53NA2501.0275NANA
622517.3NANANANA
632457.46NANANANA
642589.73NANANANA
652679.07NANANANA
662506.13NANANANA
672592.31NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293629506ilkoaru05c9wx31/16t321293629630.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293629506ilkoaru05c9wx31/16t321293629630.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t1293629506ilkoaru05c9wx31/26t321293629630.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293629506ilkoaru05c9wx31/26t321293629630.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t1293629506ilkoaru05c9wx31/3hk251293629630.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293629506ilkoaru05c9wx31/3hk251293629630.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t1293629506ilkoaru05c9wx31/4hk251293629630.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293629506ilkoaru05c9wx31/4hk251293629630.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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