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paper: analyse (min 18)

*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: Tue, 28 Dec 2010 18:57:58 +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/28/t12935625614r65dljrozen89f.htm/, Retrieved Tue, 28 Dec 2010 19:56:06 +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/28/t12935625614r65dljrozen89f.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 «
3065 2997 2901 2815 2709 2711 3509 3369 3596 3448 3160 2934 2534 2266 2088 1932 1784 1851 2700 2580 2829 2298 2045 1824 1872 1801 1735 1639 1521 1758 2603 2540 3103 2801 2590 2324 2424 2288 2163 2082 1937 2155 2874 2836 3439 3278 3129 2959 3060 2898 2783 2632 2465 2689 3321 3359 4108 3407 3241 3013 3067 2965 2823 2718 2567 2658 3436 3375 3931 3371 3038
 
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'Gwilym Jenkins' @ 72.249.127.135


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
13065NANA-30.9826388888890NA
22997NANA-188.170138888889NA
32901NANA-314.399305555556NA
42815NANA-440.305555555556NA
52709NANA-585.222222222222NA
62711NANA-400.388888888889NA
735093433.527777777783079.04166666667354.48611111111175.4722222222217
833693322.361111111113026.45833333333295.90277777777846.6388888888891
935963784.090277777782962.125821.965277777778-188.090277777777
1034483273.829861111112891.45833333333382.371527777778174.170138888890
1131602987.402777777782816.125171.277777777778172.597222222223
1229342675.215277777782741.75-66.5347222222222258.784722222223
1325342641.225694444442672.20833333333-30.9826388888890-107.225694444444
1422662417.454861111112605.625-188.170138888889-151.454861111111
1520882226.392361111112540.79166666667-314.399305555556-138.392361111111
1619322020.611111111112460.91666666667-440.305555555556-88.6111111111113
1717841781.319444444442366.54166666667-585.2222222222222.68055555555566
1818511873.444444444442273.83333333333-400.388888888889-22.4444444444443
1927002554.486111111112200354.486111111111145.513888888889
2025802448.944444444442153.04166666667295.902777777778131.055555555556
2128292940.923611111112118.95833333333821.965277777778-111.923611111111
2222982474.413194444442092.04166666667382.371527777778-176.413194444445
2320452240.152777777782068.875171.277777777778-195.152777777778
2418241987.506944444442054.04166666667-66.5347222222222-163.506944444445
2518722015.142361111112046.125-30.9826388888890-143.142361111111
2618011852.246527777782040.41666666667-188.170138888889-51.2465277777776
2717351735.767361111112050.16666666667-314.399305555556-0.767361111110858
2816391642.236111111112082.54166666667-440.305555555556-3.23611111111086
2915211540.986111111112126.20833333333-585.222222222222-19.9861111111109
3017581769.361111111112169.75-400.388888888889-11.3611111111109
3126032568.069444444442213.58333333333354.48611111111134.9305555555552
3225402552.777777777782256.875295.902777777778-12.7777777777778
3331033116.965277777782295821.965277777778-13.9652777777778
3428012713.663194444442331.29166666667382.37152777777887.3368055555557
3525902538.361111111112367.08333333333171.27777777777851.6388888888891
3623242334.423611111112400.95833333333-66.5347222222222-10.4236111111109
3724242397.809027777782428.79166666667-30.982638888889026.1909722222226
3822882264.246527777782452.41666666667-188.17013888888923.7534722222217
3921632164.350694444442478.75-314.399305555556-1.35069444444434
4020822072.319444444442512.625-440.3055555555569.6805555555552
4119371969.736111111112554.95833333333-585.222222222222-32.7361111111109
4221552203.486111111112603.875-400.388888888889-48.4861111111113
4328743011.319444444442656.83333333333354.486111111111-137.319444444444
4428363004.652777777782708.75295.902777777778-168.652777777778
4534393581.965277777782760821.965277777778-142.965277777777
4632783191.121527777782808.75382.37152777777886.8784722222226
4731293024.944444444442853.66666666667171.277777777778104.055555555556
4829592831.381944444442897.91666666667-66.5347222222222127.618055555556
4930602907.809027777782938.79166666667-30.9826388888890152.190972222222
5028982791.038194444442979.20833333333-188.170138888889106.961805555556
5127832714.475694444443028.875-314.39930555555668.5243055555561
5226322621.819444444443062.125-440.30555555555610.1805555555557
5324652486.944444444443072.16666666667-585.222222222222-21.9444444444443
5426892678.694444444443079.08333333333-400.38888888888910.3055555555557
5533213436.111111111113081.625354.486111111111-115.111111111111
5633593380.611111111113084.70833333333295.902777777778-21.6111111111109
5741083911.131944444443089.16666666667821.965277777778196.868055555555
5834073476.788194444443094.41666666667382.371527777778-69.7881944444439
5932413273.527777777783102.25171.277777777778-32.5277777777774
6030133038.673611111113105.20833333333-66.5347222222222-25.6736111111109
613067NA3108.70833333333NANA
622965NA3114.16666666667NANA
632823NA3107.45833333333NANA
642718NA3098.58333333333NANA
652567NA3088.625NANA
662658NANANANA
673436NANANANA
683375NANANANA
693931NANANANA
703371NANANANA
713038NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/28/t12935625614r65dljrozen89f/1prev1293562675.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t12935625614r65dljrozen89f/1prev1293562675.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t12935625614r65dljrozen89f/2prev1293562675.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t12935625614r65dljrozen89f/2prev1293562675.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t12935625614r65dljrozen89f/3i0wg1293562675.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t12935625614r65dljrozen89f/3i0wg1293562675.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t12935625614r65dljrozen89f/4srd11293562675.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t12935625614r65dljrozen89f/4srd11293562675.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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