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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: Tue, 21 Dec 2010 12:20:50 +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/21/t1292933956p518s7g9s0lmt32.htm/, Retrieved Tue, 21 Dec 2010 13:19:17 +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/21/t1292933956p518s7g9s0lmt32.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 «
3010 2910 3840 3580 3140 3550 3250 2820 2260 2060 2120 2210 2190 2180 2350 2440 2370 2440 2610 3040 3190 3120 3170 3600 3420 3650 4180 2960 2710 2950 3030 3770 4740 4450 5550 5580 5890 7480 10450 6360 6710 6200 4490 3480 2520 1920 2010 1950 2240 2370 2840 2700 2980 3290 3300 3000 2330 2190 1970 2170 2830 3190 3550 3240 3450 3570 3230 3260 2700
 
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'RServer@AstonUniversity' @ vre.aston.ac.uk


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
13010NANA-84.7222222222222NA
22910NANA397.881944444445NA
33840NANA1430.27777777778NA
43580NANA88.1944444444445NA
53140NANA165.902777777778NA
63550NANA195.381944444445NA
732502688.298611111112861.66666666667-173.368055555556561.701388888889
828202571.527777777782797.08333333333-225.555555555556248.472222222223
922602328.506944444442704.58333333333-376.076388888889-68.5069444444439
1020601923.090277777782595-671.909722222222136.909722222223
1121202078.923611111112515.41666666667-436.49305555555641.0763888888887
1222102127.569444444442437.08333333333-309.51388888888982.4305555555557
1321902279.444444444442364.16666666667-84.7222222222222-89.4444444444443
1421802744.548611111112346.66666666667397.881944444445-564.548611111112
1523503824.861111111112394.583333333331430.27777777778-1474.86111111111
1624402565.694444444442477.588.1944444444445-125.694444444444
1723702731.319444444442565.41666666667165.902777777778-361.319444444444
1824402862.465277777782667.08333333333195.381944444445-422.465277777778
1926102602.881944444442776.25-173.3680555555567.11805555555566
2030402663.194444444442888.75-225.555555555556376.805555555555
2131902650.173611111113026.25-376.076388888889539.826388888889
2231202452.256944444443124.16666666667-671.909722222222667.743055555555
2331702723.506944444443160-436.493055555556446.493055555555
2436002885.902777777783195.41666666667-309.513888888889714.097222222223
2534203149.444444444443234.16666666667-84.7222222222222270.555555555556
2636503679.965277777783282.08333333333397.881944444445-29.9652777777778
2741804807.361111111113377.083333333331430.27777777778-627.361111111111
2829603585.277777777783497.0833333333388.1944444444445-625.277777777777
2927103817.569444444443651.66666666667165.902777777778-1107.56944444444
3029504028.715277777783833.33333333333195.381944444445-1078.71527777778
3130303845.381944444444018.75-173.368055555556-815.381944444444
3237704055.694444444444281.25-225.555555555556-285.694444444444
3347404326.006944444454702.08333333333-376.076388888889413.993055555555
3444504433.090277777785105-671.90972222222216.9097222222235
3555504976.840277777785413.33333333333-436.493055555556573.159722222222
3655805405.902777777785715.41666666667-309.513888888889174.097222222222
3758905826.944444444445911.66666666667-84.722222222222263.0555555555566
3874806358.298611111115960.41666666667397.8819444444451121.70138888889
39104507286.111111111115855.833333333331430.277777777783163.88888888889
4063605746.111111111115657.9166666666788.1944444444445613.88888888889
4167105570.902777777785405165.9027777777781139.09722222222
4262005301.631944444445106.25195.381944444445898.368055555557
4344904629.548611111114802.91666666667-173.368055555556-139.54861111111
4434804212.361111111114437.91666666667-225.555555555556-732.36111111111
4525203531.840277777783907.91666666667-376.076388888889-1011.84027777778
4619202766.423611111113438.33333333333-671.909722222222-846.423611111111
4720102693.923611111113130.41666666667-436.493055555556-683.923611111111
4819502544.236111111112853.75-309.513888888889-594.236111111111
4922402598.194444444442682.91666666667-84.7222222222222-358.194444444445
5023703011.215277777782613.33333333333397.881944444445-641.215277777778
5128404015.694444444452585.416666666671430.27777777778-1175.69444444445
5227002676.944444444442588.7588.194444444444523.0555555555557
5329802764.236111111112598.33333333333165.902777777778215.763888888889
5432902801.215277777782605.83333333333195.381944444445488.784722222222
5533002466.215277777782639.58333333333-173.368055555556833.784722222223
5630002472.777777777782698.33333333333-225.555555555556527.222222222223
5723302386.006944444442762.08333333333-376.076388888889-56.0069444444443
5821902142.256944444442814.16666666667-671.90972222222247.7430555555552
5919702419.756944444442856.25-436.493055555556-449.756944444444
6021702577.986111111112887.5-309.513888888889-407.986111111111
612830NA2896.25NANA
623190NA2904.16666666667NANA
633550NA2930.41666666667NANA
643240NANANANA
653450NANANANA
663570NANANANA
673230NANANANA
683260NANANANA
692700NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292933956p518s7g9s0lmt32/1d7651292934046.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292933956p518s7g9s0lmt32/1d7651292934046.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t1292933956p518s7g9s0lmt32/2oy581292934046.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292933956p518s7g9s0lmt32/2oy581292934046.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t1292933956p518s7g9s0lmt32/3oy581292934046.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292933956p518s7g9s0lmt32/3oy581292934046.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t1292933956p518s7g9s0lmt32/4g75b1292934046.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292933956p518s7g9s0lmt32/4g75b1292934046.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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,m$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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