Home » date » 2010 » Nov » 29 »

*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: Mon, 29 Nov 2010 17:44:46 +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/Nov/29/t1291052605hy6tijay97b38tk.htm/, Retrieved Mon, 29 Nov 2010 18:43:25 +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/Nov/29/t1291052605hy6tijay97b38tk.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 «
10665,78 10666,71 10682,74 10777,22 10052,60 10213,97 10546,82 10767,20 10444,50 10314,68 9042,56 9220,75 9721,84 9978,53 9923,81 9892,56 10500,98 10179,35 10080,48 9492,44 8616,49 8685,40 8160,67 8048,10 8641,21 8526,63 8474,21 7916,13 7977,64 8334,59 8623,36 9098,03 9154,34 9284,73 9492,49 9682,35 9762,12 10124,63 10540,05 10601,61 10323,73 10418,40 10092,96 10364,91 10152,09 10032,80 10204,59 10001,60 10411,75 10673,38 10539,51 10723,78 10682,06 10283,19 10377,18 10486,64 10545,38 10554,27 10532,54 10324,31 10695,25 10827,81 10872,48 10971,19 11145,65 11234,68 11333,88 10997,97 11036,89 11257,35 11533,59 11963,12 12185,15 12377,62 12512,89 12631,48 12268,53 12754,80 13407,75 13480,21 13673,28 13239,71 13557,69 13901,28 13200,58 13406,97 12538,12 12419,57 12193,88 12656,63 12812,48 12056,67 11322,38 11530,75 11114,08 9181,73 8614,55 8595,56 8396,20 7690,50 7235,47 7992,12 8398,37 8593,00 8679,75 9374,63 etc...
 
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'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
110665.78NANA-97.3884244791666NA
210666.71NANA84.9603776041668NA
310682.74NANA16.2432421875003NA
410777.22NANA-88.4755078125003NA
510052.6NANA-151.524882812500NA
610213.97NANA32.8015755208334NA
710546.8210429.510846354210243.63185.880846354168117.309153645832
810767.210286.870117187510175.625111.245117187500480.329882812501
910444.510047.694596354210115.32875-67.6341536458337396.805403645832
1010314.6810072.063658854210046.845833333325.2178255208323242.616341145835
119042.5610085.161523437510028.667556.4940234375006-1042.60152343750
129220.759938.087460937510045.9075-107.820039062500-717.3374609375
139721.849927.645742187510025.0341666667-97.3884244791666-205.805742187498
149978.5310037.44871093759952.4883333333384.9603776041668-58.9187109374998
159923.819839.44949218759823.2062516.243242187500384.3605078125001
169892.569590.67699218759679.1525-88.4755078125003301.883007812497
1710500.989422.995533854179574.52041666667-151.5248828125001077.98446614583
1810179.359521.716158854179488.9145833333332.8015755208334657.633841145835
1910080.489580.908763020839395.02791666667185.880846354168499.571236979165
209492.449400.750950520839289.50583333333111.24511718750091.6890494791678
218616.499100.975846354179168.61-67.6341536458337-484.485846354166
228685.49051.076575520839025.8587525.2178255208323-365.676575520834
238160.678894.862356770838838.3683333333356.4940234375006-734.192356770833
248048.18548.544127604178656.36416666667-107.820039062500-500.444127604166
258641.218421.397408854178518.78583333333-97.3884244791666219.812591145834
268526.638526.599127604178441.6387584.96037760416680.0308723958332848
278474.218463.858658854178447.6154166666716.243242187500310.3513411458334
287916.138406.522408854178494.99791666667-88.4755078125003-490.392408854165
297977.648423.93761718758575.4625-151.524882812500-446.297617187497
308334.598731.850325520838699.0487532.8015755208334-397.260325520832
318623.368999.72792968758813.84708333333185.880846354168-376.367929687502
329098.039038.38011718758927.135111.24511718750059.6498828125004
339154.349012.160846354179079.795-67.6341536458337142.179153645833
349284.739302.98449218759277.7666666666725.2178255208323-18.2544921875015
359492.499543.909440104179487.4154166666756.4940234375006-51.4194401041696
369682.359564.174544270839671.99458333333-107.820039062500118.175455729166
379762.129722.664908854179820.05333333333-97.388424479166639.4550911458336
3810124.6310019.03371093759934.0733333333384.9603776041668105.596289062498
3910540.0510044.676158854210028.432916666716.2432421875003495.373841145833
4010601.6110012.699908854210101.1754166667-88.4755078125003588.910091145835
4110323.7310010.490950520810162.0158333333-151.524882812500313.239049479169
4210418.410237.790325520810204.9887532.8015755208334180.609674479170
4310092.9610431.239596354210245.35875185.880846354168-338.279596354167
4410364.9110406.536367187510295.29125111.245117187500-41.6263671874985
4510152.0910250.499179687510318.1333333333-67.6341536458337-98.4091796875018
4610032.810348.419075520810323.2012525.2178255208323-315.619075520834
4710204.5910399.716106770810343.222083333356.4940234375006-195.126106770833
4810001.610244.698710937510352.51875-107.820039062500-243.098710937502
4910411.7510261.339075520810358.7275-97.3884244791666150.410924479165
5010673.3810460.602460937510375.642083333384.9603776041668212.777539062499
5110539.5110413.344492187510397.1012516.2432421875003126.165507812500
5210723.7810346.740742187510435.21625-88.4755078125003377.039257812499
5310682.0610319.083867187510470.60875-151.524882812500362.976132812499
5410283.1910530.521158854210497.719583333332.8015755208334-247.331158854167
5510377.1810708.859179687510522.9783333333185.880846354168-331.679179687499
5610486.6410652.470533854210541.2254166667111.245117187500-165.830533854165
5710545.3810493.899596354210561.53375-67.634153645833751.4804036458336
5810554.2710610.934075520810585.7162525.2178255208323-56.6640755208336
5910532.5410671.835273437510615.3412556.4940234375006-139.295273437499
6010324.3110566.482877604210674.3029166667-107.820039062500-242.172877604167
6110695.2510656.422408854210753.8108333333-97.388424479166638.8275911458331
6210827.8110899.939127604210814.9787584.9603776041668-72.1291276041666
6310872.4810873.006992187510856.7637516.2432421875003-0.526992187496944
6410971.1910818.062825520810906.5383333333-88.4755078125003153.127174479167
6511145.6510826.018867187510977.54375-151.524882812500319.631132812499
6611234.6811120.339492187511087.537916666732.8015755208334114.340507812502
6711333.8811403.781679687511217.9008333333185.880846354168-69.9016796875021
6810997.9711455.800533854211344.5554166667111.245117187500-457.830533854169
6911036.8911409.847096354211477.48125-67.6341536458337-372.957096354168
7011257.3511640.228242187511615.010416666725.2178255208323-382.8782421875
7111533.5911787.469856770811730.975833333356.4940234375006-253.879856770831
7211963.1211733.280794270811841.1008333333-107.820039062500229.839205729169
7312185.1511893.461992187511990.8504166667-97.3884244791666291.688007812500
7412377.6212265.648710937512180.688333333384.9603776041668111.971289062500
7512512.8912410.207825520812393.964583333316.2432421875003102.682174479169
7612631.4812497.936992187512586.4125-88.4755078125003133.543007812501
7712268.5312601.823450520812753.3483333333-151.524882812500-333.293450520832
7812754.812951.244075520812918.442532.8015755208334-196.444075520832
7913407.7513227.389596354213041.50875185.880846354168180.360403645833
8013480.2113237.953033854213126.7079166667111.245117187500242.256966145831
8113673.2813103.014596354213170.64875-67.6341536458337570.265403645833
8213239.7113188.088242187513162.870416666725.217825520832351.6217578124997
8313557.6913207.424440104213150.930416666756.4940234375006350.265559895834
8413901.2813035.909544270813143.7295833333-107.820039062500865.370455729166
8513200.5813017.447825520813114.83625-97.3884244791666183.132174479168
8613406.9713115.679544270813030.719166666784.9603776041668291.290455729166
8712538.1212889.694075520812873.450833333316.2432421875003-351.574075520834
8812419.5712615.814492187512704.29-88.4755078125003-196.244492187501
8912193.8812379.741367187512531.26625-151.524882812500-185.861367187501
9012656.6312265.602825520812232.8012532.8015755208334391.027174479168
9112812.4812030.949596354211845.06875185.880846354168781.530403645835
9212056.6711564.753867187511453.50875111.245117187500491.916132812501
9311322.3811012.819179687511080.4533333333-67.6341536458337309.560820312501
9411530.7510736.046575520810710.8287525.2178255208323794.70342447917
9511114.0810363.677773437510307.1837556.4940234375006750.402226562501
969181.739798.40871093759906.22875-107.820039062500-616.678710937502
978614.559430.56449218759527.95291666667-97.3884244791666-816.014492187502
988595.569284.67246093759199.7120833333384.9603776041668-689.1124609375
998396.28961.526158854178945.2829166666716.2432421875003-565.326158854166
1007690.58656.85949218758745.335-88.4755078125003-966.3594921875
1017235.478442.342200520838593.86708333333-151.524882812500-1206.87220052083
1027992.128593.18949218758560.3879166666732.8015755208334-601.0694921875
1038398.378842.097513020838656.21666666667185.880846354168-443.727513020833
10485938911.718450520838800.47333333333111.245117187500-318.718450520833
1058679.758895.877513020838963.51166666667-67.6341536458337-216.127513020834
1069374.639180.356575520839155.1387525.2178255208323194.273424479166
1079634.979460.218606770839403.7245833333356.4940234375006174.751393229166
1089857.349566.824544270839674.64458333333-107.820039062500290.515455729166
10910238.83NA9889.72166666667NANA
11010433.44NA10042.55875NANA
11110471.24NA10172.0904166667NANA
11210214.51NA10277.0179166667NANA
11310677.52NA10357.8041666667NANA
11411052.15NANANANA
11510500.19NANANANA
11610159.27NANANANA
11710222.24NANANANA
11810350.4NANANANA
11910598.07NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Nov/29/t1291052605hy6tijay97b38tk/1sm401291052682.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/29/t1291052605hy6tijay97b38tk/1sm401291052682.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/29/t1291052605hy6tijay97b38tk/22e3l1291052682.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/29/t1291052605hy6tijay97b38tk/22e3l1291052682.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/29/t1291052605hy6tijay97b38tk/32e3l1291052682.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/29/t1291052605hy6tijay97b38tk/32e3l1291052682.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/29/t1291052605hy6tijay97b38tk/4vn3o1291052682.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/29/t1291052605hy6tijay97b38tk/4vn3o1291052682.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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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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