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classical decomposition

*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 17:14:41 +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/t1293642774r7kxrlicxtt22ky.htm/, Retrieved Wed, 29 Dec 2010 18:12:54 +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/t1293642774r7kxrlicxtt22ky.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 «
347553 353245 347966 364343 341713 361162 354400 345183 341807 348712 349011 416259 360289 367557 364611 378688 351763 377765 373212 365819 364686 363333 362536 428803 375361 377205 381266 390516 366117 393928 387784 385656 385320 389053 390595 462440 402532 409070 421329 428841 404864 432633 416886 414893 417914 417518 423137 506710 436264 443712 452849 453009 437876 460041 450426 447873 444955 452043 480877 546696 483668 489627 490007 496590 480846 497677 492795 488495 486769 494455 498054 558648 506424 512528 512866 529324 508431 523026 521355 514103 513885 522150 527384 654047 543115 543200 571201 568892 537223 553186 550954 543433 557195 565522 571691 633972 575265 572364 586744 600389 578540 610778 596577 590558 597294 602384 614190 690042 639497 649304 678762 691885 667973 682032 672651 671865 671463 675917 680952 754718 694413 699390 710573 714217 702996 712370 708445 707083 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
1347553NANA-3491.51844618055NA
2353245NANA-2540.44552951389NA
3347966NANA3324.54405381944NA
4364343NANA11176.5544704861NA
5341713NANA-12359.2085503472NA
6361162NANA648.390407986087NA
7354400350140.528428819356476.833333333-6336.30490451394259.4715711805
8345183345708.851345486357603.833333333-11894.9819878472-525.851345486182
9341807344982.934678819358893.708333333-13910.7736545139-3175.93467881944
10348712348653.523220486360184.958333333-11531.435112847258.4767795139342
11349011352708.783637153361201.416666667-8492.63302951389-3697.78363715281
12416259417719.770616319362311.95833333355407.8122829861-1460.77061631950
13360289360296.064887153363787.583333333-3491.51844618055-7.06488715275191
14367557362890.804470486365431.25-2540.445529513894666.19552951388
15364611370568.919053819367244.3753324.54405381944-5957.91905381944
16378688379983.429470486368806.87511176.5544704861-1295.42947048612
17351763357620.416449653369979.625-12359.2085503472-5857.41644965269
18377765371714.223741319371065.833333333648.3904079860876050.77625868062
19373212365880.195095486372216.5-6336.30490451397331.80490451393
20365819361351.518012153373246.5-11894.98198784724467.48198784725
21364686360431.684678819374342.458333333-13910.77365451394254.31532118056
22363333363997.814887153375529.25-11531.4351128472-664.81488715281
23362536368127.533637153376620.166666667-8492.63302951389-5591.53363715281
24428803433299.520616319377891.70833333355407.8122829861-4496.5206163195
25375361375680.814887153379172.333333333-3491.51844618055-319.814887152752
26377205378065.596137153380606.041666667-2540.44552951389-860.596137152752
27381266385616.877387153382292.3333333333324.54405381944-4350.87738715269
28390516395400.304470486384223.7511176.5544704861-4884.30447048607
29366117374105.333116319386464.541666667-12359.2085503472-7988.33311631944
30393928389683.598741319389035.208333333648.3904079860874244.40125868056
31387784385232.570095486391568.875-6336.30490451392551.42990451382
32385656382133.726345486394028.708333333-11894.98198784723522.27365451388
33385320383114.934678819397025.708333333-13910.77365451392205.06532118056
34389053388760.439887153400291.875-11531.4351128472292.56011284719
35390595395010.575303819403503.208333333-8492.63302951389-4415.57530381938
36462440462138.187282986406730.37555407.8122829861301.812717013934
37402532406064.148220486409555.666666667-3491.51844618055-3532.14822048607
38409070409446.012803819411986.458333333-2540.44552951389-376.012803819438
39421329417887.294053819414562.753324.544053819443441.70594618062
40428841428283.429470486417106.87511176.5544704861557.570529513876
41404864407289.624782986419648.833333333-12359.2085503472-2425.62478298612
42432633423497.723741319422849.333333333648.3904079860879135.27625868062
43416886419763.111762153426099.416666667-6336.3049045139-2877.11176215281
44414893417053.351345486428948.333333333-11894.9819878472-2160.35134548607
45417914417794.309678819431705.083333333-13910.7736545139119.690321180562
46417518422493.981553819434025.416666667-11531.4351128472-4975.9815538195
47423137427915.283637153436407.916666667-8492.63302951389-4778.28363715275
48506710494333.228949653438925.41666666755407.812282986112376.7710503472
49436264437973.398220486441464.916666667-3491.51844618055-1709.39822048612
50443712441696.137803819444236.583333333-2540.445529513892015.86219618056
51452849450062.002387153446737.4583333333324.544053819442786.99761284725
52453009460479.262803819449302.70833333311176.5544704861-7470.26280381938
53437876440787.874782986453147.083333333-12359.2085503472-2911.87478298601
54460041457867.390407986457219648.3904079860872173.60959201393
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56447873452853.559678819464748.541666667-11894.9819878472-4980.5596788195
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60546696533946.728949653478538.91666666755407.812282986112749.2710503472
61483668478380.939887153481872.458333333-3491.518446180555287.06011284731
62489627482789.971137153485330.416666667-2540.445529513896837.02886284725
63490007492089.794053819488765.253324.54405381944-2082.79405381944
64496590503451.221137153492274.66666666711176.5544704861-6861.22113715281
65480846482398.333116319494757.541666667-12359.2085503472-1552.33311631944
66497677496619.640407986495971.25648.3904079860871057.35959201393
67492795491081.111762153497417.416666667-6336.30490451391713.88823784731
68488495487424.809678819499319.791666667-11894.98198784721070.19032118056
69486769487315.684678819501226.458333333-13910.7736545139-546.68467881938
70494455492011.398220486503542.833333333-11531.43511284722443.60177951388
71498054497563.491970486506056.125-8492.63302951389490.508029513934
72558648563669.520616319508261.70833333355407.8122829861-5021.52061631944
73506424507016.398220486510507.916666667-3491.51844618055-592.398220486008
74512528510224.471137153512764.916666667-2540.445529513892303.52886284731
75512866518286.294053819514961.753324.54405381944-5420.29405381944
76529324528422.096137153517245.54166666711176.5544704861901.90386284719
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80514103521234.184678819533129.166666667-11894.9819878472-7131.18467881938
81513885522927.018012153536837.791666667-13910.7736545139-9042.01801215275
82522150529385.648220486540917.083333333-11531.4351128472-7235.64822048601
83527384535272.783637153543765.416666667-8492.63302951389-7888.78363715287
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85543115545220.189887153548711.708333333-3491.51844618055-2105.18988715287
86543200548626.63780382551167.083333333-2540.44552951389-5426.6378038195
87571201557518.29405382554193.753324.5440538194413682.7059461805
88568892568982.054470486557805.511176.5544704861-90.054470486124
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90553186563116.84874132562468.458333333648.390407986087-9930.8487413195
91550954556635.278428819562971.583333333-6336.3049045139-5681.27842881938
92543433553631.351345486565526.333333333-11894.9819878472-10198.3513454861
93557195553478.351345486567389.125-13910.77365451393716.64865451399
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95571691563890.408637153572383.041666667-8492.633029513897800.59136284725
96633972631912.062282986576504.2555407.81228298612059.93771701388
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98572364582128.929470486584669.375-2540.44552951389-9764.929470486
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104590558597923.93467882609818.916666667-11894.9819878472-7365.9346788195
105597294602948.059678819616858.833333333-13910.7736545139-5654.05967881938
106602384612973.814887153624505.25-11531.4351128472-10589.8148871528
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109639497641386.398220486644877.916666667-3491.51844618055-1889.39822048612
110649304648895.01280382651435.458333333-2540.44552951389408.987196180504
111678762661238.16905382657913.6253324.5440538194417523.8309461805
112691885675244.429470486664067.87511176.554470486116640.5705295139
113667973657554.291449653669913.5-12359.208550347210418.7085503474
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115672651674036.77842882680373.083333333-6336.3049045139-1385.77842881950
116671865672853.18467882684748.166666667-11894.9819878472-988.184678819496
117671463674249.768012153688160.541666667-13910.7736545139-2786.76801215287
118675917678885.064887153690416.5-11531.4351128472-2968.06488715275
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159749781752389.752387153749065.2083333333324.54405381944-2608.75238715287
160760911760793.01280382749616.45833333311176.5544704861117.987196180620
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162745626751309.97374132750661.583333333648.390407986087-5683.97374131938
163746246744767.52842882751103.833333333-6336.30490451391478.47157118050
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169752440752584.189887153756075.708333333-3491.51844618055-144.189887152985
170756627754220.88780382756761.333333333-2540.445529513892406.11219618050
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191765945761024.20030382769516.833333333-8492.633029513894920.79969618062
192814783825460.02061632770052.20833333355407.8122829861-10677.0206163194
193768494766896.439887153770387.958333333-3491.518446180551597.56011284713
194769222768096.679470486770637.125-2540.445529513891125.32052951388
195768977774238.79405382770914.253324.54405381944-5261.7940538195
196783341782116.38780382770939.83333333311176.55447048611224.61219618050
197764061758293.749782986770652.958333333-12359.20855034725767.25021701388
198767394770947.34874132770298.958333333648.390407986087-3553.34874131938
199763910763767.070095486770103.375-6336.3049045139142.929904513876
200761677758300.643012153770195.625-11894.98198784723376.35698784736
201759173756543.893012153770454.666666667-13910.77365451392629.10698784725
202762486759385.689887153770917.125-11531.43511284723100.31011284725
203762690762875.033637153771367.666666667-8492.63302951389-185.033637152752
204809542827174.728949653771766.91666666755407.8122829861-17632.7289496528
205769041NA772364.916666667NANA
206770889NA773073NANA
207773527NA773795.208333333NANA
208789890NA774707.708333333NANA
209768325NA775810.708333333NANA
210772712NANANANA
211772944NANANANA
212769637NANANANA
213768546NANANANA
214775013NANANANA
215776635NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293642774r7kxrlicxtt22ky/1sevr1293642876.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293642774r7kxrlicxtt22ky/1sevr1293642876.ps (open in new window)


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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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