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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, 07 Dec 2010 14:21:30 +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/07/t129173159672pfbwpqwntq0lx.htm/, Retrieved Tue, 07 Dec 2010 15:19:56 +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/07/t129173159672pfbwpqwntq0lx.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 «
1145,11 1176,86 1206,41 1192,72 1214,82 1199,07 1157,47 1100,1 1095,63 1105,63 1137,79 1124,72 1152,6 1211,85 1239,62 1244,13 1198,42 1227,99 1304,92 1340,26 1307,32 1356,51 1383,29 1437,87 1494,56 1521,42 1498,76 1488,75 1524,62 1439,27 1423,11 1466,85 1425,83 1363,45 1389,18 1395,89 1368,43 1349,03 1299,88 1365,41 1451,04 1433,75 1464,65 1475,57 1471,16 1429,12 1452,46 1538,09 1631,59 1665,5 1690,6 1711,74 1734,1 1748,09 1703,45 1745,74 1751,01 1795,65 1852,13 1877,1 1989,31 2097,76 2154,87 2152,18 2250,27 2346,9 2525,56 2409,36 2394,36 2401,33 2354,32 2450,41 2504,67 2661,39 2880,4 3064,42 3141,12 3327,7 3564,95 3403,13 3149,9 3006,84 3230,66 3361,13 3484,74 3411,13 3288,18 3280,37 3173,95 3165,26 3092,71 3053,05 3181,96 2999,93 3249,57 3210,52 3030,29 2803,47 2767,63 2882,6 2863,36 2897,06 3012,61 3142,95 3032,93 3045,78 3110,52 3013,24 2987,1 2995,55 2833,18 2848,96 2794,83 2845,26 2915,02 2 etc...
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
11145.11NANA9.13965686274509NA
21176.86NANA9.49977941176475NA
31206.41NANA-22.5539215686275NA
41192.72NANA48.7108333333335NA
51214.82NANA52.4086029411764NA
61199.07NANA20.0159803921569NA
71157.471175.823112745101155.0062520.8168627450981-18.3531127450979
81100.11163.998921568631156.776257.22267156862735-63.8989215686274
91095.631137.830514705881159.61791666667-21.7874019607844-42.2005147058824
101105.631097.721789215691163.14375-65.42196078431377.90821078431395
111137.791127.130441176471164.6025-37.472058823529410.6595588235293
121124.721144.545122549021165.12416666667-20.5790441176471-19.8251225490196
131152.61181.612573529411172.472916666679.13965686274509-29.0125735294118
141211.851198.123112745101188.623333333339.4997794117647513.7268872549016
151239.621184.896495098041207.45041666667-22.553921568627554.7235049019607
161244.131275.4351226.7241666666748.7108333333335-31.3049999999998
171198.421299.815269607841247.4066666666752.4086029411764-101.395269607843
181227.991290.699730392161270.6837520.0159803921569-62.7097303921566
191304.921318.796862745101297.9820.8168627450981-13.8768627450979
201340.261332.349754901961325.127083333337.222671568627357.91024509803924
211307.321327.035931372551348.82333333333-21.7874019607844-19.715931372549
221356.511304.391372549021369.81333333333-65.421960784313752.1186274509805
231383.291356.125441176471393.5975-37.472058823529427.1645588235294
241437.871395.413455882351415.9925-20.579044117647142.4565441176471
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261521.421449.419362745101439.919583333339.4997794117647572.0006372549021
271498.761427.578161764711450.13208333333-22.553921568627571.1818382352942
281488.751504.071455.3591666666748.7108333333335-15.3199999999999
291524.621508.302352941181455.8937552.408602941176416.3176470588235
301439.271474.405980392161454.3920.0159803921569-35.1359803921569
311423.111468.202279411761447.3854166666720.8168627450981-45.0922794117648
321466.851442.169754901961434.947083333337.2226715686273524.6802450980392
331425.831397.690098039221419.4775-21.787401960784428.1399019607843
341363.451340.629705882351406.05166666667-65.421960784313722.8202941176469
351389.181360.374607843141397.84666666667-37.472058823529428.8053921568628
361395.891373.971789215691394.55083333333-20.579044117647121.9182107843139
371368.431405.191323529411396.051666666679.13965686274509-36.7613235294118
381349.031407.645612745101398.145833333339.49977941176475-58.6156127450984
391299.881377.843995098041400.39791666667-22.5539215686275-77.9639950980395
401365.411453.733751405.0229166666748.7108333333335-88.3237500000002
411451.041462.804436274511410.3958333333352.4086029411764-11.7644362745098
421433.751438.973480392161418.957520.0159803921569-5.22348039215672
431464.651456.664362745101435.847520.81686274509817.98563725490249
441475.571467.221421568631459.998757.222671568627358.34857843137274
451471.161467.677598039221489.465-21.78740196078443.4824019607845
461429.121454.753455882351520.17541666667-65.4219607843137-25.6334558823532
471452.461508.927941176471546.4-37.4720588235294-56.4679411764705
481538.091550.712622549021571.29166666667-20.5790441176471-12.6226225490193
491631.591603.478823529411594.339166666679.1396568627450928.1111764705884
501665.51625.046029411761615.546259.4997794117647540.4539705882355
511690.61615.909828431371638.46375-22.553921568627574.6901715686272
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531734.11749.729852941181697.3212552.4086029411764-15.6298529411768
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551703.451777.946862745101757.1320.8168627450981-74.4968627450976
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571751.011805.613848039221827.40125-21.7874019607844-54.6038480392156
581795.651799.675539215691865.0975-65.4219607843137-4.02553921568574
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632154.872104.427328431372126.98125-22.553921568627550.4426715686277
642152.182227.7352179.0241666666748.7108333333335-75.5550000000003
652250.272277.594019607842225.1854166666752.4086029411764-27.3240196078432
662346.92290.013897058822269.9979166666720.015980392156956.8861029411764
672525.562336.176029411772315.3591666666720.8168627450981189.383970588235
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712354.322519.929191176472557.40125-37.4720588235294-165.609191176470
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732504.672728.700906862752719.561259.13965686274509-224.030906862745
742661.392813.776029411762804.276259.49977941176475-152.386029411764
752880.42854.610245098042877.16416666667-22.553921568627525.7897549019617
763064.422982.585416666672933.8745833333348.710833333333581.8345833333337
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803403.133228.160171568633220.93757.22267156862735174.969828431372
813149.93247.380098039223269.1675-21.7874019607844-97.480098039216
823006.843229.734289215693295.15625-65.4219607843137-222.894289215686
833230.663268.050024509803305.52208333333-37.4720588235294-37.3900245098039
843361.133279.542622549023300.12166666667-20.579044117647181.58737745098
853484.743282.816323529413273.676666666679.13965686274509201.923676470588
863411.133248.91311274513239.413333333339.49977941176475162.216887254902
873288.183203.608578431373226.1625-22.553921568627584.5714215686276
883280.373275.921253227.2104166666748.71083333333354.44875000000002
893173.953280.119019607843227.7104166666752.4086029411764-106.169019607843
903165.263242.238897058823222.2229166666720.0159803921569-76.9788970588233
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1053032.932960.221348039222982.00875-21.787401960784472.7086519607842
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1252799.432542.734019607842490.3254166666752.4086029411764256.695980392157
1262555.282457.800980392162437.78520.015980392156997.4790196078434
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1282214.952312.923504901962305.700833333337.22267156862735-97.9735049019614
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2041862.832245.003039215692265.58208333333-20.5790441176471-382.173039215686
2051905.412173.199240196082164.059583333339.13965686274509-267.789240196078
2061810.992102.084779411762092.5859.49977941176475-291.094779411765
2071670.072017.002328431372039.55625-22.5539215686275-346.932328431373
2081864.442079.778752031.0679166666748.7108333333335-215.33875
2092052.022115.469019607842063.0604166666752.4086029411764-63.4490196078427
2102029.62128.588897058822108.5729166666720.0159803921569-98.9888970588231
2112070.83NANA20.8168627450981NA
2122293.41NANA7.22267156862735NA
2132443.27NANA-21.7874019607844NA
2142513.17NANA-65.4219607843137NA
2152466.92NANA-37.4720588235294NA
2162502.66NANA-20.5790441176471NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/07/t129173159672pfbwpqwntq0lx/1zppw1291731685.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/07/t129173159672pfbwpqwntq0lx/1zppw1291731685.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/07/t129173159672pfbwpqwntq0lx/2zppw1291731685.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/07/t129173159672pfbwpqwntq0lx/2zppw1291731685.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/07/t129173159672pfbwpqwntq0lx/3sg6h1291731685.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/07/t129173159672pfbwpqwntq0lx/3sg6h1291731685.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/07/t129173159672pfbwpqwntq0lx/4sg6h1291731685.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/07/t129173159672pfbwpqwntq0lx/4sg6h1291731685.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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Software written by Ed van Stee & Patrick Wessa


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