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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: Wed, 08 Dec 2010 11:38:07 +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/08/t1291808450igmnbi9j6e0buwr.htm/, Retrieved Wed, 08 Dec 2010 12:40:55 +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/08/t1291808450igmnbi9j6e0buwr.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 «
9700 9081 9084 9743 8587 9731 9563 9998 9437 10038 9918 9252 9737 9035 9133 9487 8700 9627 8947 9283 8829 9947 9628 9318 9605 8640 9214 9567 8547 9185 9470 9123 9278 10170 9434 9655 9429 8739 9552 9687 9019 9672 9206 9069 9788 10312 10105 9863 9656 9295 9946 9701 9049 10190 9706 9765 9893 9994 10433 10073 10112 9266 9820 10097 9115 10411 9678 10408 10153 10368 10581 10597 10680 9738 9556
 
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
19700NANA1.01811015886105NA
29081NANA0.94275631726189NA
39084NANA0.9981242055042NA
49743NANA1.01580788866985NA
58587NANA0.928977968043377NA
69731NANA1.0239577741302NA
795639315.154154801019512.541666666670.979249761127741.02660673576414
899989421.892430734869512.166666666670.9905096032170931.06114563220716
994379470.705525029119512.291666666670.9956281679436630.996441075594629
10100389990.311126186869503.666666666671.051205968873791.00477351237722
1199189891.181140111019497.708333333331.041428183827961.00271139103703
1292529633.374056450779498.083333333331.014244002539190.960411164954672
1397379639.55182660979468.083333333331.018110158861051.01010920166654
1490358873.811680767199412.6250.942756317261891.01816449627640
1591339339.947253005569357.50.99812420550420.977842781399117
1694879475.836913470589328.3751.015807888669851.00117805811047
1787008651.107327403949312.50.9289779680433771.00565160860289
1896279526.049832362279303.166666666671.02395777413021.01059727477960
1989479107.430799221799300.416666666670.979249761127740.982384626053321
2092839190.402082216339278.458333333330.9905096032170931.01007550235075
2188299224.868336561029265.3750.9956281679436630.957086830714746
2299479746.869343895179272.083333333331.051205968873791.02053281408047
2396289653.041228742359269.041666666671.041428183827960.997405871564312
2493189375.92512047299244.251.014244002539190.993821930131842
2596059415.100957837459247.6251.018110158861051.02016962356675
2686408732.516077717569262.750.942756317261890.98940556457106
2792149257.394063508659274.791666666670.99812420550420.99531249688509
2895679449.84916165219302.791666666671.015807888669851.01239711199024
2985478643.2110146755793040.9289779680433770.98886860282455
3091859533.00421224499309.958333333331.02395777413020.963494801376685
3194709123.343607840129316.666666666670.979249761127741.03799663884872
3291239225.069918328939313.458333333330.9905096032170930.988935594067842
3392789290.870187194289331.666666666670.9956281679436630.998614749002518
34101709829.564213446579350.751.051205968873791.03463386363433
3594349763.823151797059375.416666666671.041428183827960.96621987651053
3696559549.487625407429415.3751.014244002539191.01104900898681
3794299595.348877212489424.666666666671.018110158861050.982663592607088
3887398872.672516883839411.416666666670.942756317261890.984934356967479
3995529412.727142990249430.416666666670.99812420550421.01479622801065
4096879607.087757752469457.583333333331.015807888669851.00831805061665
4190198817.355676268379491.458333333330.9289779680433771.02286902458459
4296729756.355001727059528.083333333331.02395777413020.991353840475042
4392069348.122230092319546.208333333330.979249761127740.984796708195063
4490699487.926404282669578.833333333330.9905096032170930.955846368697214
4597889576.366564352139618.416666666670.9956281679436631.02209955458845
461031210128.80751258609635.416666666671.051205968873791.01808628381835
471010510036.5037645969637.251.041428183827961.00682471077684
4898639797.681584862119660.083333333331.014244002539191.00666672156797
4996569878.213816349389702.51.018110158861050.977504656157413
5092959194.07385804379752.333333333330.942756317261891.01097730380619
5199469767.352355504179785.708333333330.99812420550421.01829028358900
5297019931.38442621039776.833333333331.015807888669850.976802385616824
5390499082.84983805219777.250.9289779680433770.996273213952047
541019010034.44486721799799.666666666671.02395777413021.01550211644396
5597069623.495423336119827.416666666670.979249761127741.00857324423554
5697659751.773399839619845.208333333330.9905096032170931.00135632767683
5798939795.736637355729838.750.9956281679436631.00992915247163
58999410354.378793406898501.051205968873790.965195517703459
591043310278.11510324419869.251.041428183827961.01506938725633
601007310021.95628992369881.208333333341.014244002539191.00509318825584
611011210068.34588851679889.251.018110158861051.00433577788911
6292669347.311041111979914.8750.942756317261890.99130113026577
6398209933.831155280569952.50.99812420550420.988541062003047
641009710136.6622703799978.916666666671.015807888669850.99608724555272
6591159290.3989990791310000.66666666670.9289779680433770.981120401922833
661041110268.931197493710028.66666666671.02395777413021.01383481881162
6796789865.1253018943810074.16666666670.979249761127740.981031634554257
681040810021.480910548910117.50.9905096032170931.03856905909427
691015310081.896766625510126.16666666670.9956281679436631.00705256510956
7010368NANA1.05120596887379NA
7110581NANA1.04142818382796NA
7210597NANA1.01424400253919NA
7310680NANANANA
749738NANANANA
759556NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/08/t1291808450igmnbi9j6e0buwr/1tias1291808284.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/08/t1291808450igmnbi9j6e0buwr/1tias1291808284.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/08/t1291808450igmnbi9j6e0buwr/2tias1291808284.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/08/t1291808450igmnbi9j6e0buwr/2tias1291808284.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/08/t1291808450igmnbi9j6e0buwr/3tias1291808284.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/08/t1291808450igmnbi9j6e0buwr/3tias1291808284.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/08/t1291808450igmnbi9j6e0buwr/44rru1291808284.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/08/t1291808450igmnbi9j6e0buwr/44rru1291808284.ps (open in new window)


 
Parameters (Session):
par1 = multiplicative ; par2 = 12 ;
 
Parameters (R input):
par1 = multiplicative ; 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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