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classical composition multiplicative 2

*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: Fri, 26 Nov 2010 11:44:03 +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/26/t1290771745ld5bvwgmhfdykwz.htm/, Retrieved Fri, 26 Nov 2010 12:42:25 +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/26/t1290771745ld5bvwgmhfdykwz.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'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
19700NANA1.01811015886105NA
29081NANA0.942756317261889NA
39084NANA0.998124205504201NA
49743NANA1.01580788866985NA
58587NANA0.928977968043377NA
69731NANA1.0239577741302NA
795639513.52091642789512.541666666670.979249761127741.02660673576414
899989513.157176269889512.166666666670.9905096032170931.06114563220716
994379513.287294834619512.291666666670.9956281679436630.996441075594629
10100389504.717872635549503.666666666671.051205968873791.00477351237722
1199189498.749761517169497.708333333331.041428183827961.00271139103703
1292529499.097577335879498.083333333331.014244002539190.960411164954672
1397379469.10144349229468.083333333331.018110158861051.01010920166654
1490359413.567756317269412.6250.9427563172618891.01816449627640
1591339358.49812420559357.50.9981242055042010.977842781399117
1694879329.390807888679328.3751.015807888669851.00117805811047
1787009313.428977968049312.50.9289779680433771.00565160860289
1896279304.19062444089303.166666666671.02395777413021.01059727477960
1989479301.39591642789300.416666666670.979249761127740.982384626053321
2092839279.448842936559278.458333333330.9905096032170931.01007550235075
2188299266.370628167949265.3750.9956281679436630.957086830714746
2299479273.13453930229272.083333333331.051205968873791.02053281408047
2396289270.08309485059269.041666666671.041428183827960.997405871564312
2493189245.264244002549244.251.014244002539190.993821930131842
2596059248.643110158869247.6251.018110158861051.02016962356675
2686409263.692756317269262.750.9427563172618890.98940556457106
2792149275.789790872179274.791666666670.9981242055042010.99531249688509
2895679303.807474555349302.791666666671.015807888669851.01239711199024
2985479304.9289779680493040.9289779680433770.98886860282455
3091859310.982291107469309.958333333331.02395777413020.963494801376685
3194709317.64591642789316.666666666670.979249761127741.03799663884872
3291239314.448842936559313.458333333330.9905096032170930.988935594067842
3392789332.662294834619331.666666666670.9956281679436630.998614749002518
34101709351.801205968879350.751.051205968873791.03463386363433
3594349376.45809485059375.416666666671.041428183827960.96621987651053
3696559416.389244002549415.3751.014244002539191.01104900898681
3794299425.684776825539424.666666666671.018110158861050.982663592607088
3887399412.359422983939411.416666666670.9427563172618890.98493435696748
3995529431.414790872179430.416666666670.9981242055042011.01479622801065
4096879458.5991412229457.583333333331.015807888669851.00831805061665
4190199492.387311301389491.458333333330.9289779680433771.02286902458459
4296729529.107291107469528.083333333331.02395777413020.991353840475042
4392069547.187583094469546.208333333330.979249761127740.984796708195063
4490699579.823842936559578.833333333330.9905096032170930.955846368697214
4597889619.412294834619618.416666666670.9956281679436631.02209955458845
46103129636.467872635549635.416666666671.051205968873791.01808628381835
47101059638.291428183839637.251.041428183827961.00682471077684
4898639661.097577335879660.083333333331.014244002539191.00666672156797
4996569703.518110158869702.51.018110158861050.977504656157413
5092959753.27608965069752.333333333330.9427563172618891.01097730380619
5199469786.706457538849785.708333333330.9981242055042011.01829028358900
5297019777.8491412229776.833333333331.015807888669850.976802385616824
5390499778.178977968049777.250.9289779680433770.996273213952047
54101909800.69062444089799.666666666671.02395777413021.01550211644396
5597069828.39591642789827.416666666670.979249761127741.00857324423554
5697659846.198842936559845.208333333330.9905096032170931.00135632767683
5798939839.745628167949838.750.9956281679436631.00992915247163
5899949851.0512059688798501.051205968873790.965195517703459
59104339870.291428183839869.251.041428183827961.01506938725633
60100739882.222577335889881.208333333341.014244002539191.00509318825584
61101129890.268110158869889.251.018110158861051.00433577788911
6292669915.817756317269914.8750.9427563172618890.99130113026577
6398209953.49812420559952.50.9981242055042010.988541062003047
64100979979.932474555349978.916666666671.015807888669850.99608724555272
65911510001.595644634710000.66666666670.9289779680433770.981120401922833
661041110029.690624440810028.66666666671.02395777413021.01383481881162
67967810075.145916427810074.16666666670.979249761127740.981031634554257
681040810118.490509603210117.50.9905096032170931.03856905909427
691015310127.162294834610126.16666666670.9956281679436631.00705256510956
7010368NANA1.05120596887379NA
7110581NANA1.04142818382796NA
7210597NANA1.01424400253919NA
7310680NANANANA
749738NANANANA
759556NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Nov/26/t1290771745ld5bvwgmhfdykwz/1tegx1290771838.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/26/t1290771745ld5bvwgmhfdykwz/1tegx1290771838.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/26/t1290771745ld5bvwgmhfdykwz/2tegx1290771838.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/26/t1290771745ld5bvwgmhfdykwz/2tegx1290771838.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/26/t1290771745ld5bvwgmhfdykwz/345f01290771838.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/26/t1290771745ld5bvwgmhfdykwz/345f01290771838.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/26/t1290771745ld5bvwgmhfdykwz/445f01290771838.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/26/t1290771745ld5bvwgmhfdykwz/445f01290771838.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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,m$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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