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Decompositiemodel Jeansbroeken - Stephanie Van Mechelen

R Software Module: rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Sat, 24 May 2008 11:46:28 -0600
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/May/24/t1211653590asqrfk3b23rwil3.htm/, Retrieved Sat, 24 May 2008 20:26:30 +0200
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
47,87 47,87 47,89 47,88 47,91 47,92 47,92 47,91 47,93 48,05 48,03 48,04 48,04 48,06 48,04 48,09 48,12 48,16 48,16 48,16 48,08 48,13 48,16 48,15 48,15 48,15 48,27 48,47 48,51 48,53 48,53 48,53 48,68 48,64 48,67 48,66 48,66 48,67 48,71 48,96 49,01 49,04 49,04 49,04 49,06 49,13 49,19 49,26 49,26 49,26 49,29 49,43 49,43 49,45 49,45 49,46 49,57 49,68 49,71 49,7 49,7 49,8 49,84 50,09 50,2 50,16 50,16 50,29 50,36 51,02 51,03 51,04
 
Text written by user:
 
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
147.87NANA0.999087284126528NA
247.87NANA0.998584259244463NA
347.89NANA0.99877707837678NA
447.88NANA1.00136176424665NA
547.91NANA1.00127310977741NA
647.92NANA1.00112572505239NA
747.9247.96211872552947.94208333333331.000417908251010.99912183350844
847.9147.944857974620847.95708333333330.9997450771009660.999272956974046
947.9347.971543483372347.971251.000006117901290.999133997358524
1048.0547.994049971909847.986251.000162545977441.00116577009281
1148.0348.006697103443248.003751.000061393192061.00048540928585
1248.0447.99357781322248.02250.9993977367530231.00096725830607
1348.0447.998650847648748.04250.9990872841265281.00086146488747
1448.0647.994872036711748.06291666666670.9985842592444631.00135697753790
1548.0448.020785771239648.07958333333330.998777078376781.00040012316441
1648.0948.154652774484448.08916666666661.001361764246650.998657392987813
1748.1248.159150594647948.09791666666671.001273109777410.999187058032285
1848.1648.162072953676548.10791666666671.001125725052390.999956958794557
1948.1648.137191859472948.11708333333331.000417908251011.00047381535245
2048.1648.113148395932848.12541666666670.9997450771009661.00097377963466
2148.0848.139044508120848.138751.000006117901290.998773459076222
2248.1348.171995558214948.16416666666671.000162545977440.999128216347936
2348.1648.199208921632748.196251.000061393192060.999186523544475
2448.1548.198870764980148.22791666666670.9993977367530230.998986059959405
2548.1548.214703472841148.258750.9990872841265280.998658013672582
2648.1548.221217802140448.28958333333330.9985842592444630.998523102373054
2748.2748.270896197949848.330.998777078376780.999981433989829
2848.4748.442127047636948.376251.001361764246651.00057538663271
2948.5148.480392384034848.418751.001273109777411.00061071320815
3048.5348.515804043195148.461251.001125725052391.00029260479312
3148.5348.5240201173348.503751.000417908251011.00012323551626
3248.5348.534291009661648.54666666666670.9997450771009660.999911588083965
3348.6848.586963915097448.58666666666671.000006117901291.00191483635539
3448.6448.63332053254748.62541666666671.000162545977441.00013734343820
3548.6748.669654468680148.66666666666671.000061393192061.00000709952277
3648.6648.679414510068848.708750.9993977367530230.99960117617962
3748.6648.706753960273448.751250.9990872841265280.999040092872714
3848.6748.724670699509548.793750.9985842592444630.998877966772795
3948.7148.771117051370248.83083333333330.998777078376780.998746859718104
4048.9648.933628780254748.86708333333331.001361764246651.00053891813059
4149.0148.971433404954848.90916666666671.001273109777411.00078753249320
4249.0449.010944141377248.95583333333331.001125725052391.00059284429492
4349.0449.02631327543149.00583333333331.000417908251011.00027917099318
4449.0449.042911317636749.05541666666670.9997450771009660.999940637340679
4549.0649.104467081111349.10416666666671.000006117901290.99909443918742
4649.1349.155905462820249.14791666666671.000162545977440.999472993883923
4749.1949.188019624151349.1851.000061393192061.00004026134542
4849.2649.189940187260149.21958333333330.9993977367530231.00142427115124
4949.2649.20879532054749.253750.9990872841265281.00104055949997
5049.2649.218553831060849.28833333333330.9985842592444631.00084208424899
5149.2949.266760176514649.32708333333330.998777078376781.00047171406039
5249.4349.438482003062349.371251.001361764246650.99982843318163
5349.4349.478745113908749.41583333333331.001273109777410.999014827199104
5449.4549.511507003903449.45583333333331.001125725052390.998757723050148
5549.4549.513183324113249.49251.000417908251010.998723909070851
5649.4649.520706152401249.53333333333330.9997450771009660.99877412587344
5749.5749.579053317898649.578751.000006117901290.99981739631371
5849.6849.637233688071949.62916666666671.000162545977441.00086157726268
5949.7149.691800550971849.688751.000061393192061.00036624652008
6049.749.720453819186549.75041666666670.9993977367530230.999588623642477
6149.749.76412133597449.80958333333330.9990872841265280.998711494662167
6249.849.803141699493549.873750.9985842592444630.999936917644423
6349.8449.880175765484449.941250.998777078376780.999194554452389
6450.0950.098129065259850.031.001361764246650.999837737148842
6550.250.204668118497350.14083333333331.001273109777410.999907018238099
6650.1650.308236226757650.25166666666671.001125725052390.997053440194376
6750.16NANA1.00041790825101NA
6850.29NANA0.999745077100966NA
6950.36NANA1.00000611790129NA
7051.02NANA1.00016254597744NA
7151.03NANA1.00006139319206NA
7251.04NANA0.999397736753023NA
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/24/t1211653590asqrfk3b23rwil3/1qw351211651184.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/24/t1211653590asqrfk3b23rwil3/1qw351211651184.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/24/t1211653590asqrfk3b23rwil3/2pot01211651184.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/24/t1211653590asqrfk3b23rwil3/2pot01211651184.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/24/t1211653590asqrfk3b23rwil3/31y581211651184.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/24/t1211653590asqrfk3b23rwil3/31y581211651184.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/24/t1211653590asqrfk3b23rwil3/4j36d1211651184.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/24/t1211653590asqrfk3b23rwil3/4j36d1211651184.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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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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