Home » date » 2008 » Jun » 01 »

raf mattheussen decompositiemodel eigen reeks

R Software Module: rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Sun, 01 Jun 2008 10:53:27 -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/Jun/01/t12123392459fybnso84jybnmj.htm/, Retrieved Sun, 01 Jun 2008 16:54:09 +0000
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
14,32 14,67 14,8 14,8 14,8 14,8 14,8 14,8 14,8 14,8 14,8 14,8 14,8 15,56 15,56 15,56 15,56 15,56 15,56 15,56 15,56 15,56 15,56 15,56 15,56 16,8 16,8 16,8 16,8 16,8 16,8 16,8 16,8 16,8 16,8 16,8 16,8 17,43 17,43 17,43 17,43 17,43 17,43 17,43 17,43 17,43 17,43 17,43 17,43 18,61 18,61 18,61 18,61 18,61 18,61 18,61 18,61 18,61 18,61 18,61 18,61 20 20 20 20 20 20 20 20 20 20 20 20 20,61 20,61 20,61 20,61 20,61 20,61 20,61 20,61 20,61 20,61 20,61 20,61 19,47 19,47 19,47 19,47 19,47
 
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 time6 seconds
R Server'George Udny Yule' @ 72.249.76.132


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
114.32NANA-0.479270833333334NA
214.67NANA0.4740625NA
314.8NANA0.387395833333333NA
414.8NANA0.300729166666666NA
514.8NANA0.214062500000001NA
614.8NANA0.127395833333334NA
714.814.809895833333314.76916666666670.0407291666666671-0.00989583333333677
814.814.781562514.82625-0.04468749999999970.0184374999999974
914.814.766145833333314.895-0.1288541666666660.0338541666666607
1014.814.745312514.9583333333333-0.2130208333333340.0546874999999947
1114.814.724479166666715.0216666666667-0.2971875000000010.0755208333333304
1214.814.703645833333315.085-0.3813541666666670.0963541666666643
1314.814.669062515.1483333333333-0.4792708333333340.130937499999998
1415.5615.685729166666715.21166666666670.4740625-0.125729166666670
1515.5615.662395833333315.2750.387395833333333-0.102395833333334
1615.5615.639062515.33833333333330.300729166666666-0.079062500000001
1715.5615.615729166666715.40166666666670.214062500000001-0.0557291666666675
1815.5615.592395833333315.4650.127395833333334-0.0323958333333341
1915.5615.569062515.52833333333330.0407291666666671-0.00906250000000064
2015.5615.566979166666715.6116666666667-0.0446874999999997-0.00697916666666742
2115.5615.586145833333315.715-0.128854166666666-0.0261458333333344
2215.5615.605312515.8183333333333-0.213020833333334-0.0453124999999996
2315.5615.624479166666715.9216666666667-0.297187500000001-0.0644791666666649
2415.5615.643645833333316.025-0.381354166666667-0.0836458333333319
2515.5615.649062516.1283333333333-0.479270833333334-0.0890625000000007
2616.816.705729166666716.23166666666670.47406250.0942708333333364
2716.816.722395833333316.3350.3873958333333330.0776041666666671
2816.816.739062516.43833333333330.3007291666666660.0609375000000014
2916.816.755729166666716.54166666666670.2140625000000010.0442708333333321
3016.816.772395833333316.6450.1273958333333340.0276041666666664
3116.816.789062516.74833333333330.04072916666666710.0109374999999972
3216.816.781562516.82625-0.04468749999999970.0184374999999974
3316.816.749895833333316.87875-0.1288541666666660.0501041666666673
3416.816.718229166666716.93125-0.2130208333333340.0817708333333336
3516.816.686562516.98375-0.2971875000000010.113437500000000
3616.816.654895833333317.03625-0.3813541666666670.145104166666670
3716.816.609479166666717.08875-0.4792708333333340.190520833333338
3817.4317.615312517.141250.4740625-0.185312499999998
3917.4317.581145833333317.193750.387395833333333-0.151145833333331
4017.4317.546979166666717.246250.300729166666666-0.116979166666667
4117.4317.512812517.298750.214062500000001-0.0828124999999993
4217.4317.478645833333317.351250.127395833333334-0.0486458333333353
4317.4317.444479166666717.403750.0407291666666671-0.0144791666666677
4417.4317.434479166666717.4791666666667-0.0446874999999997-0.0044791666666697
4517.4317.448645833333317.5775-0.128854166666666-0.0186458333333341
4617.4317.462812517.6758333333333-0.213020833333334-0.0328125000000021
4717.4317.476979166666717.7741666666667-0.297187500000001-0.0469791666666701
4817.4317.491145833333317.8725-0.381354166666667-0.0611458333333346
4917.4317.491562517.9708333333333-0.479270833333334-0.0615625000000009
5018.6118.543229166666718.06916666666670.47406250.066770833333333
5118.6118.554895833333318.16750.3873958333333330.0551041666666663
5218.6118.566562518.26583333333330.3007291666666660.0434374999999996
5318.6118.578229166666718.36416666666670.2140625000000010.0317708333333329
5418.6118.589895833333318.46250.1273958333333340.0201041666666661
5518.6118.601562518.56083333333330.04072916666666710.00843749999999943
5618.6118.623229166666718.6679166666667-0.0446874999999997-0.0132291666666653
5718.6118.654895833333318.78375-0.128854166666666-0.0448958333333316
5818.6118.686562518.8995833333333-0.213020833333334-0.0765624999999979
5918.6118.718229166666719.0154166666667-0.297187500000001-0.108229166666668
6018.6118.749895833333319.13125-0.381354166666667-0.139895833333334
6118.6118.767812519.2470833333333-0.479270833333334-0.157812499999995
622019.836979166666719.36291666666670.47406250.163020833333338
632019.866145833333319.478750.3873958333333330.133854166666669
642019.895312519.59458333333330.3007291666666660.104687500000001
652019.924479166666719.71041666666670.2140625000000010.0755208333333393
662019.953645833333319.826250.1273958333333340.0463541666666707
672019.982812519.94208333333330.04072916666666710.0171875000000021
682019.980729166666720.0254166666667-0.04468749999999970.0192708333333371
692019.947395833333320.07625-0.1288541666666660.0526041666666721
702019.914062520.1270833333333-0.2130208333333340.0859375000000071
712019.880729166666720.1779166666667-0.2971875000000010.119270833333335
722019.847395833333320.22875-0.3813541666666670.15260416666667
732019.800312520.2795833333333-0.4792708333333340.199687500000003
7420.6120.804479166666720.33041666666670.4740625-0.194479166666664
7520.6120.768645833333320.381250.387395833333333-0.158645833333331
7620.6120.732812520.43208333333330.300729166666666-0.122812499999998
7720.6120.696979166666720.48291666666670.214062500000001-0.0869791666666657
7820.6120.661145833333320.533750.127395833333334-0.051145833333333
7920.6120.625312520.58458333333330.0407291666666671-0.0153125000000003
8020.6120.517812520.5625-0.04468749999999970.0921874999999979
8120.6120.338645833333320.4675-0.1288541666666660.271354166666669
8220.6120.159479166666720.3725-0.2130208333333340.450520833333336
8320.6119.980312520.2775-0.2971875000000010.629687500000003
8420.6119.801145833333320.1825-0.3813541666666670.80885416666667
8520.61NANANANA
8619.47NANANANA
8719.47NANANANA
8819.47NANANANA
8919.47NANANANA
9019.47NANANANA
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t12123392459fybnso84jybnmj/1e8681212339200.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t12123392459fybnso84jybnmj/1e8681212339200.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t12123392459fybnso84jybnmj/2q8ax1212339200.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t12123392459fybnso84jybnmj/2q8ax1212339200.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t12123392459fybnso84jybnmj/34sfz1212339200.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t12123392459fybnso84jybnmj/34sfz1212339200.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t12123392459fybnso84jybnmj/4682e1212339200.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t12123392459fybnso84jybnmj/4682e1212339200.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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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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