Home » date » 2008 » Jun » 01 »

Additief model - pilsbier - niels baert (correctie)

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 07:13:33 -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/t1212326148ipdfyukmxhz8ukc.htm/, Retrieved Sun, 01 Jun 2008 13:15:48 +0000
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
4,43 4,43 4,44 4,44 4,44 4,45 4,47 4,48 4,48 4,5 4,52 4,52 4,53 4,53 4,63 4,66 4,67 4,68 4,69 4,69 4,7 4,71 4,72 4,72 4,72 4,73 4,74 4,76 4,81 4,82 4,83 4,83 4,84 4,89 4,92 4,95 4,95 5,01 5,05 5,08 5,11 5,14 5,17 5,18 5,2 5,22 5,24 5,28 5,29 5,33 5,4 5,43 5,46 5,46 5,46 5,47 5,49 5,5 5,54 5,55 5,55 5,56 5,6 5,61 5,63 5,64 5,66 5,67 5,69 5,77 5,77 5,78 5,8 5,82 5,85 5,87 5,88 5,9 5,91 5,94 5,97 5,98 6 6,01 6,02
 
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 time5 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
14.43NANA-0.0241041666666667NA
24.43NANA-0.0199374999999998NA
34.44NANA0.0120624999999997NA
44.44NANA0.0153958333333331NA
54.44NANA0.0223958333333335NA
64.45NANA0.0134791666666667NA
74.474.477229166666674.470833333333330.00639583333333344-0.00722916666666684
84.484.470229166666674.47916666666667-0.008937500000000340.0097708333333335
94.484.477395833333334.49125-0.01385416666666630.00260416666666696
104.54.508229166666674.50833333333333-0.000104166666666335-0.00822916666666629
114.524.52681254.52708333333333-0.000270833333333383-0.00681249999999967
124.524.543729166666674.54625-0.00252083333333365-0.0237291666666666
134.534.540895833333334.565-0.0241041666666667-0.0108958333333327
144.534.562979166666674.58291666666667-0.0199374999999998-0.0329791666666654
154.634.612895833333334.600833333333330.01206249999999970.0171041666666669
164.664.634145833333334.618750.01539583333333310.0258541666666678
174.674.658229166666674.635833333333330.02239583333333350.0117708333333333
184.684.665979166666674.65250.01347916666666670.0140208333333325
194.694.675145833333334.668750.006395833333333440.0148541666666668
204.694.67606254.685-0.008937500000000340.0139375000000008
214.74.68406254.69791666666667-0.01385416666666630.0159374999999997
224.714.70656254.70666666666667-0.0001041666666663350.00343750000000043
234.724.716395833333334.71666666666667-0.0002708333333333830.0036041666666673
244.724.72581254.72833333333333-0.00252083333333365-0.00581250000000022
254.724.715895833333334.74-0.02410416666666670.00410416666666613
264.734.731729166666674.75166666666667-0.0199374999999998-0.00172916666666634
274.744.775395833333334.763333333333330.0120624999999997-0.0353958333333333
284.764.79206254.776666666666670.0153958333333331-0.0320624999999994
294.814.814895833333334.79250.0223958333333335-0.00489583333333332
304.824.823895833333334.810416666666670.0134791666666667-0.00389583333333299
314.834.835979166666674.829583333333330.00639583333333344-0.0059791666666662
324.834.841895833333334.85083333333333-0.00893750000000034-0.0118958333333339
334.844.86156254.87541666666667-0.0138541666666663-0.0215624999999999
344.894.90156254.90166666666667-0.000104166666666335-0.0115624999999993
354.924.927229166666674.9275-0.000270833333333383-0.00722916666666595
364.954.95081254.95333333333333-0.00252083333333365-0.000812500000000327
374.954.956729166666674.98083333333333-0.0241041666666667-0.00672916666666623
385.014.989645833333335.00958333333333-0.01993749999999980.0203541666666665
395.055.051229166666675.039166666666670.0120624999999997-0.00122916666666661
405.085.08331255.067916666666670.0153958333333331-0.00331249999999894
415.115.117395833333335.0950.0223958333333335-0.00739583333333194
425.145.13556255.122083333333330.01347916666666670.00443750000000165
435.175.156395833333335.150.006395833333333440.0136041666666671
445.185.16856255.1775-0.008937500000000340.0114374999999987
455.25.19156255.20541666666667-0.01385416666666630.0084375000000012
465.225.234479166666675.23458333333333-0.000104166666666335-0.0144791666666668
475.245.263479166666675.26375-0.000270833333333383-0.0234791666666663
485.285.289145833333335.29166666666667-0.00252083333333365-0.0091458333333323
495.295.292979166666675.31708333333333-0.0241041666666667-0.00297916666666698
505.335.32131255.34125-0.01993749999999980.00868749999999974
515.45.377479166666675.365416666666670.01206249999999970.0225208333333331
525.435.40456255.389166666666670.01539583333333310.0254374999999989
535.465.435729166666675.413333333333330.02239583333333350.0242708333333326
545.465.45056255.437083333333330.01347916666666670.00943750000000065
555.465.46556255.459166666666670.00639583333333344-0.0055625000000008
565.475.470645833333335.47958333333333-0.00893750000000034-0.000645833333333456
575.495.483645833333335.4975-0.01385416666666630.0063541666666671
585.55.513229166666675.51333333333333-0.000104166666666335-0.0132291666666671
595.545.527645833333335.52791666666667-0.0002708333333333830.0123541666666664
605.555.539979166666675.5425-0.002520833333333650.0100208333333329
615.555.534229166666675.55833333333333-0.02410416666666670.0157708333333328
625.565.55506255.575-0.01993749999999980.00493750000000137
635.65.603729166666675.591666666666670.0120624999999997-0.00372916666666701
645.615.626645833333335.611250.0153958333333331-0.0166458333333335
655.635.654479166666675.632083333333330.0223958333333335-0.0244791666666666
665.645.664729166666675.651250.0134791666666667-0.0247291666666669
675.665.677645833333335.671250.00639583333333344-0.0176458333333329
685.675.68356255.6925-0.00893750000000034-0.0135624999999999
695.695.699895833333335.71375-0.0138541666666663-0.00989583333333321
705.775.734895833333335.735-0.0001041666666663350.0351041666666676
715.775.755979166666675.75625-0.0002708333333333830.0140208333333325
725.785.774979166666675.7775-0.002520833333333650.00502083333333303
735.85.774645833333335.79875-0.02410416666666670.0253541666666663
745.825.800479166666675.82041666666667-0.01993749999999980.0195208333333339
755.855.855395833333335.843333333333330.0120624999999997-0.00539583333333216
765.875.879145833333335.863750.0153958333333331-0.0091458333333323
775.885.904479166666675.882083333333330.0223958333333335-0.0244791666666666
785.95.914729166666675.901250.0134791666666667-0.0147291666666671
795.915.926395833333335.920.00639583333333344-0.0163958333333332
805.94NANA-0.00893750000000034NA
815.97NANA-0.0138541666666663NA
825.98NANA-0.000104166666666335NA
836NANA-0.000270833333333383NA
846.01NANA-0.00252083333333365NA
856.02NANANANA
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212326148ipdfyukmxhz8ukc/1nihm1212326005.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212326148ipdfyukmxhz8ukc/1nihm1212326005.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212326148ipdfyukmxhz8ukc/2gwbd1212326005.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212326148ipdfyukmxhz8ukc/2gwbd1212326005.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212326148ipdfyukmxhz8ukc/3o15v1212326005.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212326148ipdfyukmxhz8ukc/3o15v1212326005.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212326148ipdfyukmxhz8ukc/4fplw1212326005.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212326148ipdfyukmxhz8ukc/4fplw1212326005.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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