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Decompositie additief model - Bioscoop - Evy Heynen

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 04:42:31 -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/t1211625844m6l8s3affh6y29z.htm/, Retrieved Sat, 24 May 2008 12:44:04 +0200
 
User-defined keywords:
Decompositie additief model - Bioscoop - Evy Heynen
 
Dataseries X:
» Textbox « » Textfile « » CSV «
5,44 5,44 5,44 5,44 5,49 5,49 5,49 5,49 5,49 5,49 5,6 5,6 5,6 5,6 5,6 5,6 5,6 5,67 5,67 5,67 5,67 5,67 5,67 5,67 5,67 5,67 5,82 5,82 5,95 5,95 5,95 5,95 5,95 5,95 6,02 6,02 6,05 6,05 6,05 6,12 6,12 6,12 6,12 6,12 6,12 6,12 6,12 6,17 6,17 6,17 6,17 6,17 6,28 6,27 6,28 6,28 6,27 6,27 6,28 6,59 6,59 6,59 6,59 6,59 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,79 6,79 6,79
 
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'Herman Ole Andreas Wold' @ 193.190.124.10:1001


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
15.44NANA0.0107291666666667NA
25.44NANA-0.00827083333333339NA
35.44NANA0.00272916666666691NA
45.44NANA-0.00227083333333334NA
55.49NANA0.0356458333333337NA
65.49NANA0.0304791666666669NA
75.495.513645833333335.498333333333330.0153125000000002-0.0236458333333331
85.495.50981255.51166666666667-0.00185416666666652-0.0198124999999996
95.495.503979166666675.525-0.0210208333333332-0.0139791666666653
105.495.500145833333335.53833333333333-0.0381875000000002-0.0101458333333317
115.65.510229166666675.54958333333333-0.03935416666666710.0897708333333345
125.65.577729166666675.561666666666670.01606249999999930.0222708333333346
135.65.587395833333335.576666666666670.01072916666666670.0126041666666667
145.65.583395833333335.59166666666667-0.008270833333333390.0166041666666663
155.65.609395833333335.606666666666670.00272916666666691-0.00939583333333438
165.65.619395833333335.62166666666667-0.00227083333333334-0.0193958333333342
175.65.667729166666675.632083333333330.0356458333333337-0.067729166666668
185.675.668395833333335.637916666666670.03047916666666690.00160416666666574
195.675.65906255.643750.01531250000000020.0109374999999989
205.675.647729166666675.64958333333333-0.001854166666666520.0222708333333328
215.675.640645833333335.66166666666667-0.02102083333333320.0293541666666659
225.675.64181255.68-0.03818750000000020.0281874999999996
235.675.664395833333335.70375-0.03935416666666710.00560416666666708
245.675.74606255.730.0160624999999993-0.0760625000000008
255.675.76406255.753333333333330.0107291666666667-0.0940625000000006
265.675.768395833333335.77666666666667-0.00827083333333339-0.098395833333334
275.825.802729166666675.80.002729166666666910.0172708333333329
285.825.82106255.82333333333333-0.00227083333333334-0.00106250000000063
295.955.885229166666675.849583333333330.03564583333333370.0647708333333323
305.955.909229166666675.878750.03047916666666690.0407708333333314
315.955.924479166666675.909166666666670.01531250000000020.0255208333333314
325.955.938979166666675.94083333333333-0.001854166666666520.0110208333333324
335.955.945229166666675.96625-0.02102083333333320.00477083333333272
345.955.950145833333335.98833333333333-0.0381875000000002-0.000145833333333734
356.025.96856256.00791666666667-0.03935416666666710.0514374999999996
366.026.038145833333336.022083333333330.0160624999999993-0.0181458333333335
376.056.046979166666676.036250.01072916666666670.00302083333333325
386.056.042145833333336.05041666666667-0.008270833333333390.00785416666666627
396.056.06731256.064583333333330.00272916666666691-0.0173125000000001
406.126.076479166666676.07875-0.002270833333333340.0435208333333339
416.126.125645833333336.090.0356458333333337-0.00564583333333246
426.126.130895833333336.100416666666670.0304791666666669-0.0108958333333318
436.126.126979166666676.111666666666670.0153125000000002-0.00697916666666565
446.126.11981256.12166666666667-0.001854166666666520.000187500000001783
456.126.110645833333336.13166666666667-0.02102083333333320.0093541666666681
466.126.10056256.13875-0.03818750000000020.0194375000000022
476.126.108145833333336.1475-0.03935416666666710.0118541666666685
486.176.176479166666666.160416666666670.0160624999999993-0.00647916666666504
496.176.18406256.173333333333330.0107291666666667-0.0140624999999996
506.176.178395833333336.18666666666667-0.00827083333333339-0.00839583333333227
516.176.20231256.199583333333330.00272916666666691-0.0323124999999989
526.176.20981256.21208333333333-0.00227083333333334-0.0398125
536.286.260645833333336.2250.03564583333333370.0193541666666679
546.276.279645833333336.249166666666660.0304791666666669-0.00964583333333202
556.286.299479166666676.284166666666660.0153125000000002-0.0194791666666649
566.286.31731256.31916666666667-0.00185416666666652-0.0373124999999979
576.276.333145833333336.35416666666667-0.0210208333333332-0.0631458333333326
586.276.350979166666676.38916666666667-0.0381875000000002-0.0809791666666664
596.286.381895833333336.42125-0.0393541666666671-0.101895833333332
606.596.466895833333336.450833333333330.01606249999999930.123104166666667
616.596.491145833333336.480416666666670.01072916666666670.0988541666666674
626.596.50131256.50958333333333-0.008270833333333390.0886875000000007
636.596.541895833333336.539166666666660.002729166666666910.0481041666666684
646.596.566895833333336.56916666666667-0.002270833333333340.0231041666666671
656.636.634395833333336.598750.0356458333333337-0.00439583333333271
666.636.645479166666676.6150.0304791666666669-0.0154791666666663
676.636.633645833333336.618333333333330.0153125000000002-0.00364583333333357
686.636.61981256.62166666666667-0.001854166666666520.0101874999999998
696.636.603979166666676.625-0.02102083333333320.0260208333333320
706.636.590145833333336.62833333333333-0.03818750000000020.0398541666666654
716.636.590645833333336.63-0.03935416666666710.0393541666666657
726.636.64606256.630.0160624999999993-0.0160625000000012
736.636.640729166666676.630.0107291666666667-0.0107291666666685
746.636.621729166666676.63-0.008270833333333390.00827083333333167
756.636.632729166666676.630.00272916666666691-0.00272916666666845
766.636.634395833333346.63666666666667-0.00227083333333334-0.00439583333333537
776.636.685645833333336.650.0356458333333337-0.055645833333335
786.636.69381256.663333333333330.0304791666666669-0.0638125000000018
796.63NANA0.0153125000000002NA
806.63NANA-0.00185416666666652NA
816.63NANA-0.0210208333333332NA
826.79NANA-0.0381875000000002NA
836.79NANA-0.0393541666666671NA
846.79NANA0.0160624999999993NA
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/24/t1211625844m6l8s3affh6y29z/1eahz1211625743.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/24/t1211625844m6l8s3affh6y29z/1eahz1211625743.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/24/t1211625844m6l8s3affh6y29z/2tan01211625743.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/24/t1211625844m6l8s3affh6y29z/2tan01211625743.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/24/t1211625844m6l8s3affh6y29z/3w5e71211625743.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/24/t1211625844m6l8s3affh6y29z/3w5e71211625743.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/24/t1211625844m6l8s3affh6y29z/45b381211625743.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/24/t1211625844m6l8s3affh6y29z/45b381211625743.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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