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Clélia Comes - verbetering taak 9

R Software Module: rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Wed, 21 May 2008 08:05:50 -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/21/t12113788698egg78p87mh5omh.htm/, Retrieved Wed, 21 May 2008 16:07:49 +0200
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
1.48 1.57 1.58 1.58 1.58 1.58 1.59 1.6 1.6 1.61 1.61 1.61 1.62 1.63 1.63 1.64 1.64 1.64 1.64 1.64 1.65 1.65 1.65 1.65 1.65 1.66 1.66 1.67 1.68 1.68 1.68 1.68 1.69 1.7 1.7 1.71 1.72 1.73 1.74 1.74 1.75 1.75 1.75 1.76 1.79 1.83
 
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
11.48NANA0.998930426407109NA
21.57NANA1.00277601969426NA
31.58NANA1.00046606725575NA
41.58NANA1.00429045486190NA
51.58NANA1.00224297177349NA
61.58NANA1.00020382025412NA
71.591.585833864111431.588333333333330.9984263572579811.00262709479401
81.61.591729514806571.596666666666670.9969078380834441.00519591118749
91.61.603593279102351.601251.001463406152910.997759232874584
101.611.605744830980431.605833333333330.9999448869623851.00264996588342
111.611.607900152819911.610833333333330.9981790912487781.00130595620407
121.611.609642526527361.615833333333330.9961686600478751.00022208252252
131.621.618683511790521.620416666666670.9989304264071091.00081330797521
141.631.628675385320091.624166666666671.002776019694261.00081330797521
151.631.628675385320091.627916666666671.000466067255751.00081330797521
161.641.638667258849661.631666666666671.004290454861901.00081330797521
171.641.638667258849661.6351.002242971773491.00081330797521
181.641.638667258849661.638333333333331.000203820254121.00081330797521
191.641.638667258849661.641250.9984263572579811.00081330797521
201.641.638667258849661.643750.9969078380834441.00081330797521
211.651.648659132379231.646251.001463406152911.00081330797521
221.651.648659132379231.648750.9999448869623851.00081330797521
231.651.648659132379231.651666666666670.9981790912487781.00081330797521
241.651.648659132379231.6550.9961686600478751.00081330797521
251.651.656559623791791.658333333333330.9989304264071090.9960402126808
261.661.666279486058621.661666666666671.002776019694260.996231432895163
271.661.665776001980821.6651.000466067255750.996532545808106
281.671.675909696550791.668751.004290454861900.99647373807613
291.681.676668971529411.672916666666671.002242971773491.00198669416990
301.681.677841908476281.67751.000203820254121.00128623055177
311.681.680268357068741.682916666666670.9984263572579810.999840289161184
321.681.683528111563421.688750.9969078380834440.997904334629649
331.691.697480473429191.6951.001463406152910.995593190292154
341.71.701156238944761.701250.9999448869623850.999320321721023
351.71.703974890352601.707083333333330.9981790912487780.99766728349396
361.711.706353900607001.712916666666670.9961686600478751.00213677795192
371.72NA1.71875NANA
381.73NA1.725NANA
391.74NA1.7325NANA
401.74NA1.74208333333333NANA
411.75NANANANA
421.75NANANANA
431.75NANANANA
441.76NANANANA
451.79NANANANA
461.83NANANANA
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/21/t12113788698egg78p87mh5omh/1dm4i1211378745.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/21/t12113788698egg78p87mh5omh/1dm4i1211378745.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/21/t12113788698egg78p87mh5omh/23iag1211378745.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/21/t12113788698egg78p87mh5omh/23iag1211378745.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/21/t12113788698egg78p87mh5omh/3av341211378745.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/21/t12113788698egg78p87mh5omh/3av341211378745.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/21/t12113788698egg78p87mh5omh/4vz941211378745.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/21/t12113788698egg78p87mh5omh/4vz941211378745.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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