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jan-pieter onzea-decompositie-aardappelprijs

R Software Module: rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Mon, 11 Aug 2008 10:50:19 -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/Aug/11/t1218473474669mblsw89g2cw1.htm/, Retrieved Mon, 11 Aug 2008 16:51:15 +0000
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
0,31 0,32 0,31 0,32 0,31 0,91 0,68 0,48 0,43 0,39 0,44 0,5 0,56 0,59 0,6 0,59 0,59 0,78 0,64 0,47 0,4 0,36 0,36 0,36 0,36 0,35 0,35 0,35 0,33 0,78 0,71 0,62 0,52 0,46 0,43 0,43 0,42 0,42 0,42 0,42 0,43 0,99 1,03 0,83 0,64 0,6 0,58 0,58 0,58 0,57 0,57 0,56 0,56 0,88 0,84 0,69 0,59 0,54 0,52 0,52 0,51 0,52 0,51 0,51 0,53 0,95 0,98 0,88 0,81 0,77 0,76 0,75 0,73 0,74 0,73 0,75 0,77 1,09 1,03 0,9 0,76 0,66 0,63 0,61 0,61 0,61 0,61 0,61 0,62 0,76 0,83 0,81 0,77 0,75 0,76 0,76
 
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
10.31NANA-0.0768171296296297NA
20.32NANA-0.0771643518518519NA
30.31NANA-0.0840393518518519NA
40.32NANA-0.0882060185185185NA
50.31NANA-0.086400462962963NA
60.91NANA0.288182870370370NA
70.680.7074884259259260.4604166666666670.247071759259259-0.0274884259259258
80.480.5886689814814810.4820833333333330.106585648148148-0.108668981481481
90.430.5001273148148150.505416666666667-0.00528935185185182-0.0701273148148148
100.390.4682523148148150.52875-0.0604976851851852-0.0782523148148148
110.440.4724884259259260.551666666666667-0.0791782407407407-0.0324884259259259
120.50.4736689814814820.557916666666667-0.08424768518518520.0263310185185185
130.560.4740162037037040.550833333333333-0.07681712962962970.0859837962962964
140.590.4715856481481480.54875-0.07716435185185190.118414351851852
150.60.4630439814814810.547083333333333-0.08403935185185190.136956018518518
160.590.4563773148148150.544583333333333-0.08820601851851850.133622685185185
170.590.4535995370370370.54-0.0864004629629630.136400462962963
180.780.8190162037037040.5308333333333330.288182870370370-0.0390162037037037
190.640.7637384259259260.5166666666666670.247071759259259-0.123738425925926
200.470.6049189814814810.4983333333333330.106585648148148-0.134918981481481
210.40.4726273148148150.477916666666667-0.00528935185185182-0.0726273148148148
220.360.3970023148148150.4575-0.0604976851851852-0.0370023148148148
230.360.3574884259259260.436666666666667-0.07917824074074070.00251157407407404
240.360.3415856481481480.425833333333333-0.08424768518518520.0184143518518519
250.360.3519328703703700.42875-0.07681712962962970.00806712962962963
260.350.3607523148148150.437916666666667-0.0771643518518519-0.0107523148148149
270.350.3651273148148150.449166666666667-0.0840393518518519-0.0151273148148149
280.350.3701273148148150.458333333333333-0.0882060185185185-0.020127314814815
290.330.3790162037037040.465416666666667-0.086400462962963-0.0490162037037037
300.780.759432870370370.471250.2881828703703700.0205671296296295
310.710.7237384259259260.4766666666666670.247071759259259-0.0137384259259261
320.620.5886689814814810.4820833333333330.1065856481481480.0313310185185186
330.520.4826273148148150.487916666666667-0.005289351851851820.0373726851851852
340.460.4332523148148150.49375-0.06049768518518520.0267476851851852
350.430.4216550925925930.500833333333333-0.07917824074074070.00834490740740734
360.430.4295023148148150.51375-0.08424768518518520.000497685185185115
370.420.4590162037037040.535833333333333-0.0768171296296297-0.0390162037037036
380.420.4807523148148150.557916666666667-0.0771643518518519-0.0607523148148147
390.420.4876273148148150.571666666666667-0.0840393518518519-0.0676273148148147
400.420.4942939814814810.5825-0.0882060185185185-0.0742939814814814
410.430.508182870370370.594583333333333-0.086400462962963-0.0781828703703704
420.990.8952662037037040.6070833333333330.2881828703703700.0947337962962963
431.030.867071759259260.620.2470717592592590.162928240740741
440.830.7395023148148150.6329166666666670.1065856481481480.0904976851851852
450.640.6401273148148150.645416666666667-0.00528935185185182-0.000127314814814650
460.60.5970023148148150.6575-0.06049768518518520.00299768518518528
470.580.5895717592592590.66875-0.0791782407407407-0.00957175925925935
480.580.5853356481481480.669583333333333-0.0842476851851852-0.00533564814814824
490.580.5802662037037040.657083333333333-0.0768171296296297-0.000266203703703560
500.570.5661689814814810.643333333333333-0.07716435185185190.00383101851851853
510.570.5513773148148150.635416666666667-0.08403935185185190.0186226851851852
520.560.5426273148148150.630833333333333-0.08820601851851850.0173726851851852
530.560.539432870370370.625833333333333-0.0864004629629630.0205671296296297
540.880.9090162037037040.6208333333333330.288182870370370-0.0290162037037038
550.840.8624884259259260.6154166666666670.247071759259259-0.0224884259259259
560.690.7170023148148150.6104166666666670.106585648148148-0.0270023148148149
570.590.6005439814814810.605833333333333-0.00528935185185182-0.0105439814814814
580.540.5407523148148150.60125-0.0604976851851852-0.000752314814814747
590.520.5187384259259260.597916666666667-0.07917824074074070.00126157407407423
600.520.5153356481481480.599583333333333-0.08424768518518520.0046643518518521
610.510.5315162037037040.608333333333333-0.0768171296296297-0.0215162037037036
620.520.5449189814814810.622083333333333-0.0771643518518519-0.0249189814814814
630.510.5551273148148150.639166666666667-0.0840393518518519-0.0451273148148148
640.510.5697106481481480.657916666666667-0.0882060185185185-0.0597106481481481
650.530.5910995370370370.6775-0.086400462962963-0.061099537037037
660.950.9852662037037040.6970833333333330.288182870370370-0.0352662037037037
670.980.9629050925925920.7158333333333330.2470717592592590.0170949074074075
680.880.8407523148148150.7341666666666670.1065856481481480.0392476851851853
690.810.7472106481481480.7525-0.005289351851851820.062789351851852
700.770.7111689814814820.771666666666667-0.06049768518518520.0588310185185185
710.760.7124884259259260.791666666666667-0.07917824074074070.0475115740740741
720.750.7232523148148150.8075-0.08424768518518520.0267476851851852
730.730.7385995370370370.815416666666667-0.0768171296296297-0.0085995370370372
740.740.7411689814814810.818333333333333-0.0771643518518519-0.00116898148148148
750.730.7330439814814820.817083333333333-0.0840393518518519-0.00304398148148155
760.750.7222106481481480.810416666666667-0.08820601851851850.0277893518518519
770.770.7140162037037040.800416666666667-0.0864004629629630.0559837962962965
781.091.077349537037040.7891666666666670.2881828703703700.0126504629629631
791.031.025405092592590.7783333333333330.2470717592592590.00459490740740753
800.90.8745023148148150.7679166666666670.1065856481481480.0254976851851852
810.760.7522106481481480.7575-0.005289351851851820.00778935185185181
820.660.6861689814814820.746666666666667-0.0604976851851852-0.0261689814814815
830.630.6554050925925920.734583333333333-0.0791782407407407-0.0254050925925925
840.610.6303356481481480.714583333333333-0.0842476851851852-0.0203356481481481
850.610.615682870370370.6925-0.0768171296296297-0.0056828703703703
860.610.6032523148148150.680416666666667-0.07716435185185190.00674768518518509
870.610.5930439814814810.677083333333333-0.08403935185185190.0169560185185186
880.610.5930439814814810.68125-0.08820601851851850.0169560185185186
890.620.6040162037037030.690416666666666-0.0864004629629630.0159837962962965
900.760.9902662037037040.7020833333333330.288182870370370-0.230266203703703
910.83NANA0.247071759259259NA
920.81NANA0.106585648148148NA
930.77NANA-0.00528935185185182NA
940.75NANA-0.0604976851851852NA
950.76NANA-0.0791782407407407NA
960.76NANA-0.0842476851851852NA
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/11/t1218473474669mblsw89g2cw1/1d3nk1218473412.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/11/t1218473474669mblsw89g2cw1/1d3nk1218473412.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/11/t1218473474669mblsw89g2cw1/2bi8o1218473412.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/11/t1218473474669mblsw89g2cw1/2bi8o1218473412.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/11/t1218473474669mblsw89g2cw1/3imxt1218473412.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/11/t1218473474669mblsw89g2cw1/3imxt1218473412.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/11/t1218473474669mblsw89g2cw1/40y6t1218473412.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/11/t1218473474669mblsw89g2cw1/40y6t1218473412.ps (open in new window)


 
Parameters (Session):
 
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')
 





Copyright

Creative Commons License

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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