Maxime Jonckheere-Decompositie 2

*Unverified author*

R Software Module: rwasp_decompose.wasp (opens new window with default values)

Title produced by software: Classical Decomposition

Date of computation: Wed, 27 May 2009 15:59:50 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/May/28/t1243461651slewxj5571i0mm2.htm/, Retrieved Thu, 28 May 2009 00:00:56 +0200

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/May/28/t1243461651slewxj5571i0mm2.htm/}, year = {2009}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2009}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

66.2 66.2 66.2 66.08 66.31 66.39 66.37 66.23 66.27 66.27 66.27 66.28 66.28 66.28 66.26 66.13 65.86 65.9 65.94 65.94 65.91 65.95 65.91 66.08 66.47 66.47 66.56 66.78 67.08 67.28 67.27 67.27 67.26 67.37 67.5 67.63 67.64 67.64 67.71 67.87 67.93 68.33 68.39 68.39 68.58 68.44 68.49 68.52 68.54 68.54 68.54 68.62 68.75 68.71 68.72 68.72 68.72 68.92 68.9 69.12 69.09 69.09 69.1 69.16 68.83 68.52 68.53 68.53 68.51 68.38 68.44 68.41 68.42 68.42 68.45 68.63 68.84 68.72 68.37 68.37 68.47 68.69 68.46 68.17 68.17

Output produced by software:

Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 1 seconds R Server 'George Udny Yule' @ 72.249.76.132 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 66.2 NA NA 0.030671296296297 NA 2 66.2 NA NA 0.00192129629629948 NA 3 66.2 NA NA 0.00178240740740835 NA 4 66.08 NA NA 0.0646990740740723 NA 5 66.31 NA NA 0.0493518518518569 NA 6 66.39 NA NA 0.0493518518518522 NA 7 66.37 66.2422685185185 66.2591666666667 -0.0168981481481444 0.127731481481476 8 66.23 66.2577546296296 66.2658333333333 -0.00807870370370563 -0.0277546296296265 9 66.27 66.2307407407407 66.2716666666667 -0.0409259259259264 0.0392592592592536 10 66.27 66.2217824074074 66.27625 -0.054467592592595 0.048217592592593 11 66.27 66.1962962962963 66.2595833333333 -0.0632870370370409 0.0737037037036998 12 66.28 66.2062962962963 66.2204166666667 -0.0141203703703739 0.073703703703714 13 66.28 66.2127546296296 66.1820833333333 0.030671296296297 0.0672453703703724 14 66.28 66.1540046296296 66.1520833333333 0.00192129629629948 0.125995370370362 15 66.26 66.1267824074074 66.125 0.00178240740740835 0.133217592592601 16 66.13 66.1613657407407 66.0966666666667 0.0646990740740723 -0.0313657407407391 17 65.86 66.1176851851852 66.0683333333333 0.0493518518518569 -0.257685185185181 18 65.9 66.0943518518519 66.045 0.0493518518518522 -0.194351851851849 19 65.94 66.0276851851852 66.0445833333333 -0.0168981481481444 -0.0876851851851796 20 65.94 66.052337962963 66.0604166666667 -0.00807870370370563 -0.112337962962968 21 65.91 66.0399074074074 66.0808333333333 -0.0409259259259264 -0.129907407407401 22 65.95 66.065949074074 66.1204166666667 -0.054467592592595 -0.115949074074052 23 65.91 66.1350462962963 66.1983333333333 -0.0632870370370409 -0.225046296296298 24 66.08 66.2925462962963 66.3066666666667 -0.0141203703703739 -0.212546296296281 25 66.47 66.4502546296296 66.4195833333333 0.030671296296297 0.0197453703703729 26 66.47 66.532337962963 66.5304166666667 0.00192129629629948 -0.062337962962971 27 66.56 66.6438657407407 66.6420833333333 0.00178240740740835 -0.083865740740734 28 66.78 66.8221990740741 66.7575 0.0646990740740723 -0.0421990740740625 29 67.08 66.9322685185185 66.8829166666667 0.0493518518518569 0.147731481481486 30 67.28 67.0631018518518 67.01375 0.0493518518518522 0.216898148148161 31 67.27 67.1101851851852 67.1270833333333 -0.0168981481481444 0.159814814814823 32 67.27 67.2165046296296 67.2245833333333 -0.00807870370370563 0.0534953703703707 33 67.26 67.280324074074 67.32125 -0.0409259259259264 -0.020324074074054 34 67.37 67.3601157407407 67.4145833333333 -0.054467592592595 0.00988425925926606 35 67.5 67.4321296296296 67.4954166666667 -0.0632870370370409 0.0678703703703576 36 67.63 67.560462962963 67.5745833333333 -0.0141203703703739 0.069537037037037 37 67.64 67.6956712962963 67.665 0.030671296296297 -0.055671296296282 38 67.64 67.7602546296296 67.7583333333333 0.00192129629629948 -0.120254629629628 39 67.71 67.8617824074074 67.86 0.00178240740740835 -0.15178240740741 40 67.87 68.0242824074074 67.9595833333333 0.0646990740740723 -0.154282407407408 41 67.93 68.0947685185185 68.0454166666667 0.0493518518518569 -0.164768518518514 42 68.33 68.1731018518519 68.12375 0.0493518518518522 0.156898148148144 43 68.39 68.1814351851852 68.1983333333333 -0.0168981481481444 0.208564814814807 44 68.39 68.2652546296296 68.2733333333333 -0.00807870370370563 0.124745370370377 45 68.58 68.3044907407407 68.3454166666667 -0.0409259259259264 0.275509259259252 46 68.44 68.3567824074074 68.41125 -0.054467592592595 0.0832175925925895 47 68.49 68.4133796296296 68.4766666666667 -0.0632870370370409 0.076620370370378 48 68.52 68.5125462962963 68.5266666666667 -0.0141203703703739 0.00745370370370324 49 68.54 68.5869212962963 68.55625 0.030671296296297 -0.0469212962962899 50 68.54 68.5856712962963 68.58375 0.00192129629629948 -0.0456712962962911 51 68.54 68.6051157407407 68.6033333333333 0.00178240740740835 -0.0651157407407226 52 68.62 68.6938657407407 68.6291666666667 0.0646990740740723 -0.0738657407407288 53 68.75 68.7156018518518 68.66625 0.0493518518518569 0.0343981481481563 54 68.71 68.7576851851852 68.7083333333333 0.0493518518518522 -0.0476851851851876 55 68.72 68.7393518518519 68.75625 -0.0168981481481444 -0.0193518518518658 56 68.72 68.7940046296296 68.8020833333333 -0.00807870370370563 -0.0740046296296413 57 68.72 68.8074074074074 68.8483333333333 -0.0409259259259264 -0.0874074074074116 58 68.92 68.8396990740741 68.8941666666667 -0.054467592592595 0.0803009259259397 59 68.9 68.856712962963 68.92 -0.0632870370370409 0.0432870370370608 60 69.12 68.9012962962963 68.9154166666667 -0.0141203703703739 0.218703703703724 61 69.09 68.9302546296296 68.8995833333333 0.030671296296297 0.159745370370388 62 69.09 68.8856712962963 68.88375 0.00192129629629948 0.204328703703709 63 69.1 68.8688657407407 68.8670833333333 0.00178240740740835 0.231134259259264 64 69.16 68.9005324074074 68.8358333333333 0.0646990740740723 0.259467592592586 65 68.83 68.8435185185185 68.7941666666667 0.0493518518518569 -0.0135185185185236 66 68.52 68.7947685185185 68.7454166666667 0.0493518518518522 -0.274768518518528 67 68.53 68.6710185185185 68.6879166666667 -0.0168981481481444 -0.141018518518521 68 68.53 68.6240046296296 68.6320833333333 -0.00807870370370563 -0.094004629629623 69 68.51 68.5361574074074 68.5770833333333 -0.0409259259259264 -0.0261574074073820 70 68.38 68.473449074074 68.5279166666667 -0.054467592592595 -0.0934490740740586 71 68.44 68.442962962963 68.50625 -0.0632870370370409 -0.00296296296295395 72 68.41 68.5008796296296 68.515 -0.0141203703703739 -0.090879629629626 73 68.42 68.547337962963 68.5166666666667 0.030671296296297 -0.127337962962955 74 68.42 68.5052546296296 68.5033333333333 0.00192129629629948 -0.085254629629631 75 68.45 68.4967824074074 68.495 0.00178240740740835 -0.0467824074074059 76 68.63 68.570949074074 68.50625 0.0646990740740723 0.0590509259259306 77 68.84 68.5693518518518 68.52 0.0493518518518569 0.270648148148169 78 68.72 68.5601851851852 68.5108333333333 0.0493518518518522 0.159814814814823 79 68.37 68.4735185185185 68.4904166666666 -0.0168981481481444 -0.103518518518499 80 68.37 NA NA -0.00807870370370563 NA 81 68.47 NA NA -0.0409259259259264 NA 82 68.69 NA NA -0.054467592592595 NA 83 68.46 NA NA -0.0632870370370409 NA 84 68.17 NA NA -0.0141203703703739 NA 85 68.17 NA NA NA NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/28/t1243461651slewxj5571i0mm2/12s6m1243461588.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/28/t1243461651slewxj5571i0mm2/12s6m1243461588.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/28/t1243461651slewxj5571i0mm2/2uso81243461588.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/28/t1243461651slewxj5571i0mm2/2uso81243461588.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/28/t1243461651slewxj5571i0mm2/3qm221243461588.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/28/t1243461651slewxj5571i0mm2/3qm221243461588.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/28/t1243461651slewxj5571i0mm2/4xfo01243461588.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/28/t1243461651slewxj5571i0mm2/4xfo01243461588.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')

Copyright

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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