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

Title produced by software: Classical Decomposition

Date of computation: Sat, 17 May 2008 07:38:59 -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/17/t12110315973gfpngavlbmmdq8.htm/, Retrieved Sat, 17 May 2008 15:40:01 +0200

User-defined keywords:

Dataseries X:

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101.6 101.8 102.1 102.1 101.9 102.1 102 102.1 102.2 102.3 102.7 102.8 103.1 103.1 103.3 103.5 103.3 103.5 103.8 103.9 103.9 104.2 104.6 104.9 105.2 105.2 105.6 105.6 106.2 106.3 106.4 106.9 107.2 107.3 107.3 107.4 107.55 107.87 108.37 108.38 107.92 108.03 108.14 108.3 108.64 108.66 109.04 109.03 109.03 109.54 109.75 109.83 109.65 109.82 109.95 110.12 110.15 110.2 109.99 110.14 110.14 110.81 110.97 110.99 109.73 109.81 110.02 110.18 110.21 110.25 110.36 110.51 110.64 110.95 111.18 111.19 111.69 111.7 111.83 111.77 111.73 112.01 111.86 112.04

Text written by user:

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 101.6 NA NA -0.00279166666666081 NA 2 101.8 NA NA 0.163041666666667 NA 3 102.1 NA NA 0.322958333333333 NA 4 102.1 NA NA 0.251958333333329 NA 5 101.9 NA NA -0.178125000000000 NA 6 102.1 NA NA -0.174208333333331 NA 7 102 102.072875 102.204166666667 -0.131291666666666 -0.072874999999982 8 102.1 102.279291666667 102.320833333333 -0.0415416666666682 -0.179291666666643 9 102.2 102.392375 102.425 -0.0326250000000011 -0.192374999999984 10 102.3 102.472958333333 102.533333333333 -0.0603750000000071 -0.172958333333298 11 102.7 102.591625 102.65 -0.0583750000000004 0.108374999999995 12 102.8 102.708041666667 102.766666666667 -0.0586249999999945 0.0919583333333094 13 103.1 102.897208333333 102.9 -0.00279166666666081 0.202791666666656 14 103.1 103.213041666667 103.05 0.163041666666667 -0.113041666666675 15 103.3 103.518791666667 103.195833333333 0.322958333333333 -0.218791666666661 16 103.5 103.597791666667 103.345833333333 0.251958333333329 -0.0977916666666658 17 103.3 103.326041666667 103.504166666667 -0.178125000000000 -0.0260416666666856 18 103.5 103.496625 103.670833333333 -0.174208333333331 0.00337499999999125 19 103.8 103.714541666667 103.845833333333 -0.131291666666666 0.0854583333333352 20 103.9 103.979291666667 104.020833333333 -0.0415416666666682 -0.0792916666666486 21 103.9 104.171541666667 104.204166666667 -0.0326250000000011 -0.27154166666665 22 104.2 104.327125 104.3875 -0.0603750000000071 -0.127124999999992 23 104.6 104.537458333333 104.595833333333 -0.0583750000000004 0.0625416666666752 24 104.9 104.774708333333 104.833333333333 -0.0586249999999945 0.125291666666683 25 105.2 105.055541666667 105.058333333333 -0.00279166666666081 0.144458333333347 26 105.2 105.454708333333 105.291666666667 0.163041666666667 -0.254708333333340 27 105.6 105.877125 105.554166666667 0.322958333333333 -0.277124999999998 28 105.6 106.072791666667 105.820833333333 0.251958333333329 -0.472791666666666 29 106.2 105.884375 106.0625 -0.178125000000000 0.315625000000011 30 106.3 106.104958333333 106.279166666667 -0.174208333333331 0.195041666666654 31 106.4 106.349958333333 106.48125 -0.131291666666666 0.0500416666666865 32 106.9 106.648875 106.690416666667 -0.0415416666666682 0.251125000000016 33 107.2 106.884458333333 106.917083333333 -0.0326250000000011 0.315541666666675 34 107.3 107.087958333333 107.148333333333 -0.0603750000000071 0.212041666666678 35 107.3 107.277458333333 107.335833333333 -0.0583750000000004 0.0225416666666831 36 107.4 107.420958333333 107.479583333333 -0.0586249999999945 -0.0209583333333114 37 107.55 107.621375 107.624166666667 -0.00279166666666081 -0.0713749999999891 38 107.87 107.918041666667 107.755 0.163041666666667 -0.0480416666666628 39 108.37 108.196291666667 107.873333333333 0.322958333333333 0.173708333333352 40 108.38 108.241958333333 107.99 0.251958333333329 0.138041666666666 41 107.92 107.941041666667 108.119166666667 -0.178125000000000 -0.0210416666666617 42 108.03 108.085375 108.259583333333 -0.174208333333331 -0.0553749999999695 43 108.14 108.257875 108.389166666667 -0.131291666666666 -0.117874999999984 44 108.3 108.478875 108.520416666667 -0.0415416666666682 -0.178875000000005 45 108.64 108.614875 108.6475 -0.0326250000000011 0.0251250000000027 46 108.66 108.705041666667 108.765416666667 -0.0603750000000071 -0.0450416666666626 47 109.04 108.839541666667 108.897916666667 -0.0583750000000004 0.200458333333344 48 109.03 108.985958333333 109.044583333333 -0.0586249999999945 0.0440416666666579 49 109.03 109.191791666667 109.194583333333 -0.00279166666666081 -0.161791666666630 50 109.54 109.508875 109.345833333333 0.163041666666667 0.0311250000000030 51 109.75 109.807541666667 109.484583333333 0.322958333333333 -0.0575416666666797 52 109.83 109.863625 109.611666666667 0.251958333333329 -0.0336250000000149 53 109.65 109.537291666667 109.715416666667 -0.178125000000000 0.112708333333316 54 109.82 109.627041666667 109.80125 -0.174208333333331 0.192958333333323 55 109.95 109.762458333333 109.89375 -0.131291666666666 0.187541666666675 56 110.12 109.951375 109.992916666667 -0.0415416666666682 0.168625000000006 57 110.15 110.064041666667 110.096666666667 -0.0326250000000011 0.0859583333333234 58 110.2 110.135458333333 110.195833333333 -0.0603750000000071 0.0645416666666705 59 109.99 110.189125 110.2475 -0.0583750000000004 -0.199124999999995 60 110.14 110.191791666667 110.250416666667 -0.0586249999999945 -0.0517916666666736 61 110.14 110.250125 110.252916666667 -0.00279166666666081 -0.110124999999996 62 110.81 110.421375 110.258333333333 0.163041666666667 0.388625000000005 63 110.97 110.586291666667 110.263333333333 0.322958333333333 0.383708333333345 64 110.99 110.519875 110.267916666667 0.251958333333329 0.47012500000001 65 109.73 110.107291666667 110.285416666667 -0.178125000000000 -0.37729166666665 66 109.81 110.142041666667 110.31625 -0.174208333333331 -0.332041666666669 67 110.02 110.221208333333 110.3525 -0.131291666666666 -0.201208333333341 68 110.18 110.337625 110.379166666667 -0.0415416666666682 -0.157624999999982 69 110.21 110.361125 110.39375 -0.0326250000000011 -0.151125000000022 70 110.25 110.350458333333 110.410833333333 -0.0603750000000071 -0.100458333333336 71 110.36 110.442458333333 110.500833333333 -0.0583750000000004 -0.0824583333333635 72 110.51 110.602625 110.66125 -0.0586249999999945 -0.0926250000000124 73 110.64 110.812625 110.815416666667 -0.00279166666666081 -0.172624999999982 74 110.95 111.120125 110.957083333333 0.163041666666667 -0.170124999999985 75 111.18 111.409625 111.086666666667 0.322958333333333 -0.229624999999970 76 111.19 111.475291666667 111.223333333333 0.251958333333329 -0.285291666666666 77 111.69 111.181041666667 111.359166666667 -0.178125000000000 0.50895833333334 78 111.7 111.311208333333 111.485416666667 -0.174208333333331 0.388791666666648 79 111.83 NA NA -0.131291666666666 NA 80 111.77 NA NA -0.0415416666666682 NA 81 111.73 NA NA -0.0326250000000011 NA 82 112.01 NA NA -0.0603750000000071 NA 83 111.86 NA NA -0.0583750000000004 NA 84 112.04 NA NA -0.0586249999999945 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/17/t12110315973gfpngavlbmmdq8/16rqg1211031535.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/17/t12110315973gfpngavlbmmdq8/16rqg1211031535.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/17/t12110315973gfpngavlbmmdq8/2dm5w1211031535.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/17/t12110315973gfpngavlbmmdq8/2dm5w1211031535.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/17/t12110315973gfpngavlbmmdq8/3f84v1211031535.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/17/t12110315973gfpngavlbmmdq8/3f84v1211031535.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/17/t12110315973gfpngavlbmmdq8/435301211031535.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/17/t12110315973gfpngavlbmmdq8/435301211031535.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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