Classical Decompostion - Blue Jeans - Raf Pleysier

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

Title produced by software: Classical Decomposition

Date of computation: Sun, 25 May 2008 12:12:36 -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/25/t1211739203b7un89jlp86jju7.htm/, Retrieved Sun, 25 May 2008 20:13:23 +0200

User-defined keywords:

Dataseries X:

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48,04 48,06 48,04 48,09 48,12 48,16 48,16 48,16 48,08 48,13 48,16 48,15 48,15 48,15 48,27 48,47 48,51 48,53 48,53 48,53 48,68 48,64 48,67 48,66 48,66 48,67 48,71 48,96 49,01 49,04 49,04 49,04 49,06 49,13 49,19 49,26 49,26 49,26 49,29 49,43 49,43 49,45 49,45 49,46 49,57 49,68 49,71 49,7 49,7 49,8 49,84 50,09 50,2 50,16 50,16 50,29 50,36 51,02 51,03 51,04

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 5 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 48.04 NA NA 0.998743086385678 NA 2 48.06 NA NA 0.998075285228142 NA 3 48.04 NA NA 0.998586591685917 NA 4 48.09 NA NA 1.00175252991468 NA 5 48.12 NA NA 1.00148704763172 NA 6 48.16 NA NA 1.00108268941237 NA 7 48.16 48.1268286029633 48.1170833333333 1.00020253242622 1.00068924959320 8 48.16 48.0947714213024 48.1254166666667 0.999363221193978 1.00135625093477 9 48.08 48.155967434234 48.13875 1.00035766267786 0.998422471019033 10 48.13 48.1832331800599 48.1641666666667 1.00039586511535 0.998895192859703 11 48.16 48.2095160337659 48.19625 1.00027525033101 0.998972899173449 12 48.15 48.2123987556028 48.2279166666667 0.99967823799707 0.998705752934651 13 48.15 48.1980929201148 48.25875 0.998743086385678 0.999002182094745 14 48.15 48.1966396589648 48.2895833333333 0.998075285228142 0.999032304756207 15 48.27 48.2616899761804 48.33 0.998586591685917 1.00017218675566 16 48.47 48.4610308252852 48.37625 1.00175252991468 1.00018508014712 17 48.51 48.4907509875183 48.41875 1.00148704763172 1.00039696255656 18 48.53 48.5137184822853 48.46125 1.00108268941237 1.00033560646811 19 48.53 48.5135735821681 48.50375 1.00020253242622 1.00033859426587 20 48.53 48.5157531782303 48.5466666666667 0.999363221193978 1.00029365352151 21 48.68 48.6040443039752 48.5866666666667 1.00035766267786 1.00156274435826 22 48.64 48.6446657728444 48.6254166666667 1.00039586511535 0.999904084594472 23 48.67 48.6800621827758 48.6666666666667 1.00027525033101 0.999793299713998 24 48.66 48.6930773750398 48.70875 0.99967823799707 0.999320696558465 25 48.66 48.6899738901598 48.75125 0.998743086385678 0.999384392971182 26 48.67 48.6998359486006 48.79375 0.998075285228142 0.999387350121012 27 48.71 48.7618154275164 48.8308333333333 0.998586591685917 0.998937376981105 28 48.96 48.9527243587183 48.8670833333333 1.00175252991468 1.00014862587072 29 49.01 48.9818969271277 48.9091666666667 1.00148704763172 1.00057374406945 30 49.04 49.0088372957572 48.9558333333333 1.00108268941237 1.00063585887694 31 49.04 49.0157586036571 49.0058333333333 1.00020253242622 1.00049456332073 32 49.04 49.0241792170128 49.0554166666667 0.999363221193978 1.00032271387793 33 49.06 49.1217293944109 49.1041666666667 1.00035766267786 0.998743338331692 34 49.13 49.1673726123672 49.1479166666667 1.00039586511535 0.999239889984324 35 49.19 49.1985381875307 49.185 1.00027525033101 0.999826454446713 36 49.26 49.2037463416166 49.2195833333333 0.99967823799707 1.00114327998508 37 49.26 49.1918422910686 49.25375 0.998743086385678 1.00138554902108 38 49.26 49.1934673500864 49.2883333333333 0.998075285228142 1.00135246920979 39 49.29 49.2573640236405 49.3270833333333 0.998586591685917 1.00066256035024 40 49.43 49.4577745925503 49.37125 1.00175252991468 0.999438418069169 41 49.43 49.4893170312611 49.4158333333333 1.00148704763172 0.998801417460992 42 49.45 49.5093786404634 49.4558333333333 1.00108268941237 0.998800658741961 43 49.45 49.5025238361045 49.4925 1.00020253242622 0.998938966500407 44 49.46 49.5017915564751 49.5333333333333 0.999363221193978 0.999155756687566 45 49.57 49.5964824684901 49.57875 1.00035766267786 0.99946604139705 46 49.68 49.6488131224539 49.6291666666667 1.00039586511535 1.00062814950821 47 49.71 49.702426844885 49.68875 1.00027525033101 1.00015236992630 48 49.7 49.7344088729534 49.7504166666667 0.99967823799707 0.999308147543459 49 49.7 49.7469769899179 49.8095833333333 0.998743086385678 0.999055681515533 50 49.8 49.777757256647 49.87375 0.998075285228142 1.0004468410105 51 49.84 49.8706626220343 49.94125 0.998586591685917 0.999385157115984 52 50.09 50.1176790716316 50.03 1.00175252991468 0.99944771840707 53 50.2 50.2153951407941 50.1408333333333 1.00148704763172 0.999693417909967 54 50.16 50.3060736141207 50.2516666666667 1.00108268941237 0.99709630262061 55 50.16 NA NA 1.00020253242622 NA 56 50.29 NA NA 0.999363221193978 NA 57 50.36 NA NA 1.00035766267786 NA 58 51.02 NA NA 1.00039586511535 NA 59 51.03 NA NA 1.00027525033101 NA 60 51.04 NA NA 0.99967823799707 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211739203b7un89jlp86jju7/1ymjl1211739148.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211739203b7un89jlp86jju7/1ymjl1211739148.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211739203b7un89jlp86jju7/2hyay1211739148.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211739203b7un89jlp86jju7/2hyay1211739148.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211739203b7un89jlp86jju7/342hy1211739148.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211739203b7un89jlp86jju7/342hy1211739148.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211739203b7un89jlp86jju7/4qy6v1211739148.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211739203b7un89jlp86jju7/4qy6v1211739148.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')

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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