decomposition of Multitive - Katrien Devillé - oef. 2

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

Title produced by software: Classical Decomposition

Date of computation: Mon, 19 May 2008 12:48:31 -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/19/t1211223331d76gt9fv8tycsq2.htm/, Retrieved Mon, 19 May 2008 20:55:36 +0200

User-defined keywords:

Dataseries X:

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20,18 20,19 20,3 20,47 20,47 20,46 20,46 20,46 20,52 20,64 20,65 20,66 20,66 20,66 20,67 20,71 20,73 20,73 20,74 20,74 20,75 20,75 20,77 20,78 20,78 20,8 20,84 20,85 20,86 20,86 20,86 20,86 20,9 20,92 20,95 20,95 20,95 20,96 21,1 21,18 21,19 21,19 21,19 21,19 21,19 21,21 21,22 21,22 21,22 21,23 21,41 21,42 21,43 21,44 21,44 21,44 21,48 21,53 21,54 21,54 21,54 21,54 21,54 21,54 21,54 21,54 21,54 21,54 21,57 21,6 21,61 21,6 21,6 21,71 21,75 21,84 21,85 21,92 21,92 21,93 22 22 21,99 22,01 22,01 22,06 22,03 22,05 22,05 22,06 22,06 22,13 22,06 22,25 22,28 22,18

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 2 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 20.18 NA NA 0.998345170366853 NA 2 20.19 NA NA 0.998549080320233 NA 3 20.3 NA NA 1.00080722987181 NA 4 20.47 NA NA 1.00168075967552 NA 5 20.47 NA NA 1.00127391259998 NA 6 20.46 NA NA 1.00100925441320 NA 7 20.46 20.4793303139573 20.475 1.00021149274517 0.999056106148932 8 20.46 20.5021666727242 20.5145833333333 0.999394739809852 0.997943306510121 9 20.52 20.5489835203779 20.5495833333333 0.999970811429811 0.99858953994736 10 20.64 20.5755621538531 20.575 1.00002732217998 1.00313176600790 11 20.65 20.5900308139401 20.5958333333333 0.999718267316534 1.00291253503221 12 20.66 20.5975453252539 20.6179166666667 0.99901195927105 1.00303214163435 13 20.66 20.6066762706805 20.6408333333333 0.998345170366853 1.00258769190233 14 20.66 20.634184620584 20.6641666666667 0.998549080320233 1.00125109762710 15 20.67 20.7021145529108 20.6854166666667 1.00080722987181 0.998448730788888 16 20.71 20.7343743583002 20.6995833333333 1.00168075967552 0.998824446888102 17 20.73 20.7355483350184 20.7091666666667 1.00127391259998 0.999732424003035 18 20.73 20.7400775770629 20.7191666666667 1.00100925441320 0.999514101284074 19 20.74 20.7335507350301 20.7291666666667 1.00021149274517 1.00031105453438 20 20.74 20.7274469036563 20.74 0.999394739809852 1.00060562675191 21 20.75 20.7523109187019 20.7529166666667 0.999970811429811 0.999888642825805 22 20.75 20.7664007011692 20.7658333333333 1.00002732217998 0.999210228994174 23 20.77 20.7712297498912 20.7770833333333 0.999718267316534 0.999940795518319 24 20.78 20.7673773583300 20.7879166666667 0.99901195927105 1.00060781106118 25 20.78 20.7639156350133 20.7983333333333 0.998345170366853 1.00077463062697 26 20.8 20.7781421129968 20.8083333333333 0.998549080320233 1.00105196542041 27 20.84 20.8363895229187 20.8195833333333 1.00080722987181 1.0001732774806 28 20.85 20.8679317929236 20.8329166666667 1.00168075967552 0.999140700999912 29 20.86 20.874057892928 20.8475 1.00127391259998 0.9993265376095 30 20.86 20.8831384830061 20.8620833333333 1.00100925441320 0.998892001648844 31 20.86 20.8806651754213 20.87625 1.00021149274517 0.999010320061754 32 20.86 20.8773561146278 20.89 0.999394739809852 0.999168663190276 33 20.9 20.9068897399688 20.9075 0.999970811429811 0.99967045600496 34 20.92 20.9326552434816 20.9320833333333 1.00002732217998 0.99939543056844 35 20.95 20.9536783336765 20.9595833333333 0.999718267316534 0.999824454035328 36 20.95 20.9663472402181 20.9870833333333 0.99901195927105 0.99922031052759 37 20.95 20.9798077781051 21.0145833333333 0.998345170366853 0.998579215862206 38 20.96 21.0115529605217 21.0420833333333 0.998549080320233 0.997546446918105 39 21.1 21.0849233183368 21.0679166666667 1.00080722987181 1.00071504560086 40 21.18 21.1275340564728 21.0920833333333 1.00168075967552 1.00248329707513 41 21.19 21.1423158620121 21.1154166666667 1.00127391259998 1.00225538859126 42 21.19 21.1592502023484 21.1379166666667 1.00100925441320 1.00145325554344 43 21.19 21.1648919412764 21.1604166666667 1.00021149274517 1.00118630696501 44 21.19 21.1700954904971 21.1829166666667 0.999394739809852 1.00094021822017 45 21.19 21.2064643288930 21.2070833333333 0.999970811429811 0.999223617448076 46 21.21 21.2305800498810 21.23 1.00002732217998 0.999030641186784 47 21.22 21.2440131804763 21.25 0.999718267316534 0.99886964952091 48 21.22 21.2494006286782 21.2704166666667 0.99901195927105 0.998616401978013 49 21.22 21.2560166085733 21.29125 0.998345170366853 0.99830558052167 50 21.23 21.2811612122082 21.3120833333333 0.998549080320233 0.997595938882376 51 21.41 21.3518052463026 21.3345833333333 1.00080722987181 1.00272551913180 52 21.42 21.3959010266692 21.36 1.00168075967552 1.00112633598841 53 21.43 21.4139114108048 21.3866666666667 1.00127391259998 1.00075131482925 54 21.44 21.4349448345014 21.4133333333333 1.00100925441320 1.00023583757913 55 21.44 21.4445344044564 21.44 1.00021149274517 0.999788551974555 56 21.44 21.4532573334432 21.46625 0.999394739809852 0.99938203633895 57 21.48 21.4839562290647 21.4845833333333 0.999970811429811 0.999815851930503 58 21.53 21.4955872902587 21.495 1.00002732217998 1.00160091972723 59 21.54 21.498524789364 21.5045833333333 0.999718267316534 1.00192921193628 60 21.54 21.4920772837845 21.5133333333333 0.99901195927105 1.00222978521726 61 21.54 21.4860519749120 21.5216666666667 0.998345170366853 1.00251083936458 62 21.54 21.4987616992946 21.53 0.998549080320233 1.00191817097571 63 21.54 21.5553027163766 21.5379166666667 1.00080722987181 0.999290071840887 64 21.54 21.580794600226 21.5445833333333 1.00168075967552 0.99810968034395 65 21.54 21.5778700139931 21.5504166666667 1.00127391259998 0.998244960509609 66 21.54 21.5775886532553 21.5558333333333 1.00100925441320 0.998257977114156 67 21.54 21.5653932931631 21.5608333333333 1.00021149274517 0.998822498026448 68 21.54 21.5573609521734 21.5704166666667 0.999394739809852 0.999194662453723 69 21.57 21.5856199282268 21.58625 0.999970811429811 0.99927637342459 70 21.6 21.608090364004 21.6075 1.00002732217998 0.999625586349016 71 21.61 21.6268219670030 21.6329166666667 0.999718267316534 0.999222171106386 72 21.6 21.6402640577430 21.6616666666667 0.99901195927105 0.998139391569548 73 21.6 21.6574345624916 21.6933333333333 0.998345170366853 0.997348044048067 74 21.71 21.6938948320739 21.7254166666667 0.998549080320233 1.00074238250212 75 21.75 21.7771483189982 21.7595833333333 1.00080722987181 0.998753357482785 76 21.84 21.8307974231617 21.7941666666667 1.00168075967552 1.00042154103031 77 21.85 21.8544719323488 21.8266666666667 1.00127391259998 0.999795376783174 78 21.92 21.8816452142833 21.8595833333333 1.00100925441320 1.00175282915618 79 21.92 21.8983803692895 21.89375 1.00021149274517 1.00098727076368 80 21.93 21.9121460848059 21.9254166666667 0.999394739809852 1.00081479537079 81 22 21.9510259289034 21.9516666666667 0.999970811429811 1.00223106069189 82 22 21.9726836585488 21.9720833333333 1.00002732217998 1.00124319550018 83 21.99 21.9829715997345 21.9891666666667 0.999718267316534 1.0003197202086 84 22.01 21.9815931438273 22.0033333333333 0.99901195927105 1.00129230197224 85 22.01 21.9785689256263 22.015 0.998345170366853 1.00143007829491 86 22.06 21.9972041152211 22.0291666666667 0.998549080320233 1.0028547211932 87 22.03 22.0577913463747 22.04 1.00080722987181 0.998740066675838 88 22.05 22.0899823197277 22.0529166666667 1.00168075967552 0.998190024819893 89 22.05 22.1035388181081 22.0754166666667 1.00127391259998 0.997577816903046 90 22.06 22.1168823890704 22.0945833333333 1.00100925441320 0.997428100938019 91 22.06 NA NA 1.00021149274517 NA 92 22.13 NA NA 0.999394739809852 NA 93 22.06 NA NA 0.999970811429811 NA 94 22.25 NA NA 1.00002732217998 NA 95 22.28 NA NA 0.999718267316534 NA 96 22.18 NA NA 0.99901195927105 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211223331d76gt9fv8tycsq2/1wt7v1211222909.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211223331d76gt9fv8tycsq2/1wt7v1211222909.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211223331d76gt9fv8tycsq2/2hyvz1211222909.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211223331d76gt9fv8tycsq2/2hyvz1211222909.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211223331d76gt9fv8tycsq2/3djdf1211222909.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211223331d76gt9fv8tycsq2/3djdf1211222909.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211223331d76gt9fv8tycsq2/4yqi31211222909.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211223331d76gt9fv8tycsq2/4yqi31211222909.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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