Nicky Van Calster 2MAR04b-Opgave 9- additief decompositie model Pond

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 13:57:02 -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/t12117455034257n91xiajt0y4.htm/, Retrieved Sun, 25 May 2008 21:58:23 +0200

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Dataseries X:

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0.70291 0.6885 0.67127 0.66502 0.65825 0.65025 0.65779 0.66014 0.64683 0.64587 0.63702 0.62651 0.61834 0.61466 0.61063 0.59802 0.60151 0.62927 0.62304 0.6071 0.60773 0.58933 0.60039 0.61342 0.6348 0.634 0.62915 0.62168 0.61328 0.6089 0.60857 0.62672 0.62291 0.62393 0.61838 0.62012 0.61659 0.6116 0.61573 0.61407 0.62823 0.64405 0.6387 0.63633 0.63059 0.62994 0.63709 0.64217 0.65711 0.66977 0.68255 0.68902 0.71322 0.70224 0.70045 0.69919 0.69693 0.69763 0.69278 0.70196 0.69215 0.6769 0.67124 0.66532 0.67157 0.66428 0.66576 0.66942 0.6813 0.69144 0.69862 0.695 0.69867 0.68968 0.69233 0.68293 0.68399 0.66895 0.68756 0.68527 0.6776 0.68137 0.67933 0.67922 0.68598 0.68297 0.68935 0.69463 0.6833 0.68666 0.68782 0.67669 0.67511 0.67254 0.67397 0.67286 0.66341 0.668 0.68021 0.67934 0.68136 0.67562 0.6744 0.67766 0.68887 0.69614 0.70896 0.72064 0.74725

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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 6 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 0.70291 NA NA 0.00351919642857146 NA 2 0.6885 NA NA -0.000195208333333318 NA 3 0.67127 NA NA 0.00116651785714289 NA 4 0.66502 NA NA -0.00277627976190475 NA 5 0.65825 NA NA 0.00104931547619044 NA 6 0.65025 NA NA 0.00187491071428568 NA 7 0.65779 0.658082232142857 0.655672916666666 0.00240931547619049 -0.000292232142857007 8 0.66014 0.649298898809524 0.6490725 0.000226398809523835 0.0108411011904761 9 0.64683 0.641742470238095 0.643469166666667 -0.00172669642857145 0.00508752976190474 10 0.64587 0.634670208333333 0.638150833333333 -0.00348062500000003 0.0111997916666666 11 0.63702 0.63060931547619 0.632995 -0.00238568452380952 0.00641068452380966 12 0.62651 0.630075505952381 0.629756666666667 0.000318839285714258 -0.00356550595238092 13 0.61834 0.630953779761905 0.627434583333333 0.00351919642857146 -0.0126137797619048 14 0.61466 0.623581458333333 0.623776666666667 -0.000195208333333318 -0.00892145833333335 15 0.61063 0.621104017857143 0.6199375 0.00116651785714289 -0.0104740178571429 16 0.59802 0.613176220238095 0.6159525 -0.00277627976190475 -0.0151562202380954 17 0.60151 0.613119732142857 0.612070416666667 0.00104931547619044 -0.0116097321428571 18 0.62927 0.611873660714286 0.60999875 0.00187491071428568 0.0173963392857144 19 0.62304 0.612548482142857 0.610139166666667 0.00240931547619049 0.0104915178571430 20 0.6071 0.611857232142857 0.611630833333333 0.000226398809523835 -0.00475723214285706 21 0.60773 0.611481636904762 0.613208333333333 -0.00172669642857145 -0.00375163690476177 22 0.58933 0.611485208333333 0.614965833333333 -0.00348062500000003 -0.0221552083333333 23 0.60039 0.614056398809524 0.616442083333333 -0.00238568452380952 -0.0136663988095239 24 0.61342 0.616402589285714 0.61608375 0.000318839285714258 -0.00298258928571427 25 0.6348 0.618151279761905 0.614632083333333 0.00351919642857146 0.0166487202380953 26 0.634 0.614651458333333 0.614846666666667 -0.000195208333333318 0.0193485416666667 27 0.62915 0.61746318452381 0.616296666666667 0.00116651785714289 0.0116868154761905 28 0.62168 0.615594553571429 0.618370833333333 -0.00277627976190475 0.00608544642857134 29 0.61328 0.621611398809524 0.620562083333333 0.00104931547619044 -0.00833139880952383 30 0.6089 0.623465744047619 0.621590833333333 0.00187491071428568 -0.0145657440476191 31 0.60857 0.62352056547619 0.62111125 0.00240931547619049 -0.0149505654761904 32 0.62672 0.61964556547619 0.619419166666667 0.000226398809523835 0.00707443452380963 33 0.62291 0.616199970238095 0.617926666666667 -0.00172669642857145 0.00671002976190471 34 0.62393 0.613569791666667 0.617050416666667 -0.00348062500000003 0.0103602083333334 35 0.61838 0.61497056547619 0.61735625 -0.00238568452380952 0.00340943452380948 36 0.62012 0.619762589285714 0.61944375 0.000318839285714258 0.000357410714285633 37 0.61659 0.625682946428571 0.62216375 0.00351919642857146 -0.0090929464285715 38 0.6116 0.623624375 0.623819583333333 -0.000195208333333318 -0.0120243750000000 39 0.61573 0.625706517857143 0.62454 0.00116651785714289 -0.00997651785714282 40 0.61407 0.622334136904762 0.625110416666667 -0.00277627976190475 -0.0082641369047618 41 0.62823 0.627189732142857 0.626140416666667 0.00104931547619044 0.00104026785714262 42 0.64405 0.629713660714286 0.62783875 0.00187491071428568 0.0143363392857142 43 0.6387 0.632855148809524 0.630445833333333 0.00240931547619049 0.00584485119047617 44 0.63633 0.63478431547619 0.634557916666667 0.000226398809523835 0.00154568452380954 45 0.63059 0.638039136904762 0.639765833333333 -0.00172669642857145 -0.0074491369047619 46 0.62994 0.642192291666667 0.645672916666667 -0.00348062500000003 -0.0122522916666666 47 0.63709 0.649951398809524 0.652337083333333 -0.00238568452380952 -0.0128613988095236 48 0.64217 0.658621755952381 0.658302916666667 0.000318839285714258 -0.0164517559523809 49 0.65711 0.666819613095238 0.663300416666667 0.00351919642857146 -0.0097096130952381 50 0.66977 0.668297291666667 0.6684925 -0.000195208333333318 0.00147270833333346 51 0.68255 0.675042351190476 0.673875833333333 0.00116651785714289 0.00750764880952393 52 0.68902 0.676684136904762 0.679460416666667 -0.00277627976190475 0.0123358630952382 53 0.71322 0.68565056547619 0.68460125 0.00104931547619044 0.0275694345238094 54 0.70224 0.691287827380953 0.689412916666667 0.00187491071428568 0.0109521726190475 55 0.70045 0.695773482142857 0.693364166666667 0.00240931547619049 0.00467651785714285 56 0.69919 0.695347648809524 0.69512125 0.000226398809523835 0.00384235119047616 57 0.69693 0.693220386904762 0.694947083333333 -0.00172669642857145 0.00370961309523821 58 0.69763 0.690007708333333 0.693488333333333 -0.00348062500000003 0.0076222916666665 59 0.69278 0.688379732142857 0.690765416666667 -0.00238568452380952 0.00440026785714276 60 0.70196 0.687767172619048 0.687448333333333 0.000318839285714258 0.0141928273809524 61 0.69215 0.687940446428571 0.68442125 0.00351919642857146 0.0042095535714286 62 0.6769 0.681540208333333 0.681735416666667 -0.000195208333333318 -0.00464020833333334 63 0.67124 0.681010267857143 0.67984375 0.00116651785714289 -0.00977026785714274 64 0.66532 0.676158303571428 0.678934583333333 -0.00277627976190475 -0.0108383035714285 65 0.67157 0.67996931547619 0.67892 0.00104931547619044 -0.00839931547619044 66 0.66428 0.680748244047619 0.678873333333333 0.00187491071428568 -0.0164682440476189 67 0.66576 0.68126431547619 0.678855 0.00240931547619049 -0.0155043154761904 68 0.66942 0.67988556547619 0.679659166666667 0.000226398809523835 -0.0104655654761904 69 0.6813 0.679343720238095 0.681070416666667 -0.00172669642857145 0.00195627976190482 70 0.69144 0.679202291666667 0.682682916666667 -0.00348062500000003 0.0122377083333334 71 0.69862 0.681548482142857 0.683934166666667 -0.00238568452380952 0.017071517857143 72 0.695 0.684965089285714 0.68464625 0.000318839285714258 0.0100349107142856 73 0.69867 0.689268363095238 0.685749166666667 0.00351919642857146 0.00940163690476192 74 0.68968 0.687122708333333 0.687317916666667 -0.000195208333333318 0.00255729166666663 75 0.69233 0.68899068452381 0.687824166666667 0.00116651785714289 0.00333931547619049 76 0.68293 0.684474136904762 0.687250416666667 -0.00277627976190475 -0.00154413690476185 77 0.68399 0.687076398809524 0.686027083333333 0.00104931547619044 -0.00308639880952366 78 0.66895 0.686440744047619 0.684565833333333 0.00187491071428568 -0.0174907440476190 79 0.68756 0.685788898809524 0.683379583333333 0.00240931547619049 0.00177110119047597 80 0.68527 0.682797648809524 0.68257125 0.000226398809523835 0.00247235119047617 81 0.6776 0.680440803571429 0.6821675 -0.00172669642857145 -0.00284080357142857 82 0.68137 0.679050208333333 0.682530833333333 -0.00348062500000003 0.00231979166666663 83 0.67933 0.680603898809524 0.682989583333333 -0.00238568452380952 -0.00127389880952389 84 0.67922 0.684017589285714 0.68369875 0.000318839285714258 -0.00479758928571428 85 0.68598 0.687966696428571 0.6844475 0.00351919642857146 -0.0019866964285713 86 0.68297 0.683905625 0.684100833333333 -0.000195208333333318 -0.000935625000000107 87 0.68935 0.684806101190476 0.683639583333333 0.00116651785714289 0.00454389880952388 88 0.69463 0.680391636904762 0.683167916666667 -0.00277627976190475 0.0142383630952381 89 0.6833 0.683625982142857 0.682576666666667 0.00104931547619044 -0.000325982142857173 90 0.68666 0.683963244047619 0.682088333333333 0.00187491071428568 0.00269675595238095 91 0.68782 0.683292232142857 0.680882916666667 0.00240931547619049 0.00452776785714282 92 0.67669 0.679545148809524 0.67931875 0.000226398809523835 -0.00285514880952376 93 0.67511 0.676587470238095 0.678314166666667 -0.00172669642857145 -0.00147747023809519 94 0.67254 0.673815625 0.67729625 -0.00348062500000003 -0.00127562500000000 95 0.67397 0.674192648809524 0.676578333333333 -0.00238568452380952 -0.00022264880952394 96 0.67286 0.676356339285714 0.6760375 0.000318839285714258 -0.00349633928571447 97 0.66341 0.678537529761905 0.675018333333333 0.00351919642857146 -0.0151275297619047 98 0.668 0.674304375 0.674499583333333 -0.000195208333333318 -0.00630437500000003 99 0.68021 0.676279851190476 0.675113333333333 0.00116651785714289 0.0039301488095238 100 0.67934 0.673893720238095 0.67667 -0.00277627976190475 0.00544627976190493 101 0.68136 0.68016056547619 0.67911125 0.00104931547619044 0.00119943452380955 102 0.67562 0.684434910714286 0.68256 0.00187491071428568 -0.00881491071428575 103 0.6744 0.690453482142857 0.688044166666667 0.00240931547619049 -0.0160534821428571 104 0.67766 NA NA 0.000226398809523835 NA 105 0.68887 NA NA -0.00172669642857145 NA 106 0.69614 NA NA -0.00348062500000003 NA 107 0.70896 NA NA -0.00238568452380952 NA 108 0.72064 NA NA 0.000318839285714258 NA 109 0.74725 NA NA NA NA

Charts produced by software:

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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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