datareeks - decompositiemodel gemiddelde gokuitgaven - Frederik Verbraken

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Fri, 20 May 2011 14:31:28 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2011/May/20/t1305901728a8yn1qhwc5cka11.htm/, Retrieved Fri, 20 May 2011 16:28:51 +0200

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

KDGP2W92

Dataseries X:

» Textbox « » Textfile « » CSV «

5.81 5.76 5.99 6.12 6.03 6.25 5.80 5.67 5.89 5.91 5.86 6.07 6.27 6.68 6.77 6.71 6.62 6.50 5.89 6.05 6.43 6.47 6.62 6.77 6.70 6.95 6.73 7.07 7.28 7.32 6.76 6.93 6.99 7.16 7.28 7.08 7.34 7.87 6.28 6.30 6.36 6.28 5.89 6.04 5.96 6.10 6.26 6.02 6.25 6.41 6.22 6.57 6.18 6.26 6.10 6.02 6.06 6.35 6.21 6.48 6.74 6.53 6.80 6.75 6.56 6.66 6.18 6.40 6.43 6.54 6.44 6.64 6.82 6.97 7.00 6.91 6.74 6.98 6.37 6.56 6.63 6.87 6.68 6.75 6.84 7.15 7.09 6.97 7.15

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' @ www.wessa.org Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 5.81 NA NA 0.160700231481481 NA 2 5.76 NA NA 0.365561342592593 NA 3 5.99 NA NA 0.0859085648148153 NA 4 6.12 NA NA 0.15910300925926 NA 5 6.03 NA NA 0.0517418981481485 NA 6 6.25 NA NA 0.0846585648148148 NA 7 5.8 5.55681134259259 5.94916666666667 -0.392355324074075 0.243188657407408 8 5.67 5.74208912037037 6.00666666666667 -0.264577546296297 -0.0720891203703706 9 5.89 5.89076967592593 6.0775 -0.186730324074074 -0.000769675925926805 10 5.91 6.10882523148148 6.13458333333333 -0.025758101851852 -0.19882523148148 11 5.86 6.15250578703704 6.18375 -0.0312442129629633 -0.292505787037037 12 6.07 6.21174189814815 6.21875 -0.00700810185185185 -0.141741898148148 13 6.27 6.39361689814815 6.23291666666667 0.160700231481481 -0.123616898148148 14 6.68 6.61806134259259 6.2525 0.365561342592593 0.0619386574074063 15 6.77 6.37674189814815 6.29083333333333 0.0859085648148153 0.393258101851853 16 6.71 6.49576967592593 6.33666666666667 0.15910300925926 0.214230324074074 17 6.62 6.44340856481482 6.39166666666667 0.0517418981481485 0.176591435185185 18 6.5 6.53715856481482 6.4525 0.0846585648148148 -0.0371585648148152 19 5.89 6.10722800925926 6.49958333333333 -0.392355324074075 -0.21722800925926 20 6.05 6.2641724537037 6.52875 -0.264577546296297 -0.214172453703705 21 6.43 6.35160300925926 6.53833333333333 -0.186730324074074 0.0783969907407407 22 6.47 6.52590856481481 6.55166666666667 -0.025758101851852 -0.055908564814815 23 6.62 6.5629224537037 6.59416666666667 -0.0312442129629633 0.0570775462962958 24 6.77 6.64882523148148 6.65583333333333 -0.00700810185185185 0.121174768518519 25 6.7 6.88695023148148 6.72625 0.160700231481481 -0.186950231481481 26 6.95 7.16472800925926 6.79916666666667 0.365561342592593 -0.214728009259259 27 6.73 6.94507523148148 6.85916666666667 0.0859085648148153 -0.215075231481481 28 7.07 7.07035300925926 6.91125 0.15910300925926 -0.000353009259259629 29 7.28 7.01924189814815 6.9675 0.0517418981481485 0.260758101851852 30 7.32 7.09257523148148 7.00791666666667 0.0846585648148148 0.227424768518519 31 6.76 6.65514467592592 7.0475 -0.392355324074075 0.104855324074075 32 6.93 6.8479224537037 7.1125 -0.264577546296297 0.082077546296297 33 6.99 6.94535300925926 7.13208333333333 -0.186730324074074 0.0446469907407412 34 7.16 7.05549189814815 7.08125 -0.025758101851852 0.104508101851853 35 7.28 6.97958912037037 7.01083333333333 -0.0312442129629633 0.300410879629629 36 7.08 6.92215856481481 6.92916666666667 -0.00700810185185185 0.157841435185187 37 7.34 7.01028356481481 6.84958333333333 0.160700231481481 0.329716435185186 38 7.87 7.14181134259259 6.77625 0.365561342592593 0.728188657407408 39 6.28 6.78215856481481 6.69625 0.0859085648148153 -0.502158564814814 40 6.3 6.76826967592593 6.60916666666667 0.15910300925926 -0.468269675925925 41 6.36 6.57424189814815 6.5225 0.0517418981481485 -0.214241898148148 42 6.28 6.52049189814815 6.43583333333333 0.0846585648148148 -0.240491898148147 43 5.89 5.95389467592593 6.34625 -0.392355324074075 -0.0638946759259262 44 6.04 5.9754224537037 6.24 -0.264577546296297 0.0645775462962961 45 5.96 5.98993634259259 6.17666666666667 -0.186730324074074 -0.0299363425925918 46 6.1 6.15965856481481 6.18541666666667 -0.025758101851852 -0.0596585648148151 47 6.26 6.1579224537037 6.18916666666667 -0.0312442129629633 0.102077546296297 48 6.02 6.17382523148148 6.18083333333333 -0.00700810185185185 -0.153825231481481 49 6.25 6.34945023148148 6.18875 0.160700231481481 -0.0994502314814811 50 6.41 6.56222800925926 6.19666666666667 0.365561342592593 -0.152228009259259 51 6.22 6.28590856481481 6.2 0.0859085648148153 -0.0659085648148148 52 6.57 6.37368634259259 6.21458333333333 0.15910300925926 0.196313657407408 53 6.18 6.27465856481481 6.22291666666667 0.0517418981481485 -0.0946585648148144 54 6.26 6.32465856481481 6.24 0.0846585648148148 -0.0646585648148141 55 6.1 5.88722800925926 6.27958333333333 -0.392355324074075 0.212771990740741 56 6.02 6.0404224537037 6.305 -0.264577546296297 -0.0204224537037039 57 6.06 6.14743634259259 6.33416666666667 -0.186730324074074 -0.087436342592592 58 6.35 6.34007523148148 6.36583333333333 -0.025758101851852 0.00992476851851976 59 6.21 6.3579224537037 6.38916666666667 -0.0312442129629633 -0.147922453703703 60 6.48 6.41465856481481 6.42166666666667 -0.00700810185185185 0.0653414351851866 61 6.74 6.60236689814815 6.44166666666667 0.160700231481481 0.137633101851852 62 6.53 6.82639467592593 6.46083333333333 0.365561342592593 -0.296394675925925 63 6.8 6.57799189814815 6.49208333333333 0.0859085648148153 0.222008101851851 64 6.75 6.67451967592593 6.51541666666667 0.15910300925926 0.075480324074074 65 6.56 6.58465856481481 6.53291666666667 0.0517418981481485 -0.024658564814815 66 6.66 6.63382523148148 6.54916666666667 0.0846585648148148 0.0261747685185192 67 6.18 6.16681134259259 6.55916666666667 -0.392355324074075 0.013188657407408 68 6.4 6.31625578703704 6.58083333333333 -0.264577546296297 0.083744212962964 69 6.43 6.42076967592592 6.6075 -0.186730324074074 0.00923032407407476 70 6.54 6.59674189814815 6.6225 -0.025758101851852 -0.0567418981481476 71 6.44 6.6054224537037 6.63666666666667 -0.0312442129629633 -0.165422453703703 72 6.64 6.65049189814815 6.6575 -0.00700810185185185 -0.0104918981481479 73 6.82 6.83945023148148 6.67875 0.160700231481481 -0.0194502314814811 74 6.97 7.05889467592593 6.69333333333333 0.365561342592593 -0.0888946759259266 75 7 6.79424189814815 6.70833333333333 0.0859085648148153 0.205758101851853 76 6.91 6.88951967592593 6.73041666666667 0.15910300925926 0.0204803240740743 77 6.74 6.80590856481481 6.75416666666667 0.0517418981481485 -0.0659085648148139 78 6.98 6.85340856481481 6.76875 0.0846585648148148 0.126591435185186 79 6.37 6.38181134259259 6.77416666666667 -0.392355324074075 -0.0118113425925923 80 6.56 6.5179224537037 6.7825 -0.264577546296297 0.0420775462962961 81 6.63 6.60701967592593 6.79375 -0.186730324074074 0.0229803240740729 82 6.87 6.77424189814815 6.8 -0.025758101851852 0.0957581018518514 83 6.68 6.78833912037037 6.81958333333333 -0.0312442129629633 -0.108339120370371 84 6.75 NA NA -0.00700810185185185 NA 85 6.84 NA NA NA NA 86 7.15 NA NA NA NA 87 7.09 NA NA NA NA 88 6.97 NA NA NA NA 89 7.15 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/May/20/t1305901728a8yn1qhwc5cka11/1d5g51305901885.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/20/t1305901728a8yn1qhwc5cka11/1d5g51305901885.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/20/t1305901728a8yn1qhwc5cka11/2uid11305901885.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/20/t1305901728a8yn1qhwc5cka11/2uid11305901885.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/20/t1305901728a8yn1qhwc5cka11/3rhs21305901885.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/20/t1305901728a8yn1qhwc5cka11/3rhs21305901885.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/20/t1305901728a8yn1qhwc5cka11/4sguj1305901885.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/20/t1305901728a8yn1qhwc5cka11/4sguj1305901885.ps (open in new window)

Parameters (Session):

par1 = 48 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;

Parameters (R input):

par1 = additive ; par2 = 12 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;

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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