isabelle regnard, oef 9, Classical Deomposition

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Fri, 13 May 2011 09:47:54 +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/13/t1305279912byslay8g37mot4s.htm/, Retrieved Fri, 13 May 2011 11:45:15 +0200

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

KDGP2W92

Dataseries X:

» Textbox « » Textfile « » CSV «

106,42 106,22 106,32 105,81 105,92 107,54 107,34 107,24 107,74 105,71 105,41 106,22 106,32 106,12 106,22 105,92 105,71 105,71 105,92 105,71 105,41 104,49 101,35 99,72 99,01 97,89 95,86 94,95 95,35 95,15 95,46 95,56 95,05 94,64 93,63 93,12 93,53 97,18 96,27 95,15 97,08 101,95 103,07 103,68 102,87 102,56 103,38 103,27 102,89 102,69 101,54 102,9 101,53 101,96 101,99 101,11 101,75 101,71 104,11 103,57 103,32 103,64 103,68 103,79 103,01 101,54 101,9 103,68 104,62 104,11 105,04 104,83 105,05 104,68 107,32 109,9 109,77 110,69 110,54 110,89 110,95 109,73 110,85 110,39 110,58 110,4 111,07 110,86 111,38 111,44 110,36 110,06 108,34 107,94 107,39 107,1 107,61 107,74 106,9 106,71 106,6 108,21 110,54 110,91 109,51 110,27 111,39 112,13 111,64

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 4 seconds R Server 'Gwilym Jenkins' @ www.wessa.org Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 106.42 NA NA -0.474292534722225 NA 2 106.22 NA NA -0.256323784722219 NA 3 106.32 NA NA -0.469657118055554 NA 4 105.81 NA NA -0.337625868055552 NA 5 105.92 NA NA -0.361271701388894 NA 6 107.54 NA NA 0.354301215277776 NA 7 107.34 107.173728298611 106.486666666667 0.687061631944445 0.166271701388908 8 107.24 107.272061631944 106.478333333333 0.793728298611123 -0.032061631944444 9 107.74 107.027790798611 106.47 0.557790798611112 0.712209201388887 10 105.71 106.607842881944 106.470416666667 0.137426215277778 -0.897842881944442 11 105.41 106.412374131944 106.46625 -0.0538758680555628 -1.00237413194442 12 106.22 105.803988715278 106.38125 -0.577261284722228 0.416011284722217 13 106.32 105.771540798611 106.245833333333 -0.474292534722225 0.54845920138888 14 106.12 105.866592881944 106.122916666667 -0.256323784722219 0.253407118055577 15 106.22 105.492426215278 105.962083333333 -0.469657118055554 0.727573784722225 16 105.92 105.476540798611 105.814166666667 -0.337625868055552 0.44345920138889 17 105.71 105.232894965278 105.594166666667 -0.361271701388894 0.4771050347222 18 105.71 105.508467881944 105.154166666667 0.354301215277776 0.201532118055553 19 105.92 105.265811631944 104.57875 0.687061631944445 0.654188368055543 20 105.71 104.724978298611 103.93125 0.793728298611123 0.985021701388874 21 105.41 103.714457465278 103.156666666667 0.557790798611112 1.69554253472222 22 104.49 102.405342881944 102.267916666667 0.137426215277778 2.08465711805555 23 101.35 101.325290798611 101.379166666667 -0.0538758680555628 0.0247092013888732 24 99.72 99.9302387152778 100.5075 -0.577261284722228 -0.210238715277782 25 99.01 99.1573741319444 99.6316666666666 -0.474292534722225 -0.147374131944417 26 97.89 98.5165928819444 98.7729166666667 -0.256323784722219 -0.626592881944447 27 95.86 97.4486762152778 97.9183333333333 -0.469657118055554 -1.58867621527777 28 94.95 96.7386241319444 97.07625 -0.337625868055552 -1.78862413194445 29 95.35 95.9828949652778 96.3441666666667 -0.361271701388894 -0.632894965277785 30 95.15 96.1018012152778 95.7475 0.354301215277776 -0.951801215277754 31 95.46 95.9312282986111 95.2441666666667 0.687061631944445 -0.471228298611109 32 95.56 95.7799782986111 94.98625 0.793728298611123 -0.21997829861111 33 95.05 95.5315407986111 94.97375 0.557790798611112 -0.481540798611107 34 94.64 95.1365928819444 94.9991666666667 0.137426215277778 -0.496592881944437 35 93.63 95.0257074652778 95.0795833333333 -0.0538758680555628 -1.39570746527779 36 93.12 94.8577387152778 95.435 -0.577261284722228 -1.73773871527779 37 93.53 95.5611241319445 96.0354166666667 -0.474292534722225 -2.03112413194445 38 97.18 96.4345095486111 96.6908333333333 -0.256323784722219 0.745490451388875 39 96.27 96.8853428819445 97.355 -0.469657118055554 -0.615342881944457 40 95.15 97.6732074652778 98.0108333333333 -0.337625868055552 -2.52320746527776 41 97.08 98.3858116319445 98.7470833333334 -0.361271701388894 -1.30581163194447 42 101.95 99.9305512152778 99.57625 0.354301215277776 2.01944878472224 43 103.07 101.076228298611 100.389166666667 0.687061631944445 1.99377170138889 44 103.68 101.802478298611 101.00875 0.793728298611123 1.87752170138889 45 102.87 102.015707465278 101.457916666667 0.557790798611112 0.854292534722205 46 102.56 102.137842881944 102.000416666667 0.137426215277778 0.422157118055537 47 103.38 102.454874131944 102.50875 -0.0538758680555628 0.925125868055545 48 103.27 102.117322048611 102.694583333333 -0.577261284722228 1.15267795138888 49 102.89 102.175707465278 102.65 -0.474292534722225 0.714292534722219 50 102.69 102.241592881944 102.497916666667 -0.256323784722219 0.448407118055542 51 101.54 101.874509548611 102.344166666667 -0.469657118055554 -0.334509548611109 52 102.9 101.924457465278 102.262083333333 -0.337625868055552 0.975542534722237 53 101.53 101.895811631944 102.257083333333 -0.361271701388894 -0.365811631944439 54 101.96 102.654301215278 102.3 0.354301215277776 -0.69430121527779 55 101.99 103.017478298611 102.330416666667 0.687061631944445 -1.0274782986111 56 101.11 103.181644965278 102.387916666667 0.793728298611123 -2.07164496527778 57 101.75 103.074457465278 102.516666666667 0.557790798611112 -1.32445746527779 58 101.71 102.780342881944 102.642916666667 0.137426215277778 -1.07034288194444 59 104.11 102.687790798611 102.741666666667 -0.0538758680555628 1.4222092013889 60 103.57 102.208572048611 102.785833333333 -0.577261284722228 1.3614279513889 61 103.32 102.290290798611 102.764583333333 -0.474292534722225 1.0297092013889 62 103.64 102.611592881944 102.867916666667 -0.256323784722219 1.02840711805555 63 103.68 102.624926215278 103.094583333333 -0.469657118055554 1.05507378472224 64 103.79 102.976540798611 103.314166666667 -0.337625868055552 0.813459201388895 65 103.01 103.091644965278 103.452916666667 -0.361271701388894 -0.0816449652777749 66 101.54 103.898467881944 103.544166666667 0.354301215277776 -2.35846788194443 67 101.9 104.355811631944 103.66875 0.687061631944445 -2.45581163194446 68 103.68 104.577894965278 103.784166666667 0.793728298611123 -0.897894965277771 69 104.62 104.536957465278 103.979166666667 0.557790798611112 0.0830425347222388 70 104.11 104.522842881944 104.385416666667 0.137426215277778 -0.412842881944442 71 105.04 104.867790798611 104.921666666667 -0.0538758680555628 0.172209201388895 72 104.83 105.007322048611 105.584583333333 -0.577261284722228 -0.177322048611103 73 105.05 105.851540798611 106.325833333333 -0.474292534722225 -0.8015407986111 74 104.68 106.729926215278 106.98625 -0.256323784722219 -2.04992621527776 75 107.32 107.080759548611 107.550416666667 -0.469657118055554 0.239240451388895 76 109.9 107.710707465278 108.048333333333 -0.337625868055552 2.18929253472224 77 109.77 108.163311631944 108.524583333333 -0.361271701388894 1.60668836805556 78 110.69 109.352634548611 108.998333333333 0.354301215277776 1.33736545138892 79 110.54 110.147478298611 109.460416666667 0.687061631944445 0.392521701388915 80 110.89 110.722894965278 109.929166666667 0.793728298611123 0.167105034722226 81 110.95 110.881540798611 110.32375 0.557790798611112 0.0684592013889187 82 109.73 110.657426215278 110.52 0.137426215277778 -0.927426215277762 83 110.85 110.573207465278 110.627083333333 -0.0538758680555628 0.276792534722233 84 110.39 110.148155381944 110.725416666667 -0.577261284722228 0.241844618055552 85 110.58 110.274874131944 110.749166666667 -0.474292534722225 0.305125868055555 86 110.4 110.450759548611 110.707083333333 -0.256323784722219 -0.0507595486111114 87 111.07 110.094092881944 110.56375 -0.469657118055554 0.975907118055545 88 110.86 110.042790798611 110.380416666667 -0.337625868055552 0.817209201388891 89 111.38 109.800394965278 110.161666666667 -0.361271701388894 1.57960503472222 90 111.44 110.234717881944 109.880416666667 0.354301215277776 1.20528211805556 91 110.36 110.306644965278 109.619583333333 0.687061631944445 0.0533550347222302 92 110.06 110.178728298611 109.385 0.793728298611123 -0.118728298611103 93 108.34 109.658207465278 109.100416666667 0.557790798611112 -1.31820746527775 94 107.94 108.891176215278 108.75375 0.137426215277778 -0.951176215277783 95 107.39 108.327790798611 108.381666666667 -0.0538758680555628 -0.937790798611118 96 107.1 107.470655381944 108.047916666667 -0.577261284722228 -0.370655381944445 97 107.61 107.446540798611 107.920833333333 -0.474292534722225 0.163459201388889 98 107.74 107.707426215278 107.96375 -0.256323784722219 0.032573784722203 99 106.9 107.578259548611 108.047916666667 -0.469657118055554 -0.678259548611109 100 106.71 107.856124131944 108.19375 -0.337625868055552 -1.14612413194445 101 106.6 108.096228298611 108.4575 -0.361271701388894 -1.49622829861109 102 108.21 109.188051215278 108.83375 0.354301215277776 -0.978051215277787 103 110.54 109.898311631944 109.21125 0.687061631944445 0.641688368055569 104 110.91 NA NA 0.793728298611123 NA 105 109.51 NA NA 0.557790798611112 NA 106 110.27 NA NA 0.137426215277778 NA 107 111.39 NA NA -0.0538758680555628 NA 108 112.13 NA NA -0.577261284722228 NA 109 111.64 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/May/13/t1305279912byslay8g37mot4s/15twm1305280070.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/13/t1305279912byslay8g37mot4s/15twm1305280070.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/13/t1305279912byslay8g37mot4s/259wz1305280070.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/13/t1305279912byslay8g37mot4s/259wz1305280070.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/13/t1305279912byslay8g37mot4s/362oi1305280070.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/13/t1305279912byslay8g37mot4s/362oi1305280070.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/13/t1305279912byslay8g37mot4s/4xwwq1305280070.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/13/t1305279912byslay8g37mot4s/4xwwq1305280070.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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