Mathieu Demoor - Opgave 9.2

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

Title produced by software: Classical Decomposition

Date of computation: Sun, 18 May 2008 15:48:26 -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/18/t1211147339kqjinjm9wg0fn9q.htm/, Retrieved Sun, 18 May 2008 23:48:59 +0200

User-defined keywords:

Dataseries X:

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113 110 107 103 98 98 137 148 147 139 130 128 127 123 118 114 108 111 151 159 158 148 138 137 136 133 126 120 114 116 153 162 161 149 139 135 130 127 122 117 112 113 149 157 157 147 137 132 125 123 117 114 111 112 144 150 149 134 123 116 117 111 105 102 95 93 124 130 124 115 106 105

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 3 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 113 NA NA -3.40885416666666 NA 2 110 NA NA -6.50260416666666 NA 3 107 NA NA -12.2942708333333 NA 4 103 NA NA -16.7630208333333 NA 5 98 NA NA -21.6380208333333 NA 6 98 NA NA -19.6901041666667 NA 7 137 138.872395833333 122.083333333333 16.7890625 -1.87239583333334 8 148 147.9765625 123.208333333333 24.7682291666667 0.0234375000000142 9 147 148.486979166667 124.208333333333 24.2786458333333 -1.48697916666667 10 139 137.9140625 125.125 12.7890625 1.0859375 11 130 128.799479166667 126 2.79947916666667 1.20052083333334 12 128 125.830729166667 126.958333333333 -1.12760416666667 2.16927083333337 13 127 124.674479166667 128.083333333333 -3.40885416666666 2.32552083333334 14 123 122.622395833333 129.125 -6.50260416666666 0.377604166666686 15 118 117.747395833333 130.041666666667 -12.2942708333333 0.252604166666686 16 114 114.111979166667 130.875 -16.7630208333333 -0.111979166666629 17 108 109.9453125 131.583333333333 -21.6380208333333 -1.94531249999997 18 111 112.6015625 132.291666666667 -19.6901041666667 -1.60156249999997 19 151 149.830729166667 133.041666666667 16.7890625 1.16927083333334 20 159 158.6015625 133.833333333333 24.7682291666667 0.3984375 21 158 158.861979166667 134.583333333333 24.2786458333333 -0.861979166666657 22 148 147.955729166667 135.166666666667 12.7890625 0.0442708333333428 23 138 138.466145833333 135.666666666667 2.79947916666667 -0.466145833333286 24 137 134.997395833333 136.125 -1.12760416666667 2.00260416666669 25 136 133.0078125 136.416666666667 -3.40885416666666 2.9921875 26 133 130.122395833333 136.625 -6.50260416666666 2.87760416666666 27 126 124.580729166667 136.875 -12.2942708333333 1.41927083333334 28 120 120.278645833333 137.041666666667 -16.7630208333333 -0.278645833333314 29 114 115.486979166667 137.125 -21.6380208333333 -1.48697916666666 30 116 117.393229166667 137.083333333333 -19.6901041666667 -1.39322916666666 31 153 153.5390625 136.75 16.7890625 -0.5390625 32 162 161.018229166667 136.25 24.7682291666667 0.981770833333371 33 161 160.111979166667 135.833333333333 24.2786458333333 0.888020833333343 34 149 148.330729166667 135.541666666667 12.7890625 0.669270833333314 35 139 138.1328125 135.333333333333 2.79947916666667 0.8671875 36 135 133.997395833333 135.125 -1.12760416666667 1.00260416666669 37 130 131.424479166667 134.833333333333 -3.40885416666666 -1.42447916666666 38 127 127.955729166667 134.458333333333 -6.50260416666666 -0.955729166666657 39 122 121.7890625 134.083333333333 -12.2942708333333 0.210937500000028 40 117 117.0703125 133.833333333333 -16.7630208333333 -0.0703124999999716 41 112 112.028645833333 133.666666666667 -21.6380208333333 -0.0286458333333144 42 113 113.768229166667 133.458333333333 -19.6901041666667 -0.768229166666629 43 149 149.9140625 133.125 16.7890625 -0.9140625 44 157 157.518229166667 132.75 24.7682291666667 -0.518229166666657 45 157 156.653645833333 132.375 24.2786458333333 0.346354166666657 46 147 144.830729166667 132.041666666667 12.7890625 2.16927083333334 47 137 134.674479166667 131.875 2.79947916666667 2.32552083333334 48 132 130.6640625 131.791666666667 -1.12760416666667 1.33593750000003 49 125 128.1328125 131.541666666667 -3.40885416666666 -3.1328125 50 123 124.5390625 131.041666666667 -6.50260416666666 -1.5390625 51 117 118.122395833333 130.416666666667 -12.2942708333333 -1.12239583333331 52 114 112.778645833333 129.541666666667 -16.7630208333333 1.22135416666666 53 111 106.778645833333 128.416666666667 -21.6380208333333 4.22135416666669 54 112 107.4765625 127.166666666667 -19.6901041666667 4.52343750000001 55 144 142.955729166667 126.166666666667 16.7890625 1.04427083333334 56 150 150.1015625 125.333333333333 24.7682291666667 -0.101562499999986 57 149 148.611979166667 124.333333333333 24.2786458333333 0.388020833333329 58 134 136.122395833333 123.333333333333 12.7890625 -2.12239583333333 59 123 124.966145833333 122.166666666667 2.79947916666667 -1.96614583333333 60 116 119.580729166667 120.708333333333 -1.12760416666667 -3.58072916666667 61 117 115.674479166667 119.083333333333 -3.40885416666666 1.32552083333331 62 111 110.9140625 117.416666666667 -6.50260416666666 0.0859374999999858 63 105 103.247395833333 115.541666666667 -12.2942708333333 1.75260416666666 64 102 96.9453125 113.708333333333 -16.7630208333333 5.05468750000001 65 95 90.5703125 112.208333333333 -21.6380208333333 4.4296875 66 93 91.3515625 111.041666666667 -19.6901041666667 1.64843750000001 67 124 NA NA 16.7890625 NA 68 130 NA NA 24.7682291666667 NA 69 124 NA NA 24.2786458333333 NA 70 115 NA NA 12.7890625 NA 71 106 NA NA 2.79947916666667 NA 72 105 NA NA -1.12760416666667 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/18/t1211147339kqjinjm9wg0fn9q/1glod1211147301.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/18/t1211147339kqjinjm9wg0fn9q/1glod1211147301.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/18/t1211147339kqjinjm9wg0fn9q/2ywo51211147301.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/18/t1211147339kqjinjm9wg0fn9q/2ywo51211147301.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/18/t1211147339kqjinjm9wg0fn9q/3tl421211147301.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/18/t1211147339kqjinjm9wg0fn9q/3tl421211147301.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/18/t1211147339kqjinjm9wg0fn9q/4lo0q1211147301.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/18/t1211147339kqjinjm9wg0fn9q/4lo0q1211147301.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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