Van der Linden Kevin, Classical decomposition, productie van bakstenen

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Tue, 02 Jun 2009 02:52:40 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Jun/02/t124393295414noqcgbcriq0r7.htm/, Retrieved Tue, 02 Jun 2009 10:55:58 +0200

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Jun/02/t124393295414noqcgbcriq0r7.htm/}, year = {2009}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2009}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

155 176 180 181 208 183 185 205 180 196 183 147 128 158 186 165 191 168 171 169 157 175 156 129 89 138 146 151 156 129 146 141 137 155 147 128 92 136 159 131 134 148 146 144 161 140 141 139 94 136 164 141 159 162 154 166 156 147 161 135 98 150 173 144 167 161 156 175 163 159 167 148 119 150 161 136 166 155 140 141

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 'George Udny Yule' @ 72.249.76.132 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 155 NA NA -50.3965277777778 NA 2 176 NA NA -6.50486111111111 NA 3 180 NA NA 15.8868055555556 NA 4 181 NA NA -2.86319444444444 NA 5 208 NA NA 12.5784722222222 NA 6 183 NA NA 4.90347222222223 NA 7 185 186.428472222222 180.458333333333 5.97013888888889 -1.42847222222224 8 205 189.095138888889 178.583333333333 10.5118055555556 15.9048611111111 9 180 184.670138888889 178.083333333333 6.58680555555556 -4.67013888888889 10 196 185.103472222222 177.666666666667 7.43680555555557 10.8965277777778 11 183 183.378472222222 176.291666666667 7.08680555555555 -0.378472222222172 12 147 163.761805555556 174.958333333333 -11.1965277777778 -16.7618055555556 13 128 123.353472222222 173.75 -50.3965277777778 4.64652777777778 14 158 165.161805555556 171.666666666667 -6.50486111111111 -7.16180555555556 15 186 185.095138888889 169.208333333333 15.8868055555556 0.904861111111103 16 165 164.511805555556 167.375 -2.86319444444444 0.488194444444446 17 191 177.953472222222 165.375 12.5784722222222 13.0465277777778 18 168 168.403472222222 163.5 4.90347222222223 -0.403472222222206 19 171 167.095138888889 161.125 5.97013888888889 3.9048611111111 20 169 169.178472222222 158.666666666667 10.5118055555556 -0.178472222222211 21 157 162.753472222222 156.166666666667 6.58680555555556 -5.7534722222222 22 175 161.353472222222 153.916666666667 7.43680555555557 13.6465277777778 23 156 158.961805555556 151.875 7.08680555555555 -2.96180555555551 24 129 137.595138888889 148.791666666667 -11.1965277777778 -8.59513888888887 25 89 95.7284722222222 146.125 -50.3965277777778 -6.7284722222222 26 138 137.411805555556 143.916666666667 -6.50486111111111 0.588194444444468 27 146 157.803472222222 141.916666666667 15.8868055555556 -11.8034722222222 28 151 137.386805555556 140.25 -2.86319444444444 13.6131944444445 29 156 151.620138888889 139.041666666667 12.5784722222222 4.3798611111111 30 129 143.528472222222 138.625 4.90347222222223 -14.5284722222222 31 146 144.678472222222 138.708333333333 5.97013888888889 1.32152777777776 32 141 149.261805555556 138.75 10.5118055555556 -8.26180555555555 33 137 145.795138888889 139.208333333333 6.58680555555556 -8.79513888888889 34 155 146.353472222222 138.916666666667 7.43680555555557 8.64652777777778 35 147 144.253472222222 137.166666666667 7.08680555555555 2.74652777777777 36 128 125.845138888889 137.041666666667 -11.1965277777778 2.15486111111110 37 92 87.4368055555556 137.833333333333 -50.3965277777778 4.56319444444446 38 136 131.453472222222 137.958333333333 -6.50486111111111 4.54652777777778 39 159 154.970138888889 139.083333333333 15.8868055555556 4.0298611111111 40 131 136.595138888889 139.458333333333 -2.86319444444444 -5.5951388888889 41 134 151.161805555556 138.583333333333 12.5784722222222 -17.1618055555555 42 148 143.695138888889 138.791666666667 4.90347222222223 4.30486111111111 43 146 145.303472222222 139.333333333333 5.97013888888889 0.69652777777776 44 144 149.928472222222 139.416666666667 10.5118055555556 -5.92847222222221 45 161 146.211805555556 139.625 6.58680555555556 14.7881944444445 46 140 147.686805555556 140.25 7.43680555555557 -7.68680555555557 47 141 148.795138888889 141.708333333333 7.08680555555555 -7.79513888888891 48 139 132.136805555556 143.333333333333 -11.1965277777778 6.86319444444445 49 94 93.8534722222222 144.25 -50.3965277777778 0.146527777777806 50 136 138.995138888889 145.5 -6.50486111111111 -2.99513888888887 51 164 162.095138888889 146.208333333333 15.8868055555556 1.90486111111113 52 141 143.428472222222 146.291666666667 -2.86319444444444 -2.42847222222221 53 159 159.995138888889 147.416666666667 12.5784722222222 -0.995138888888903 54 162 152.986805555556 148.083333333333 4.90347222222223 9.01319444444442 55 154 154.053472222222 148.083333333333 5.97013888888889 -0.0534722222222115 56 166 159.345138888889 148.833333333333 10.5118055555556 6.65486111111113 57 156 156.378472222222 149.791666666667 6.58680555555556 -0.3784722222222 58 147 157.728472222222 150.291666666667 7.43680555555557 -10.7284722222222 59 161 157.836805555556 150.75 7.08680555555555 3.16319444444446 60 135 139.845138888889 151.041666666667 -11.1965277777778 -4.84513888888893 61 98 100.686805555556 151.083333333333 -50.3965277777778 -2.68680555555554 62 150 145.036805555556 151.541666666667 -6.50486111111111 4.96319444444447 63 173 168.095138888889 152.208333333333 15.8868055555556 4.9048611111111 64 144 150.136805555556 153 -2.86319444444444 -6.13680555555555 65 167 166.328472222222 153.75 12.5784722222222 0.671527777777754 66 161 159.445138888889 154.541666666667 4.90347222222223 1.55486111111111 67 156 161.928472222222 155.958333333333 5.97013888888889 -5.92847222222224 68 175 167.345138888889 156.833333333333 10.5118055555556 7.6548611111111 69 163 162.920138888889 156.333333333333 6.58680555555556 0.0798611111111143 70 159 162.936805555556 155.5 7.43680555555557 -3.93680555555554 71 167 162.211805555556 155.125 7.08680555555555 4.78819444444446 72 148 143.636805555556 154.833333333333 -11.1965277777778 4.36319444444442 73 119 NA 153.916666666667 NA NA 74 150 NA 151.833333333333 NA NA 75 161 NA NA NA NA 76 136 NA NA NA NA 77 166 NA NA NA NA 78 155 NA NA NA NA 79 140 NA NA NA NA 80 141 NA NA NA NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t124393295414noqcgbcriq0r7/1k22z1243932758.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t124393295414noqcgbcriq0r7/1k22z1243932758.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t124393295414noqcgbcriq0r7/2ndpq1243932758.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t124393295414noqcgbcriq0r7/2ndpq1243932758.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t124393295414noqcgbcriq0r7/3tm1o1243932758.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t124393295414noqcgbcriq0r7/3tm1o1243932758.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t124393295414noqcgbcriq0r7/49crh1243932758.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t124393295414noqcgbcriq0r7/49crh1243932758.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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