Tijdreeks 1 - stap 29

*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 Aug 2010 14:52:27 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Aug/13/t1281711698q8o6ijm14hdopiy.htm/, Retrieved Fri, 13 Aug 2010 17:01:39 +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/2010/Aug/13/t1281711698q8o6ijm14hdopiy.htm/}, year = {2010}, } @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 = {2010}, 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:

Reuben Vermoet

Dataseries X:

» Textbox « » Textfile « » CSV «

210 209 208 206 226 225 210 200 201 201 202 204 197 196 187 196 221 218 200 191 194 192 199 196 182 178 169 177 207 213 191 182 188 189 194 195 171 165 156 170 201 208 189 175 184 187 193 199 179 188 171 182 212 216 192 182 183 183 187 190 167 167 158 171 201 208 181 169 173 180 181 192 169 168 156 161 195 208 176 164 170 175 170 175 148 151 143 139 166 186 149 142 138 137 130 138 118 113 99 93 125 146 109 97 97 94 92 103 78 72 57 40 70 89 53 46 43 38 29 34

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 'RServer@AstonUniversity' @ vre.aston.ac.uk Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 210 NA NA -9.77430555555556 NA 2 209 NA NA -9.55671296296297 NA 3 208 NA NA -19.4456018518519 NA 4 206 NA NA -14.2928240740741 NA 5 226 NA NA 17.1516203703704 NA 6 225 NA NA 29.1840277777778 NA 7 210 210.827546296296 207.958333333333 2.86921296296296 -0.827546296296276 8 200 200.434027777778 206.875 -6.44097222222222 -0.434027777777771 9 201 203.239583333333 205.458333333333 -2.21875 -2.23958333333331 10 201 204.52662037037 204.166666666667 0.359953703703702 -3.52662037037038 11 202 206.503472222222 203.541666666667 2.96180555555556 -4.50347222222217 12 204 212.244212962963 203.041666666667 9.2025462962963 -8.24421296296296 13 197 192.559027777778 202.333333333333 -9.77430555555556 4.44097222222223 14 196 191.984953703704 201.541666666667 -9.55671296296297 4.0150462962963 15 187 181.429398148148 200.875 -19.4456018518519 5.57060185185185 16 196 185.915509259259 200.208333333333 -14.2928240740741 10.0844907407408 17 221 216.859953703704 199.708333333333 17.1516203703704 4.14004629629633 18 218 228.434027777778 199.25 29.1840277777778 -10.4340277777778 19 200 201.16087962963 198.291666666667 2.86921296296296 -1.16087962962965 20 191 190.475694444444 196.916666666667 -6.44097222222222 0.524305555555571 21 194 193.197916666667 195.416666666667 -2.21875 0.802083333333343 22 192 194.234953703704 193.875 0.359953703703702 -2.23495370370364 23 199 195.461805555556 192.5 2.96180555555556 3.53819444444449 24 196 200.91087962963 191.708333333333 9.2025462962963 -4.91087962962962 25 182 181.350694444444 191.125 -9.77430555555556 0.649305555555571 26 178 180.818287037037 190.375 -9.55671296296297 -2.81828703703704 27 169 170.304398148148 189.75 -19.4456018518519 -1.30439814814815 28 177 175.082175925926 189.375 -14.2928240740741 1.91782407407408 29 207 206.193287037037 189.041666666667 17.1516203703704 0.806712962962962 30 213 217.975694444444 188.791666666667 29.1840277777778 -4.97569444444446 31 191 191.16087962963 188.291666666667 2.86921296296296 -0.160879629629676 32 182 180.850694444444 187.291666666667 -6.44097222222222 1.14930555555554 33 188 183.989583333333 186.208333333333 -2.21875 4.01041666666669 34 189 185.734953703704 185.375 0.359953703703702 3.2650462962963 35 194 187.795138888889 184.833333333333 2.96180555555556 6.20486111111114 36 195 193.577546296296 184.375 9.2025462962963 1.42245370370372 37 171 174.309027777778 184.083333333333 -9.77430555555556 -3.30902777777777 38 165 174.15162037037 183.708333333333 -9.55671296296297 -9.15162037037038 39 156 163.804398148148 183.25 -19.4456018518519 -7.80439814814815 40 170 168.707175925926 183 -14.2928240740741 1.2928240740741 41 201 200.02662037037 182.875 17.1516203703704 0.97337962962962 42 208 212.184027777778 183 29.1840277777778 -4.18402777777774 43 189 186.369212962963 183.5 2.86921296296296 2.63078703703707 44 175 178.350694444444 184.791666666667 -6.44097222222222 -3.3506944444444 45 184 184.15625 186.375 -2.21875 -0.156249999999972 46 187 187.859953703704 187.5 0.359953703703702 -0.859953703703695 47 193 191.420138888889 188.458333333333 2.96180555555556 1.57986111111111 48 199 198.452546296296 189.25 9.2025462962963 0.547453703703695 49 179 179.934027777778 189.708333333333 -9.77430555555556 -0.934027777777828 50 188 180.568287037037 190.125 -9.55671296296297 7.43171296296293 51 171 170.929398148148 190.375 -19.4456018518519 0.0706018518518192 52 182 175.873842592593 190.166666666667 -14.2928240740741 6.12615740740742 53 212 206.90162037037 189.75 17.1516203703704 5.09837962962962 54 216 218.309027777778 189.125 29.1840277777778 -2.30902777777774 55 192 191.119212962963 188.25 2.86921296296296 0.880787037037038 56 182 180.434027777778 186.875 -6.44097222222222 1.56597222222226 57 183 183.239583333333 185.458333333333 -2.21875 -0.239583333333314 58 183 184.818287037037 184.458333333333 0.359953703703702 -1.81828703703701 59 187 186.503472222222 183.541666666667 2.96180555555556 0.4965277777778 60 190 191.952546296296 182.75 9.2025462962963 -1.95254629629628 61 167 172.184027777778 181.958333333333 -9.77430555555556 -5.1840277777778 62 167 171.40162037037 180.958333333333 -9.55671296296297 -4.40162037037038 63 158 160.554398148148 180 -19.4456018518519 -2.55439814814818 64 171 165.165509259259 179.458333333333 -14.2928240740741 5.83449074074073 65 201 196.234953703704 179.083333333333 17.1516203703704 4.7650462962963 66 208 208.100694444444 178.916666666667 29.1840277777778 -0.1006944444444 67 181 181.952546296296 179.083333333333 2.86921296296296 -0.952546296296276 68 169 172.767361111111 179.208333333333 -6.44097222222222 -3.76736111111109 69 173 176.947916666667 179.166666666667 -2.21875 -3.94791666666666 70 180 179.02662037037 178.666666666667 0.359953703703702 0.973379629629648 71 181 180.961805555556 178 2.96180555555556 0.0381944444444287 72 192 186.952546296296 177.75 9.2025462962963 5.0474537037037 73 169 167.767361111111 177.541666666667 -9.77430555555556 1.23263888888889 74 168 167.568287037037 177.125 -9.55671296296297 0.43171296296299 75 156 157.346064814815 176.791666666667 -19.4456018518519 -1.34606481481484 76 161 162.165509259259 176.458333333333 -14.2928240740741 -1.16550925925927 77 195 192.943287037037 175.791666666667 17.1516203703704 2.05671296296296 78 208 203.809027777778 174.625 29.1840277777778 4.19097222222223 79 176 175.91087962963 173.041666666667 2.86921296296296 0.0891203703703809 80 164 165.017361111111 171.458333333333 -6.44097222222222 -1.01736111111111 81 170 167.989583333333 170.208333333333 -2.21875 2.01041666666666 82 175 169.109953703704 168.75 0.359953703703702 5.89004629629628 83 170 169.586805555556 166.625 2.96180555555556 0.413194444444457 84 175 173.702546296296 164.5 9.2025462962963 1.29745370370372 85 148 152.684027777778 162.458333333333 -9.77430555555556 -4.68402777777777 86 151 150.859953703704 160.416666666667 -9.55671296296297 0.140046296296305 87 143 138.721064814815 158.166666666667 -19.4456018518519 4.27893518518519 88 139 140.957175925926 155.25 -14.2928240740741 -1.9571759259259 89 166 169.15162037037 152 17.1516203703704 -3.15162037037038 90 186 177.975694444444 148.791666666667 29.1840277777778 8.02430555555557 91 149 148.869212962963 146 2.86921296296296 0.130787037037038 92 142 136.725694444444 143.166666666667 -6.44097222222222 5.27430555555557 93 138 137.53125 139.75 -2.21875 0.46875 94 137 136.359953703704 136 0.359953703703702 0.640046296296305 95 130 135.336805555556 132.375 2.96180555555556 -5.33680555555556 96 138 138.202546296296 129 9.2025462962963 -0.202546296296305 97 118 115.892361111111 125.666666666667 -9.77430555555556 2.1076388888889 98 113 112.568287037037 122.125 -9.55671296296297 0.431712962962962 99 99 99.0960648148148 118.541666666667 -19.4456018518519 -0.0960648148147953 100 93 100.748842592593 115.041666666667 -14.2928240740741 -7.74884259259258 101 125 128.818287037037 111.666666666667 17.1516203703704 -3.81828703703705 102 146 137.809027777778 108.625 29.1840277777778 8.19097222222223 103 109 108.369212962963 105.5 2.86921296296296 0.630787037037024 104 97 95.6840277777778 102.125 -6.44097222222222 1.31597222222221 105 97 96.4479166666667 98.6666666666667 -2.21875 0.552083333333329 106 94 95.068287037037 94.7083333333333 0.359953703703702 -1.06828703703702 107 92 93.1701388888889 90.2083333333333 2.96180555555556 -1.17013888888887 108 103 94.744212962963 85.5416666666666 9.2025462962963 8.25578703703705 109 78 71.0590277777778 80.8333333333333 -9.77430555555556 6.94097222222223 110 72 66.818287037037 76.375 -9.55671296296297 5.18171296296296 111 57 52.5543981481481 72 -19.4456018518519 4.44560185185186 112 40 53.1238425925926 67.4166666666667 -14.2928240740741 -13.1238425925926 113 70 79.6099537037037 62.4583333333333 17.1516203703704 -9.6099537037037 114 89 86.1423611111111 56.9583333333333 29.1840277777778 2.85763888888889 115 53 NA NA 2.86921296296296 NA 116 46 NA NA -6.44097222222222 NA 117 43 NA NA -2.21875 NA 118 38 NA NA 0.359953703703702 NA 119 29 NA NA 2.96180555555556 NA 120 34 NA NA 9.2025462962963 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Aug/13/t1281711698q8o6ijm14hdopiy/12igc1281711143.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/13/t1281711698q8o6ijm14hdopiy/12igc1281711143.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/13/t1281711698q8o6ijm14hdopiy/22igc1281711143.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/13/t1281711698q8o6ijm14hdopiy/22igc1281711143.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/13/t1281711698q8o6ijm14hdopiy/3drgf1281711143.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/13/t1281711698q8o6ijm14hdopiy/3drgf1281711143.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/13/t1281711698q8o6ijm14hdopiy/4drgf1281711143.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/13/t1281711698q8o6ijm14hdopiy/4drgf1281711143.ps (open in new window)

Parameters (Session):

par1 = 0.01 ; par2 = 0.99 ; par3 = 0.01 ;

Parameters (R input):

par1 = additive ; par2 = 12 ; par3 = 0.01 ;

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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