additieve decompositie

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Fri, 02 Jul 2010 18:26:49 +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/Jul/02/t1278095319ptyl3lnwf6tossu.htm/, Retrieved Fri, 02 Jul 2010 20:28:40 +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/Jul/02/t1278095319ptyl3lnwf6tossu.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:

thomas talboom

Dataseries X:

» Textbox « » Textfile « » CSV «

237 236 235 233 231 230 231 233 234 234 235 237 246 245 240 239 231 224 229 231 238 240 237 239 248 239 237 232 216 209 214 217 217 227 218 220 229 224 216 208 191 190 196 196 200 204 193 194 207 209 193 175 157 150 162 157 160 167 159 161 179 180 169 152 128 125 131 135 141 154 152 147 163 165 147 130 106 107 115 114 124 141 139 129

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 'RServer@AstonUniversity' @ vre.aston.ac.uk Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 237 NA NA 17.6921296296296 NA 2 236 NA NA 17.6574074074074 NA 3 235 NA NA 9.2476851851852 NA 4 233 NA NA -0.342592592592585 NA 5 231 NA NA -16.8634259259259 NA 6 230 NA NA -19.4467592592593 NA 7 231 225.331018518519 234.208333333333 -8.87731481481481 5.6689814814815 8 233 228.087962962963 234.958333333333 -6.87037037037039 4.91203703703707 9 234 233.275462962963 235.541666666667 -2.2662037037037 0.724537037037067 10 234 241.060185185185 236 5.06018518518518 -7.06018518518516 11 235 237.560185185185 236.25 1.31018518518518 -2.56018518518516 12 237 239.699074074074 236 3.69907407407406 -2.69907407407408 13 246 253.358796296296 235.666666666667 17.6921296296296 -7.3587962962963 14 245 253.157407407407 235.5 17.6574074074074 -8.15740740740742 15 240 244.831018518519 235.583333333333 9.2476851851852 -4.8310185185185 16 239 235.657407407407 236 -0.342592592592585 3.34259259259261 17 231 219.469907407407 236.333333333333 -16.8634259259259 11.5300925925926 18 224 217.053240740741 236.5 -19.4467592592593 6.9467592592593 19 229 227.789351851852 236.666666666667 -8.87731481481481 1.21064814814818 20 231 229.62962962963 236.5 -6.87037037037039 1.37037037037038 21 238 233.858796296296 236.125 -2.2662037037037 4.1412037037037 22 240 240.768518518519 235.708333333333 5.06018518518518 -0.768518518518533 23 237 236.101851851852 234.791666666667 1.31018518518518 0.898148148148152 24 239 237.240740740741 233.541666666667 3.69907407407406 1.75925925925927 25 248 249.983796296296 232.291666666667 17.6921296296296 -1.98379629629628 26 239 248.740740740741 231.083333333333 17.6574074074074 -9.74074074074073 27 237 238.872685185185 229.625 9.2476851851852 -1.87268518518516 28 232 227.865740740741 228.208333333333 -0.342592592592585 4.1342592592593 29 216 210.011574074074 226.875 -16.8634259259259 5.98842592592592 30 209 205.844907407407 225.291666666667 -19.4467592592593 3.15509259259261 31 214 214.831018518519 223.708333333333 -8.87731481481481 -0.831018518518505 32 217 215.421296296296 222.291666666667 -6.87037037037039 1.57870370370372 33 217 218.525462962963 220.791666666667 -2.2662037037037 -1.52546296296296 34 227 223.976851851852 218.916666666667 5.06018518518518 3.02314814814815 35 218 218.185185185185 216.875 1.31018518518518 -0.18518518518519 36 220 218.740740740741 215.041666666667 3.69907407407406 1.25925925925924 37 229 231.19212962963 213.5 17.6921296296296 -2.19212962962962 38 224 229.532407407407 211.875 17.6574074074074 -5.53240740740742 39 216 219.539351851852 210.291666666667 9.2476851851852 -3.53935185185185 40 208 208.282407407407 208.625 -0.342592592592585 -0.282407407407419 41 191 189.761574074074 206.625 -16.8634259259259 1.2384259259259 42 190 185.053240740741 204.5 -19.4467592592593 4.9467592592593 43 196 193.622685185185 202.5 -8.87731481481481 2.37731481481484 44 196 194.087962962963 200.958333333333 -6.87037037037039 1.91203703703704 45 200 197.108796296296 199.375 -2.2662037037037 2.8912037037037 46 204 202.101851851852 197.041666666667 5.06018518518518 1.89814814814815 47 193 195.560185185185 194.25 1.31018518518518 -2.56018518518522 48 194 194.865740740741 191.166666666667 3.69907407407406 -0.865740740740705 49 207 205.775462962963 188.083333333333 17.6921296296296 1.22453703703704 50 209 202.699074074074 185.041666666667 17.6574074074074 6.30092592592595 51 193 190.997685185185 181.75 9.2476851851852 2.00231481481481 52 175 178.199074074074 178.541666666667 -0.342592592592585 -3.19907407407408 53 157 158.719907407407 175.583333333333 -16.8634259259259 -1.71990740740739 54 150 153.344907407407 172.791666666667 -19.4467592592593 -3.34490740740739 55 162 161.372685185185 170.25 -8.87731481481481 0.62731481481481 56 157 161.00462962963 167.875 -6.87037037037039 -4.00462962962965 57 160 163.400462962963 165.666666666667 -2.2662037037037 -3.40046296296296 58 167 168.768518518519 163.708333333333 5.06018518518518 -1.7685185185185 59 159 162.851851851852 161.541666666667 1.31018518518518 -3.85185185185185 60 161 162.990740740741 159.291666666667 3.69907407407406 -1.99074074074076 61 179 174.650462962963 156.958333333333 17.6921296296296 4.34953703703701 62 180 172.407407407407 154.75 17.6574074074074 7.59259259259258 63 169 162.289351851852 153.041666666667 9.2476851851852 6.71064814814818 64 152 151.365740740741 151.708333333333 -0.342592592592585 0.634259259259238 65 128 134.011574074074 150.875 -16.8634259259259 -6.01157407407408 66 125 130.553240740741 150 -19.4467592592593 -5.55324074074073 67 131 139.872685185185 148.75 -8.87731481481481 -8.87268518518522 68 135 140.587962962963 147.458333333333 -6.87037037037039 -5.58796296296296 69 141 143.650462962963 145.916666666667 -2.2662037037037 -2.65046296296296 70 154 149.143518518519 144.083333333333 5.06018518518518 4.85648148148147 71 152 143.560185185185 142.25 1.31018518518518 8.4398148148148 72 147 144.282407407407 140.583333333333 3.69907407407406 2.71759259259258 73 163 156.858796296296 139.166666666667 17.6921296296296 6.1412037037037 74 165 155.282407407407 137.625 17.6574074074074 9.71759259259258 75 147 145.289351851852 136.041666666667 9.2476851851852 1.71064814814815 76 130 134.449074074074 134.791666666667 -0.342592592592585 -4.44907407407405 77 106 116.844907407407 133.708333333333 -16.8634259259259 -10.8449074074074 78 107 112.969907407407 132.416666666667 -19.4467592592593 -5.9699074074074 79 115 NA NA -8.87731481481481 NA 80 114 NA NA -6.87037037037039 NA 81 124 NA NA -2.2662037037037 NA 82 141 NA NA 5.06018518518518 NA 83 139 NA NA 1.31018518518518 NA 84 129 NA NA 3.69907407407406 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jul/02/t1278095319ptyl3lnwf6tossu/1yn2s1278095206.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jul/02/t1278095319ptyl3lnwf6tossu/1yn2s1278095206.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jul/02/t1278095319ptyl3lnwf6tossu/2yn2s1278095206.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jul/02/t1278095319ptyl3lnwf6tossu/2yn2s1278095206.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jul/02/t1278095319ptyl3lnwf6tossu/39e1v1278095206.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jul/02/t1278095319ptyl3lnwf6tossu/39e1v1278095206.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jul/02/t1278095319ptyl3lnwf6tossu/49e1v1278095206.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jul/02/t1278095319ptyl3lnwf6tossu/49e1v1278095206.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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