Omzet Product Y

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Sat, 24 Jul 2010 09:36:49 +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/24/t127996422859o54ky6odu4jvt.htm/, Retrieved Sat, 24 Jul 2010 11:37:13 +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/24/t127996422859o54ky6odu4jvt.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:

Febiri Lordina

Dataseries X:

» Textbox « » Textfile « » CSV «

297 296 295 293 291 290 291 293 294 294 295 297 302 297 301 298 295 287 290 288 288 287 274 282 296 292 298 296 292 296 293 295 294 291 279 284 299 296 299 299 291 298 288 284 277 270 251 257 269 271 268 268 258 261 255 251 239 229 210 218 226 227 222 215 203 205 194 190 182 179 158 163 165 169 163 154 142 146 133 131 128 120 88 95

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 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 297 NA NA 3.53009259259259 NA 2 296 NA NA 4.91898148148149 NA 3 295 NA NA 7.0300925925926 NA 4 293 NA NA 5.89120370370371 NA 5 291 NA NA 0.370370370370355 NA 6 290 NA NA 5.21064814814814 NA 7 291 294.960648148148 294.041666666667 0.918981481481498 -3.96064814814815 8 293 295.342592592593 294.291666666667 1.05092592592594 -2.34259259259255 9 294 292.932870370370 294.583333333333 -1.65046296296297 1.06712962962962 10 294 291.273148148148 295.041666666667 -3.76851851851851 2.72685185185190 11 295 279.814814814815 295.416666666667 -15.6018518518519 15.1851851851852 12 297 287.557870370370 295.458333333333 -7.90046296296296 9.44212962962968 13 302 298.821759259259 295.291666666667 3.53009259259259 3.17824074074070 14 297 299.960648148148 295.041666666667 4.91898148148149 -2.96064814814810 15 301 301.613425925926 294.583333333333 7.0300925925926 -0.613425925925924 16 298 299.932870370370 294.041666666667 5.89120370370371 -1.93287037037038 17 295 293.245370370370 292.875 0.370370370370355 1.75462962962956 18 287 296.585648148148 291.375 5.21064814814814 -9.58564814814815 19 290 291.418981481481 290.5 0.918981481481498 -1.41898148148147 20 288 291.092592592593 290.041666666667 1.05092592592594 -3.09259259259255 21 288 288.057870370370 289.708333333333 -1.65046296296297 -0.057870370370324 22 287 285.731481481481 289.5 -3.76851851851851 1.26851851851853 23 274 273.689814814815 289.291666666667 -15.6018518518519 0.310185185185162 24 282 281.641203703704 289.541666666667 -7.90046296296296 0.358796296296362 25 296 293.571759259259 290.041666666667 3.53009259259259 2.42824074074076 26 292 295.377314814815 290.458333333333 4.91898148148149 -3.37731481481484 27 298 298.030092592593 291 7.0300925925926 -0.0300925925926094 28 296 297.307870370370 291.416666666667 5.89120370370371 -1.30787037037032 29 292 292.162037037037 291.791666666667 0.370370370370355 -0.162037037037067 30 296 297.293981481481 292.083333333333 5.21064814814814 -1.29398148148147 31 293 293.210648148148 292.291666666667 0.918981481481498 -0.210648148148152 32 295 293.634259259259 292.583333333333 1.05092592592594 1.36574074074076 33 294 291.141203703704 292.791666666667 -1.65046296296297 2.85879629629630 34 291 289.189814814815 292.958333333333 -3.76851851851851 1.81018518518522 35 279 277.439814814815 293.041666666667 -15.6018518518519 1.56018518518516 36 284 285.182870370370 293.083333333333 -7.90046296296296 -1.18287037037032 37 299 296.488425925926 292.958333333333 3.53009259259259 2.51157407407413 38 296 297.210648148148 292.291666666667 4.91898148148149 -1.21064814814810 39 299 298.155092592593 291.125 7.0300925925926 0.844907407407447 40 299 295.432870370370 289.541666666667 5.89120370370371 3.56712962962968 41 291 287.870370370370 287.5 0.370370370370355 3.12962962962968 42 298 290.418981481481 285.208333333333 5.21064814814814 7.58101851851853 43 288 283.752314814815 282.833333333333 0.918981481481498 4.24768518518516 44 284 281.592592592593 280.541666666667 1.05092592592594 2.40740740740745 45 277 276.557870370370 278.208333333333 -1.65046296296297 0.442129629629676 46 270 271.856481481481 275.625 -3.76851851851851 -1.85648148148141 47 251 257.356481481481 272.958333333333 -15.6018518518519 -6.35648148148147 48 257 262.141203703704 270.041666666667 -7.90046296296296 -5.1412037037037 49 269 270.655092592593 267.125 3.53009259259259 -1.65509259259261 50 271 269.293981481481 264.375 4.91898148148149 1.70601851851859 51 268 268.446759259259 261.416666666667 7.0300925925926 -0.446759259259238 52 268 264.016203703704 258.125 5.89120370370371 3.98379629629630 53 258 255.078703703704 254.708333333333 0.370370370370355 2.92129629629628 54 261 256.585648148148 251.375 5.21064814814814 4.41435185185182 55 255 248.877314814815 247.958333333333 0.918981481481498 6.12268518518516 56 251 245.384259259259 244.333333333333 1.05092592592594 5.61574074074076 57 239 238.932870370370 240.583333333333 -1.65046296296297 0.0671296296296475 58 229 232.689814814815 236.458333333333 -3.76851851851851 -3.68981481481481 59 210 216.356481481481 231.958333333333 -15.6018518518519 -6.35648148148144 60 218 219.432870370370 227.333333333333 -7.90046296296296 -1.43287037037035 61 226 225.988425925926 222.458333333333 3.53009259259259 0.0115740740741046 62 227 222.293981481481 217.375 4.91898148148149 4.7060185185185 63 222 219.488425925926 212.458333333333 7.0300925925926 2.51157407407408 64 215 213.891203703704 208 5.89120370370371 1.10879629629633 65 203 204.120370370370 203.75 0.370370370370355 -1.12037037037032 66 205 204.502314814815 199.291666666667 5.21064814814814 0.49768518518519 67 194 195.377314814815 194.458333333333 0.918981481481498 -1.37731481481478 68 190 190.550925925926 189.5 1.05092592592594 -0.550925925925924 69 182 182.974537037037 184.625 -1.65046296296297 -0.97453703703701 70 179 175.856481481481 179.625 -3.76851851851851 3.14351851851850 71 158 158.939814814815 174.541666666667 -15.6018518518519 -0.93981481481481 72 163 161.641203703704 169.541666666667 -7.90046296296296 1.35879629629630 73 165 168.071759259259 164.541666666667 3.53009259259259 -3.07175925925927 74 169 164.460648148148 159.541666666667 4.91898148148149 4.53935185185188 75 163 161.863425925926 154.833333333333 7.0300925925926 1.13657407407408 76 154 156.016203703704 150.125 5.89120370370371 -2.01620370370367 77 142 145.120370370370 144.75 0.370370370370355 -3.12037037037035 78 146 144.210648148148 139 5.21064814814814 1.78935185185188 79 133 NA NA 0.918981481481498 NA 80 131 NA NA 1.05092592592594 NA 81 128 NA NA -1.65046296296297 NA 82 120 NA NA -3.76851851851851 NA 83 88 NA NA -15.6018518518519 NA 84 95 NA NA -7.90046296296296 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jul/24/t127996422859o54ky6odu4jvt/1rb9x1279964207.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jul/24/t127996422859o54ky6odu4jvt/1rb9x1279964207.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jul/24/t127996422859o54ky6odu4jvt/2kk8i1279964207.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jul/24/t127996422859o54ky6odu4jvt/2kk8i1279964207.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jul/24/t127996422859o54ky6odu4jvt/3kk8i1279964207.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jul/24/t127996422859o54ky6odu4jvt/3kk8i1279964207.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jul/24/t127996422859o54ky6odu4jvt/4cbql1279964207.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jul/24/t127996422859o54ky6odu4jvt/4cbql1279964207.ps (open in new window)

Parameters (Session):

par1 = 48 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;

Parameters (R input):

par1 = additive ; par2 = 12 ; par3 = 1 ; 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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