tijdreeks 2 stap 24

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Wed, 11 Aug 2010 21:35:45 +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/11/t1281562596wbthwpcatf93ysw.htm/, Retrieved Wed, 11 Aug 2010 23:36:37 +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/11/t1281562596wbthwpcatf93ysw.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:

Marianne Nykjaer

Dataseries X:

» Textbox « » Textfile « » CSV «

190 189 188 186 184 183 184 186 187 187 188 190 190 190 197 187 185 182 182 191 183 192 178 181 179 175 183 179 178 175 170 179 169 178 161 168 167 165 181 181 184 181 177 183 162 166 151 162 159 152 164 158 160 161 151 149 131 138 130 147 151 140 149 143 145 139 136 133 118 130 121 142 148 131 137 128 130 119 107 113 93 106 98 118

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 1 190 NA NA 1.02072479965765 NA 2 189 NA NA 0.98036155180561 NA 3 188 NA NA 1.04831871464382 NA 4 186 NA NA 1.01876627948945 NA 5 184 NA NA 1.03450009649748 NA 6 183 NA NA 1.01266812413458 NA 7 184 185.341488881319 186.833333333333 0.992015105519996 0.99276206914374 8 186 189.61652889615 186.875 1.01467038874194 0.98092714323375 9 187 176.877092793759 187.291666666667 0.944393821368234 1.05723130704124 10 187 186.288666600861 187.708333333333 0.992436847596153 1.00381844699475 11 188 175.365379334507 187.791666666667 0.933829399606873 1.0720474058987 12 190 189.165338471604 187.791666666667 1.0073148709382 1.00441233861943 13 190 191.556020735753 187.666666666667 1.02072479965765 0.99187694163944 14 190 184.103729749495 187.791666666667 0.98036155180561 1.03202689189691 15 197 196.909198567265 187.833333333333 1.04831871464382 1.00046113352447 16 187 191.400714759081 187.875 1.01876627948945 0.977007845740702 17 185 194.141184776028 187.666666666667 1.03450009649748 0.952914757440192 18 182 189.24235569765 186.875 1.01266812413458 0.961729731851253 19 182 184.556143589449 186.041666666667 0.992015105519996 0.986149777841395 20 191 187.671743984394 184.958333333333 1.01467038874194 1.0177344545585 21 183 173.532364676413 183.75 0.944393821368234 1.05455832600012 22 192 181.45053696883 182.833333333333 0.992436847596153 1.05813960767161 23 178 170.151498520036 182.208333333333 0.933829399606873 1.04612654927068 24 181 182.953563434151 181.625 1.0073148709382 0.989322080436796 25 179 184.581067938092 180.833333333333 1.02072479965765 0.969763594931825 26 175 176.301685733042 179.833333333333 0.98036155180561 0.992616714198563 27 183 187.386970242583 178.75 1.04831871464382 0.976588712454744 28 179 180.915911799335 177.583333333333 1.01876627948945 0.989409932049204 29 178 182.373746178369 176.291666666667 1.03450009649748 0.976017676502126 30 175 177.259116228724 175.041666666667 1.01266812413458 0.98725528888563 31 170 172.610628360479 174 0.992015105519996 0.984875622171844 32 179 175.622533118084 173.083333333333 1.01467038874194 1.0192313982833 33 169 162.986633671134 172.583333333333 0.944393821368234 1.0368948434201 34 178 171.278059280969 172.583333333333 0.992436847596153 1.03924577816475 35 161 161.474667015355 172.916666666667 0.933829399606873 0.997060424250264 36 168 174.685187201867 173.416666666667 1.0073148709382 0.96173008536699 37 167 177.563584940446 173.958333333333 1.02072479965765 0.94050815687243 38 165 170.991393994095 174.416666666667 0.98036155180561 0.964960844787885 39 181 182.71321597313 174.291666666667 1.04831871464382 0.99062346987871 40 181 176.75594949142 173.5 1.01876627948945 1.02401079296505 41 184 178.537474987191 172.583333333333 1.03450009649748 1.0305959575893 42 181 174.094528340803 171.916666666667 1.01266812413458 1.03966507003413 43 177 169.965254745759 171.333333333333 0.992015105519996 1.04138931374394 44 183 172.959023347636 170.458333333333 1.01467038874194 1.05805407811642 45 162 159.799304524017 169.208333333333 0.944393821368234 1.01377162111274 46 166 166.274523507672 167.541666666667 0.992436847596153 0.99834897432342 47 151 154.626584751571 165.583333333333 0.933829399606873 0.97654617569548 48 162 164.947810116131 163.75 1.0073148709382 0.982128831452472 49 159 165.187296744597 161.833333333333 1.02072479965765 0.962543749631284 50 152 156.204273921027 159.333333333333 0.98036155180561 0.97308477024673 51 164 164.192918681089 156.625 1.04831871464382 0.998825048713193 52 158 157.059801421291 154.166666666667 1.01876627948945 1.00598624581339 53 160 157.37332717968 152.125 1.03450009649748 1.01669071161799 54 161 152.533136197771 150.625 1.01266812413458 1.05550835715625 55 151 148.471594126159 149.666666666667 0.992015105519996 1.01702955968596 56 149 151.016776191092 148.833333333333 1.01467038874194 0.986645349993831 57 131 139.4948373646 147.708333333333 0.944393821368234 0.939102854807476 58 138 145.35064663752 146.458333333333 0.992436847596153 0.949428180695672 59 130 135.599810734581 145.208333333333 0.933829399606873 0.958703403019181 60 147 144.717569791455 143.666666666667 1.0073148709382 1.01577161786115 61 151 145.070512151344 142.125 1.02072479965765 1.04087314341642 62 140 138.067585212623 140.833333333333 0.98036155180561 1.01399615111976 63 149 146.371500532144 139.625 1.04831871464382 1.01795772714155 64 143 141.353821279161 138.75 1.01876627948945 1.01164580275186 65 145 142.80411748734 138.041666666667 1.03450009649748 1.01537688514377 66 139 139.199672563332 137.458333333333 1.01266812413458 0.998565567291536 67 136 136.030071344429 137.125 0.992015105519996 0.999778936053387 68 133 138.629341861867 136.625 1.01467038874194 0.95939285445446 69 118 128.201461250738 135.75 0.944393821368234 0.920426326258594 70 130 133.606810607632 134.625 0.992436847596153 0.973004290790053 71 121 124.549496172567 133.375 0.933829399606873 0.971501320505956 72 142 132.881620057931 131.916666666667 1.0073148709382 1.06862032490342 73 148 132.566633355538 129.875 1.02072479965765 1.11641969214886 74 131 125.32288503915 127.833333333333 0.98036155180561 1.04529990639041 75 137 132.044478098678 125.958333333333 1.04831871464382 1.03752918692759 76 128 126.242121466735 123.916666666667 1.01876627948945 1.01392465932006 77 130 126.165907602006 121.958333333333 1.03450009649748 1.03038929034688 78 119 121.520174896149 120 1.01266812413458 0.979261263421459 79 107 NA NA 0.992015105519996 NA 80 113 NA NA 1.01467038874194 NA 81 93 NA NA 0.944393821368234 NA 82 106 NA NA 0.992436847596153 NA 83 98 NA NA 0.933829399606873 NA 84 118 NA NA 1.0073148709382 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Aug/11/t1281562596wbthwpcatf93ysw/1x6yo1281562541.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/11/t1281562596wbthwpcatf93ysw/1x6yo1281562541.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/11/t1281562596wbthwpcatf93ysw/2x6yo1281562541.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/11/t1281562596wbthwpcatf93ysw/2x6yo1281562541.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/11/t1281562596wbthwpcatf93ysw/3qxf91281562541.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/11/t1281562596wbthwpcatf93ysw/3qxf91281562541.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/11/t1281562596wbthwpcatf93ysw/4qxf91281562541.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/11/t1281562596wbthwpcatf93ysw/4qxf91281562541.ps (open in new window)

Parameters (Session):

par1 = multiplicative ; par2 = 12 ;

Parameters (R input):

par1 = multiplicative ; 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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