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: Fri, 06 Aug 2010 07:48:35 +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/06/t1281080895pjgdfqs26he8zsn.htm/, Retrieved Fri, 06 Aug 2010 09:48:17 +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/06/t1281080895pjgdfqs26he8zsn.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:

Van de Walle Mathias

Dataseries X:

» Textbox « » Textfile « » CSV «

239 238 237 235 233 232 233 235 236 236 237 239 238 237 244 230 237 244 239 240 230 228 231 228 225 227 238 214 222 233 228 218 203 209 207 203 195 199 207 182 181 189 186 174 153 158 153 147 143 156 168 142 146 150 145 133 111 115 109 105 96 112 127 107 116 125 120 107 86 87 79 83 75 89 104 86 98 98 85 74 49 54 47 56

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 1 239 NA NA -9.94965277777778 NA 2 238 NA NA 0.196180555555558 NA 3 237 NA NA 13.9461805555556 NA 4 235 NA NA -4.65798611111111 NA 5 233 NA NA 4.42534722222222 NA 6 232 NA NA 13.515625 NA 7 233 243.786458333333 235.791666666667 7.99479166666666 -10.7864583333333 8 235 238.543402777778 235.708333333333 2.83506944444444 -3.54340277777774 9 236 226.085069444444 235.958333333333 -9.87326388888888 9.91493055555557 10 236 230.460069444444 236.041666666667 -5.58159722222222 5.5399305555556 11 237 229.557291666667 236 -6.44270833333333 7.44270833333331 12 239 230.258680555556 236.666666666667 -6.4079861111111 8.74131944444449 13 238 227.467013888889 237.416666666667 -9.94965277777778 10.5329861111111 14 237 238.071180555556 237.875 0.196180555555558 -1.07118055555554 15 244 251.779513888889 237.833333333333 13.9461805555556 -7.77951388888891 16 230 232.592013888889 237.25 -4.65798611111111 -2.59201388888889 17 237 241.092013888889 236.666666666667 4.42534722222222 -4.09201388888889 18 244 249.473958333333 235.958333333333 13.515625 -5.47395833333331 19 239 242.953125 234.958333333333 7.99479166666666 -3.95312499999997 20 240 236.835069444444 234 2.83506944444444 3.16493055555557 21 230 223.460069444444 233.333333333333 -9.87326388888888 6.53993055555557 22 228 226.835069444444 232.416666666667 -5.58159722222222 1.16493055555557 23 231 224.682291666667 231.125 -6.44270833333333 6.31770833333334 24 228 223.633680555556 230.041666666667 -6.4079861111111 4.36631944444449 25 225 219.175347222222 229.125 -9.94965277777778 5.82465277777777 26 227 227.946180555556 227.75 0.196180555555558 -0.946180555555543 27 238 239.654513888889 225.708333333333 13.9461805555556 -1.65451388888889 28 214 219.133680555556 223.791666666667 -4.65798611111111 -5.13368055555551 29 222 226.425347222222 222 4.42534722222222 -4.42534722222223 30 233 233.473958333333 219.958333333333 13.515625 -0.473958333333343 31 228 225.661458333333 217.666666666667 7.99479166666666 2.33854166666669 32 218 218.085069444444 215.25 2.83506944444444 -0.0850694444444287 33 203 202.918402777778 212.791666666667 -9.87326388888888 0.081597222222257 34 209 204.585069444444 210.166666666667 -5.58159722222222 4.4149305555556 35 207 200.682291666667 207.125 -6.44270833333333 6.31770833333337 36 203 197.175347222222 203.583333333333 -6.4079861111111 5.8246527777778 37 195 190.050347222222 200 -9.94965277777778 4.9496527777778 38 199 196.612847222222 196.416666666667 0.196180555555558 2.38715277777777 39 207 206.446180555556 192.5 13.9461805555556 0.553819444444457 40 182 183.633680555556 188.291666666667 -4.65798611111111 -1.63368055555554 41 181 188.342013888889 183.916666666667 4.42534722222222 -7.34201388888889 42 189 192.848958333333 179.333333333333 13.515625 -3.84895833333334 43 186 182.828125 174.833333333333 7.99479166666666 3.171875 44 174 173.710069444444 170.875 2.83506944444444 0.2899305555556 45 153 157.585069444444 167.458333333333 -9.87326388888888 -4.58506944444443 46 158 158.585069444444 164.166666666667 -5.58159722222222 -0.585069444444457 47 153 154.598958333333 161.041666666667 -6.44270833333333 -1.59895833333334 48 147 151.550347222222 157.958333333333 -6.4079861111111 -4.55034722222223 49 143 144.675347222222 154.625 -9.94965277777778 -1.67534722222223 50 156 151.404513888889 151.208333333333 0.196180555555558 4.59548611111109 51 168 161.696180555556 147.75 13.9461805555556 6.30381944444446 52 142 139.550347222222 144.208333333333 -4.65798611111111 2.44965277777777 53 146 145.008680555556 140.583333333333 4.42534722222222 0.991319444444457 54 150 150.515625 137 13.515625 -0.515625 55 145 141.286458333333 133.291666666667 7.99479166666666 3.71354166666669 56 133 132.335069444444 129.5 2.83506944444444 0.664930555555571 57 111 116.085069444444 125.958333333333 -9.87326388888888 -5.08506944444443 58 115 117.210069444444 122.791666666667 -5.58159722222222 -2.21006944444443 59 109 113.640625 120.083333333333 -6.44270833333333 -4.64062499999999 60 105 111.383680555556 117.791666666667 -6.4079861111111 -6.38368055555554 61 96 105.758680555556 115.708333333333 -9.94965277777778 -9.75868055555553 62 112 113.779513888889 113.583333333333 0.196180555555558 -1.77951388888887 63 127 125.404513888889 111.458333333333 13.9461805555556 1.59548611111113 64 107 104.592013888889 109.25 -4.65798611111111 2.40798611111113 65 116 111.258680555556 106.833333333333 4.42534722222222 4.74131944444446 66 125 118.182291666667 104.666666666667 13.515625 6.81770833333336 67 120 110.869791666667 102.875 7.99479166666666 9.13020833333336 68 107 103.876736111111 101.041666666667 2.83506944444444 3.12326388888889 69 86 89.2517361111111 99.125 -9.87326388888888 -3.25173611111113 70 87 91.7100694444444 97.2916666666667 -5.58159722222222 -4.71006944444444 71 79 89.2239583333333 95.6666666666667 -6.44270833333333 -10.2239583333333 72 83 87.3836805555556 93.7916666666667 -6.4079861111111 -4.38368055555556 73 75 81.2586805555556 91.2083333333333 -9.94965277777778 -6.25868055555556 74 89 88.5711805555555 88.375 0.196180555555558 0.428819444444457 75 104 99.4045138888889 85.4583333333333 13.9461805555556 4.59548611111113 76 86 77.8836805555555 82.5416666666667 -4.65798611111111 8.11631944444446 77 98 84.2586805555555 79.8333333333333 4.42534722222222 13.7413194444445 78 98 90.890625 77.375 13.515625 7.10937500000001 79 85 NA NA 7.99479166666666 NA 80 74 NA NA 2.83506944444444 NA 81 49 NA NA -9.87326388888888 NA 82 54 NA NA -5.58159722222222 NA 83 47 NA NA -6.44270833333333 NA 84 56 NA NA -6.4079861111111 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Aug/06/t1281080895pjgdfqs26he8zsn/1d1lm1281080910.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/06/t1281080895pjgdfqs26he8zsn/1d1lm1281080910.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/06/t1281080895pjgdfqs26he8zsn/2d1lm1281080910.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/06/t1281080895pjgdfqs26he8zsn/2d1lm1281080910.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/06/t1281080895pjgdfqs26he8zsn/3ot381281080910.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/06/t1281080895pjgdfqs26he8zsn/3ot381281080910.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/06/t1281080895pjgdfqs26he8zsn/4ot381281080910.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/06/t1281080895pjgdfqs26he8zsn/4ot381281080910.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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