Kristof Nollekens - Classical decompose - productie bakstenen 9 oef2

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Fri, 05 Jun 2009 10:17:50 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Jun/05/t12442187404kwzt126dg30543.htm/, Retrieved Fri, 05 Jun 2009 18:19:05 +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/2009/Jun/05/t12442187404kwzt126dg30543.htm/}, year = {2009}, } @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 = {2009}, 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:

Dataseries X:

» Textbox « » Textfile « » CSV «

155 176 180 181 208 183 185 205 180 196 183 147 128 158 186 165 191 168 171 169 157 175 156 129 89 138 146 151 156 129 146 141 137 155 147 128 92 136 159 131 134 148 146 144 161 140 141 139 94 136 164 141 159 162 154 166 156 147 161 135 98 150 173 144 167 161 156 175 163 159 167 148 119 150 161 136 166 155 140 141

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 'George Udny Yule' @ 72.249.76.132 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 155 NA NA 0.662786595715704 NA 2 176 NA NA 0.958040856351961 NA 3 180 NA NA 1.10603500391105 NA 4 181 NA NA 0.981482951604662 NA 5 208 NA NA 1.08185247772957 NA 6 183 NA NA 1.03216798546247 NA 7 185 187.769203215367 180.458333333333 1.04051278623154 0.98525209050288 8 205 190.943066492877 178.583333333333 1.06920989170066 1.07361845478504 9 180 186.178768202390 178.083333333333 1.04545868901670 0.96681271306042 10 196 186.62903369605 177.666666666667 1.05044484256689 1.05021172814521 11 183 184.71977130013 176.291666666667 1.04780773131721 0.99068983635035 12 147 161.696524627345 174.958333333333 0.924200188391587 0.909110448346276 13 128 115.159171005604 173.75 0.662786595715704 1.11150504890116 14 158 164.46368034042 171.666666666667 0.958040856351961 0.960698433070201 15 186 187.150339620116 169.208333333333 1.10603500391105 0.993853392826053 16 165 164.275709024830 167.375 0.981482951604662 1.00440899618982 17 191 178.911353504527 165.375 1.08185247772957 1.06756779968783 18 168 168.759465623114 163.5 1.03216798546247 0.995499715406719 19 171 167.652622681557 161.125 1.04051278623154 1.01996614943985 20 169 169.647969483172 158.666666666667 1.06920989170066 0.996180505518894 21 157 163.265798601441 156.166666666667 1.04545868901670 0.961622099330572 22 175 161.680968685087 153.916666666667 1.05044484256689 1.08237847300912 23 156 159.135799193801 151.875 1.04780773131721 0.980294822348663 24 129 137.513286364432 148.791666666667 0.924200188391587 0.938091172209571 25 89 96.8496912989573 146.125 0.662786595715704 0.918949754060375 26 138 137.878046576653 143.916666666667 0.958040856351961 1.00088450211165 27 146 156.964800971710 141.916666666667 1.10603500391105 0.930144842003871 28 151 137.652983962554 140.25 0.981482951604662 1.09696132734091 29 156 150.422571590982 139.041666666667 1.08185247772957 1.03707840086782 30 129 143.084286984735 138.625 1.03216798546247 0.901566501245258 31 146 144.3277943902 138.708333333333 1.04051278623154 1.01158616479151 32 141 148.352872473467 138.75 1.06920989170066 0.950436601928405 33 137 145.536561666866 139.208333333333 1.04545868901670 0.94134421227838 34 155 145.924296046583 138.916666666667 1.05044484256689 1.06219460500614 35 147 143.724293812344 137.166666666667 1.04780773131721 1.02279159702766 36 128 126.653934150830 137.041666666667 0.924200188391587 1.01062790396678 37 92 91.3540857761479 137.833333333333 0.662786595715704 1.00707044702341 38 136 132.169719807556 137.958333333333 0.958040856351961 1.02898001295623 39 159 153.831035127295 139.083333333333 1.10603500391105 1.03360157375544 40 131 136.875976625867 139.458333333333 0.981482951604662 0.957070796711624 41 134 149.926722538689 138.583333333333 1.08185247772957 0.893769954621805 42 148 143.256314982312 138.791666666667 1.03216798546247 1.03311326986370 43 146 144.978114881595 139.333333333333 1.04051278623154 1.00704854742552 44 144 149.065679067934 139.416666666667 1.06920989170066 0.966017133523905 45 161 145.972169453956 139.625 1.04545868901670 1.10294997054753 46 140 147.324889170006 140.25 1.05044484256689 0.95028070809167 47 141 148.483087258743 141.708333333333 1.04780773131721 0.949603100279675 48 139 132.468693669461 143.333333333333 0.924200188391587 1.04930452735373 49 94 95.6069664319903 144.25 0.662786595715704 0.983191952511814 50 136 139.394944599210 145.5 0.958040856351961 0.975645138286962 51 164 161.711534530161 146.208333333333 1.10603500391105 1.01415152899567 52 141 143.582776795165 146.291666666667 0.981482951604662 0.98201193170369 53 159 159.483086091967 147.416666666667 1.08185247772957 0.99697092585926 54 162 152.846875847234 148.083333333333 1.03216798546247 1.05988427373494 55 154 154.082601761121 148.083333333333 1.04051278623154 0.999463912471775 56 166 159.134072214782 148.833333333333 1.06920989170066 1.04314555449792 57 156 156.600999458960 149.791666666667 1.04545868901670 0.996162224627966 58 147 157.873106130782 150.291666666667 1.05044484256689 0.931127559359132 59 161 157.957015496070 150.75 1.04780773131721 1.01926463661252 60 135 139.592736788313 151.041666666667 0.924200188391587 0.967099027542691 61 98 100.136008169381 151.083333333333 0.662786595715704 0.978668930303594 62 150 145.183108106337 151.541666666667 0.958040856351961 1.03317804637531 63 173 168.347744553628 152.208333333333 1.10603500391105 1.02763479521931 64 144 150.166891595513 153 0.981482951604662 0.958933080854305 65 167 166.334818450921 153.75 1.08185247772957 1.00399905176363 66 161 159.512960753346 154.541666666667 1.03216798546247 1.00932237254973 67 156 162.276639952694 155.958333333333 1.04051278623154 0.961321358671688 68 175 167.687751348387 156.833333333333 1.06920989170066 1.04360633733123 69 163 163.440041716277 156.333333333333 1.04545868901670 0.9973076260159 70 159 163.344173019151 155.5 1.05044484256689 0.973404787334278 71 167 162.541174320582 155.125 1.04780773131721 1.02743197653183 72 148 143.096995835964 154.833333333333 0.924200188391587 1.03426350172757 73 119 NA 153.916666666667 NA NA 74 150 NA 151.833333333333 NA NA 75 161 NA NA NA NA 76 136 NA NA NA NA 77 166 NA NA NA NA 78 155 NA NA NA NA 79 140 NA NA NA NA 80 141 NA NA NA NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t12442187404kwzt126dg30543/1zaot1244218668.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t12442187404kwzt126dg30543/1zaot1244218668.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t12442187404kwzt126dg30543/22rzj1244218668.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t12442187404kwzt126dg30543/22rzj1244218668.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t12442187404kwzt126dg30543/33x3p1244218668.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t12442187404kwzt126dg30543/33x3p1244218668.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t12442187404kwzt126dg30543/4v75v1244218668.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t12442187404kwzt126dg30543/4v75v1244218668.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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