prijsindex van de grondstoffen

*The author of this computation has been verified*

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

Title produced by software: Classical Decomposition

Date of computation: Fri, 04 Dec 2009 12:37:58 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/04/t1259955587ejgyqmyjq79938t.htm/, Retrieved Fri, 04 Dec 2009 20:39:52 +0100

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/Dec/04/t1259955587ejgyqmyjq79938t.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 «

226.9 235.9 216.2 226.2 198.3 176.7 166.2 157.6 163.4 159.7 191.0 239.4 321.9 362.7 413.6 407.1 383.2 347.7 333.8 312.3 295.4 283.3 287.6 265.7 250.2 234.7 244.0 231.2 223.8 223.5 210.5 201.6 190.7 207.5 198.8 196.6 204.2 227.4 229.7 217.9 221.4 216.3 197.0 193.8 196.8 180.5 174.8 181.6 190.0 190.6 179.0 174.1 161.1 168.6 169.4 152.2 148.3 137.7 145.0 153.4

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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 226.9 NA NA 1.04044708895866 NA 2 235.9 NA NA 1.09024465270875 NA 3 216.2 NA NA 1.13035167962194 NA 4 226.2 NA NA 1.09365063068418 NA 5 198.3 NA NA 1.05767723915094 NA 6 176.7 NA NA 1.04419812895061 NA 7 166.2 191.400841221751 200.416666666667 0.95501459237464 0.868334741577474 8 157.6 192.002856628187 209.658333333333 0.915789291918695 0.82082112093359 9 163.4 201.584330789209 223.166666666667 0.903290503909825 0.810578874658976 10 159.7 213.517486784689 238.929166666667 0.893643458283059 0.747948106756433 11 191 233.467879006364 254.170833333333 0.91854708876129 0.818099692398342 12 239.4 257.472178418223 269 0.95714564467741 0.929809199078326 13 321.9 294.559241276604 283.108333333333 1.04044708895866 1.09281921899616 14 362.7 323.298423702622 296.5375 1.09024465270875 1.12187370370114 15 413.6 348.694653968707 308.483333333333 1.13035167962194 1.18613805887921 16 407.1 349.020371272344 319.133333333333 1.09365063068418 1.16640756101407 17 383.2 347.244251590248 328.308333333333 1.05767723915094 1.10354598598850 18 347.7 348.166111970895 333.429166666667 1.04419812895061 0.998661236820963 19 333.8 316.623150419407 331.5375 0.95501459237464 1.05425013792529 20 312.3 295.998362302988 323.216666666667 0.915789291918695 1.05507340503569 21 295.4 280.757743456905 310.816666666667 0.903290503909825 1.05215263651434 22 283.3 264.894538607146 296.420833333333 0.893643458283059 1.06948222296176 23 287.6 259.443625220626 282.45 0.91854708876129 1.10852598423041 24 265.7 259.035516304530 270.633333333333 0.95714564467741 1.02572806922598 25 250.2 270.850053236958 260.320833333333 1.04044708895866 0.923758356366678 26 234.7 273.183511166443 250.570833333333 1.09024465270875 0.859129451107333 27 244 273.088255997995 241.595833333333 1.13035167962194 0.89348404642414 28 231.2 255.996271377399 234.075 1.09365063068418 0.903138154145834 29 223.8 240.32189668908 227.216666666667 1.05767723915094 0.931250972480233 30 223.5 230.389264676341 220.6375 1.04419812895061 0.970097284324342 31 210.5 206.131941309130 215.841666666667 0.95501459237464 1.02119059599948 32 201.6 195.631671697415 213.620833333333 0.915789291918695 1.03050798600656 33 190.7 192.148708733784 212.720833333333 0.903290503909825 0.992460481554463 34 207.5 189.068891171829 211.570833333333 0.893643458283059 1.09748356122436 35 198.8 193.736890137902 210.916666666667 0.91854708876129 1.02613394825577 36 196.6 201.495110632006 210.516666666667 0.95714564467741 0.975706057498606 37 204.2 218.134067396386 209.654166666667 1.04044708895866 0.936121544137047 38 227.4 227.606741997164 208.766666666667 1.09024465270875 0.99909167015287 39 229.7 235.899685738433 208.695833333333 1.13035167962194 0.973718974151973 40 217.9 227.287942321939 207.825 1.09365063068418 0.958695818942116 41 221.4 217.564208093349 205.7 1.05767723915094 1.01763062012942 42 216.3 213.094733165596 204.075 1.04419812895061 1.01504151128838 43 197 193.732668518132 202.858333333333 0.95501459237464 1.01686515499353 44 193.8 183.829437197813 200.733333333333 0.915789291918695 1.05423811851993 45 196.8 178.027267189328 197.0875 0.903290503909825 1.10544863776799 46 180.5 172.607233967373 193.15 0.893643458283059 1.04572673955322 47 174.8 173.433172196741 188.8125 0.91854708876129 1.00788100561125 48 181.6 176.413906634605 184.3125 0.95714564467741 1.02939730469286 49 190 188.503001342085 181.175 1.04044708895866 1.00794151099588 50 190.6 194.381536205865 178.291666666667 1.09024465270875 0.980545805534431 51 179 197.288756282014 174.5375 1.13035167962194 0.90729955104045 52 174.1 186.722617678812 170.733333333333 1.09365063068418 0.93239909639375 53 161.1 177.381286982606 167.708333333333 1.05767723915094 0.908213051897618 54 168.6 172.597249064462 165.291666666667 1.04419812895061 0.97684059806209 55 169.4 NA NA 0.95501459237464 NA 56 152.2 NA NA 0.915789291918695 NA 57 148.3 NA NA 0.903290503909825 NA 58 137.7 NA NA 0.893643458283059 NA 59 145 NA NA 0.91854708876129 NA 60 153.4 NA NA 0.95714564467741 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259955587ejgyqmyjq79938t/15rnz1259955473.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259955587ejgyqmyjq79938t/15rnz1259955473.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259955587ejgyqmyjq79938t/2u7ya1259955473.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259955587ejgyqmyjq79938t/2u7ya1259955473.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259955587ejgyqmyjq79938t/3ralj1259955473.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259955587ejgyqmyjq79938t/3ralj1259955473.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259955587ejgyqmyjq79938t/4n4zv1259955473.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259955587ejgyqmyjq79938t/4n4zv1259955473.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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