klassiek model

*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 06:17:00 -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/t1259932683ymanc5mfm4ng4fe.htm/, Retrieved Fri, 04 Dec 2009 14:18:08 +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/t1259932683ymanc5mfm4ng4fe.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 «

5594 5585 5710 5511 5403 5826 5884 5965 5960 6064 6046 5954 5952 5960 5983 5996 6021 6094 6202 6276 6306 6342 6345 6328 6191 6261 6253 6198 6247 6293 6381 6448 6470 6516 6532 6526 6533 6498 6507 6464 6453 6468 6497 6808 6793 6907 6792 6757 6734 6654 6589 6469 6521 6448 6410 6528 6445 6458 6215 6167

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 1 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 5594 NA NA -18.1510416666663 NA 2 5585 NA NA -38.744791666667 NA 3 5710 NA NA -59.9114583333328 NA 4 5511 NA NA -120.317708333333 NA 5 5403 NA NA -97.4322916666668 NA 6 5826 NA NA -86.161458333334 NA 7 5884 5791.24479166667 5806.75 -15.5052083333335 92.755208333334 8 5965 5932.02604166667 5837.29166666667 94.7343750000002 32.9739583333339 9 5960 5946.734375 5864.29166666667 82.4427083333328 13.265625 10 6064 6034.18229166667 5895.875 138.307291666667 29.817708333333 11 6046 6030.015625 5941.83333333333 88.1822916666667 15.984375 12 5954 6011.30729166667 5978.75 32.5572916666669 -57.307291666666 13 5952 5985.015625 6003.16666666667 -18.1510416666663 -33.015625 14 5960 5990.63020833333 6029.375 -38.744791666667 -30.630208333333 15 5983 5996.83854166667 6056.75 -59.9114583333328 -13.8385416666661 16 5996 5962.43229166667 6082.75 -120.317708333333 33.567708333333 17 6021 6009.359375 6106.79166666667 -97.4322916666668 11.640625 18 6094 6048.671875 6134.83333333333 -86.161458333334 45.3281250000009 19 6202 6144.86979166667 6160.375 -15.5052083333335 57.130208333333 20 6276 6277.609375 6182.875 94.7343750000002 -1.60937499999909 21 6306 6289.109375 6206.66666666667 82.4427083333328 16.890625 22 6342 6364.640625 6226.33333333333 138.307291666667 -22.6406249999991 23 6345 6332.34895833333 6244.16666666667 88.1822916666667 12.6510416666670 24 6328 6294.43229166667 6261.875 32.5572916666669 33.567708333333 25 6191 6259.47395833333 6277.625 -18.1510416666663 -68.473958333334 26 6261 6253.50520833333 6292.25 -38.744791666667 7.49479166666697 27 6253 6246.33854166667 6306.25 -59.9114583333328 6.66145833333394 28 6198 6200.015625 6320.33333333333 -120.317708333333 -2.015625 29 6247 6237.94270833333 6335.375 -97.4322916666668 9.05729166666788 30 6293 6265.25520833333 6351.41666666667 -86.161458333334 27.744791666667 31 6381 6358.41145833333 6373.91666666667 -15.5052083333335 22.5885416666679 32 6448 6492.77604166667 6398.04166666667 94.7343750000002 -44.7760416666661 33 6470 6500.94270833333 6418.5 82.4427083333328 -30.942708333333 34 6516 6578.47395833333 6440.16666666667 138.307291666667 -62.473958333333 35 6532 6548.015625 6459.83333333333 88.1822916666667 -16.015625 36 6526 6508.265625 6475.70833333333 32.5572916666669 17.7343750000009 37 6533 6469.68229166667 6487.83333333333 -18.1510416666663 63.317708333333 38 6498 6468.921875 6507.66666666667 -38.744791666667 29.078125 39 6507 6476.21354166667 6536.125 -59.9114583333328 30.7864583333339 40 6464 6445.55729166667 6565.875 -120.317708333333 18.4427083333339 41 6453 6495.56770833333 6593 -97.4322916666668 -42.567708333333 42 6468 6527.296875 6613.45833333333 -86.161458333334 -59.296875 43 6497 6615.953125 6631.45833333333 -15.5052083333335 -118.953124999998 44 6808 6741.06770833333 6646.33333333333 94.7343750000002 66.932291666667 45 6793 6738.69270833333 6656.25 82.4427083333328 54.307291666667 46 6907 6798.18229166667 6659.875 138.307291666667 108.817708333334 47 6792 6751.09895833333 6662.91666666667 88.1822916666667 40.901041666667 48 6757 6697.47395833333 6664.91666666667 32.5572916666669 59.526041666667 49 6734 6642.30729166667 6660.45833333333 -18.1510416666663 91.6927083333348 50 6654 6606.421875 6645.16666666667 -38.744791666667 47.5781250000009 51 6589 6559.08854166667 6619 -59.9114583333328 29.9114583333339 52 6469 6465.47395833333 6585.79166666667 -120.317708333333 3.52604166666697 53 6521 6445.609375 6543.04166666667 -97.4322916666668 75.3906250000009 54 6448 6408.25520833333 6494.41666666667 -86.161458333334 39.744791666667 55 6410 NA NA -15.5052083333335 NA 56 6528 NA NA 94.7343750000002 NA 57 6445 NA NA 82.4427083333328 NA 58 6458 NA NA 138.307291666667 NA 59 6215 NA NA 88.1822916666667 NA 60 6167 NA NA 32.5572916666669 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259932683ymanc5mfm4ng4fe/15lgx1259932618.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259932683ymanc5mfm4ng4fe/15lgx1259932618.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259932683ymanc5mfm4ng4fe/2a6ja1259932618.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259932683ymanc5mfm4ng4fe/2a6ja1259932618.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259932683ymanc5mfm4ng4fe/3q0p41259932618.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259932683ymanc5mfm4ng4fe/3q0p41259932618.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259932683ymanc5mfm4ng4fe/42ayf1259932618.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259932683ymanc5mfm4ng4fe/42ayf1259932618.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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