*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: Mon, 21 Dec 2009 01:12:17 -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/21/t1261383602jffv4ihmko2agyl.htm/, Retrieved Mon, 21 Dec 2009 09:20:08 +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/21/t1261383602jffv4ihmko2agyl.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 «

43.9 51 51.9 54.3 50.3 57.2 48.8 41.1 58 63 53.8 54.7 55.5 56.1 69.6 69.4 57.2 68 53.3 47.9 60.8 61.7 57.8 51.4 50.5 48.1 58.7 54 56.1 60.4 51.2 50.7 56.4 53.3 52.6 47.7 49.5 48.5 55.3 49.8 57.4 64.6 53 41.5 55.9 58.4 53.5 50.6 58.5 49.1 61.1 52.3 58.4 65.5 61.7 45.1 52.1 59.3 57.9 45 64.9 63.8 69.4 71.1 62.9 73.5 62.7 51.9 73.3 66.7 62.5 70.3

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 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 43.9 NA NA 0.98637144227593 NA 2 51 NA NA 0.93494232384568 NA 3 51.9 NA NA 1.10265600726763 NA 4 54.3 NA NA 1.03489917282345 NA 5 50.3 NA NA 1.02461027121193 NA 6 57.2 NA NA 1.15941232296446 NA 7 48.8 51.5820705287634 52.8166666666667 0.976624875899592 0.94606516372366 8 41.1 43.9226852190966 53.5125 0.820792996385827 0.935735139939274 9 58 55.717759557962 54.4625 1.02304814428207 1.04096073604079 10 63 59.2592687292291 55.8291666666667 1.06143924882566 1.06312483010655 11 53.8 55.9218429434615 56.7458333333333 0.985479279420718 0.962056991834 12 54.7 51.1442963689177 57.4833333333333 0.88972391479706 1.06952297486770 13 55.5 57.3287302012789 58.1208333333333 0.98637144227593 0.96810098191852 14 56.1 54.7798289913248 58.5916666666667 0.93494232384568 1.02409958251027 15 69.6 65.0475156287293 58.9916666666667 1.10265600726763 1.06998705987873 16 69.4 61.1151082351115 59.0541666666667 1.03489917282345 1.13556208937758 17 57.2 60.6227743800392 59.1666666666667 1.02461027121193 0.943539793171092 18 68 68.6323786348173 59.1958333333333 1.15941232296446 0.99078600148507 19 53.3 57.474373946691 58.85 0.976624875899592 0.927369823104766 20 47.9 47.8590716309302 58.3083333333333 0.820792996385827 1.0008551851859 21 60.8 58.8465817992246 57.5208333333333 1.02304814428207 1.03319510056574 22 61.7 59.8917096149877 56.425 1.06143924882566 1.03019266600731 23 57.8 54.9281513367123 55.7375 0.985479279420718 1.05228373053524 24 51.4 49.2684617818872 55.375 0.88972391479706 1.04326374603594 25 50.5 54.2216601581097 54.9708333333333 0.98637144227593 0.93136211345692 26 48.1 51.4218278115124 55 0.93494232384568 0.935400432989496 27 58.7 60.5725699992349 54.9333333333333 1.10265600726763 0.969085511820639 28 54 56.2985150015957 54.4 1.03489917282345 0.95917272415568 29 56.1 55.1581862669089 53.8333333333333 1.02461027121193 1.01707477705184 30 60.4 61.9850813164877 53.4625 1.15941232296446 0.97442801908423 31 51.2 52.0215517229182 53.2666666666667 0.976624875899592 0.984207473716008 32 50.7 43.7003871159087 53.2416666666667 0.820792996385827 1.16017278898528 33 56.4 54.3409072637824 53.1166666666667 1.02304814428207 1.03789213025506 34 53.3 56.0439923379947 52.8 1.06143924882566 0.95103859979414 35 52.6 51.9142272071506 52.6791666666667 0.985479279420718 1.01320972746282 36 47.7 47.0738094587211 52.9083333333333 0.88972391479706 1.01330231286737 37 49.5 52.4338619189846 53.1583333333333 0.98637144227593 0.944046427029965 38 48.5 49.4117018152442 52.85 0.93494232384568 0.981548868349988 39 55.3 57.8297131811567 52.4458333333333 1.10265600726763 0.956255823485894 40 49.8 54.4745052094944 52.6375 1.03489917282345 0.914189120368924 41 57.4 54.189075718721 52.8875 1.02461027121193 1.05925408836914 42 64.6 61.5019928485858 53.0458333333333 1.15941232296446 1.05037246775143 43 53 52.2901235637906 53.5416666666667 0.976624875899592 1.01357572688355 44 41.5 44.2749422133788 53.9416666666667 0.820792996385827 0.937324769392013 45 55.9 55.4577348212903 54.2083333333333 1.02304814428207 1.00797481505754 46 58.4 57.9059336869764 54.5541666666667 1.06143924882566 1.00853222254725 47 53.5 53.9057165843133 54.7 0.985479279420718 0.992473588887763 48 50.6 48.7383346159873 54.7791666666667 0.88972391479706 1.03819714807001 49 58.5 54.4271542085839 55.1791666666667 0.98637144227593 1.07483113623408 50 49.1 52.0684962521724 55.6916666666667 0.93494232384568 0.942988631017964 51 61.1 61.3995620046856 55.6833333333333 1.10265600726763 0.995121105185364 52 52.3 57.501585290003 55.5625 1.03489917282345 0.909540141132295 53 58.4 57.1561762957722 55.7833333333333 1.02461027121193 1.02176184246111 54 65.5 64.6179134665528 55.7333333333333 1.15941232296446 1.01365080495680 55 61.7 54.4631139126672 55.7666666666667 0.976624875899592 1.13287683291369 56 45.1 46.4945032744388 56.6458333333333 0.820792996385827 0.970007136839217 57 52.1 58.9318358112481 57.6041666666667 1.02304814428207 0.884072238422544 58 59.3 62.3418652143603 58.7333333333333 1.06143924882566 0.95120670188643 59 57.9 58.8372191450811 59.7041666666667 0.985479279420718 0.984070981621852 60 45 53.5836227686529 60.225 0.88972391479706 0.839808838500661 61 64.9 59.7741094019213 60.6 0.98637144227593 1.08575436170219 62 63.8 56.9613610802981 60.925 0.93494232384568 1.12005750547396 63 69.4 68.465749251259 62.0916666666667 1.10265600726763 1.01364551997105 64 71.1 65.4918693201774 63.2833333333333 1.03489917282345 1.08563094530720 65 62.9 65.3530584654676 63.7833333333333 1.02461027121193 0.962464519288507 66 73.5 75.3956171854433 65.0291666666667 1.15941232296446 0.97485772706415 67 62.7 NA NA 0.976624875899592 NA 68 51.9 NA NA 0.820792996385827 NA 69 73.3 NA NA 1.02304814428207 NA 70 66.7 NA NA 1.06143924882566 NA 71 62.5 NA NA 0.985479279420718 NA 72 70.3 NA NA 0.88972391479706 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/21/t1261383602jffv4ihmko2agyl/1hd1q1261383134.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261383602jffv4ihmko2agyl/1hd1q1261383134.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261383602jffv4ihmko2agyl/2ydu41261383134.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261383602jffv4ihmko2agyl/2ydu41261383134.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261383602jffv4ihmko2agyl/3e2gx1261383134.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261383602jffv4ihmko2agyl/3e2gx1261383134.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261383602jffv4ihmko2agyl/4m1ub1261383134.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261383602jffv4ihmko2agyl/4m1ub1261383134.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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