tijdreeks B-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: Thu, 19 Aug 2010 21:20:04 +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/19/t128225283104jg4qrb3f77lmh.htm/, Retrieved Thu, 19 Aug 2010 23:20:31 +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/19/t128225283104jg4qrb3f77lmh.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:

gilian keirsebelik

Dataseries X:

» Textbox « » Textfile « » CSV «

51 50 49 47 45 44 45 47 48 48 49 51 45 42 40 37 28 33 32 38 38 34 38 48 41 41 43 37 22 30 32 41 44 37 53 67 62 63 68 62 50 64 71 76 73 68 78 89 74 74 73 65 55 69 80 81 80 86 90 100 90 89 83 63 48 62 69 73 76 77 75 77 78 73 74 55 36 41 52 53 49 47 44 55

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 51 NA NA 6.33043981481482 NA 2 50 NA NA 4.9068287037037 NA 3 49 NA NA 4.69155092592592 NA 4 47 NA NA -5.64178240740741 NA 5 45 NA NA -18.9334490740741 NA 6 44 NA NA -8.92650462962963 NA 7 45 44.5943287037037 47.5833333333333 -2.98900462962963 0.405671296296291 8 47 48.1637731481481 47 1.16377314814815 -1.16377314814815 9 48 47.6221064814815 46.2916666666667 1.33043981481481 0.377893518518519 10 48 45.1012731481481 45.5 -0.398726851851851 2.89872685185185 11 49 49.4832175925926 44.375 5.10821759259259 -0.483217592592588 12 51 56.5665509259259 43.2083333333333 13.3582175925926 -5.56655092592592 13 45 48.5387731481481 42.2083333333333 6.33043981481482 -3.53877314814815 14 42 46.1984953703704 41.2916666666667 4.9068287037037 -4.19849537037037 15 40 45.1915509259259 40.5 4.69155092592592 -5.19155092592592 16 37 33.8582175925926 39.5 -5.64178240740741 3.14178240740740 17 28 19.5248842592592 38.4583333333333 -18.9334490740741 8.47511574074075 18 33 28.9484953703704 37.875 -8.92650462962963 4.05150462962963 19 32 34.5943287037037 37.5833333333333 -2.98900462962963 -2.5943287037037 20 38 38.5387731481482 37.375 1.16377314814815 -0.538773148148152 21 38 38.7887731481482 37.4583333333333 1.33043981481481 -0.788773148148152 22 34 37.1846064814815 37.5833333333333 -0.398726851851851 -3.18460648148148 23 38 42.4415509259259 37.3333333333333 5.10821759259259 -4.44155092592592 24 48 50.3165509259259 36.9583333333333 13.3582175925926 -2.31655092592592 25 41 43.1637731481481 36.8333333333333 6.33043981481482 -2.16377314814815 26 41 41.865162037037 36.9583333333333 4.9068287037037 -0.865162037037038 27 43 42.0248842592593 37.3333333333333 4.69155092592592 0.97511574074074 28 37 32.0665509259259 37.7083333333333 -5.64178240740741 4.93344907407408 29 22 19.5248842592593 38.4583333333333 -18.9334490740741 2.47511574074075 30 30 30.9484953703704 39.875 -8.92650462962963 -0.94849537037036 31 32 38.5526620370370 41.5416666666667 -2.98900462962963 -6.55266203703705 32 41 44.4971064814815 43.3333333333333 1.16377314814815 -3.49710648148149 33 44 46.6221064814815 45.2916666666667 1.33043981481481 -2.62210648148148 34 37 46.9762731481481 47.375 -0.398726851851851 -9.97627314814814 35 53 54.6915509259259 49.5833333333333 5.10821759259259 -1.69155092592592 36 67 65.5248842592593 52.1666666666667 13.3582175925926 1.47511574074075 37 62 61.5387731481481 55.2083333333333 6.33043981481482 0.461226851851855 38 63 63.1984953703704 58.2916666666667 4.9068287037037 -0.198495370370360 39 68 65.6498842592593 60.9583333333333 4.69155092592592 2.35011574074075 40 62 57.8165509259259 63.4583333333333 -5.64178240740741 4.18344907407408 41 50 46.8582175925926 65.7916666666667 -18.9334490740741 3.14178240740742 42 64 58.8234953703704 67.75 -8.92650462962963 5.17650462962963 43 71 66.177662037037 69.1666666666666 -2.98900462962963 4.82233796296299 44 76 71.2887731481481 70.125 1.16377314814815 4.71122685185186 45 73 72.1221064814815 70.7916666666667 1.33043981481481 0.877893518518533 46 68 70.7262731481482 71.125 -0.398726851851851 -2.72627314814815 47 78 76.5665509259259 71.4583333333333 5.10821759259259 1.43344907407409 48 89 85.2332175925926 71.875 13.3582175925926 3.76678240740740 49 74 78.7887731481481 72.4583333333333 6.33043981481482 -4.78877314814814 50 74 77.9484953703704 73.0416666666667 4.9068287037037 -3.94849537037037 51 73 78.2332175925926 73.5416666666667 4.69155092592592 -5.2332175925926 52 65 68.9415509259259 74.5833333333333 -5.64178240740741 -3.94155092592592 53 55 56.8998842592593 75.8333333333333 -18.9334490740741 -1.89988425925927 54 69 67.865162037037 76.7916666666667 -8.92650462962963 1.13483796296296 55 80 74.927662037037 77.9166666666667 -2.98900462962963 5.07233796296298 56 81 80.3721064814815 79.2083333333333 1.16377314814815 0.627893518518547 57 80 81.5804398148148 80.25 1.33043981481481 -1.58043981481480 58 86 80.1846064814815 80.5833333333333 -0.398726851851851 5.81539351851853 59 90 85.316550925926 80.2083333333333 5.10821759259259 4.68344907407406 60 100 92.9832175925926 79.625 13.3582175925926 7.0167824074074 61 90 85.2054398148148 78.875 6.33043981481482 4.79456018518519 62 89 82.990162037037 78.0833333333333 4.9068287037037 6.00983796296295 63 83 82.2748842592593 77.5833333333333 4.69155092592592 0.725115740740748 64 63 71.3998842592593 77.0416666666667 -5.64178240740741 -8.39988425925925 65 48 57.1082175925926 76.0416666666667 -18.9334490740741 -9.10821759259258 66 62 65.5318287037037 74.4583333333333 -8.92650462962963 -3.53182870370370 67 69 70.0109953703704 73 -2.98900462962963 -1.01099537037037 68 73 72.9971064814815 71.8333333333333 1.16377314814815 0.00289351851853326 69 76 72.1221064814815 70.7916666666667 1.33043981481481 3.87789351851852 70 77 69.6846064814815 70.0833333333333 -0.398726851851851 7.31539351851852 71 75 74.3582175925926 69.25 5.10821759259259 0.641782407407405 72 77 81.2332175925926 67.875 13.3582175925926 -4.23321759259258 73 78 72.6221064814815 66.2916666666667 6.33043981481482 5.37789351851852 74 73 69.6568287037037 64.75 4.9068287037037 3.34317129629630 75 74 67.4832175925926 62.7916666666667 4.69155092592592 6.51678240740742 76 55 54.7748842592593 60.4166666666667 -5.64178240740741 0.225115740740740 77 36 38.9415509259259 57.875 -18.9334490740741 -2.94155092592592 78 41 46.740162037037 55.6666666666667 -8.92650462962963 -5.74016203703704 79 52 NA NA -2.98900462962963 NA 80 53 NA NA 1.16377314814815 NA 81 49 NA NA 1.33043981481481 NA 82 47 NA NA -0.398726851851851 NA 83 44 NA NA 5.10821759259259 NA 84 55 NA NA 13.3582175925926 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Aug/19/t128225283104jg4qrb3f77lmh/1sq7m1282252800.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/19/t128225283104jg4qrb3f77lmh/1sq7m1282252800.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/19/t128225283104jg4qrb3f77lmh/2sq7m1282252800.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/19/t128225283104jg4qrb3f77lmh/2sq7m1282252800.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/19/t128225283104jg4qrb3f77lmh/3lh671282252800.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/19/t128225283104jg4qrb3f77lmh/3lh671282252800.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/19/t128225283104jg4qrb3f77lmh/4lh671282252800.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/19/t128225283104jg4qrb3f77lmh/4lh671282252800.ps (open in new window)

Parameters (Session):

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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