klassieke decompositie

*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: Sun, 12 Dec 2010 17:12:25 +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/Dec/12/t1292173867rfvp4okua0tx50s.htm/, Retrieved Sun, 12 Dec 2010 18:11:12 +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/2010/Dec/12/t1292173867rfvp4okua0tx50s.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:

Dataseries X:

» Textbox « » Textfile « » CSV «

62.027 56.493 65.566 62.653 53.470 59.600 42.542 42.018 44.038 44.988 43.309 26.843 69.770 64.886 79.354 63.025 54.003 55.926 45.629 40.361 43.039 44.570 43.269 25.563 68.707 60.223 74.283 61.232 61.531 65.305 51.699 44.599 35.221 55.066 45.335 28.702 69.517 69.240 71.525 77.740 62.107 65.450 51.493 43.067 49.172 54.483 38.158 27.898 58.648 56.000 62.381 59.849 48.345 55.376 45.400 38.389 44.098 48.290 41.267 31.238

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 62.027 NA NA 13.727375 NA 2 56.493 NA NA 9.66215625 NA 3 65.566 NA NA 18.9978333333333 NA 4 62.653 NA NA 12.5385625 NA 5 53.47 NA NA 3.5604375 NA 6 59.6 NA NA 7.55367708333334 NA 7 42.542 45.19059375 50.6182083333333 -5.42761458333333 -2.64859374999999 8 42.018 40.5737604166667 51.2905416666667 -10.71678125 1.44423958333334 9 44.038 41.89253125 52.21475 -10.32221875 2.14546875000001 10 44.988 49.4541666666667 52.80475 -3.35058333333334 -4.46616666666666 11 43.309 42.31546875 52.8424583333333 -10.5269895833333 0.993531250000004 12 26.843 27.0157291666667 52.7115833333333 -25.6958541666667 -0.17272916666667 13 69.77 66.4145 52.687125 13.727375 3.35549999999999 14 64.886 62.4088645833333 52.7467083333333 9.66215625 2.47713541666667 15 79.354 71.633875 52.6360416666667 18.9978333333333 7.720125 16 63.025 65.1155625 52.577 12.5385625 -2.09056250000000 17 54.003 56.1183541666667 52.5579166666667 3.5604375 -2.11535416666667 18 55.926 60.05659375 52.5029166666667 7.55367708333334 -4.13059375 19 45.629 46.9776770833333 52.4052916666667 -5.42761458333333 -1.34867708333334 20 40.361 41.4499270833333 52.1667083333333 -10.71678125 -1.08892708333333 21 43.039 41.43890625 51.761125 -10.32221875 1.60009375000001 22 44.57 48.1245416666667 51.475125 -3.35058333333334 -3.55454166666666 23 43.269 41.18709375 51.7140833333333 -10.5269895833333 2.08190625000001 24 25.563 26.7226875 52.4185416666667 -25.6958541666667 -1.15968749999999 25 68.707 66.789625 53.06225 13.727375 1.917375 26 60.223 63.15390625 53.49175 9.66215625 -2.93090624999999 27 74.283 72.3404166666667 53.3425833333333 18.9978333333333 1.94258333333335 28 61.232 65.9927291666667 53.4541666666667 12.5385625 -4.76072916666666 29 61.531 57.5380208333333 53.9775833333333 3.5604375 3.99297916666666 30 65.305 61.7481354166667 54.1944583333333 7.55367708333334 3.55686458333334 31 51.699 48.9313854166667 54.359 -5.42761458333333 2.76761458333334 32 44.599 44.0516770833333 54.7684583333333 -10.71678125 0.547322916666666 33 35.221 44.70703125 55.02925 -10.32221875 -9.48603125 34 55.066 52.2515833333333 55.6021666666667 -3.35058333333334 2.81441666666667 35 45.335 45.7870104166667 56.314 -10.5269895833333 -0.45201041666666 36 28.702 30.6481875 56.3440416666667 -25.6958541666667 -1.94618749999999 37 69.517 70.068875 56.3415 13.727375 -0.551874999999995 38 69.24 65.9312395833333 56.2690833333333 9.66215625 3.30876041666667 39 71.525 75.784375 56.7865416666667 18.9978333333333 -4.25937499999999 40 77.74 69.8821041666667 57.3435416666667 12.5385625 7.85789583333334 41 62.107 60.5806458333333 57.0202083333333 3.5604375 1.52635416666666 42 65.45 64.24134375 56.6876666666667 7.55367708333334 1.20865625000000 43 51.493 50.7736770833333 56.2012916666667 -5.42761458333333 0.71932291666667 44 43.067 44.47996875 55.19675 -10.71678125 -1.41296874999999 45 49.172 43.9418645833333 54.2640833333333 -10.32221875 5.23013541666667 46 54.483 49.7870416666667 53.137625 -3.35058333333334 4.69595833333333 47 38.158 41.2917604166667 51.81875 -10.5269895833333 -3.13376041666667 48 27.898 25.1297291666667 50.8255833333333 -25.6958541666667 2.76827083333333 49 58.648 63.8793333333333 50.1519583333333 13.727375 -5.23133333333332 50 56 59.3653229166667 49.7031666666667 9.66215625 -3.36532291666666 51 62.381 68.2946666666667 49.2968333333333 18.9978333333333 -5.91366666666666 52 59.849 61.3659375 48.827375 12.5385625 -1.51693750000000 53 48.345 52.2593125 48.698875 3.5604375 -3.91431249999999 54 55.376 56.5212604166667 48.9675833333333 7.55367708333334 -1.14526041666666 55 45.4 NA NA -5.42761458333333 NA 56 38.389 NA NA -10.71678125 NA 57 44.098 NA NA -10.32221875 NA 58 48.29 NA NA -3.35058333333334 NA 59 41.267 NA NA -10.5269895833333 NA 60 31.238 NA NA -25.6958541666667 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292173867rfvp4okua0tx50s/1a3q61292173940.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292173867rfvp4okua0tx50s/1a3q61292173940.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292173867rfvp4okua0tx50s/2a3q61292173940.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292173867rfvp4okua0tx50s/2a3q61292173940.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292173867rfvp4okua0tx50s/3lc791292173940.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292173867rfvp4okua0tx50s/3lc791292173940.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292173867rfvp4okua0tx50s/4el6c1292173940.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292173867rfvp4okua0tx50s/4el6c1292173940.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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