Broodprijs - classical decomposition - Clélia Comes

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

Title produced by software: Classical Decomposition

Date of computation: Sat, 17 May 2008 10:45:24 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/May/17/t12110428568tpmvcfd3nenr10.htm/, Retrieved Sat, 17 May 2008 18:47:42 +0200

User-defined keywords:

Dataseries X:

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1.43 1.43 1.43 1.43 1.43 1.43 1.44 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.57 1.58 1.58 1.58 1.58 1.59 1.6 1.6 1.61 1.61 1.61 1.62 1.63 1.63 1.64 1.64 1.64 1.64 1.64 1.65 1.65 1.65 1.65 1.65 1.66 1.66 1.67 1.68 1.68 1.68 1.68 1.69 1.7 1.7 1.71 1.72 1.73 1.74 1.74 1.75 1.75 1.75 1.76 1.79 1.83

Text written by user:

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 1.43 NA NA -0.0107118055555556 NA 2 1.43 NA NA 0.0105381944444444 NA 3 1.43 NA NA 0.00960069444444446 NA 4 1.43 NA NA 0.0085590277777778 NA 5 1.43 NA NA 0.00501736111111113 NA 6 1.43 NA NA 0.00147569444444441 NA 7 1.44 1.45418402777778 1.45375 0.000434027777777799 -0.0141840277777778 8 1.48 1.45720486111111 1.45791666666667 -0.000711805555555435 0.0227951388888890 9 1.48 1.46012152777778 1.46208333333333 -0.00196180555555558 0.0198784722222223 10 1.48 1.46293402777778 1.46625 -0.00331597222222223 0.0170659722222226 11 1.48 1.46303819444444 1.47041666666667 -0.00737847222222226 0.0169618055555558 12 1.48 1.46303819444444 1.47458333333333 -0.0115451388888889 0.0169618055555560 13 1.48 1.46762152777778 1.47833333333333 -0.0107118055555556 0.0123784722222224 14 1.48 1.49053819444444 1.48 0.0105381944444444 -0.0105381944444443 15 1.48 1.48960069444444 1.48 0.00960069444444446 -0.00960069444444422 16 1.48 1.48855902777778 1.48 0.0085590277777778 -0.00855902777777762 17 1.48 1.48501736111111 1.48 0.00501736111111113 -0.00501736111111084 18 1.48 1.48147569444444 1.48 0.00147569444444441 -0.00147569444444429 19 1.48 1.48043402777778 1.48 0.000434027777777799 -0.000434027777777679 20 1.48 1.47928819444444 1.48 -0.000711805555555435 0.000711805555555722 21 1.48 1.47803819444444 1.48 -0.00196180555555558 0.00196180555555570 22 1.48 1.47668402777778 1.48 -0.00331597222222223 0.00331597222222246 23 1.48 1.47262152777778 1.48 -0.00737847222222226 0.00737847222222254 24 1.48 1.46845486111111 1.48 -0.0115451388888889 0.0115451388888892 25 1.48 1.46928819444444 1.48 -0.0107118055555556 0.0107118055555557 26 1.48 1.49053819444444 1.48 0.0105381944444444 -0.0105381944444443 27 1.48 1.48960069444444 1.48 0.00960069444444446 -0.00960069444444422 28 1.48 1.48855902777778 1.48 0.0085590277777778 -0.00855902777777762 29 1.48 1.48501736111111 1.48 0.00501736111111113 -0.00501736111111084 30 1.48 1.48147569444444 1.48 0.00147569444444441 -0.00147569444444429 31 1.48 1.48043402777778 1.48 0.000434027777777799 -0.000434027777777679 32 1.48 1.48303819444444 1.48375 -0.000711805555555435 -0.0030381944444442 33 1.48 1.48970486111111 1.49166666666667 -0.00196180555555558 -0.00970486111111102 34 1.48 1.49668402777778 1.5 -0.00331597222222223 -0.0166840277777776 35 1.48 1.50095486111111 1.50833333333333 -0.00737847222222226 -0.0209548611111108 36 1.48 1.50512152777778 1.51666666666667 -0.0115451388888889 -0.0251215277777774 37 1.48 1.51470486111111 1.52541666666667 -0.0107118055555556 -0.0347048611111112 38 1.57 1.54553819444444 1.535 0.0105381944444444 0.0244618055555557 39 1.58 1.55460069444444 1.545 0.00960069444444446 0.0253993055555557 40 1.58 1.56397569444444 1.55541666666667 0.0085590277777778 0.0160243055555558 41 1.58 1.57126736111111 1.56625 0.00501736111111113 0.00873263888888931 42 1.58 1.57855902777778 1.57708333333333 0.00147569444444441 0.00144097222222239 43 1.59 1.58876736111111 1.58833333333333 0.000434027777777799 0.00123263888888903 44 1.6 1.59595486111111 1.59666666666667 -0.000711805555555435 0.00404513888888935 45 1.6 1.59928819444444 1.60125 -0.00196180555555558 0.000711805555555722 46 1.61 1.60251736111111 1.60583333333333 -0.00331597222222223 0.00748263888888911 47 1.61 1.60345486111111 1.61083333333333 -0.00737847222222226 0.00654513888888908 48 1.61 1.60428819444444 1.61583333333333 -0.0115451388888889 0.00571180555555562 49 1.62 1.60970486111111 1.62041666666667 -0.0107118055555556 0.010295138888889 50 1.63 1.63470486111111 1.62416666666667 0.0105381944444444 -0.00470486111111135 51 1.63 1.63751736111111 1.62791666666667 0.00960069444444446 -0.00751736111111101 52 1.64 1.64022569444444 1.63166666666667 0.0085590277777778 -0.000225694444444535 53 1.64 1.64001736111111 1.635 0.00501736111111113 -1.73611111111693e-05 54 1.64 1.63980902777778 1.63833333333333 0.00147569444444441 0.000190972222221975 55 1.64 1.64168402777778 1.64125 0.000434027777777799 -0.00168402777777787 56 1.64 1.64303819444444 1.64375 -0.000711805555555435 -0.00303819444444442 57 1.65 1.64428819444444 1.64625 -0.00196180555555558 0.00571180555555539 58 1.65 1.64543402777778 1.64875 -0.00331597222222223 0.00456597222222221 59 1.65 1.64428819444444 1.65166666666667 -0.00737847222222226 0.00571180555555562 60 1.65 1.64345486111111 1.655 -0.0115451388888889 0.00654513888888908 61 1.65 1.64762152777778 1.65833333333333 -0.0107118055555556 0.00237847222222221 62 1.66 1.67220486111111 1.66166666666667 0.0105381944444444 -0.0122048611111110 63 1.66 1.67460069444444 1.665 0.00960069444444446 -0.0146006944444443 64 1.67 1.67730902777778 1.66875 0.0085590277777778 -0.00730902777777787 65 1.68 1.67793402777778 1.67291666666667 0.00501736111111113 0.00206597222222227 66 1.68 1.67897569444444 1.6775 0.00147569444444441 0.00102430555555544 67 1.68 1.68335069444444 1.68291666666667 0.000434027777777799 -0.00335069444444436 68 1.68 1.68803819444444 1.68875 -0.000711805555555435 -0.00803819444444409 69 1.69 1.69303819444444 1.695 -0.00196180555555558 -0.00303819444444398 70 1.7 1.69793402777778 1.70125 -0.00331597222222223 0.00206597222222271 71 1.7 1.69970486111111 1.70708333333333 -0.00737847222222226 0.000295138888889213 72 1.71 1.70137152777778 1.71291666666667 -0.0115451388888889 0.00862847222222252 73 1.72 NA 1.71875 NA NA 74 1.73 NA 1.725 NA NA 75 1.74 NA 1.7325 NA NA 76 1.74 NA 1.74208333333333 NA NA 77 1.75 NA NA NA NA 78 1.75 NA NA NA NA 79 1.75 NA NA NA NA 80 1.76 NA NA NA NA 81 1.79 NA NA NA NA 82 1.83 NA NA NA NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/17/t12110428568tpmvcfd3nenr10/19uzp1211042721.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/17/t12110428568tpmvcfd3nenr10/19uzp1211042721.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/17/t12110428568tpmvcfd3nenr10/2qbfe1211042721.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/17/t12110428568tpmvcfd3nenr10/2qbfe1211042721.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/17/t12110428568tpmvcfd3nenr10/3xv6x1211042721.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/17/t12110428568tpmvcfd3nenr10/3xv6x1211042721.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/17/t12110428568tpmvcfd3nenr10/4yhgi1211042721.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/17/t12110428568tpmvcfd3nenr10/4yhgi1211042721.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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