decomp verkoop

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

Title produced by software: Classical Decomposition

Date of computation: Mon, 28 Apr 2008 06:19:26 -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/Apr/28/t1209385997s8nla1r02soh3s6.htm/, Retrieved Mon, 28 Apr 2008 14:33:27 +0200

User-defined keywords:

eerste test,

Dataseries X:

» Textbox « » Textfile « » CSV «

13328 12873 14000 13477 14237 13674 13529 14058 12975 14326 14008 16193 14483 14011 15057 14884 15414 14440 14900 15074 14442 15307 14938 17193 15528 14765 15838 15723 16150 15486 15986 15983 15692 16490 15686 18897 16316 15636 17163 16534 16518 16375 16290 16352 15943 16362 16393 19051 16747 16320 17910 16961 17480 17049 16879 17473 16998 17307 17418 20169 17871 17226 19062 17804 19100 18522 18060 18869 18127 18871 18890 21263 19547 18450 20254 19240 20216 19420 19415 20018 18652 19978 19509 21971

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 13328 NA NA -95.6243055555557 NA 2 12873 NA NA -770.874305555556 NA 3 14000 NA NA 560.500694444444 NA 4 13477 NA NA -145.107638888889 NA 5 14237 NA NA 327.534027777778 NA 6 13674 NA NA -313.399305555556 NA 7 13529 13588.7090277778 13937.9583333333 -349.249305555555 -59.709027777777 8 14058 13932.2590277778 14033.5 -101.240972222222 125.740972222224 9 12975 13433.6173611111 14124.9583333333 -691.340972222222 -458.61736111111 10 14326 14083.6756944444 14227.625 -143.949305555555 242.324305555559 11 14008 13912.6256944444 14335.2916666667 -422.665972222222 95.3743055555587 12 16193 16561.6673611111 14416.25 2145.41736111111 -368.667361111111 13 14483 14409.6673611111 14505.2916666667 -95.6243055555557 73.3326388888872 14 14011 13833.8756944444 14604.75 -770.874305555556 177.124305555555 15 15057 15268.7090277778 14708.2083333333 560.500694444444 -211.709027777777 16 14884 14665.1006944444 14810.2083333333 -145.107638888889 218.899305555557 17 15414 15217.3673611111 14889.8333333333 327.534027777778 196.632638888892 18 14440 14656.8506944444 14970.25 -313.399305555556 -216.850694444443 19 14900 14706.2090277778 15055.4583333333 -349.249305555555 193.790972222221 20 15074 15029.1756944444 15130.4166666667 -101.240972222222 44.8243055555558 21 14442 14503.0340277778 15194.375 -691.340972222222 -61.0340277777777 22 15307 15117.9256944444 15261.875 -143.949305555555 189.074305555558 23 14938 14904.8340277778 15327.5 -422.665972222222 33.165972222223 24 17193 17547.1673611111 15401.75 2145.41736111111 -354.167361111109 25 15528 15394.9590277778 15490.5833333333 -95.6243055555557 133.040972222221 26 14765 14802.8340277778 15573.7083333333 -770.874305555556 -37.8340277777788 27 15838 16224.1673611111 15663.6666666667 560.500694444444 -386.167361111111 28 15723 15619.9340277778 15765.0416666667 -145.107638888889 103.065972222224 29 16150 16173.0340277778 15845.5 327.534027777778 -23.0340277777759 30 15486 15634.2673611111 15947.6666666667 -313.399305555556 -148.267361111109 31 15986 15702.2506944444 16051.5 -349.249305555555 283.749305555557 32 15983 16019.3840277778 16120.625 -101.240972222222 -36.3840277777781 33 15692 15520.7840277778 16212.125 -691.340972222222 171.215972222226 34 16490 16157.1756944444 16301.125 -143.949305555555 332.824305555556 35 15686 15927.5840277778 16350.25 -422.665972222222 -241.584027777773 36 18897 18548.0423611111 16402.625 2145.41736111111 348.957638888893 37 16316 16356.7090277778 16452.3333333333 -95.6243055555557 -40.7090277777716 38 15636 15709.5006944444 16480.375 -770.874305555556 -73.5006944444394 39 17163 17066.7090277778 16506.2083333333 560.500694444444 96.2909722222248 40 16534 16366.2256944444 16511.3333333333 -145.107638888889 167.774305555558 41 16518 16862.9923611111 16535.4583333333 327.534027777778 -344.99236111111 42 16375 16257.9340277778 16571.3333333333 -313.399305555556 117.065972222219 43 16290 16246.4590277778 16595.7083333333 -349.249305555555 43.5409722222248 44 16352 16540.9256944444 16642.1666666667 -101.240972222222 -188.925694444442 45 15943 16010.4506944444 16701.7916666667 -691.340972222222 -67.4506944444402 46 16362 16606.7590277778 16750.7083333333 -143.949305555555 -244.759027777778 47 16393 16385.9173611111 16808.5833333333 -422.665972222222 7.08263888888541 48 19051 19022.1673611111 16876.75 2145.41736111111 28.8326388888891 49 16747 16833.7506944444 16929.375 -95.6243055555557 -86.7506944444431 50 16320 16229.7506944444 17000.625 -770.874305555556 90.2493055555569 51 17910 17651.7923611111 17091.2916666667 560.500694444444 258.207638888889 52 16961 17029.5173611111 17174.625 -145.107638888889 -68.5173611111095 53 17480 17584.2423611111 17256.7083333333 327.534027777778 -104.242361111112 54 17049 17032.6006944444 17346 -313.399305555556 16.3993055555547 55 16879 17090.1673611111 17439.4166666667 -349.249305555555 -211.167361111111 56 17473 17422.7590277778 17524 -101.240972222222 50.2409722222219 57 16998 16918.4090277778 17609.75 -691.340972222222 79.5909722222241 58 17307 17548.9256944444 17692.875 -143.949305555555 -241.925694444446 59 17418 17372.8340277778 17795.5 -422.665972222222 45.1659722222212 60 20169 20069.7923611111 17924.375 2145.41736111111 99.207638888889 61 17871 17939.3340277778 18034.9583333333 -95.6243055555557 -68.3340277777752 62 17226 17371.4590277778 18142.3333333333 -770.874305555556 -145.459027777775 63 19062 18808.0423611111 18247.5416666667 560.500694444444 253.957638888893 64 17804 18214.6423611111 18359.75 -145.107638888889 -410.642361111109 65 19100 18813.7840277778 18486.25 327.534027777778 286.215972222220 66 18522 18279.7673611111 18593.1666666667 -313.399305555556 242.232638888891 67 18060 18359.3340277778 18708.5833333333 -349.249305555555 -299.334027777772 68 18869 18728.1756944444 18829.4166666667 -101.240972222222 140.824305555558 69 18127 18238.7423611111 18930.0833333333 -691.340972222222 -111.742361111112 70 18871 18895.6340277778 19039.5833333333 -143.949305555555 -24.6340277777781 71 18890 18723.2506944444 19145.9166666667 -422.665972222222 166.749305555557 72 21263 21375.2506944444 19229.8333333333 2145.41736111111 -112.250694444443 73 19547 19228.0840277778 19323.7083333333 -95.6243055555557 318.915972222225 74 18450 18657.1673611111 19428.0416666667 -770.874305555556 -207.167361111104 75 20254 20058.2923611111 19497.7916666667 560.500694444444 195.707638888889 76 19240 19420.6840277778 19565.7916666667 -145.107638888889 -180.684027777777 77 20216 19965.2423611111 19637.7083333333 327.534027777778 250.757638888888 78 19420 19379.6006944444 19693 -313.399305555556 40.3993055555547 79 19415 NA NA -349.249305555555 NA 80 20018 NA NA -101.240972222222 NA 81 18652 NA NA -691.340972222222 NA 82 19978 NA NA -143.949305555555 NA 83 19509 NA NA -422.665972222222 NA 84 21971 NA NA 2145.41736111111 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/28/t1209385997s8nla1r02soh3s6/17l0a1209385163.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/28/t1209385997s8nla1r02soh3s6/17l0a1209385163.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/28/t1209385997s8nla1r02soh3s6/205v11209385163.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/28/t1209385997s8nla1r02soh3s6/205v11209385163.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/28/t1209385997s8nla1r02soh3s6/3rfzc1209385163.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/28/t1209385997s8nla1r02soh3s6/3rfzc1209385163.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/28/t1209385997s8nla1r02soh3s6/4jc6h1209385163.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/28/t1209385997s8nla1r02soh3s6/4jc6h1209385163.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$trend)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) a<-table.element(a,m$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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