Evelyne Van Haevermaet verbetering opd9 oef 2

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

Title produced by software: Classical Decomposition

Date of computation: Wed, 28 May 2008 06:40:57 -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/28/t1211978642625jtcxk5vyi8xy.htm/, Retrieved Wed, 28 May 2008 14:44:02 +0200

User-defined keywords:

Dataseries X:

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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 19514 22148

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 13328 NA NA 0.994578556481803 NA 2 12873 NA NA 0.953159362044224 NA 3 14000 NA NA 1.0334682729003 NA 4 13477 NA NA 0.992228846279956 NA 5 14237 NA NA 1.01993449653857 NA 6 13674 NA NA 0.980599134598712 NA 7 13529 13660.6825481740 13937.9583333333 0.980106427460313 0.990360470810323 8 14058 13948.3301709530 14033.5 0.993930963120608 1.00786257764928 9 12975 13548.5607180001 14124.9583333333 0.959192968805225 0.957666299030709 10 14326 14116.1885441894 14227.625 0.992167599595114 1.01486318032334 11 14008 13976.3898992687 14335.2916666667 0.974963762458175 1.00226167851349 12 16193 16227.9345110828 14416.25 1.12566960971700 0.997847260779928 13 14483 14426.6520471809 14505.2916666667 0.994578556481803 1.00390582323847 14 14011 13920.6541928154 14604.75 0.953159362044224 1.00649005470097 15 15057 15200.4666637078 14708.2083333333 1.0334682729003 0.990561693474166 16 14884 14695.1159277491 14810.2083333333 0.992228846279956 1.01285352719771 17 15414 15186.6546643765 14889.8333333333 1.01993449653857 1.01497007343933 18 14440 14679.8141947264 14970.25 0.980599134598712 0.983663676423608 19 14900 14755.9514808609 15055.4583333333 0.980106427460313 1.00976206240078 20 15074 15038.5896099161 15130.4166666667 0.993930963120608 1.00235463504241 21 14442 14574.3376653899 15194.375 0.959192968805225 0.990919816157124 22 15307 15142.3378840707 15261.875 0.992167599595114 1.01087428620270 23 14938 14943.7570690777 15327.5 0.974963762458175 0.999614750892224 24 17193 17337.2819114589 15401.75 1.12566960971700 0.9916779393566 25 15528 15406.6020107277 15490.5833333333 0.994578556481803 1.00787960831257 26 14765 14844.2258996628 15573.7083333333 0.953159362044224 0.994662847345605 27 15838 16187.902537286 15663.6666666667 1.0334682729003 0.978384936746434 28 15723 15642.5291044721 15765.0416666667 0.992228846279956 1.00514436604148 29 16150 16161.3720649018 15845.5 1.01993449653857 0.999296342856524 30 15486 15638.2681322021 15947.6666666667 0.980599134598712 0.990263107723003 31 15986 15732.1783203792 16051.5 0.980106427460313 1.01613391829484 32 15983 16022.7883323561 16120.625 0.993930963120608 0.997516766025312 33 15692 15550.5563093914 16212.125 0.959192968805225 1.00909573186929 34 16490 16173.4480619499 16301.125 0.992167599595114 1.01957232229254 35 15686 15940.9012571318 16350.25 0.974963762458175 0.98400960817584 36 18897 18463.9364820844 16402.625 1.12566960971700 1.02345456064235 37 16316 16363.1379374241 16452.3333333333 0.994578556481803 0.997119260522989 38 15636 15708.4237212496 16480.375 0.953159362044224 0.995389497855752 39 17163 17058.6426183825 16506.2083333333 1.0334682729003 1.00611756655861 40 16534 16383.0212238771 16511.3333333333 0.992228846279956 1.0092155637266 41 16518 16865.0843702428 16535.4583333333 1.01993449653857 0.979419944625052 42 16375 16249.8351258134 16571.3333333333 0.980599134598712 1.00770253194678 43 16290 16265.5604057567 16595.7083333333 0.980106427460313 1.00150253625658 44 16352 16541.1647434137 16642.1666666667 0.993930963120608 0.988564001003074 45 15943 16020.2411331164 16701.7916666667 0.959192968805225 0.995178528682899 46 16362 16619.5100786012 16750.7083333333 0.992167599595114 0.98450555537538 47 16393 16387.7596482584 16808.5833333333 0.974963762458175 1.00031977230897 48 19051 18997.6445857915 16876.75 1.12566960971700 1.00280852786605 49 16747 16837.5933496391 16929.375 0.994578556481803 0.994619578477879 50 16320 16204.3048793531 17000.625 0.953159362044224 1.00713977683759 51 17910 17663.3076803853 17091.2916666667 1.0334682729003 1.01396637164899 52 16961 17041.1583490409 17174.625 0.992228846279956 0.99529619129175 53 17480 17600.7121258712 17256.7083333333 1.01993449653857 0.99314163398572 54 17049 17009.4725887492 17346 0.980599134598712 1.00232384696495 55 16879 17092.4843661585 17439.4166666667 0.980106427460313 0.987510044673132 56 17473 17417.6461977255 17524 0.993930963120608 1.00317803000739 57 16998 16891.1483824178 17609.75 0.959192968805225 1.00632589419991 58 17307 17554.2973186864 17692.875 0.992167599595114 0.985912434192216 59 17418 17349.9676348245 17795.5 0.974963762458175 1.00392118110001 60 20169 20176.9242106712 17924.375 1.12566960971700 0.999607263694481 61 17871 17937.1828253761 18034.9583333333 0.994578556481803 0.996310299893777 62 17226 17292.5348659937 18142.3333333333 0.953159362044224 0.996152393705766 63 19062 18858.2553709263 18247.5416666667 1.0334682729003 1.01080400201749 64 17804 18217.0735604884 18359.75 0.992228846279956 0.977324922188142 65 19100 18854.7640866361 18486.25 1.01993449653857 1.01300657553906 66 18522 18232.4431427829 18593.1666666667 0.980599134598712 1.01588140738734 67 18060 18336.4027736769 18708.5833333333 0.980106427460313 0.984926008820351 68 18869 18715.1402424992 18829.4166666667 0.993930963120608 1.00822113836750 69 18127 18157.6028322303 18930.0833333333 0.959192968805225 0.998314599536455 70 18871 18890.4576931245 19039.5833333333 0.992167599595114 0.998969972382853 71 18890 18666.574949044 19145.9166666667 0.974963762458175 1.01196925796864 72 21263 21646.4389832564 19229.8333333333 1.12566960971700 0.982286278886196 73 19547 19218.9459400421 19323.7083333333 0.994578556481803 1.01706930551662 74 18450 18518.0198007686 19428.0416666667 0.953159362044224 0.99632683183729 75 20254 20150.3490791199 19497.7916666667 1.0334682729003 1.00514387718412 76 19240 19413.7428919706 19565.7916666667 0.992228846279956 0.991050520606075 77 20216 20029.388648483 19637.9166666667 1.01993449653857 1.00931687705462 78 19420 19318.5792592428 19700.7916666667 0.980599134598712 1.00524990680713 79 19415 NA NA 0.980106427460313 NA 80 20018 NA NA 0.993930963120608 NA 81 18652 NA NA 0.959192968805225 NA 82 19978 NA NA 0.992167599595114 NA 83 19514 NA NA 0.974963762458175 NA 84 22148 NA NA 1.12566960971700 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/28/t1211978642625jtcxk5vyi8xy/1jyhk1211978452.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/28/t1211978642625jtcxk5vyi8xy/1jyhk1211978452.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/28/t1211978642625jtcxk5vyi8xy/2vvs91211978452.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/28/t1211978642625jtcxk5vyi8xy/2vvs91211978452.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/28/t1211978642625jtcxk5vyi8xy/3stqq1211978452.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/28/t1211978642625jtcxk5vyi8xy/3stqq1211978452.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/28/t1211978642625jtcxk5vyi8xy/4sws31211978452.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/28/t1211978642625jtcxk5vyi8xy/4sws31211978452.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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