Classical Decomposition - Niels Braspennincx - Gem p badpak

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

Title produced by software: Classical Decomposition

Date of computation: Sat, 31 May 2008 14:24:45 -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/31/t121226584675fykq61bp1anp8.htm/, Retrieved Sat, 31 May 2008 20:30:51 +0000

User-defined keywords:

Dataseries X:

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38 38 37,93 38 38,02 38,02 38,02 37,96 37,96 37,99 38 38 38 38,01 38,06 38,02 38,05 38,05 38,05 38,05 38,03 37,98 38,03 38,04 38,04 38,08 38,14 38,37 38,49 38,55 38,55 38,55 38,48 38,51 38,48 38,48 38,48 38,43 38,43 38,45 38,48 38,47 38,47 38,42 38,55 38,56 38,57 38,57 38,57 38,57 38,62 38,73 38,74 38,68 38,69 38,69 38,61 38,77 38,78 38,81 38,81 38,81 38,76 38,93 38,95 38,97 38,97 38,96 38,92 38,95 38,95 38,97 38,97 39,05 39,08 38,83 38,86 38,87 38,87 38,75 38,86 38,93 38,96 38,95

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 6 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 38 NA NA 0.99909714446951 NA 2 38 NA NA 0.998677364269339 NA 3 37.93 NA NA 0.998831856288834 NA 4 38 NA NA 1.00095573525070 NA 5 38.02 NA NA 1.00163610137355 NA 6 38.02 NA NA 1.00127309669591 NA 7 38.02 38.0260303594107 37.9916666666667 1.00090450606038 0.999841414963548 8 37.96 37.9981124913608 37.9920833333333 1.00015869511484 0.998996989880236 9 37.96 37.9713782145421 37.9979166666667 0.999301581390439 0.999700347601875 10 37.99 37.997728812713 38.0041666666667 0.999830601365105 0.999796598034817 11 38 37.9944197935946 38.00625 0.999688729974534 1.00014686910435 12 38 37.9952412245236 38.00875 0.999644587746864 1.00012524661834 13 38 37.9769313327167 38.01125 0.99909714446951 1.00060743894975 14 38.01 37.9659683494043 38.01625 0.998677364269339 1.00115976629887 15 38.06 37.9785004356823 38.0229166666667 0.998831856288834 1.00214593950216 16 38.02 38.0617588977974 38.0254166666667 1.00095573525070 0.998902864738607 17 38.05 38.0884647998559 38.02625 1.00163610137355 0.998990119448026 18 38.05 38.0775814730983 38.0291666666667 1.00127309669591 0.999275650605126 19 38.05 38.0669006267415 38.0325 1.00090450606038 0.999556028295889 20 38.05 38.0431196326409 38.0370833333333 1.00015869511484 1.00018085707548 21 38.03 38.0167631613636 38.0433333333333 0.999301581390439 1.0003481842623 22 37.98 38.0548024762076 38.06125 0.999830601365105 0.998034348588345 23 38.03 38.0823090944382 38.0941666666667 0.999688729974534 0.998626420096835 24 38.04 38.1197802794137 38.1333333333333 0.999644587746864 0.997907115968955 25 38.04 38.1405334901235 38.175 0.99909714446951 0.997364129944602 26 38.08 38.1661199378266 38.2166666666667 0.998677364269339 0.997743550091891 27 38.14 38.2115612021497 38.25625 0.998831856288834 0.998127236891183 28 38.37 38.3336852058739 38.2970833333333 1.00095573525070 1.00094733375962 29 38.49 38.400641384784 38.3379166666667 1.00163610137355 1.00232700840386 30 38.55 38.4238550857056 38.375 1.00127309669591 1.00328298433390 31 38.55 38.4464102519560 38.4116666666667 1.00090450606038 1.00269439324413 32 38.55 38.4506843009002 38.4445833333333 1.00015869511484 1.00258293710256 33 38.48 38.4443809630669 38.47125 0.999301581390439 1.00092650827093 34 38.51 38.4801470778717 38.4866666666667 0.999830601365105 1.00077580062436 35 38.48 38.477602679749 38.4895833333333 0.999688729974534 1.00006230430391 36 38.48 38.4721549965945 38.4858333333333 0.999644587746864 1.00020391380223 37 38.48 38.444425538233 38.4791666666667 0.99909714446951 1.00092534772646 38 38.43 38.4195343190099 38.4704166666667 0.998677364269339 1.00027240520156 39 38.43 38.4229806117308 38.4679166666667 0.998831856288834 1.00018268723970 40 38.45 38.5096865893221 38.4729166666667 1.00095573525070 0.99845008893584 41 38.48 38.5417051357274 38.47875 1.00163610137355 0.998399003481809 42 38.47 38.5352467177131 38.48625 1.00127309669591 0.998306830154974 43 38.47 38.5285678301618 38.49375 1.00090450606038 0.998479885615785 44 38.42 38.5094436242382 38.5033333333333 1.00015869511484 0.997677358699051 45 38.55 38.4901822855473 38.5170833333333 0.999301581390439 1.00155410317387 46 38.56 38.5301386079399 38.5366666666667 0.999830601365105 1.00077501387586 47 38.57 38.5471643538764 38.5591666666667 0.999688729974534 1.0005924079373 48 38.57 38.5650386395393 38.57875 0.999644587746864 1.00012864917645 49 38.57 38.5618194527082 38.5966666666667 0.99909714446951 1.00021214111284 50 38.57 38.5660069991028 38.6170833333333 0.998677364269339 1.00010353679854 51 38.62 38.5857069683179 38.6308333333333 0.998831856288834 1.00088874960125 52 38.73 38.6790149345354 38.6420833333333 1.00095573525070 1.00131815832308 53 38.74 38.7228343307258 38.6595833333333 1.00163610137355 1.00044329578583 54 38.68 38.7275745917034 38.6783333333333 1.00127309669591 0.998771557676798 55 38.69 38.73333621036 38.6983333333333 1.00090450606038 0.998881165047992 56 38.69 38.7244777436879 38.7183333333333 1.00015869511484 0.999109665366797 57 38.61 38.7071140038408 38.7341666666667 0.999301581390439 0.997491055421203 58 38.77 38.7417694185622 38.7483333333333 0.999830601365105 1.00072868590830 59 38.78 38.7533501544336 38.7654166666667 0.999688729974534 1.00068767849644 60 38.81 38.7724648914968 38.78625 0.999644587746864 1.00096808672361 61 38.81 38.7749601768617 38.81 0.99909714446951 1.00090367141523 62 38.81 38.7815548635575 38.8329166666667 0.998677364269339 1.00073347075801 63 38.76 38.8116926758032 38.8570833333333 0.998831856288834 0.998668115914577 64 38.93 38.914656597209 38.8775 1.00095573525070 1.00039428339173 65 38.95 38.9557147242952 38.8920833333333 1.00163610137355 0.999853302029352 66 38.97 38.9553642212017 38.9058333333333 1.00127309669591 1.00037570637808 67 38.97 38.9543692887817 38.9191666666667 1.00090450606038 1.00040125694508 68 38.96 38.9420122598754 38.9358333333333 1.00015869511484 1.00046191090498 69 38.92 38.9319568596537 38.9591666666667 0.999301581390439 0.999692878020574 70 38.95 38.9617321508625 38.9683333333333 0.999830601365105 0.999698880152014 71 38.95 38.9482894567787 38.9604166666667 0.999688729974534 1.00004391831439 72 38.97 38.9386558042097 38.9525 0.999644587746864 1.00080496347763 73 38.97 38.9090057104113 38.9441666666667 0.99909714446951 1.00156761367902 74 39.05 38.8797581377107 38.93125 0.998677364269339 1.00437867596003 75 39.08 38.8745358467614 38.92 0.998831856288834 1.00528531463497 76 38.83 38.9538606968396 38.9166666666667 1.00095573525070 0.996820322950694 77 38.86 38.9799209300784 38.91625 1.00163610137355 0.996923520437779 78 38.87 38.9653769521687 38.9158333333333 1.00127309669591 0.997552264096257 79 38.87 NA NA 1.00090450606038 NA 80 38.75 NA NA 1.00015869511484 NA 81 38.86 NA NA 0.999301581390439 NA 82 38.93 NA NA 0.999830601365105 NA 83 38.96 NA NA 0.999688729974534 NA 84 38.95 NA NA 0.999644587746864 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/31/t121226584675fykq61bp1anp8/1d8cd1212265477.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/31/t121226584675fykq61bp1anp8/1d8cd1212265477.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/31/t121226584675fykq61bp1anp8/2vj9e1212265477.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/31/t121226584675fykq61bp1anp8/2vj9e1212265477.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/31/t121226584675fykq61bp1anp8/3y1lo1212265477.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/31/t121226584675fykq61bp1anp8/3y1lo1212265477.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/31/t121226584675fykq61bp1anp8/40wzq1212265477.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/31/t121226584675fykq61bp1anp8/40wzq1212265477.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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