verbetering opgave 9 deel 2- Elke Van Buggenhout

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

Title produced by software: Classical Decomposition

Date of computation: Wed, 21 May 2008 09:40:00 -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/21/t1211384496tag13k52prq5jfs.htm/, Retrieved Wed, 21 May 2008 17:41:36 +0200

User-defined keywords:

Dataseries X:

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10.893 10.756 10.940 10.997 10.827 10.166 10.186 10.457 10.368 10.244 10.511 10.812 10.738 10.171 9.721 9.897 9.828 9.924 10.371 10.846 10.413 10.709 10.662 10.570 10.297 10.635 10.872 10.296 10.383 10.431 10.574 10.653 10.805 10.872 10.625 10.407 10.463 10.556 10.646 10.702 11.353 11.346 11.451 11.964 12.574 13.031 13.812 14.544 14.931 14.886 16.005 17.064 15.168 16.050 15.839 15.137 14.954 15.648 15.305 15.579 16.348 15.928 16.171 15.937 15.713 15.594 15.683 16.438 17.032 17.696 17.745 19.394

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 2 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 10.893 NA NA 1.01012797708450 NA 2 10.756 NA NA 0.997519199631532 NA 3 10.94 NA NA 1.0055568496333 NA 4 10.997 NA NA 1.00662338252075 NA 5 10.827 NA NA 0.981907092822072 NA 6 10.166 NA NA 0.991580581953606 NA 7 10.186 10.5515905978611 10.5899583333333 0.996376970119757 0.965352086543687 8 10.457 10.5608494516576 10.559125 1.00016331387853 0.990166562629928 9 10.368 10.4056116235092 10.4839583333333 0.992526991491844 0.996385448076478 10 10.244 10.4991745652705 10.3873333333333 1.01076707835863 0.975695749824504 11 10.511 10.3311011376663 10.299875 1.00303170064358 1.01741332893140 12 10.812 10.2873029995044 10.2481666666667 1.00381886186191 1.05100433034011 13 10.738 10.3495608098792 10.2457916666667 1.01012797708450 1.03753194915769 14 10.171 10.2442312371159 10.2697083333333 0.997519199631532 0.992851465822968 15 9.721 10.3449593780170 10.2877916666667 1.0055568496333 0.939684695201128 16 9.897 10.3773223930473 10.3090416666667 1.00662338252075 0.95371422657456 17 9.828 10.1477234147474 10.3347083333333 0.981907092822072 0.968493089367933 18 9.924 10.2439363604475 10.3309166666667 0.991580581953606 0.968768220614603 19 10.371 10.2651322189517 10.3024583333333 0.996376970119757 1.01031333827857 20 10.846 10.3050993576046 10.3034166666667 1.00016331387853 1.05248863922852 21 10.413 10.2932079417227 10.3707083333333 0.992526991491844 1.01163797126761 22 10.709 10.5476492697368 10.4352916666667 1.01076707835863 1.01529731659983 23 10.662 10.5067988572290 10.4750416666667 1.00303170064358 1.01477149652144 24 10.57 10.5594633884268 10.5192916666667 1.00381886186191 1.00099783589238 25 10.297 10.6557137642672 10.548875 1.01012797708450 0.966336017257696 26 10.635 10.5231209800129 10.5492916666667 0.997519199631532 1.01063173370330 27 10.872 10.6162502364077 10.5575833333333 1.0055568496333 1.02409040460588 28 10.296 10.6507884119655 10.5807083333333 1.00662338252075 0.966689000077507 29 10.383 10.3944275718189 10.5859583333333 0.981907092822072 0.998900605950644 30 10.431 10.4885675531870 10.577625 0.991580581953606 0.994511399874664 31 10.574 10.5394264956843 10.57775 0.996376970119757 1.00328039711932 32 10.653 10.5831030853914 10.581375 1.00016331387853 1.00660457656366 33 10.805 10.4896869307468 10.5686666666667 0.992526991491844 1.03005934031539 34 10.872 10.6900410819006 10.5761666666667 1.01076707835863 1.01702134881478 35 10.625 10.6657375887935 10.6335 1.00303170064358 0.996180518369747 36 10.407 10.7529494740506 10.7120416666667 1.00381886186191 0.96782748074047 37 10.463 10.8959558681505 10.7867083333333 1.01012797708450 0.960264535448786 38 10.556 10.8508891636919 10.877875 0.997519199631532 0.972823502365264 39 10.646 11.0673681780744 11.0062083333333 1.0055568496333 0.961926975655404 40 10.702 11.2438573548339 11.169875 1.00662338252075 0.951808588660102 41 11.353 11.1864992933621 11.392625 0.981907092822072 1.01488407608775 42 11.346 11.5993030684054 11.6977916666667 0.991580581953606 0.978162216564947 43 11.451 12.0126528774205 12.0563333333333 0.996376970119757 0.953244892435358 44 11.964 12.4249455013702 12.4229166666667 1.00016331387853 0.962901607792218 45 12.574 12.7307715222441 12.826625 0.992526991491844 0.987685622825753 46 13.031 13.4583636483452 13.315 1.01076707835863 0.968245497037249 47 13.812 13.7806943281297 13.7390416666667 1.00303170064358 1.00227170497545 48 14.544 14.1478230390817 14.094 1.00381886186191 1.02800267997585 49 14.931 14.6194138576811 14.4728333333333 1.01012797708450 1.02131317611993 50 14.886 14.7511892342511 14.787875 0.997519199631532 1.00913897609257 51 16.005 15.1027097138549 15.01925 1.0055568496333 1.05974360252169 52 17.064 15.3283156146938 15.2274583333333 1.00662338252075 1.11323386267193 53 15.168 15.1201009327983 15.3987083333333 0.981907092822072 1.00316790657778 54 16.05 15.3735066584663 15.5040416666667 0.991580581953606 1.04400384092989 55 15.839 15.5496665742244 15.6062083333333 0.996376970119757 1.01860705015085 56 15.137 15.7112321099465 15.7086666666667 1.00016331387853 0.9634508543997 57 14.954 15.6412328589200 15.759 0.992526991491844 0.956062743575354 58 15.648 15.8882055894244 15.7189583333333 1.01076707835863 0.984881515532235 59 15.305 15.7422899906883 15.6947083333333 1.00303170064358 0.97222195811747 60 15.579 15.7583667513673 15.6984166666667 1.00381886186191 0.988617681375405 61 16.348 15.8316516075139 15.6729166666667 1.01012797708450 1.03261494159213 62 15.928 15.6816252677075 15.720625 0.997519199631532 1.01571104576768 63 16.171 15.9495561740545 15.8614166666667 1.0055568496333 1.01388401178873 64 15.937 16.1395282330827 16.0333333333333 1.00662338252075 0.98745141554587 65 15.713 15.9268603479383 16.2203333333333 0.981907092822072 0.986572347388858 66 15.594 16.3421982553198 16.4809583333333 0.991580581953606 0.954216792402684 67 15.683 NA NA 0.996376970119757 NA 68 16.438 NA NA 1.00016331387853 NA 69 17.032 NA NA 0.992526991491844 NA 70 17.696 NA NA 1.01076707835863 NA 71 17.745 NA NA 1.00303170064358 NA 72 19.394 NA NA 1.00381886186191 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/21/t1211384496tag13k52prq5jfs/1xpla1211384395.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/21/t1211384496tag13k52prq5jfs/1xpla1211384395.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/21/t1211384496tag13k52prq5jfs/2l8nm1211384395.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/21/t1211384496tag13k52prq5jfs/2l8nm1211384395.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/21/t1211384496tag13k52prq5jfs/3xagr1211384396.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/21/t1211384496tag13k52prq5jfs/3xagr1211384396.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/21/t1211384496tag13k52prq5jfs/4i4ly1211384396.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/21/t1211384496tag13k52prq5jfs/4i4ly1211384396.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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