opgave9oef1-MerelBoels-MAR204A

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Tue, 02 Jun 2009 12:44:54 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Jun/02/t124396840613rrs0uvrjc95na.htm/, Retrieved Tue, 02 Jun 2009 20:46:51 +0200

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Jun/02/t124396840613rrs0uvrjc95na.htm/}, year = {2009}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2009}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

672.1 674.4 676.6 678.7 680.8 682.9 684 684.1 684.1 684.2 685.9 689.2 692.4 695.7 697.2 696.8 696.4 695.9 696.2 697.2 705.2 706.2 707.4 708.7 710 711.3 711.5 710.7 710 709.2 707.9 706.1 704.4 702.7 701.5 700.8 700 699.3 698.8 698.4 696.8 695.1 694.3 693.4 692.4 691 689.7 688.3 686 683.6 682.6 681.9 681 679.9 678.5 677.5 678 679 679.8 681.3 684.2 687 688.4 689.5 691.1 693.3 695.9 698 699.6 701.6 703.5 705.5 708.1 709.6 710.3

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 1 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 672.1 NA NA 1.00073281980099 NA 2 674.4 NA NA 1.00165891680969 NA 3 676.6 NA NA 1.00176323928649 NA 4 678.7 NA NA 1.00102382820104 NA 5 680.8 NA NA 1.00003181360352 NA 6 682.9 NA NA 0.999082528407648 NA 7 684 681.257998162266 682.2625 0.99852769009328 1.00402490957190 8 684.1 682.628896419973 683.995833333333 0.998001542046391 1.00215505611869 9 684.1 685.599365391093 685.741666666667 0.999792485592621 0.997813058957197 10 684.2 687.477967656593 687.354166666667 1.00018011237282 0.995231894241256 11 685.9 688.557870604987 688.758333333333 0.999708950558353 0.996139945938528 12 689.2 689.602315723082 689.95 0.999496073227164 0.999416597488278 13 692.4 691.506378482485 691 1.00073281980099 1.00129228239294 14 695.7 693.202226956965 692.054166666667 1.00165891680969 1.00360323863067 15 697.2 694.701936377693 693.479166666667 1.00176323928649 1.00359587830622 16 696.8 695.986842152476 695.275 1.00102382820104 1.00116835232834 17 696.4 697.109676865344 697.0875 1.00003181360352 0.9989819724372 18 695.9 698.154708007396 698.795833333333 0.999082528407648 0.996770475108832 19 696.2 699.310546692745 700.341666666667 0.99852769009328 0.99555198086536 20 697.2 700.322632092504 701.725 0.998001542046391 0.995541152107031 21 705.2 702.82495675745 702.970833333333 0.999792485592621 1.00337928131282 22 706.2 704.272658710187 704.145833333333 1.00018011237282 1.00273664079668 23 707.4 705.086391920885 705.291666666667 0.999708950558353 1.00328131148980 24 708.7 706.056519828584 706.4125 0.999496073227164 1.00374400646007 25 710 707.972603088294 707.454166666667 1.00073281980099 1.00286366577303 26 711.3 709.487531512762 708.3125 1.00165891680969 1.00255461640513 27 711.5 709.899519520368 708.65 1.00176323928649 1.00225451692193 28 710.7 709.196185752113 708.470833333333 1.00102382820104 1.00212044886605 29 710 708.101693216536 708.079166666667 1.00003181360352 1.00268083920947 30 709.2 706.85505169228 707.504166666667 0.999082528407648 1.00331743870558 31 707.9 705.71776603751 706.758333333333 0.99852769009328 1.00309221911012 32 706.1 704.431071773928 705.841666666667 0.998001542046391 1.00236918598986 33 704.4 704.666241251749 704.8125 0.999792485592621 0.999622173965257 34 702.7 703.897591168047 703.770833333333 1.00018011237282 0.998298628688217 35 701.5 702.503810465276 702.708333333333 0.999708950558353 0.998571096056246 36 700.8 701.217293007376 701.570833333333 0.999496073227164 0.999404902001794 37 700 700.929945868944 700.416666666667 1.00073281980099 0.998673268456534 38 699.3 700.480948419115 699.320833333333 1.00165891680969 0.998314089167192 39 698.8 699.522921966759 698.291666666667 1.00176323928649 0.998966549995636 40 698.4 698.018086337201 697.304166666667 1.00102382820104 1.00054714006739 41 696.8 696.34715260747 696.325 1.00003181360352 1.00065031843791 42 695.1 694.674570533443 695.3125 0.999082528407648 1.00061241548864 43 694.3 693.186243526839 694.208333333333 0.99852769009328 1.00160672039234 44 693.4 691.58596025984 692.970833333333 0.998001542046391 1.00262301412174 45 692.4 691.49814105609 691.641666666667 0.999792485592621 1.00130421022178 46 691 690.403494485283 690.279166666667 1.00018011237282 1.00086399550333 47 689.7 688.732819671335 688.933333333333 0.999708950558353 1.00140428958958 48 688.3 687.295145620716 687.641666666667 0.999496073227164 1.00146204201454 49 686 686.85297087041 686.35 1.00073281980099 0.998758146347785 50 683.6 686.165573066377 685.029166666667 1.00165891680969 0.996261000016495 51 682.6 684.972310916123 683.766666666667 1.00176323928649 0.996536632388906 52 681.9 683.365600051908 682.666666666667 1.00102382820104 0.997855320707105 53 681 681.775855723423 681.754166666667 1.00003181360352 0.998862007627713 54 679.9 680.425155972029 681.05 0.999082528407648 0.999228194361394 55 678.5 679.681156518327 680.683333333333 0.99852769009328 0.9982621902829 56 677.5 679.389549748081 680.75 0.998001542046391 0.997218753587273 57 678 680.99198835332 681.133333333333 0.999792485592621 0.995606426500618 58 679 681.814447770282 681.691666666667 1.00018011237282 0.995872120663495 59 679.8 682.230546038745 682.429166666667 0.999708950558353 0.996437353834628 60 681.3 683.063945577388 683.408333333333 0.999496073227164 0.997417598178313 61 684.2 685.193422277574 684.691666666667 1.00073281980099 0.998550157889328 62 687 687.409299554749 686.270833333333 1.00165891680969 0.999404576640127 63 688.4 689.238152710085 688.025 1.00176323928649 0.998783943246918 64 689.5 690.572971614956 689.866666666667 1.00102382820104 0.99844625889072 65 691.1 691.817841851691 691.795833333333 1.00003181360352 0.998962383147318 66 693.3 693.155132521489 693.791666666667 0.999082528407648 1.00020899719516 67 695.9 694.771406234862 695.795833333333 0.99852769009328 1.00162441020890 68 698 696.338942603836 697.733333333333 0.998001542046391 1.0023854150537 69 699.6 699.442325514528 699.5875 0.999792485592621 1.00022542885914 70 701.6 NA NA 1.00018011237282 NA 71 703.5 NA NA 0.999708950558353 NA 72 705.5 NA NA 0.999496073227164 NA 73 708.1 NA NA NA NA 74 709.6 NA NA NA NA 75 710.3 NA NA NA NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t124396840613rrs0uvrjc95na/162mv1243968292.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t124396840613rrs0uvrjc95na/162mv1243968292.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t124396840613rrs0uvrjc95na/28h0x1243968292.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t124396840613rrs0uvrjc95na/28h0x1243968292.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t124396840613rrs0uvrjc95na/3nafo1243968292.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t124396840613rrs0uvrjc95na/3nafo1243968292.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t124396840613rrs0uvrjc95na/4k7bz1243968292.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t124396840613rrs0uvrjc95na/4k7bz1243968292.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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