niet werkende werkzoekende tss 20-25 jaar frederic.ledent@student.kdg.be

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Fri, 08 Jan 2010 14:26:41 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Jan/08/t1262986171z0renuperw5swf4.htm/, Retrieved Fri, 08 Jan 2010 22:29:37 +0100

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/2010/Jan/08/t1262986171z0renuperw5swf4.htm/}, year = {2010}, } @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 = {2010}, 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:

KDGP2W52

Dataseries X:

» Textbox « » Textfile « » CSV «

89507 87562 85209 82360 79054 79069 107551 115759 115585 110260 103444 102303 101397 97994 94044 91159 87239 89235 118647 125620 125154 117529 109459 108483 107137 104699 100804 96066 91971 93228 120144 127233 127166 118194 109940 106683 102834 99882 96666 92540 88744 89321 115870 122401 122030 113802 105791 103076 98658 96945 92497 90687 88796 90015 113228 118711 117460 106556 97347 92657 93118 89037 83570 81693 75956 73993 97088 102394 96549 89727 82336 82653 82303 79596 74472 73562 66618 69029 89899 93774 90305 83799 80320 82497

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 89507 NA NA 0.979587828450488 NA 2 87562 NA NA 0.95323689445307 NA 3 85209 NA NA 0.911835187016945 NA 4 82360 NA NA 0.888881007393845 NA 5 79054 NA NA 0.845661412944441 NA 6 79069 NA NA 0.857753804700136 NA 7 107551 108119.478555268 96967.3333333333 1.11500930095291 0.994742126369234 8 115759 115723.435611216 97897.4166666666 1.18208875730854 1.00030732226879 9 115585 115347.392594197 98700.2083333333 1.16866412484807 1.00205992871151 10 110260 108451.946793441 99434.9583333333 1.09068227725183 1.01667146842465 11 103444 101400.451974576 100142.625 1.01256035553868 1.02015324375414 12 102303 100305.746841438 100907.25 0.994039049141048 1.01991165233752 13 101397 99715.3470860986 101793.166666667 0.979587828450488 1.01686453452796 14 97994 97865.3764697542 102666.375 0.95323689445307 1.00131429045578 15 94044 94353.0198186326 103475.958333333 0.911835187016945 0.996724855026086 16 91159 92601.4381844809 104177.541666667 0.888881007393845 0.984423155700808 17 87239 88567.0006749765 104731.041666667 0.845661412944441 0.985005694391187 18 89235 90269.295611805 105239.166666667 0.857753804700136 0.988542110528336 19 118647 117896.437610673 105735.833333333 1.11500930095291 1.0063662855684 20 125620 125602.102102345 106254.375 1.18208875730854 1.00014249680025 21 125154 124831.345439032 106815.416666667 1.16866412484807 1.00258472389153 22 117529 117031.889817632 107301.541666667 1.09068227725183 1.00424764722797 23 109459 109055.956732641 107703.166666667 1.01256035553868 1.00369574738908 24 108483 107422.527995470 108066.708333333 0.994039049141048 1.00987197028704 25 107137 106084.912859800 108295.458333333 0.979587828450488 1.00991740589531 26 104699 103354.749999278 108425.041666667 0.95323689445307 1.01300617534009 27 100804 99003.4932518174 108576.083333333 0.911835187016945 1.01818629514014 28 96066 96610.3656012444 108687.625 0.888881007393845 0.994365349951254 29 91971 91953.3108595437 108735.375 0.845661412944441 1.00019237089226 30 93228 93221.0408922294 108680.416666667 0.857753804700136 1.0000746516849 31 120144 120896.137841283 108426.125 1.11500930095291 0.993778644589372 32 127233 127720.109633253 108046.125 1.18208875730854 0.996186116386436 33 127166 125833.572314767 107673 1.16866412484807 1.01058880917646 34 118194 117088.741631334 107353.666666667 1.09068227725183 1.00943949309957 35 109940 108417.157718341 107072.291666667 1.01256035553868 1.01404613728775 36 106683 106138.560890329 106775.041666667 0.994039049141048 1.00512951282836 37 102834 104261.614197937 106434.166666667 0.979587828450488 0.9863073844682 38 99882 101095.300531997 106054.75 0.95323689445307 0.987998447745723 39 96666 96325.737252611 105639.416666667 0.911835187016945 1.00353241778463 40 92540 93547.9853472294 105242.416666667 0.888881007393845 0.989224937945077 41 88744 88698.5010246894 104886.541666667 0.845661412944441 1.00051296216717 42 89321 89689.632738537 104563.375 0.857753804700136 0.995889906923672 43 115870 116227.547439472 104239.083333333 1.11500930095291 0.996923728949383 44 122401 122869.506925034 103942.708333333 1.18208875730854 0.99618695527671 45 122030 121128.092299081 103646.625 1.16866412484807 1.00744590031759 46 113802 112771.866623066 103395.708333333 1.09068227725183 1.00913466636477 47 105791 104618.410974493 103320.666666667 1.01256035553868 1.01120824733032 48 103076 102735.675297063 103351.75 0.994039049141048 1.00331262438245 49 98658 101162.606470315 103270.583333333 0.979587828450488 0.975241776010881 50 96945 98189.8344777037 103006.75 0.95323689445307 0.987322165432651 51 92497 93611.3558733927 102662.583333333 0.911835187016945 0.98809593277444 52 90687 90817.194745681 102170.25 0.888881007393845 0.998566408640505 53 88796 85848.5868271744 101516.5 0.845661412944441 1.03433269296278 54 90015 86402.0053640889 100730.541666667 0.857753804700136 1.04181609698393 55 113228 111574.056121945 100065.583333333 1.11500930095291 1.01482373174860 56 118711 117624.037318175 99505.25 1.18208875730854 1.00924099109848 57 117460 115468.446719796 98803.7916666667 1.16866412484807 1.01724759738941 58 106556 106949.122950672 98057.0833333333 1.09068227725183 0.996324205941794 59 97347 98367.5383796344 97147.3333333333 1.01256035553868 0.989625252431388 60 92657 95372.8280600756 95944.75 0.994039049141048 0.971524090086069 61 93118 92673.5799812823 94604.6666666667 0.979587828450488 1.00479554171542 62 89037 88891.5249089652 93252.2916666667 0.95323689445307 1.00163654624199 63 83570 83616.3124640392 91701.125 0.911835187016945 0.999446131231162 64 81693 80113.622985022 90128.625 0.888881007393845 1.01971421284085 65 75956 75096.3895564 88801.9583333333 0.845661412944441 1.01144676127145 66 73993 75276.187982549 87759.6666666667 0.857753804700136 0.98295360037564 67 97088 96885.6204720047 86892.2083333333 1.11500930095291 1.00208884999662 68 102394 101716.619657376 86048.2083333333 1.18208875730854 1.00665948539094 69 96549 99658.709744513 85275.75 1.16866412484807 0.968796407735107 70 89727 92225.7756645756 84557.875 1.09068227725183 0.972905886162849 71 82336 84882.9346048073 83830 1.01256035553868 0.969994739028932 72 82653 82737.9290527934 83234.0833333333 0.994039049141048 0.998973517300158 73 82303 81039.0561589353 82727.7083333333 0.979587828450488 1.01559672460382 74 79596 78231.198690869 82069 0.95323689445307 1.01744574200536 75 74472 74268.6720374678 81449.6666666667 0.911835187016945 1.00273773526514 76 73562 71948.2509409763 80942.5 0.888881007393845 1.02242930214311 77 66618 68170.0349895708 80611.5 0.845661412944441 0.977232885536757 78 69029 69067.1941082596 80521 0.857753804700136 0.999447000725124 79 89899 NA NA 1.11500930095291 NA 80 93774 NA NA 1.18208875730854 NA 81 90305 NA NA 1.16866412484807 NA 82 83799 NA NA 1.09068227725183 NA 83 80320 NA NA 1.01256035553868 NA 84 82497 NA NA 0.994039049141048 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jan/08/t1262986171z0renuperw5swf4/1asaa1262985998.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/08/t1262986171z0renuperw5swf4/1asaa1262985998.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/08/t1262986171z0renuperw5swf4/2q6jd1262985998.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/08/t1262986171z0renuperw5swf4/2q6jd1262985998.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/08/t1262986171z0renuperw5swf4/3tpc91262985998.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/08/t1262986171z0renuperw5swf4/3tpc91262985998.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/08/t1262986171z0renuperw5swf4/4h3hm1262985998.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/08/t1262986171z0renuperw5swf4/4h3hm1262985998.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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