WS9

*The author of this computation has been verified*

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

Title produced by software: Decomposition by Loess

Date of computation: Fri, 11 Dec 2009 05:47:20 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/11/t1260535686rhojp057lyt6qo6.htm/, Retrieved Fri, 11 Dec 2009 13:48:11 +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/2009/Dec/11/t1260535686rhojp057lyt6qo6.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 «

7.55 7.55 7.59 7.59 7.59 7.57 7.57 7.59 7.6 7.64 7.64 7.76 7.76 7.76 7.77 7.83 7.94 7.94 7.94 8.09 8.18 8.26 8.28 8.28 8.28 8.29 8.3 8.3 8.31 8.33 8.33 8.34 8.48 8.59 8.67 8.67 8.67 8.71 8.72 8.72 8.72 8.74 8.74 8.74 8.74 8.79 8.85 8.86 8.87 8.92 8.96 8.97 8.99 8.98 8.98 9.01 9.01 9.03 9.05 9.05

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 Seasonal Decomposition by Loess - Parameters Component Window Degree Jump Seasonal 601 0 61 Trend 19 1 2 Low-pass 13 1 2 Seasonal Decomposition by Loess - Time Series Components t Observed Fitted Seasonal Trend Remainder 1 7.55 7.55365324602313 -0.00367028860727190 7.55001704258414 0.00365324602313244 2 7.55 7.54866754914876 -0.0092773548552403 7.56060980570648 -0.00133245085124400 3 7.59 7.62168186273052 -0.0128844315593433 7.57120256882883 0.0316818627305153 4 7.59 7.61631703774549 -0.0191280186707920 7.5828109809253 0.0263170377454909 5 7.59 7.59695220622538 -0.0113715992471585 7.59441939302177 0.0069522062253835 6 7.57 7.56056721045888 -0.0278219799409109 7.60725476948203 -0.00943278954111992 7 7.57 7.56618223037658 -0.0462723763188668 7.62009014594229 -0.00381776962342162 8 7.59 7.56989393346754 -0.0236829488296599 7.63378901536212 -0.0201060665324642 9 7.6 7.547605599962 0.00490651525604124 7.64748788478196 -0.0523944000380014 10 7.64 7.57221757037849 0.0414016793500263 7.66638075027149 -0.0677824296215146 11 7.64 7.54082954471602 0.0538968395229606 7.68527361576102 -0.0991704552839767 12 7.76 7.7498144779604 0.0539039334506507 7.71628158858895 -0.0101855220396043 13 7.76 7.77638072719038 -0.00367028860727190 7.74728956141689 0.0163807271903815 14 7.76 7.74019361884409 -0.0092773548552403 7.78908373601115 -0.0198063811559122 15 7.77 7.72200652095393 -0.0128844315593433 7.83087791060541 -0.0479934790460712 16 7.83 7.79934689585813 -0.0191280186707920 7.87978112281266 -0.0306531041418694 17 7.94 7.96268726422725 -0.0113715992471585 7.9286843350199 0.0226872642272493 18 7.94 7.93056133940415 -0.0278219799409109 7.97726064053676 -0.0094386605958503 19 7.94 7.90043543026525 -0.0462723763188668 8.02583694605361 -0.0395645697347469 20 8.09 8.13333721637362 -0.0236829488296599 8.07034573245604 0.043337216373617 21 8.18 8.24023896588549 0.00490651525604124 8.11485451885847 0.060238965885489 22 8.26 8.32506764071619 0.0414016793500263 8.15353067993379 0.0650676407161885 23 8.28 8.31389631946794 0.0538968395229606 8.1922068410091 0.0338963194679369 24 8.28 8.28323537935748 0.0539039334506507 8.22286068719187 0.00323537935748064 25 8.28 8.31015575523264 -0.00367028860727190 8.25351453337463 0.0301557552326361 26 8.29 8.31058755162883 -0.0092773548552403 8.2786898032264 0.0205875516288341 27 8.3 8.30901935848117 -0.0128844315593433 8.30386507307818 0.00901935848116509 28 8.3 8.2890550260394 -0.0191280186707920 8.3300729926314 -0.0109449739606049 29 8.31 8.27509068706254 -0.0113715992471585 8.35628091218462 -0.0349093129374562 30 8.33 8.30011391324243 -0.0278219799409109 8.38770806669848 -0.0298860867575659 31 8.33 8.28713715510653 -0.0462723763188668 8.41913522121233 -0.0428628448934685 32 8.34 8.2490507991701 -0.0236829488296599 8.45463214965957 -0.0909492008299075 33 8.48 8.46496440663716 0.00490651525604124 8.4901290781068 -0.015035593362839 34 8.59 8.61136704025606 0.0414016793500263 8.52723128039391 0.0213670402560613 35 8.67 8.721769677796 0.0538968395229606 8.56433348268103 0.0517696777960097 36 8.67 8.6862420809514 0.0539039334506507 8.59985398559795 0.0162420809513968 37 8.67 8.7082958000924 -0.00367028860727190 8.63537448851488 0.0382958000923921 38 8.71 8.76549708021783 -0.0092773548552403 8.66378027463741 0.0554970802178314 39 8.72 8.7606983707994 -0.0128844315593433 8.69218606075994 0.0406983707994026 40 8.72 8.74815296831509 -0.0191280186707920 8.7109750503557 0.0281529683150890 41 8.72 8.72160755929569 -0.0113715992471585 8.72976403995147 0.00160755929569234 42 8.74 8.76379875903365 -0.0278219799409109 8.74402322090726 0.0237987590336513 43 8.74 8.76798997445581 -0.0462723763188668 8.75828240186305 0.0279899744558136 44 8.74 8.72909979009003 -0.0236829488296599 8.77458315873963 -0.0109002099099715 45 8.74 8.68420956912775 0.00490651525604124 8.79088391561621 -0.0557904308722499 46 8.79 8.72756227179334 0.0414016793500263 8.81103604885663 -0.0624377282066586 47 8.85 8.81491497837998 0.0538968395229606 8.83118818209705 -0.0350850216200147 48 8.86 8.81191916506848 0.0539039334506507 8.85417690148087 -0.04808083493152 49 8.87 8.86650466774258 -0.00367028860727190 8.87716562086469 -0.00349533225741716 50 8.92 8.94848383172275 -0.0092773548552403 8.90079352313249 0.0284838317227525 51 8.96 9.00846300615905 -0.0128844315593433 8.92442142540029 0.0484630061590519 52 8.97 9.01939147984129 -0.0191280186707920 8.9397365388295 0.0493914798412849 53 8.99 9.03631994698844 -0.0113715992471585 8.95505165225872 0.0463199469884383 54 8.98 9.01866334351904 -0.0278219799409109 8.96915863642187 0.0386633435190422 55 8.98 9.02300675573385 -0.0462723763188668 8.98326562058502 0.0430067557338525 56 9.01 9.04724143636939 -0.0236829488296599 8.99644151246027 0.0372414363693867 57 9.01 9.00547608040843 0.00490651525604124 9.00961740433553 -0.00452391959157161 58 9.03 8.997241974424 0.0414016793500263 9.02135634622597 -0.0327580255759941 59 9.05 9.01300787236063 0.0538968395229606 9.0330952881164 -0.0369921276393672 60 9.05 9.00245741683268 0.0539039334506507 9.04363864971667 -0.0475425831673224

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/11/t1260535686rhojp057lyt6qo6/1omed1260535638.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260535686rhojp057lyt6qo6/1omed1260535638.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260535686rhojp057lyt6qo6/264fl1260535638.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260535686rhojp057lyt6qo6/264fl1260535638.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260535686rhojp057lyt6qo6/34q8m1260535638.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260535686rhojp057lyt6qo6/34q8m1260535638.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260535686rhojp057lyt6qo6/41jnw1260535638.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260535686rhojp057lyt6qo6/41jnw1260535638.ps (open in new window)

Parameters (Session):

par1 = 12 ;

Parameters (R input):

par1 = 12 ; par2 = periodic ; par3 = 0 ; par5 = 1 ; par7 = 1 ; par8 = FALSE ;

R code (references can be found in the software module):

par1 <- as.numeric(par1) #seasonal period if (par2 != 'periodic') par2 <- as.numeric(par2) #s.window par3 <- as.numeric(par3) #s.degree if (par4 == '') par4 <- NULL else par4 <- as.numeric(par4)#t.window par5 <- as.numeric(par5)#t.degree if (par6 != '') par6 <- as.numeric(par6)#l.window par7 <- as.numeric(par7)#l.degree if (par8 == 'FALSE') par8 <- FALSE else par9 <- TRUE #robust nx <- length(x) x <- ts(x,frequency=par1) if (par6 != '') { m <- stl(x,s.window=par2, s.degree=par3, t.window=par4, t.degre=par5, l.window=par6, l.degree=par7, robust=par8) } else { m <- stl(x,s.window=par2, s.degree=par3, t.window=par4, t.degre=par5, l.degree=par7, robust=par8) } m$time.series m$win m$deg m$jump m$inner m$outer bitmap(file='test1.png') plot(m,main=main) dev.off() mylagmax <- nx/2 bitmap(file='test2.png') op <- par(mfrow = c(2,2)) acf(as.numeric(x),lag.max = mylagmax,main='Observed') acf(as.numeric(m$time.series[,'trend']),na.action=na.pass,lag.max = mylagmax,main='Trend') acf(as.numeric(m$time.series[,'seasonal']),na.action=na.pass,lag.max = mylagmax,main='Seasonal') acf(as.numeric(m$time.series[,'remainder']),na.action=na.pass,lag.max = mylagmax,main='Remainder') par(op) dev.off() bitmap(file='test3.png') op <- par(mfrow = c(2,2)) spectrum(as.numeric(x),main='Observed') spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'trend']),'trend']),main='Trend') spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'seasonal']),'seasonal']),main='Seasonal') spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'remainder']),'remainder']),main='Remainder') par(op) dev.off() bitmap(file='test4.png') op <- par(mfrow = c(2,2)) cpgram(as.numeric(x),main='Observed') cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'trend']),'trend']),main='Trend') cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'seasonal']),'seasonal']),main='Seasonal') cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'remainder']),'remainder']),main='Remainder') par(op) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Seasonal Decomposition by Loess - Parameters',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Component',header=TRUE) a<-table.element(a,'Window',header=TRUE) a<-table.element(a,'Degree',header=TRUE) a<-table.element(a,'Jump',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Seasonal',header=TRUE) a<-table.element(a,m$win['s']) a<-table.element(a,m$deg['s']) a<-table.element(a,m$jump['s']) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Trend',header=TRUE) a<-table.element(a,m$win['t']) a<-table.element(a,m$deg['t']) a<-table.element(a,m$jump['t']) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Low-pass',header=TRUE) a<-table.element(a,m$win['l']) a<-table.element(a,m$deg['l']) a<-table.element(a,m$jump['l']) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'t',header=TRUE) a<-table.element(a,'Observed',header=TRUE) a<-table.element(a,'Fitted',header=TRUE) a<-table.element(a,'Seasonal',header=TRUE) a<-table.element(a,'Trend',header=TRUE) a<-table.element(a,'Remainder',header=TRUE) a<-table.row.end(a) for (i in 1:nx) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]+m$time.series[i,'remainder']) a<-table.element(a,m$time.series[i,'seasonal']) a<-table.element(a,m$time.series[i,'trend']) a<-table.element(a,m$time.series[i,'remainder']) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable1.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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