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: Sun, 06 Dec 2009 12:42:08 -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/06/t1260128572szn7x3bp7jft2ny.htm/, Retrieved Sun, 06 Dec 2009 20:42:57 +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/06/t1260128572szn7x3bp7jft2ny.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 «

286602 283042 276687 277915 277128 277103 275037 270150 267140 264993 287259 291186 292300 288186 281477 282656 280190 280408 276836 275216 274352 271311 289802 290726 292300 278506 269826 265861 269034 264176 255198 253353 246057 235372 258556 260993 254663 250643 243422 247105 248541 245039 237080 237085 225554 226839 247934 248333 246969 245098 246263 255765 264319 268347 273046 273963 267430 271993 292710 295881 293299

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 611 0 62 Trend 19 1 2 Low-pass 13 1 2 Seasonal Decomposition by Loess - Time Series Components t Observed Fitted Seasonal Trend Remainder 1 286602 288283.547340159 7904.64296547268 277015.809694368 1681.54734015925 2 283042 285396.800809141 3511.75019532022 277175.448995539 2354.80080914067 3 276687 278162.958227938 -2124.04652464782 277335.08829671 1475.95822793769 4 277915 278219.611934075 49.4823079391573 277560.905757986 304.611934074841 5 277128 274589.663859774 1879.61292096409 277786.723219262 -2538.33614022593 6 277103 275179.303914318 970.464403580195 278056.231682101 -1923.69608568161 7 275037 274434.345679252 -2686.08582419262 278325.740144941 -602.654320748406 8 270150 266004.7109927 -4308.76108191526 278604.050089215 -4145.28900729993 9 267140 265689.877617028 -10292.2376505172 278882.360033489 -1450.12238297204 10 264993 263361.204257918 -12669.4331204611 279294.228862543 -1631.79574208159 11 287259 286702.927421792 8108.97488661175 279706.097691596 -556.072578207823 12 291186 292615.327498452 9655.63833319157 280101.034168356 1429.32749845221 13 292300 296199.386389411 7904.64296547268 280495.970645116 3899.38638941105 14 288186 292026.438709217 3511.75019532022 280833.811095463 3840.43870921677 15 281477 283906.394978838 -2124.04652464782 281171.651545810 2429.39497883816 16 282656 283847.735129900 49.4823079391573 281414.782562161 1191.73512989952 17 280190 276842.473500523 1879.61292096409 281657.913578513 -3347.52649947704 18 280408 278208.717836694 970.464403580195 281636.817759726 -2199.28216330591 19 276836 274742.363883254 -2686.08582419262 281615.721940938 -2093.63611674588 20 275216 273583.984508351 -4308.76108191526 281156.776573564 -1632.01549164904 21 274352 278298.406444327 -10292.2376505172 280697.83120619 3946.40644432709 22 271311 275516.152405758 -12669.4331204611 279775.280714703 4205.15240575804 23 289802 292642.294890172 8108.97488661175 278852.730223216 2840.29489017237 24 290726 294423.711135668 9655.63833319157 277372.65053114 3697.71113566816 25 292300 300802.786195463 7904.64296547268 275892.570839065 8502.78619546269 26 278506 279757.350176907 3511.75019532022 273742.899627773 1251.35017690674 27 269826 270182.818108166 -2124.04652464782 271593.228416481 356.818108166393 28 265861 262770.534978654 49.4823079391573 268901.982713407 -3090.46502134629 29 269034 269977.650068703 1879.61292096409 266210.737010333 943.650068703108 30 264176 263922.619465147 970.464403580195 263458.916131273 -253.380534852738 31 255198 252374.990571980 -2686.08582419262 260707.095252212 -2823.00942801972 32 253353 252723.747014535 -4308.76108191526 258291.01406738 -629.252985464729 33 246057 246531.304767970 -10292.2376505172 255874.932882548 474.30476796953 34 235372 229412.835734903 -12669.4331204611 254000.597385558 -5959.16426509718 35 258556 256876.763224819 8108.97488661175 252126.261888569 -1679.23677518056 36 260993 261738.142857124 9655.63833319157 250592.218809685 745.142857123632 37 254663 252363.181303727 7904.64296547268 249058.175730801 -2299.81869627346 38 250643 250038.258321462 3511.75019532022 247735.991483218 -604.741678538441 39 243422 242554.239289012 -2124.04652464782 246413.807235636 -867.760710987815 40 247105 248859.458439718 49.4823079391573 245301.059252343 1754.45843971783 41 248541 251014.075809985 1879.61292096409 244188.311269050 2473.07580998546 42 245039 245797.888515985 970.464403580195 243309.647080435 758.88851598493 43 237080 234415.102932373 -2686.08582419262 242430.982891819 -2664.89706762668 44 237085 236443.310999216 -4308.76108191526 242035.450082699 -641.689000783546 45 225554 219760.320376939 -10292.2376505172 241639.917273578 -5793.67962306115 46 226839 224109.410416003 -12669.4331204611 242238.022704458 -2729.58958399663 47 247934 244922.896978051 8108.97488661175 242836.128135337 -3011.1030219488 48 248333 242186.790398631 9655.63833319157 244823.571268177 -6146.20960136876 49 246969 239222.34263351 7904.64296547268 246811.014401017 -7746.65736648996 50 245098 236659.842804353 3511.75019532022 250024.407000326 -8438.15719564664 51 246263 241412.246925012 -2124.04652464782 253237.799599636 -4850.7530749877 52 255765 254284.757702114 49.4823079391573 257195.759989947 -1480.24229788600 53 264319 265604.666698778 1879.61292096409 261153.720380258 1285.66669877773 54 268347 270652.326406955 970.464403580195 265071.209189464 2305.32640695537 55 273046 279789.387825522 -2686.08582419262 268988.697998671 6743.38782552193 56 273963 279294.05454129 -4308.76108191526 272940.706540625 5331.05454129027 57 267430 268259.522567938 -10292.2376505172 276892.715082579 829.522567937907 58 271993 275815.739322695 -12669.4331204611 280839.693797766 3822.73932269472 59 292710 292524.352600435 8108.97488661175 284786.672512953 -185.647399565205 60 295881 293443.921487678 9655.63833319157 288662.440179131 -2437.07851232245 61 293299 286155.149189219 7904.64296547268 292538.207845308 -7143.85081078089

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/06/t1260128572szn7x3bp7jft2ny/1189b1260128526.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260128572szn7x3bp7jft2ny/1189b1260128526.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260128572szn7x3bp7jft2ny/21t6t1260128526.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260128572szn7x3bp7jft2ny/21t6t1260128526.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260128572szn7x3bp7jft2ny/3c11f1260128526.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260128572szn7x3bp7jft2ny/3c11f1260128526.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260128572szn7x3bp7jft2ny/4poyx1260128526.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260128572szn7x3bp7jft2ny/4poyx1260128526.ps (open in new window)

Parameters (Session):

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

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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