*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, 04 Dec 2009 12:50:57 -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/04/t125995633657twig41dfmcoa6.htm/, Retrieved Fri, 04 Dec 2009 20:52:22 +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/04/t125995633657twig41dfmcoa6.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 «

6802.96 7132.68 7073.29 7264.5 7105.33 7218.71 7225.72 7354.25 7745.46 8070.26 8366.33 8667.51 8854.34 9218.1 9332.9 9358.31 9248.66 9401.2 9652.04 9957.38 10110.63 10169.26 10343.78 10750.21 11337.5 11786.96 12083.04 12007.74 11745.93 11051.51 11445.9 11924.88 12247.63 12690.91 12910.7 13202.12 13654.67 13862.82 13523.93 14211.17 14510.35 14289.23 14111.82 13086.59 13351.54 13747.69 12855.61 12926.93 12121.95 11731.65 11639.51 12163.78 12029.53 11234.18 9852.13 9709.04 9332.75 7108.6 6691.49 6143.05

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 2 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 6802.96 7047.9981955608 95.2013427908154 6462.72046164838 245.038195560802 2 7132.68 7398.40910271792 221.718874162140 6645.23202311994 265.729102717917 3 7073.29 7178.66613023296 140.170285175537 6827.7435845915 105.376130232960 4 7264.5 7122.3315014571 392.520918634440 7014.14757990846 -142.168498542904 5 7105.33 6708.94272607836 301.165698696214 7200.55157522542 -396.387273921638 6 7218.71 7039.54468837006 10.9871364938157 7386.88817513612 -179.165311629939 7 7225.72 7049.85616631122 -171.640941358045 7573.22477504682 -175.863833688777 8 7354.25 7161.91328977108 -210.717381838794 7757.30409206771 -192.336710228918 9 7745.46 7597.06231302596 -47.5257221145658 7941.3834090886 -148.397686974036 10 8070.26 8233.17685292282 -225.888163239239 8133.23131031642 162.916852922819 11 8366.33 8735.33606932375 -327.755280867987 8325.07921154424 369.00606932375 12 8667.51 8999.94847293375 -178.236957973296 8513.30848503955 332.438472933751 13 8854.34 8911.94089867433 95.2013427908154 8701.53775853486 57.6008986743273 14 9218.1 9329.24804991451 221.718874162140 8885.23307592334 111.148049914515 15 9332.9 9456.70132151263 140.170285175537 9068.92839331183 123.801321512630 16 9358.31 9077.8110999521 392.520918634440 9246.28798141346 -280.498900047902 17 9248.66 8772.5067317887 301.165698696214 9423.64756951509 -476.153268211303 18 9401.2 9181.34552485335 10.9871364938157 9610.06733865284 -219.854475146651 19 9652.04 9679.23383356746 -171.640941358045 9796.48710779058 27.193833567464 20 9957.38 10111.0188179484 -210.717381838794 10014.4585638904 153.638817948437 21 10110.63 10036.3557021244 -47.5257221145658 10232.4300199901 -74.2742978755668 22 10169.26 10113.8561375643 -225.888163239239 10450.5520256750 -55.4038624357272 23 10343.78 10346.6412495082 -327.755280867987 10668.6740313598 2.86124950818885 24 10750.21 10829.8689224344 -178.236957973296 10848.7880355389 79.6589224343734 25 11337.5 11550.8966174911 95.2013427908154 11028.9020397180 213.396617491138 26 11786.96 12163.777366734 221.718874162140 11188.4237591039 376.817366733992 27 12083.04 12677.9642363348 140.170285175537 11347.9454784897 594.924236334778 28 12007.74 12097.6996924060 392.520918634440 11525.2593889595 89.9596924060224 29 11745.93 11488.1210018744 301.165698696214 11702.5732994294 -257.808998125600 30 11051.51 10201.3585303702 10.9871364938157 11890.674333136 -850.151469629813 31 11445.9 10984.6655745154 -171.640941358045 12078.7753668426 -461.234425484563 32 11924.88 11794.0084906326 -210.717381838794 12266.4688912062 -130.871509367367 33 12247.63 12088.6233065449 -47.5257221145658 12454.1624155697 -159.006693455149 34 12690.91 12945.1036190797 -225.888163239239 12662.6045441595 254.193619079690 35 12910.7 13278.1086081186 -327.755280867987 12871.0466727494 367.408608118601 36 13202.12 13496.1172438692 -178.236957973296 13086.3597141041 293.997243869202 37 13654.67 13912.4659017504 95.2013427908154 13301.6727554588 257.795901750382 38 13862.82 14056.1681123165 221.718874162140 13447.7530135214 193.348112316495 39 13523.93 13313.8564432405 140.170285175537 13593.8332715839 -210.073556759458 40 14211.17 14379.9439681536 392.520918634440 13649.8751132119 168.773968153630 41 14510.35 15013.6173464638 301.165698696214 13705.9169548399 503.267346463848 42 14289.23 14901.4264589434 10.9871364938157 13666.0464045628 612.196458943403 43 14111.82 14769.1050870724 -171.640941358045 13626.1758542856 657.285087072423 44 13086.59 12896.9659265421 -210.717381838794 13486.9314552967 -189.624073457904 45 13351.54 13402.9186658068 -47.5257221145658 13347.6870563078 51.3786658067911 46 13747.69 14582.5661237739 -225.888163239239 13138.7020394654 834.876123773884 47 12855.61 13109.2582582451 -327.755280867987 12929.7170226229 253.648258245054 48 12926.93 13368.7079975166 -178.236957973296 12663.3889604567 441.777997516607 49 12121.95 11751.6377589187 95.2013427908154 12397.0608982904 -370.312241081261 50 11731.65 11178.3983661700 221.718874162140 12063.1827596679 -553.251633830012 51 11639.51 11409.5450937792 140.170285175537 11729.3046210453 -229.964906220828 52 12163.78 12698.7994763662 392.520918634440 11236.2396049994 535.019476366198 53 12029.53 13014.7197123504 301.165698696214 10743.1745889534 985.189712350357 54 11234.18 12223.4437951139 10.9871364938157 10233.9290683923 989.263795113886 55 9852.13 10151.2173935269 -171.640941358045 9724.68354783117 299.087393526879 56 9709.04 10418.7661444101 -210.717381838794 9210.03123742865 709.726144410148 57 9332.75 10017.6467950884 -47.5257221145658 8695.37892702613 684.896795088434 58 7108.6 6277.58327167398 -225.888163239239 8165.50489156526 -831.016728326023 59 6691.49 6075.10442476359 -327.755280867987 7635.63085610439 -616.385575236407 60 6143.05 5374.41278905665 -178.236957973296 7089.92416891664 -768.637210943346

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t125995633657twig41dfmcoa6/162oi1259956255.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t125995633657twig41dfmcoa6/162oi1259956255.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t125995633657twig41dfmcoa6/20av61259956255.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t125995633657twig41dfmcoa6/20av61259956255.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t125995633657twig41dfmcoa6/33oum1259956255.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t125995633657twig41dfmcoa6/33oum1259956255.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t125995633657twig41dfmcoa6/4yek01259956255.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t125995633657twig41dfmcoa6/4yek01259956255.ps (open in new window)

Parameters (Session):

par1 = Aandelenkoers ; par2 = belgostat ; par3 = euronext brussel ;

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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