Paper - Ontleden tijdreeks 25-50 jaar Loess

*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, 28 Nov 2010 19:33:07 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/28/t1290972689rmiazk2a4wu8h99.htm/, Retrieved Sun, 28 Nov 2010 20:31:33 +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/Nov/28/t1290972689rmiazk2a4wu8h99.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:

Dataseries X:

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376.974 377.632 378.205 370.861 369.167 371.551 382.842 381.903 384.502 392.058 384.359 388.884 386.586 387.495 385.705 378.67 377.367 376.911 389.827 387.82 387.267 380.575 372.402 376.74 377.795 376.126 370.804 367.98 367.866 366.121 379.421 378.519 372.423 355.072 344.693 342.892 344.178 337.606 327.103 323.953 316.532 306.307 327.225 329.573 313.761 307.836 300.074 304.198 306.122 300.414 292.133 290.616 280.244 285.179 305.486 305.957 293.886 289.441 288.776 299.149 306.532 309.914 313.468 314.901 309.16 316.15 336.544 339.196 326.738 320.838 318.62 331.533 335.378

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 'George Udny Yule' @ 72.249.76.132 Seasonal Decomposition by Loess - Parameters Component Window Degree Jump Seasonal 731 0 74 Trend 19 1 2 Low-pass 13 1 2 Seasonal Decomposition by Loess - Time Series Components t Observed Fitted Seasonal Trend Remainder 1 376.974 378.146070292971 2.43799250765485 373.363937199374 1.17207029297140 2 377.632 379.026831435046 1.82576035660079 374.411408208354 1.39483143504555 3 378.205 382.164831564073 -1.21371078140615 375.458879217334 3.9598315640726 4 370.861 369.178292472759 -4.0016360254215 376.545343552663 -1.68270752724106 5 369.167 368.526420136467 -7.82422802445897 377.631807887991 -0.640579863532537 6 371.551 371.379142046755 -6.98504846886772 378.707906422113 -0.171857953245024 7 382.842 375.838692134644 10.0613029091218 379.784004956234 -7.00330786535574 8 381.903 372.273135861161 10.7613433498147 380.771520789025 -9.62986413883942 9 384.502 383.452581946728 3.79238143145696 381.759036621815 -1.04941805327246 10 392.058 402.683493192339 -1.21978999518490 382.652296802846 10.6254931923393 11 384.359 392.094233689629 -6.92179067350455 383.545556983876 7.73523368962873 12 388.884 394.32572335582 -0.712579424928549 384.154856069109 5.44172335581965 13 386.586 385.969852338003 2.43799250765485 384.764155154342 -0.61614766199682 14 387.495 388.344577764674 1.82576035660079 384.819661878725 0.849577764673825 15 385.705 387.748542178297 -1.21371078140615 384.875168603109 2.04354217829734 16 378.67 377.075685751845 -4.0016360254215 384.265950273577 -1.59431424815517 17 377.367 378.901496080414 -7.82422802445897 383.656731944045 1.53449608041444 18 376.911 378.013926668525 -6.98504846886772 382.793121800342 1.10292666852536 19 389.827 387.663185434238 10.0613029091218 381.92951165664 -2.16381456576198 20 387.82 383.883826887997 10.7613433498147 380.994829762188 -3.93617311200296 21 387.267 390.681470700807 3.79238143145696 380.060147867736 3.4144707008067 22 380.575 383.264914833509 -1.21978999518490 379.104875161676 2.68991483350925 23 372.402 373.576188217890 -6.92179067350455 378.149602455615 1.17418821788965 24 376.74 376.952160734155 -0.712579424928549 377.240418690774 0.212160734155020 25 377.795 376.820772566413 2.43799250765485 376.331234925932 -0.974227433586975 26 376.126 375.176190958002 1.82576035660079 375.250048685397 -0.949809041997923 27 370.804 368.652848336544 -1.21371078140615 374.168862444862 -2.15115166345589 28 367.98 367.508450449821 -4.0016360254215 372.4531855756 -0.471549550178679 29 367.866 372.818719318121 -7.82422802445897 370.737508706338 4.95271931812067 30 366.121 370.964444320670 -6.98504846886772 368.262604148197 4.84344432067041 31 379.421 382.992997500822 10.0613029091218 365.787699590056 3.57199750082185 32 378.519 383.779573548317 10.7613433498147 362.497083101868 5.26057354831676 33 372.423 381.847151954862 3.79238143145696 359.206466613681 9.4241519548624 34 355.072 356.228241626078 -1.21978999518490 355.135548369107 1.15624162607764 35 344.693 345.243160548971 -6.92179067350455 351.064630124534 0.550160548970666 36 342.892 340.028118983699 -0.712579424928549 346.468460441229 -2.86388101630081 37 344.178 344.04571673442 2.43799250765485 341.872290757925 -0.132283265579645 38 337.606 336.018231789198 1.82576035660079 337.368007854201 -1.58776821080153 39 327.103 322.555985830930 -1.21371078140615 332.863724950477 -4.54701416907051 40 323.953 323.051195811567 -4.0016360254215 328.856440213854 -0.901804188432493 41 316.532 316.039072547228 -7.82422802445897 324.849155477231 -0.492927452772165 42 306.307 298.119219851727 -6.98504846886772 321.479828617141 -8.1877801482728 43 327.225 326.278195333828 10.0613029091218 318.11050175705 -0.946804666171715 44 329.573 333.252615509579 10.7613433498147 315.132041140606 3.67961550957921 45 313.761 311.576038044381 3.79238143145696 312.153580524162 -2.18496195561914 46 307.836 307.45107364901 -1.21978999518490 309.440716346175 -0.384926350990327 47 300.074 300.341938505316 -6.92179067350455 306.727852168188 0.267938505316351 48 304.198 304.709580951647 -0.712579424928549 304.398998473282 0.51158095164692 49 306.122 307.73586271397 2.43799250765485 302.070144778375 1.61386271397015 50 300.414 298.830766271164 1.82576035660079 300.171473372235 -1.58323372883558 51 292.133 287.206908815312 -1.21371078140615 298.272801966095 -4.92609118468846 52 290.616 288.311298745325 -4.0016360254215 296.922337280096 -2.30470125467497 53 280.244 272.740355430361 -7.82422802445897 295.571872594098 -7.50364456963922 54 285.179 282.217850617449 -6.98504846886772 295.125197851419 -2.9611493825513 55 305.486 306.232173982138 10.0613029091218 294.67852310874 0.74617398213843 56 305.957 305.645980238085 10.7613433498147 295.506676412100 -0.311019761915190 57 293.886 287.644788853082 3.79238143145696 296.334829715461 -6.24121114691809 58 289.441 281.816602890682 -1.21978999518490 298.285187104503 -7.6243971093179 59 288.776 284.23824617996 -6.92179067350455 300.235544493544 -4.53775382003982 60 299.149 296.138404276962 -0.712579424928549 302.872175147967 -3.01059572303848 61 306.532 305.117201689955 2.43799250765485 305.508805802390 -1.41479831004449 62 309.914 309.590330266282 1.82576035660079 308.411909377117 -0.323669733718248 63 313.468 316.834697829561 -1.21371078140615 311.315012951845 3.36669782956091 64 314.901 319.949969174159 -4.0016360254215 313.853666851262 5.04896917415942 65 309.16 309.75190727378 -7.82422802445897 316.392320750679 0.59190727378018 66 316.15 320.64633068945 -6.98504846886772 318.638717779418 4.49633068944968 67 336.544 342.141582282721 10.0613029091218 320.885114808157 5.59758228272091 68 339.196 344.526340100862 10.7613433498147 323.104316549323 5.33034010086192 69 326.738 324.360100278053 3.79238143145696 325.323518290490 -2.37789972194651 70 320.838 315.462279908409 -1.21978999518490 327.433510086776 -5.37572009159095 71 318.62 314.618288790442 -6.92179067350455 329.543501883062 -4.0017112095577 72 331.533 332.235669255320 -0.712579424928549 331.542910169609 0.702669255319506 73 335.378 334.775689036189 2.43799250765485 333.542318456156 -0.602310963810737

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290972689rmiazk2a4wu8h99/1jzd71290972783.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290972689rmiazk2a4wu8h99/1jzd71290972783.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290972689rmiazk2a4wu8h99/2jzd71290972783.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290972689rmiazk2a4wu8h99/2jzd71290972783.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290972689rmiazk2a4wu8h99/3jzd71290972783.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290972689rmiazk2a4wu8h99/3jzd71290972783.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290972689rmiazk2a4wu8h99/4u9us1290972783.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290972689rmiazk2a4wu8h99/4u9us1290972783.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')

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