Kim Van Assche opdracht 9 Loess eigen reeks

*Unverified author*

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

Title produced by software: Decomposition by Loess

Date of computation: Mon, 01 Jun 2009 11:43:04 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Jun/01/t12438782152dwjw3bgybtfe7g.htm/, Retrieved Mon, 01 Jun 2009 19:43:39 +0200

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/Jun/01/t12438782152dwjw3bgybtfe7g.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 «

519 517 510 509 501 507 569 580 578 565 547 555 562 561 555 544 537 543 594 611 613 611 594 595 591 589 584 573 567 569 621 629 628 612 595 597 593 590 580 574 573 573 620 626 620 588 566 557 561 549 532 526 511 499 555 565 542 527 510 514 517 508 493 490 469 478 528 534 518 506 502 516

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 721 0 73 Trend 19 1 2 Low-pass 13 1 2 Seasonal Decomposition by Loess - Time Series Components t Observed Fitted Seasonal Trend Remainder 1 519 518.705066627814 1.19195314747985 518.102980224707 -0.294933372186392 2 517 515.944947298012 -3.69379264886424 521.748845350852 -1.05505270198751 3 510 508.387729642824 -13.7824401198209 525.394710476997 -1.61227035717593 4 509 508.620556683349 -19.6172212212476 528.996664537898 -0.379443316650736 5 501 501.211511408574 -31.8101300073742 532.5986185988 0.211511408574324 6 507 506.764080871408 -28.9280901418061 536.164009270398 -0.235919128592286 7 569 573.891628892948 24.3789711650554 539.729399941997 4.89162889294767 8 580 582.006070603261 34.7055252392002 543.288404157538 2.00607060326149 9 578 579.236667449969 29.9159241769509 546.84740837308 1.23666744996945 10 565 565.873886346361 13.9529578768212 550.173155776818 0.873886346361246 11 547 545.345037623251 -4.84394080380629 553.498903180555 -1.65496237674915 12 555 555.189932449049 -1.46971856140716 556.279786112358 0.189932449049252 13 562 563.74737780836 1.19195314747985 559.06066904416 1.74737780835983 14 561 564.036240885513 -3.69379264886424 561.657551763351 3.03624088551305 15 555 559.528005637279 -13.7824401198209 564.254434482542 4.52800563727897 16 544 540.732853492546 -19.6172212212476 566.884367728702 -3.26714650745441 17 537 536.295829032512 -31.8101300073742 569.514300974862 -0.70417096748804 18 543 542.742588475903 -28.9280901418061 572.185501665903 -0.257411524096597 19 594 588.764326478002 24.3789711650554 574.856702356943 -5.23567352199848 20 611 609.893064033461 34.7055252392002 577.401410727339 -1.10693596653925 21 613 616.137956725314 29.9159241769509 579.946119097735 3.13795672531398 22 611 625.67703379132 13.9529578768212 582.370008331859 14.6770337913194 23 594 608.050043237823 -4.84394080380629 584.793897565984 14.0500432378226 24 595 604.372353878194 -1.46971856140716 587.097364683214 9.3723538781935 25 591 591.407215052077 1.19195314747985 589.400831800444 0.407215052076594 26 589 590.32261440106 -3.69379264886424 591.371178247804 1.32261440105981 27 584 588.440915424656 -13.7824401198209 593.341524695165 4.4409154246556 28 573 571.208663492073 -19.6172212212476 594.408557729175 -1.7913365079271 29 567 570.33453924419 -31.8101300073742 595.475590763184 3.33453924419007 30 569 571.028867684827 -28.9280901418061 595.899222456979 2.02886768482745 31 621 621.298174684171 24.3789711650554 596.322854150773 0.298174684171386 32 629 626.899026111809 34.7055252392002 596.395448648991 -2.10097388819111 33 628 629.616032675841 29.9159241769509 596.468043147208 1.61603267584064 34 612 613.797543112255 13.9529578768212 596.249499010924 1.79754311225463 35 595 598.812985929166 -4.84394080380629 596.03095487464 3.81298592916619 36 597 599.855365030379 -1.46971856140716 595.614353531028 2.85536503037895 37 593 589.610294665104 1.19195314747985 595.197752187416 -3.38970533489623 38 590 589.861578202421 -3.69379264886424 593.832214446443 -0.138421797578985 39 580 581.315763414351 -13.7824401198209 592.46667670547 1.31576341435084 40 574 578.159482959709 -19.6172212212476 589.457738261538 4.15948295970918 41 573 591.361330189768 -31.8101300073742 586.448799817607 18.3613301897675 42 573 591.586823979417 -28.9280901418061 583.341266162389 18.5868239794174 43 620 635.387296327774 24.3789711650554 580.233732507171 15.3872963277739 44 626 640.207736880395 34.7055252392002 577.086737880405 14.2077368803949 45 620 636.14433256941 29.9159241769509 573.939743253639 16.1443325694100 46 588 591.354900481598 13.9529578768212 570.692141641581 3.35490048159761 47 566 569.399400774283 -4.84394080380629 567.444540029523 3.39940077428287 48 557 552.507065145519 -1.46971856140716 562.962653415888 -4.49293485448072 49 561 562.327280050268 1.19195314747985 558.480766802252 1.32728005026797 50 549 547.882000161726 -3.69379264886424 553.811792487138 -1.11799983827382 51 532 528.639621947797 -13.7824401198209 549.142818172024 -3.36037805220303 52 526 526.898342131504 -19.6172212212476 544.718879089744 0.89834213150391 53 511 513.515189999911 -31.8101300073742 540.294940007463 2.51518999991072 54 499 490.481120777170 -28.9280901418061 536.446969364637 -8.5188792228305 55 555 553.022030113135 24.3789711650554 532.59899872181 -1.97796988686503 56 565 566.136592444616 34.7055252392002 529.157882316184 1.13659244461621 57 542 528.367309912492 29.9159241769509 525.716765910557 -13.6326900875084 58 527 517.490780441002 13.9529578768212 522.556261682177 -9.5092195589981 59 510 505.44818335001 -4.84394080380629 519.395757453796 -4.5518166499902 60 514 512.820535108731 -1.46971856140716 516.649183452677 -1.17946489126939 61 517 518.905437400964 1.19195314747985 513.902609451557 1.90543740096359 62 508 508.135293094875 -3.69379264886424 511.558499553989 0.135293094874783 63 493 490.568050463399 -13.7824401198209 509.214389656422 -2.43194953660139 64 490 492.689726278834 -19.6172212212476 506.927494942414 2.68972627883386 65 469 465.169529778969 -31.8101300073742 504.640600228405 -3.83047022103096 66 478 482.60186155960 -28.9280901418061 502.326228582206 4.60186155960037 67 528 531.609171898938 24.3789711650554 500.011856936006 3.60917189893837 68 534 535.606803495469 34.7055252392002 497.687671265331 1.60680349546885 69 518 510.720590228393 29.9159241769509 495.363485594656 -7.27940977160654 70 506 505.054074234971 13.9529578768212 492.992967888207 -0.945925765028676 71 502 518.221490622047 -4.84394080380629 490.622450181759 16.2214906220469 72 516 545.254851693819 -1.46971856140716 488.214866867588 29.2548516938194

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438782152dwjw3bgybtfe7g/1gnzy1243878181.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438782152dwjw3bgybtfe7g/1gnzy1243878181.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438782152dwjw3bgybtfe7g/2jfy21243878181.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438782152dwjw3bgybtfe7g/2jfy21243878181.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438782152dwjw3bgybtfe7g/3ppg41243878181.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438782152dwjw3bgybtfe7g/3ppg41243878181.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438782152dwjw3bgybtfe7g/42dym1243878181.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438782152dwjw3bgybtfe7g/42dym1243878181.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

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

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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Software written by Ed van Stee & Patrick Wessa

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