Home » date » 2010 » Dec » 27 »

LOESS Werkloosheid België 2000 - 2010

*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: Mon, 27 Dec 2010 20:06:09 +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/Dec/27/t1293480303ul1taknxnrcufpp.htm/, Retrieved Mon, 27 Dec 2010 21:05:07 +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/Dec/27/t1293480303ul1taknxnrcufpp.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:
Data Paper Statistiek
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
464 460 467 460 448 443 436 431 484 510 513 503 471 471 476 475 470 461 455 456 517 525 523 519 509 512 519 517 510 509 501 507 569 580 578 565 547 555 562 561 555 544 537 543 594 611 613 611 594 595 591 589 584 573 567 569 621 629 628 612 595 597 593 590 580 574 573 573 620 626 620 588 566 557 561 549 532 526 511 499 555 565 542 527 510 514 517 508 493 490 469 478 528 534 518 506 502 516 528 533 536 537 524 536 587 597 581 564 558 575 580 575 563 552 537 545 601 604 586 564 549
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal12110122
Trend1912
Low-pass1312


Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
1464472.39118155618-4.88540625077001460.4942246945898.39118155618053
2460460.189303700079-1.82317134286365461.6338676427850.189303700078597
3467469.9214267527751.30506265624482462.7735105909812.92142675277455
4460459.179009699465-3.19779482071541464.01878512125-0.820990300534675
5448443.336592538571-12.6006521900907465.264059651519-4.66340746142879
6443438.820081165493-19.3611453964424466.541064230949-4.17991883450679
7436434.003567712426-29.8216365228043467.818068810379-1.99643228757441
8431420.732273568545-27.7830468667714469.050773298227-10.2677264314551
9484472.26097978328225.4555424306439470.283477786074-11.7390202167182
10510513.15855157981835.119009443292471.7224389768903.15855157981753
11513526.45612075843926.3824790738543473.16140016770713.4561207584391
12503519.94311652907511.2107552613780474.84612820954716.9431165290749
13471470.354549999383-4.88540625077001476.530856251387-0.645450000617359
14471465.654291417655-1.82317134286365478.168879925209-5.34570858234514
15476470.8880337447251.30506265624482479.80690359903-5.11196625527509
16475471.97783926897-3.19779482071541481.219955551746-3.02216073103034
17470469.967644685629-12.6006521900907482.633007504461-0.0323553143705340
18461456.757403263254-19.3611453964424484.603742133188-4.24259673674590
19455453.247159760889-29.8216365228043486.574476761915-1.75284023911098
20456450.055844098537-27.7830468667714489.727202768234-5.94415590146298
21517515.66452879480325.4555424306439492.879928774553-1.33547120519739
22525518.31751497885235.119009443292496.563475577856-6.68248502114761
23523519.37049854498826.3824790738543500.247022381158-3.62950145501196
24519522.61916569850711.2107552613780504.1700790401153.61916569850706
25509514.792270551698-4.88540625077001508.0931356990725.79227055169787
26512513.545320289436-1.82317134286365512.2778510534271.54532028943629
27519520.2323709359731.30506265624482516.4625664077831.23237093597254
28517516.518536437012-3.19779482071541520.679258383703-0.481463562987983
29510507.704701830467-12.6006521900907524.895950359624-2.29529816953345
30509508.547026016782-19.3611453964424528.81411937966-0.45297398321793
31501499.089348123108-29.8216365228043532.732288399697-1.91065187689219
32507505.378769252153-27.7830468667714536.404277614618-1.62123074784699
33569572.46819073981625.4555424306439540.076266829543.46819073981567
34580581.21391731815135.119009443292543.6670732385571.21391731815129
35578582.35964127857326.3824790738543547.2578796475734.35964127857289
36565568.30260390153911.2107552613780550.4866408370833.3026039015391
37547545.170004224177-4.88540625077001553.715402026593-1.82999577582291
38555555.406728543308-1.82317134286365556.4164427995560.406728543308191
39562563.5774537712371.30506265624482559.1174835725181.57745377123706
40561563.448569585617-3.19779482071541561.7492252350982.44856958561741
41555558.219685292413-12.6006521900907564.3809668976783.21968529241269
42544539.801314151122-19.3611453964424567.55983124532-4.1986858488782
43537533.082940929841-29.8216365228043570.738695592963-3.91705907015887
44543539.760293901557-27.7830468667714574.022752965215-3.23970609844309
45594585.2376472318925.4555424306439577.306810337466-8.76235276810962
46611606.69510848470535.119009443292580.185882072003-4.30489151529491
47613616.55256711960626.3824790738543583.064953806543.55256711960567
48611625.156343738211.2107552613780585.63290100042214.1563437381996
49594604.684558056465-4.88540625077001588.20084819430510.6845580564652
50595601.531060774981-1.82317134286365590.2921105678836.53106077498092
51591588.3115644022941.30506265624482592.383372941461-2.68843559770562
52589587.586789768086-3.19779482071541593.61100505263-1.41321023191426
53584585.762015026292-12.6006521900907594.8386371637991.76201502629203
54573570.014759109419-19.3611453964424595.346386287024-2.98524089058117
55567567.967501112556-29.8216365228043595.8541354102490.967501112555851
56569569.648849755218-27.7830468667714596.1341971115530.648849755218066
57621620.13019875649825.4555424306439596.414258812858-0.869801243502138
58629626.35749713486235.119009443292596.523493421846-2.64250286513845
59628632.98479289531126.3824790738543596.6327280308354.9847928953111
60612616.12861257523611.2107552613780596.6606321633864.12861257523616
61595598.196869954833-4.88540625077001596.6885362959373.19686995483301
62597599.108621491816-1.82317134286365596.7145498510482.10862149181605
63593587.9543739375971.30506265624482596.740563406158-5.04562606240313
64590586.858624302273-3.19779482071541596.339170518442-3.14137569772674
65580576.662874559365-12.6006521900907595.937777630726-3.33712544063542
66574572.84331831915-19.3611453964424594.517827077292-1.15668168084983
67573582.723759998946-29.8216365228043593.0978765238589.72375999894598
68573583.212900088565-27.7830468667714590.57014677820610.2129000885651
69620626.50204053680225.4555424306439588.0424170325546.50204053680181
70626632.47562268247635.119009443292584.4053678742326.47562268247646
71620632.84920221023726.3824790738543580.76831871590912.8492022102370
72588588.78862003016111.2107552613780576.0006247084610.788620030161383
73566565.652475549758-4.88540625077001571.232930701012-0.347524450242418
74557550.163279265907-1.82317134286365565.659892076956-6.83672073409264
75561560.6080838908551.30506265624482560.0868534529-0.3919161091452
76549546.711577871465-3.19779482071541554.486216949251-2.28842212853533
77532527.71507174449-12.6006521900907548.885580445601-4.28492825551041
78526527.531706363858-19.3611453964424543.8294390325841.53170636385789
79511513.048338903237-29.8216365228043538.7732976195682.04833890323653
80499491.143785625552-27.7830468667714534.63926124122-7.85621437444809
81555554.03923270648525.4555424306439530.505224862871-0.960767293515119
82565567.84840105063935.119009443292527.0325895060692.84840105063904
83542534.05756677687926.3824790738543523.559954149267-7.94243322312093
84527522.21278333231811.2107552613780520.576461406304-4.78721666768217
85510507.292437587428-4.88540625077001517.592968663342-2.70756241257163
86514514.690807227291-1.82317134286365515.1323641155730.690807227290975
87517520.0231777759511.30506265624482512.6717595678043.02317777595147
88508508.6308765428-3.19779482071541510.5669182779150.630876542799967
89493490.138575202064-12.6006521900907508.462076988027-2.86142479793642
90490492.438642142274-19.3611453964424506.9225032541682.43864214227449
91469462.438707002496-29.8216365228043505.382929520309-6.56129299750421
92478478.60482076577-27.7830468667714505.1782261010010.604820765770341
93528525.57093488766225.4555424306439504.973522681694-2.42906511233753
94534525.99918898091335.119009443292506.881801575795-8.00081101908722
95518500.82744045624926.3824790738543508.790080469897-17.1725595437510
96506488.05859770057411.2107552613780512.730647038048-17.9414022994259
97502492.214192644571-4.88540625077001516.671213606199-9.78580735542897
98516511.864292048209-1.82317134286365521.958879294654-4.13570795179078
99528527.4483923606451.30506265624482527.24654498311-0.551607639354756
100533536.312201479235-3.19779482071541532.885593341483.31220147923523
101536546.07601049024-12.6006521900907538.5246416998510.0760104902400
102537549.792479583077-19.3611453964424543.56866581336612.7924795830767
103524529.208946595924-29.8216365228043548.6126899268815.20894659592363
104536547.097460070462-27.7830468667714552.68558679630911.0974600704621
105587591.78597390361825.4555424306439556.7584836657384.78597390361824
106597599.12410450052435.119009443292559.7568860561842.12410450052357
107581572.86223247951526.3824790738543562.755288446631-8.13776752048511
108564552.21493389076411.2107552613780564.574310847858-11.7850661092363
109558554.492073001684-4.88540625077001566.393333249086-3.50792699831561
110575584.19738910233-1.82317134286365567.6257822405349.19738910232979
111580589.8367061117731.30506265624482568.8582312319829.83670611177308
112575584.06005876519-3.19779482071541569.1377360555269.06005876518986
113563569.183411311022-12.6006521900907569.4172408790696.18341131102159
114552554.756639038768-19.3611453964424568.6045063576752.75663903876773
115537536.029864686524-29.8216365228043567.79177183628-0.970135313475794
116545550.961173945125-27.7830468667714566.8218729216465.96117394512544
117601610.69248356234425.4555424306439565.8519740070129.69248356234436
118604608.17698327085335.119009443292564.7040072858554.17698327085282
119586582.06148036144726.3824790738543563.556040564699-3.93851963855286
120564554.60693413491611.2107552613780562.182310603706-9.39306586508394
121549542.076825608057-4.88540625077001560.808580642713-6.92317439194335
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/27/t1293480303ul1taknxnrcufpp/1ofo21293480365.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/27/t1293480303ul1taknxnrcufpp/1ofo21293480365.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/27/t1293480303ul1taknxnrcufpp/2ofo21293480365.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/27/t1293480303ul1taknxnrcufpp/2ofo21293480365.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/27/t1293480303ul1taknxnrcufpp/3hp551293480365.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/27/t1293480303ul1taknxnrcufpp/3hp551293480365.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/27/t1293480303ul1taknxnrcufpp/4hp551293480365.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/27/t1293480303ul1taknxnrcufpp/4hp551293480365.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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