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Aantal openstaande VDAB-vacatures: 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: Wed, 22 Dec 2010 18:09:02 +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/22/t1293041346cv8up5won95ytu8.htm/, Retrieved Wed, 22 Dec 2010 19:09:06 +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/22/t1293041346cv8up5won95ytu8.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:

» Textbox « » Textfile « » CSV «

27.951 29.781 32.914 33.488 35.652 36.488 35.387 35.676 34.844 32.447 31.068 29.010 29.812 30.951 32.974 32.936 34.012 32.946 31.948 30.599 27.691 25.073 23.406 22.248 22.896 25.317 26.558 26.471 27.543 26.198 24.725 25.005 23.462 20.780 19.815 19.761 21.454 23.899 24.939 23.580 24.562 24.696 23.785 23.812 21.917 19.713 19.282 18.788 21.453 24.482 27.474 27.264 27.349 30.632 29.429 30.084 26.290 24.379 23.335 21.346 21.106 24.514 28.353 30.805 31.348 34.556 33.855 34.787 32.529 29.998 29.257 28.155 30.466 35.704 39.327 39.351 42.234 43.630 43.722 43.121 37.985 37.135 34.646 33.026 35.087 38.846 42.013 43.908 42.868 44.423 44.167 43.636 44.382 42.142 43.452 36.912 42.413 45.344 44.873 47.510 49.554 47.369 45.998 48.140 48.441 44.928 40.454 38.661 37.246 36.843 36.424 37.594 38.144 38.737 34.560 36.080 33.508 35.462 33.374 32.110 35.533 35.532 37.903 36.763 40.399 44.164 44.496 43.110 etc...

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	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Seasonal Decomposition by Loess - Parameters
Component	Window	Degree	Jump
Seasonal	1311	0	132
Trend	19	1	2
Low-pass	13	1	2

Seasonal Decomposition by Loess - Time Series Components
t	Observed	Fitted	Seasonal	Trend	Remainder
1	27.951	27.4946532216489	-3.27134477734183	31.6786915556929	-0.456346778351111
2	29.781	28.6959423051780	-1.02208218022269	31.8881398750447	-1.08505769482197
3	32.914	32.7993235422317	0.931088263371945	32.0975881943964	-0.114676457768322
4	33.488	33.3153544345412	1.37959852932444	32.2810470361343	-0.172645565458772
5	35.652	36.2771139109043	2.56238021122342	32.4645058778723	0.625113910904297
6	36.488	36.9511995654082	3.40036639354699	32.6244340410449	0.463199565408161
7	35.387	35.7459215677261	2.24371622805647	32.7843622042174	0.358921567726107
8	35.676	36.1086523899609	2.31616278254882	32.9271848274903	0.432652389960921
9	34.844	36.1474755713929	0.470516977844029	33.0700074507631	1.30347557139287
10	32.447	33.2110649194411	-1.3849629549096	33.0678980354685	0.764064919441118
11	31.068	31.8223809466073	-2.75216956678121	33.0657886201739	0.754380946607341
12	29.01	30.0797027943594	-4.87326943009693	32.8135666357375	1.06970279435944
13	29.812	30.3340001260407	-3.27134477734183	32.5613446513011	0.522000126040716
14	30.951	30.8083878376383	-1.02208218022269	32.1156943425844	-0.142612162361740
15	32.974	33.3468677027603	0.931088263371945	31.6700440338678	0.372867702760299
16	32.936	33.4154702378051	1.37959852932444	31.0769312328705	0.479470237805074
17	34.012	34.9778013569034	2.56238021122342	30.4838184318732	0.965801356903356
18	32.946	32.6165712105756	3.40036639354699	29.8750623958774	-0.329428789424409
19	31.948	32.3859774120619	2.24371622805647	29.2663063598816	0.437977412061919
20	30.599	30.1752926052688	2.31616278254882	28.7065446121824	-0.423707394731206
21	27.691	26.7647001576728	0.470516977844029	28.1467828644832	-0.9262998423272
22	25.073	23.9101253916822	-1.3849629549096	27.6208375632274	-1.16287460831778
23	23.406	22.4692773048096	-2.75216956678121	27.0948922619716	-0.936722695190387
24	22.248	22.7831813956968	-4.87326943009693	26.5860880344001	0.535181395696839
25	22.896	22.9860609705132	-3.27134477734183	26.0772838068286	0.090060970513239
26	25.317	26.0323730504758	-1.02208218022269	25.6237091297469	0.715373050475815
27	26.558	27.0147772839629	0.931088263371945	25.1701344526652	0.456777283962886
28	26.471	26.7699504421061	1.37959852932444	24.7924510285694	0.298950442106115
29	27.543	28.1088521843029	2.56238021122342	24.4147676044737	0.565852184302862
30	26.198	24.8463042392444	3.40036639354699	24.1493293672086	-1.35169576075560
31	24.725	23.3223926420000	2.24371622805647	23.8838911299435	-1.40260735799997
32	25.005	23.9636457214876	2.31616278254882	23.7301914959636	-1.04135427851245
33	23.462	22.8769911601722	0.470516977844029	23.5764918619838	-0.585008839827779
34	20.78	19.4794245779142	-1.3849629549096	23.4655383769954	-1.30057542208585
35	19.815	19.0275846747741	-2.75216956678121	23.3545848920071	-0.787415325225925
36	19.761	21.1450298408636	-4.87326943009693	23.2502395892333	1.3840298408636
37	21.454	23.0334504908823	-3.27134477734183	23.1458942864595	1.57945049088231
38	23.899	25.7910502594933	-1.02208218022269	23.0290319207294	1.89205025949333
39	24.939	26.0347421816289	0.931088263371945	22.9121695549992	1.09574218162885
40	23.58	23.0186404156713	1.37959852932444	22.7617610550043	-0.561359584328734
41	24.562	23.9502672337672	2.56238021122342	22.6113525550094	-0.611732766232805
42	24.696	23.4806365751735	3.40036639354699	22.5109970312796	-1.21536342482655
43	23.785	22.9156422643938	2.24371622805647	22.4106415075497	-0.869357735606211
44	23.812	22.80952586238	2.31616278254882	22.4983113550712	-1.00247413761998
45	21.917	20.7775018195634	0.470516977844029	22.5859812025926	-1.13949818043662
46	19.713	17.9205008549678	-1.3849629549096	22.8904620999418	-1.79249914503223
47	19.282	18.1212265694901	-2.75216956678121	23.1949429972911	-1.16077343050987
48	18.788	18.8031488447357	-4.87326943009693	23.6461205853612	0.0151488447357266
49	21.453	22.0800466039105	-3.27134477734183	24.0972981734313	0.627046603910507
50	24.482	25.409095947951	-1.02208218022269	24.5769862322717	0.927095947951013
51	27.474	28.960237445516	0.931088263371945	25.0566742911120	1.48623744551601
52	27.264	27.7196180414967	1.37959852932444	25.4287834291789	0.455618041496681
53	27.349	26.3347272215309	2.56238021122342	25.8008925672457	-1.01427277846913
54	30.632	31.8989652794983	3.40036639354699	25.9646683269547	1.26696527949828
55	29.429	30.4858396852798	2.24371622805647	26.1284440866638	1.05683968527978
56	30.084	31.6652589966722	2.31616278254882	26.1865782207790	1.58125899667215
57	26.29	25.8647706672617	0.470516977844029	26.2447123548943	-0.425229332738343
58	24.379	23.7528861924726	-1.3849629549096	26.390076762437	-0.626113807527421
59	23.335	22.8867283968015	-2.75216956678121	26.5354411699797	-0.448271603198517
60	21.346	20.7216154493907	-4.87326943009693	26.8436539807062	-0.624384550609271
61	21.106	18.3314779859092	-3.27134477734183	27.1518667914327	-2.77452201409085
62	24.514	22.4435456741271	-1.02208218022269	27.6065365060956	-2.0704543258729
63	28.353	27.7137055158695	0.931088263371945	28.0612062207585	-0.639294484130456
64	30.805	31.5970376748876	1.37959852932444	28.6333637957880	0.79203767488761
65	31.348	30.9280984179592	2.56238021122342	29.2055213708174	-0.419901582040808
66	34.556	35.8490791161464	3.40036639354699	29.8625544903066	1.29307911614643
67	33.855	34.9466961621477	2.24371622805647	30.5195876097958	1.09169616214775
68	34.787	35.9800856152719	2.31616278254882	31.2777516021793	1.19308561527188
69	32.529	32.5515674275932	0.470516977844029	32.0359155945628	0.0225674275931524
70	29.998	28.5567230175878	-1.3849629549096	32.8242399373218	-1.44127698241221
71	29.257	27.6536052867004	-2.75216956678121	33.6125642800808	-1.60339471329958
72	28.155	26.7604542934808	-4.87326943009693	34.4228151366162	-1.39454570651922
73	30.466	28.9702787841903	-3.27134477734183	35.2330659931515	-1.49572121580968
74	35.704	36.4424389445552	-1.02208218022269	35.9876432356675	0.73843894455522
75	39.327	40.9806912584446	0.931088263371945	36.7422204781834	1.65369125844462
76	39.351	39.9800657261394	1.37959852932444	37.3423357445362	0.629065726139366
77	42.234	43.9631687778876	2.56238021122342	37.9424510108890	1.72916877788763
78	43.63	45.540723062682	3.40036639354699	38.318910543771	1.91072306268200
79	43.722	46.5049136952905	2.24371622805647	38.6953700766531	2.78291369529045
80	43.121	45.0145337200102	2.31616278254882	38.911303497441	1.89353372001017
81	37.985	36.372246103927	0.470516977844029	39.1272369182290	-1.61275389607298
82	37.135	36.3888340994395	-1.3849629549096	39.2661288554701	-0.746165900560456
83	34.646	32.6391487740700	-2.75216956678121	39.4050207927112	-2.00685122592996
84	33.026	31.3910212571088	-4.87326943009693	39.5342481729881	-1.63497874289121
85	35.087	33.7818692240767	-3.27134477734183	39.6634755532651	-1.30513077592328
86	38.846	38.7887403842079	-1.02208218022269	39.9253417960148	-0.0572596157921268
87	42.013	42.9077036978635	0.931088263371945	40.1872080387645	0.894703697863534
88	43.908	45.7796910794553	1.37959852932444	40.6567103912202	1.87169107945535
89	42.868	42.0474070451007	2.56238021122342	41.1262127436759	-0.820592954899332
90	44.423	43.8123509129309	3.40036639354699	41.6332826935221	-0.610649087069064
91	44.167	43.9499311285753	2.24371622805647	42.1403526433682	-0.217068871424701
92	43.636	42.3735669527793	2.31616278254882	42.5822702646719	-1.26243304722073
93	44.382	45.2692951361804	0.470516977844029	43.0241878859756	0.887295136180363
94	42.142	42.2462986938028	-1.3849629549096	43.4226642611068	0.104298693802775
95	43.452	45.8350289305432	-2.75216956678121	43.8211406362380	2.38302893054316
96	36.912	34.5428439622502	-4.87326943009693	44.1544254678467	-2.36915603774977
97	42.413	43.6096344778865	-3.27134477734183	44.4877102994553	1.19663447788650
98	45.344	46.9661997224578	-1.02208218022269	44.7438824577649	1.62219972245782
99	44.873	43.8148571205536	0.931088263371945	45.0000546160744	-1.05814287944637
100	47.51	48.5097477462982	1.37959852932444	45.1306537243773	0.999747746298219
101	49.554	51.2843669560963	2.56238021122342	45.2612528326803	1.73036695609632
102	47.369	46.1959912751579	3.40036639354699	45.1416423312952	-1.17300872484215
103	45.998	44.7302519420335	2.24371622805647	45.0220318299101	-1.26774805796653
104	48.14	49.4516934514088	2.31616278254882	44.5121437660424	1.31169345140876
105	48.441	52.4092273199812	0.470516977844029	44.0022557021748	3.96822731998120
106	44.928	48.0364114763526	-1.3849629549096	43.204551478557	3.10841147635263
107	40.454	41.2533223118420	-2.75216956678121	42.4068472549392	0.799322311842047
108	38.661	40.7729085772384	-4.87326943009693	41.4223608528585	2.11190857723838
109	37.246	37.3254703265639	-3.27134477734183	40.4378744507779	0.0794703265639143
110	36.843	35.3435059891765	-1.02208218022269	39.3645761910462	-1.49949401082351
111	36.424	33.6256338053135	0.931088263371945	38.2912779313145	-2.79836619468646
112	37.594	36.4074694988072	1.37959852932444	37.4009319718683	-1.18653050119277
113	38.144	37.2150337763544	2.56238021122342	36.5105860124222	-0.928966223645574
114	38.737	37.9909639663133	3.40036639354699	36.0826696401397	-0.746036033686714
115	34.56	31.2215305040862	2.24371622805647	35.6547532678573	-3.33846949591377
116	36.08	34.2439707622908	2.31616278254882	35.5998664551604	-1.83602923770917
117	33.508	31.0005033796926	0.470516977844029	35.5449796424634	-2.50749662030742
118	35.462	36.5896601974299	-1.3849629549096	35.7193027574797	1.12766019742993
119	33.374	33.6065436942853	-2.75216956678121	35.893625872496	0.232543694285248
120	32.11	32.7682007177203	-4.87326943009693	36.3250687123767	0.658200717720263
121	35.533	37.5808332250845	-3.27134477734183	36.7565115522574	2.04783322508447
122	35.532	34.6319432327838	-1.02208218022269	37.4541389474389	-0.900056767216192
123	37.903	36.7231453940076	0.931088263371945	38.1517663426204	-1.17985460599235
124	36.763	33.1574733745145	1.37959852932444	38.988928096161	-3.60552662548546
125	40.399	38.4095299390750	2.56238021122342	39.8260898497016	-1.98947006092504
126	44.164	44.2558665153299	3.40036639354699	40.6717670911231	0.0918665153298974
127	44.496	45.2308394393989	2.24371622805647	41.5174443325446	0.734839439398925
128	43.11	41.5132488073339	2.31616278254882	42.3905884101173	-1.59675119266612
129	43.88	44.025750534466	0.470516977844029	43.26373248769	0.145750534465989
130	43.93	45.069215629344	-1.3849629549096	44.1757473255656	1.13921562934398
131	44.327	46.3184074033399	-2.75216956678121	45.0877621634413	1.99140740333994

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293041346cv8up5won95ytu8/1lx721293041337.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293041346cv8up5won95ytu8/1lx721293041337.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293041346cv8up5won95ytu8/2lx721293041337.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293041346cv8up5won95ytu8/2lx721293041337.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293041346cv8up5won95ytu8/3v6651293041337.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293041346cv8up5won95ytu8/3v6651293041337.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293041346cv8up5won95ytu8/4v6651293041337.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293041346cv8up5won95ytu8/4v6651293041337.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')

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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