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Paper statistiek: werkloosheid - 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, 26 Dec 2010 18:24:41 +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/26/t1293387981qhu75xv0o0chr45.htm/, Retrieved Sun, 26 Dec 2010 19:26:21 +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/26/t1293387981qhu75xv0o0chr45.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 «

493 514 522 490 484 506 501 462 465 454 464 427 460 473 465 422 415 413 420 363 376 380 384 346 389 407 393 346 348 353 364 305 307 312 312 286 324 336 327 302 299 311 315 264 278 278 287 279 324 354 354 360 363 385 412 370 389 395 417 404

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	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	493	480.818230902225	9.00162999748834	496.180139100287	-12.1817690977751
2	514	504.680140347835	29.7643269514014	493.555532700764	-9.31985965216535
3	522	525.94205240748	27.1270212912793	490.930926301241	3.94205240747965
4	490	491.662097950526	0.301846850505914	488.036055198968	1.66209795052595
5	484	483.382129116384	-0.523313213078847	485.141184096695	-0.61787088361649
6	506	517.825780203494	12.1543793146487	482.019840481857	11.8257802034944
7	501	501.269423840608	21.8320792923734	478.898496867019	0.269423840608113
8	462	475.876649257514	-27.4751000466452	475.598450789131	13.8766492575138
9	465	474.683868008634	-16.9822727198782	472.298404711244	9.68386800863397
10	454	456.726657374498	-16.1842799895038	467.457622615006	2.72665737449779
11	464	472.56945654299	-7.1862970617575	462.616840518768	8.56945654298977
12	427	430.176734517691	-31.8300260244715	455.653291506781	3.17673451769053
13	460	462.308627507718	9.00162999748834	448.689742494794	2.30862750771752
14	473	475.182720107169	29.7643269514014	441.052952941429	2.18272010716925
15	465	469.456815320656	27.1270212912793	433.416163388064	4.45681532065618
16	422	417.478822971285	0.301846850505914	426.219330178209	-4.52117702871493
17	415	411.500816244725	-0.523313213078847	419.022496968353	-3.49918375527466
18	413	401.218936839048	12.1543793146487	412.626683846303	-11.7810631609519
19	420	411.937049983374	21.8320792923734	406.230870724253	-8.06295001662636
20	363	352.952128596696	-27.4751000466452	400.52297144995	-10.0478714033044
21	376	374.167200544232	-16.9822727198782	394.815072175646	-1.83279945576811
22	380	386.745184002376	-16.1842799895038	389.439095987128	6.7451840023756
23	384	391.123177263147	-7.1862970617575	384.06311979861	7.1231772631474
24	346	344.949862429613	-31.8300260244715	378.880163594859	-1.05013757038745
25	389	395.301162611404	9.00162999748834	373.697207391108	6.30116261140398
26	407	415.966416741832	29.7643269514014	368.269256306766	8.96641674183223
27	393	396.031673486296	27.1270212912793	362.841305222425	3.03167348629563
28	346	334.609198693265	0.301846850505914	357.088954456229	-11.390801306735
29	348	345.186709523046	-0.523313213078847	351.336603690033	-2.81329047695425
30	353	348.11330853846	12.1543793146487	345.732312146891	-4.88669146153978
31	364	366.039900103878	21.8320792923734	340.128020603749	2.03990010387753
32	305	302.498470097238	-27.4751000466452	334.976629949407	-2.50152990276194
33	307	301.157033424813	-16.9822727198782	329.825239295065	-5.84296657518695
34	312	314.790843368745	-16.1842799895038	325.393436620759	2.79084336874524
35	312	310.224663115306	-7.1862970617575	320.961633946452	-1.77533688469447
36	286	286.723896936261	-31.8300260244715	317.10612908821	0.723896936261156
37	324	325.747745772543	9.00162999748834	313.250624229969	1.74774577254306
38	336	332.307479169094	29.7643269514014	309.928193879505	-3.69252083090595
39	327	320.26721517968	27.1270212912793	306.605763529040	-6.73278482031981
40	302	299.570618966688	0.301846850505914	304.127534182806	-2.42938103331238
41	299	296.874008376506	-0.523313213078847	301.649304836572	-2.12599162349363
42	311	309.134005159677	12.1543793146487	300.711615525674	-1.86599484032263
43	315	308.393994492851	21.8320792923734	299.773926214775	-6.60600550714878
44	264	254.433114810935	-27.4751000466452	301.04198523571	-9.56688518906486
45	278	270.672228463234	-16.9822727198782	302.310044256645	-7.32777153676648
46	278	265.902654006528	-16.1842799895038	306.281625982976	-12.0973459934719
47	287	270.933089352451	-7.1862970617575	310.253207709307	-16.0669106475493
48	279	272.916242463392	-31.8300260244715	316.913783561079	-6.083757536608
49	324	315.424010589660	9.00162999748834	323.574359412852	-8.57598941034047
50	354	345.765151955643	29.7643269514014	332.470521092956	-8.23484804435725
51	354	339.506295935661	27.1270212912793	341.366682773060	-14.4937040643389
52	360	367.892328276689	0.301846850505914	351.805824872805	7.89232827668877
53	363	364.278346240528	-0.523313213078847	362.244966972551	1.27834624052775
54	385	385.066107562784	12.1543793146487	372.779513122568	0.0661075627836567
55	412	418.853861435042	21.8320792923734	383.314059272584	6.85386143504235
56	370	373.520776035701	-27.4751000466452	393.954324010944	3.52077603570126
57	389	390.387683970575	-16.9822727198782	404.594588749304	1.38768397057464
58	395	390.921088565861	-16.1842799895038	415.263191423643	-4.07891143413946
59	417	415.254502963775	-7.1862970617575	425.931794097983	-1.74549703622546
60	404	403.243438185595	-31.8300260244715	436.586587838876	-0.756561814404733

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293387981qhu75xv0o0chr45/1pg241293387876.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293387981qhu75xv0o0chr45/1pg241293387876.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293387981qhu75xv0o0chr45/2pg241293387876.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293387981qhu75xv0o0chr45/2pg241293387876.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293387981qhu75xv0o0chr45/3i71p1293387876.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293387981qhu75xv0o0chr45/3i71p1293387876.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293387981qhu75xv0o0chr45/4i71p1293387876.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293387981qhu75xv0o0chr45/4i71p1293387876.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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