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Paper: faillissementen 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 18:30:15 +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/t1293388110u2ptc9upesc5102.htm/, Retrieved Sun, 26 Dec 2010 19:28:34 +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/t1293388110u2ptc9upesc5102.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 «

797 840 988 819 831 904 814 798 828 789 930 744 832 826 907 776 835 715 729 733 736 712 711 667 799 661 692 649 729 622 671 635 648 745 624 477 710 515 461 590 415 554 585 513 591 561 684 668 795 776 1043 964 762 1030 939 779 918 839 874 840

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	'Gwilym Jenkins' @ 72.249.127.135

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	797	712.746156799127	47.825417874594	833.42842532628	-84.2538432008733
2	840	861.724651528597	-16.099269625205	834.374618096608	21.7246515285973
3	988	1063.10317448963	77.5760146434371	835.320810866936	75.1031744896267
4	819	781.982577691397	19.9937815776385	836.023640730964	-37.0174223086027
5	831	849.461978801919	-24.1884493969109	836.726470594992	18.4619788019186
6	904	945.423442381555	25.3479888751701	837.228568743275	41.4234423815551
7	814	783.384725331435	6.88460777700814	837.730666891557	-30.6152746685651
8	798	810.337517610217	-51.8616976125129	837.524180002296	12.3375176102165
9	828	820.690367789851	-2.00806090288690	837.317693113036	-7.30963221014883
10	789	764.328198088699	-20.0533055737423	833.725107485044	-24.6718019113015
11	930	1017.56613922262	12.3013389203291	830.132521857052	87.5661392226189
12	744	740.262079554157	-75.7184193711462	823.45633981699	-3.73792044584343
13	832	799.394424348479	47.825417874594	816.780157776927	-32.6055756515212
14	826	859.756879277821	-16.099269625205	808.342390347384	33.7568792778212
15	907	936.519362438723	77.5760146434371	799.90462291784	29.5193624387226
16	776	741.932509525646	19.9937815776385	790.073708896715	-34.0674904743538
17	835	913.94565452132	-24.1884493969109	780.24279487559	78.9456545213204
18	715	634.135219479075	25.3479888751701	770.516791645755	-80.8647805209255
19	729	690.324603807072	6.88460777700814	760.79078841592	-38.6753961929281
20	733	767.71310251613	-51.8616976125129	750.148595096383	34.7131025161297
21	736	734.50165912604	-2.00806090288690	739.506401776846	-1.49834087395959
22	712	715.188956072798	-20.0533055737423	728.864349500944	3.18895607279842
23	711	691.47636385463	12.3013389203291	718.222297225041	-19.5236361453706
24	667	700.348217525064	-75.7184193711462	709.370201846082	33.348217525064
25	799	849.656475658283	47.825417874594	700.518106467123	50.6564756582832
26	661	644.244307212295	-16.099269625205	693.85496241291	-16.7556927877046
27	692	619.232166997867	77.5760146434371	687.191818358696	-72.7678330021334
28	649	596.712719567113	19.9937815776385	681.293498855249	-52.2872804328872
29	729	806.79327004511	-24.1884493969109	675.395179351801	77.7932700451097
30	622	550.414493572832	25.3479888751701	668.237517551998	-71.5855064271682
31	671	674.035536470797	6.88460777700814	661.079855752195	3.03553647079696
32	635	671.825378083024	-51.8616976125129	650.036319529489	36.8253780830239
33	648	659.015277596104	-2.00806090288690	638.992783306783	11.0152775961038
34	745	885.289241562326	-20.0533055737423	624.764064011416	140.289241562326
35	624	625.163316363621	12.3013389203291	610.53534471605	1.16331636362111
36	477	433.743265255534	-75.7184193711462	595.975154115612	-43.256734744466
37	710	790.759618610231	47.825417874594	581.414963515175	80.7596186102313
38	515	475.632535925924	-16.099269625205	570.466733699281	-39.3674640740763
39	461	284.905481473175	77.5760146434371	559.518503883388	-176.094518526825
40	590	604.299980791732	19.9937815776385	555.70623763063	14.2999807917320
41	415	302.29447801904	-24.1884493969109	551.893971377871	-112.705521980960
42	554	520.963355328479	25.3479888751701	561.68865579635	-33.0366446715207
43	585	591.632052008162	6.88460777700814	571.48334021483	6.63205200816196
44	513	480.930790935153	-51.8616976125129	596.93090667736	-32.0692090648471
45	591	561.629587762997	-2.00806090288690	622.37847313989	-29.3704122370032
46	561	485.01159830119	-20.0533055737423	657.041707272552	-75.9884016988099
47	684	663.993719674457	12.3013389203291	691.704941405214	-20.0062803255435
48	668	685.358696200341	-75.7184193711462	726.359723170806	17.3586962003405
49	795	781.160077189009	47.825417874594	761.014504936397	-13.8399228109909
50	776	777.787017127957	-16.099269625205	790.312252497248	1.78701712795657
51	1043	1188.81398529846	77.5760146434371	819.6100000581	145.813985298463
52	964	1073.83533700516	19.9937815776385	834.170881417201	109.835337005160
53	762	699.456686620608	-24.1884493969109	848.731762776303	-62.5433133793916
54	1030	1172.19923445834	25.3479888751701	862.452776666495	142.199234458335
55	939	994.941601666305	6.88460777700814	876.173790556687	55.941601666305
56	779	721.43666751386	-51.8616976125129	888.425030098653	-57.5633324861406
57	918	937.331791262267	-2.00806090288690	900.67626964062	19.3317912622670
58	839	787.120689881116	-20.0533055737423	910.932615692626	-51.8793101188838
59	874	814.509699335038	12.3013389203291	921.188961744632	-59.4903006649616
60	840	825.708022960949	-75.7184193711462	930.010396410197	-14.2919770390507

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293388110u2ptc9upesc5102/1xxif1293388212.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293388110u2ptc9upesc5102/1xxif1293388212.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293388110u2ptc9upesc5102/2xxif1293388212.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293388110u2ptc9upesc5102/2xxif1293388212.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293388110u2ptc9upesc5102/377hi1293388212.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293388110u2ptc9upesc5102/377hi1293388212.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293388110u2ptc9upesc5102/477hi1293388212.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293388110u2ptc9upesc5102/477hi1293388212.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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