Home » date » 2010 » Nov » 28 »

Paper - Ontleden tijdreeks 25-50 jaar Loess (6)

*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, 28 Nov 2010 19:38:12 +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/Nov/28/t1290973024o5y7odpgkvdqlm8.htm/, Retrieved Sun, 28 Nov 2010 20:37:09 +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/Nov/28/t1290973024o5y7odpgkvdqlm8.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 «

376.974 377.632 378.205 370.861 369.167 371.551 382.842 381.903 384.502 392.058 384.359 388.884 386.586 387.495 385.705 378.67 377.367 376.911 389.827 387.82 387.267 380.575 372.402 376.74 377.795 376.126 370.804 367.98 367.866 366.121 379.421 378.519 372.423 355.072 344.693 342.892 344.178 337.606 327.103 323.953 316.532 306.307 327.225 329.573 313.761 307.836 300.074 304.198 306.122 300.414 292.133 290.616 280.244 285.179 305.486 305.957 293.886 289.441 288.776 299.149 306.532 309.914 313.468 314.901 309.16 316.15 336.544 339.196 326.738 320.838 318.62 331.533 335.378

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	'George Udny Yule' @ 72.249.76.132

Seasonal Decomposition by Loess - Parameters
Component	Window	Degree	Jump
Seasonal	6	0	1
Trend	25	1	3
Low-pass	13	1	2

Seasonal Decomposition by Loess - Time Series Components
t	Observed	Fitted	Seasonal	Trend	Remainder
1	376.974	376.852841184103	1.39666215551412	375.698496660383	-0.121158815897445
2	377.632	377.652504996997	1.18998682196537	376.421508181038	0.0205049969970901
3	378.205	380.746717714999	-1.48123741669063	377.144519701692	2.54171771499887
4	370.861	369.968599135819	-6.1141303581653	377.867531222346	-0.892400864180672
5	369.167	367.111941419978	-7.31788517761092	378.539943757633	-2.05505858002232
6	371.551	371.234064615834	-7.34442090875446	379.212356292921	-0.316935384166129
7	382.842	378.118027671348	7.68120350044368	379.884768828208	-4.72397232865154
8	381.903	375.081984655238	8.25224580165441	380.471769543108	-6.82101534476243
9	384.502	381.923832389835	6.02139735215633	381.058770258008	-2.5781676101646
10	392.058	400.037281374092	2.43294765299928	381.645770972909	7.97928137409218
11	384.359	391.061330536261	-4.55047896364941	382.207148427388	6.70233053626146
12	388.884	395.193898905675	-0.194424787542112	382.768525881867	6.30989890567474
13	386.586	388.302669936854	1.53942672679881	383.329903336347	1.71666993685437
14	387.495	390.481568539271	1.16726920006560	383.341162260663	2.98656853927099
15	385.705	389.897128039618	-1.83954922459815	383.35242118498	4.19212803961818
16	378.67	379.928814311847	-5.952494421143	383.363680109297	1.25881431184649
17	377.367	379.213596445601	-7.30729016235381	382.827693716753	1.84659644560105
18	376.911	379.061004349502	-7.53071167371151	382.291707324209	2.15000434950241
19	389.827	389.727019397309	8.17125967102592	381.755720931665	-0.0999806026912324
20	387.82	385.705995986309	9.05740758540277	380.876596428288	-2.1140040136911
21	387.267	388.495218024254	6.04131005083486	379.997471924911	1.22821802425375
22	380.575	380.075095031988	1.95655754647763	379.118347421534	-0.499904968012061
23	372.402	371.567684662562	-4.81474381260356	378.051059150042	-0.834315337438227
24	376.74	376.817042993964	-0.320813872513268	376.983770878549	0.0770429939641417
25	377.795	377.842420414208	1.83109697873571	375.916482607057	0.0474204142077497
26	376.126	376.892506930430	1.09098004130532	374.268513028264	0.766506930430467
27	370.804	371.803314661848	-2.81585811131975	372.620543449472	0.999314661847848
28	367.98	370.558059008139	-5.57063287881822	370.972573870680	2.57805900813872
29	367.866	374.434809960277	-7.21577249658098	368.512962536304	6.56880996027701
30	366.121	374.069973454769	-7.88132465669714	366.053351201928	7.94897345476875
31	379.421	385.633188734011	9.61507139843627	363.593739867553	6.21218873401091
32	378.519	385.427804852678	11.293203595027	360.316991552295	6.90880485267792
33	372.423	381.509790306020	6.29596645694237	357.040243237037	9.08679030602036
34	355.072	356.081136964723	0.299368113497655	353.763494921780	1.00913696472281
35	344.693	345.358824089799	-5.8496985705883	349.876874480789	0.665824089799003
36	342.892	340.739947517666	-0.94620155746526	345.990254039799	-2.15205248233383
37	344.178	343.796320402158	2.45604599903296	342.103633598809	-0.38167959784181
38	337.606	336.2368104728	0.863323497079608	338.11186603012	-1.36918952719975
39	327.103	323.793065805356	-3.70716426678783	334.120098461431	-3.30993419464352
40	323.953	322.317167691408	-4.53949858415075	330.128330892743	-1.63583230859194
41	316.532	314.904498609133	-8.35667965792747	326.516181048794	-1.62750139086677
42	306.307	297.837777467816	-8.12780867266217	322.904031204846	-8.46922253218366
43	327.225	323.637724641526	11.5203939975769	319.291881360897	-3.58727535847424
44	329.573	329.37143346681	13.5496104506251	316.224956082565	-0.201566533189862
45	313.761	309.912849613437	4.45111958233082	313.158030804232	-3.84815038656268
46	307.836	308.293553688150	-2.71265921404871	310.091105525899	0.457553688149574
47	300.074	300.227383970857	-7.79076278732193	307.711378816465	0.153383970856737
48	304.198	304.739870446485	-1.67552255351604	305.331652107031	0.541870446484836
49	306.122	306.580204503371	2.71187009903231	302.951925397597	0.458204503370496
50	300.414	297.962658145620	1.39117363855984	301.47416821582	-2.45134185437962
51	292.133	286.796816722185	-2.52722775622684	299.996411034042	-5.33618327781545
52	290.616	285.869205888449	-3.15585974071365	298.518653852265	-4.7467941115512
53	280.244	270.711369557786	-8.39257657497088	298.169207017185	-9.5326304422137
54	285.179	279.841522686635	-7.30328286873933	297.819760182104	-5.33747731336501
55	305.486	301.012726910721	12.4889597422547	297.470313347024	-4.47327308927873
56	305.957	299.298031887502	14.2018962157057	298.414071896793	-6.65896811249843
57	293.886	285.089408335703	3.32476121773556	299.357830446561	-8.79659166429695
58	289.441	282.265972006695	-3.68556100302478	300.30158899633	-7.17502799330538
59	288.776	283.747179243298	-8.44487164022667	302.249692396929	-5.02882075670237
60	299.149	295.38079875964	-1.28059455716791	304.197795797528	-3.76820124036004
61	306.532	304.101985229258	2.81611557261548	306.145899198127	-2.43001477074239
62	309.914	309.783939120134	1.51295955279666	308.531101327070	-0.130060879866221
63	313.468	318.293411740831	-2.27371519684366	310.916303456012	4.82541174083138
64	314.901	319.290905071698	-2.79041065665266	313.301505584955	4.38990507169768
65	309.16	311.215967727038	-8.6208032073962	315.724835480358	2.05596772703819
66	316.15	321.331898080463	-7.18006345622393	318.148165375761	5.18189808046282
67	336.544	339.768442989961	12.7480617388747	320.571495271164	3.22444298996118
68	339.196	341.065266997502	14.3247096689275	323.002023333570	1.86926699750222
69	326.738	325.275502309456	2.76794629456754	325.432551395977	-1.46249769054413
70	320.838	317.89624404803	-4.08332350641307	327.863079458383	-2.9417559519697
71	318.62	315.669227870992	-8.67999865978614	330.250770788794	-2.95077212900833
72	331.533	331.554585706432	-1.12704782563754	332.638462119206	0.0215857064314946
73	335.378	332.839033513642	2.89081303674004	335.026153449618	-2.53896648635777

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290973024o5y7odpgkvdqlm8/1i2601290973088.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290973024o5y7odpgkvdqlm8/1i2601290973088.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290973024o5y7odpgkvdqlm8/2i2601290973088.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290973024o5y7odpgkvdqlm8/2i2601290973088.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290973024o5y7odpgkvdqlm8/3btn31290973088.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290973024o5y7odpgkvdqlm8/3btn31290973088.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290973024o5y7odpgkvdqlm8/4btn31290973088.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290973024o5y7odpgkvdqlm8/4btn31290973088.ps (open in new window)

Parameters (Session):

par1 = 12 ; par2 = 6 ; par3 = 0 ; par5 = 1 ; par7 = 1 ; par8 = FALSE ;

Parameters (R input):

par1 = 12 ; par2 = 6 ; 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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