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prijsindex van de grondstoffen

*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: Sat, 05 Dec 2009 02:15:37 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/05/t1260004605jyvu6kp43lqbca2.htm/, Retrieved Sat, 05 Dec 2009 10:16:50 +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/2009/Dec/05/t1260004605jyvu6kp43lqbca2.htm/},
    year = {2009},
}
@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 = {2009},
    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 «

226.9 235.9 216.2 226.2 198.3 176.7 166.2 157.6 163.4 159.7 191.0 239.4 321.9 362.7 413.6 407.1 383.2 347.7 333.8 312.3 295.4 283.3 287.6 265.7 250.2 234.7 244.0 231.2 223.8 223.5 210.5 201.6 190.7 207.5 198.8 196.6 204.2 227.4 229.7 217.9 221.4 216.3 197.0 193.8 196.8 180.5 174.8 181.6 190.0 190.6 179.0 174.1 161.1 168.6 169.4 152.2 148.3 137.7 145.0 153.4

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	2 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	226.9	264.741832188982	17.2474663692209	171.810701441797	37.8418321889818
2	235.9	266.984466538638	28.0506694377768	176.764864023585	31.0844665386383
3	216.2	217.207085125667	33.4738882689606	181.719026605373	1.00708512566683
4	226.2	237.401444921454	27.0883237348385	187.910231343707	11.2014449214544
5	198.3	190.335791555581	12.1627723623776	194.101436082042	-7.96420844441931
6	176.7	151.053874071938	0.926110161893321	201.420015766168	-25.6461259280617
7	166.2	134.151960091103	-10.4905555413983	208.738595450295	-32.0480399088968
8	157.6	119.516274641975	-21.1284565884058	216.812181946431	-38.0837253580249
9	163.4	126.38059554495	-24.4663639875165	224.885768442566	-37.0194044550499
10	159.7	109.831424233001	-28.3649926793949	237.933568446394	-49.8685757669986
11	191	152.402269886137	-21.3836383363578	250.981368450221	-38.5977301138627
12	239.4	224.151111928491	-13.1151675322482	267.764055603757	-15.2488880715090
13	321.9	342.005790873485	17.2474663692209	284.546742757294	20.1057908734850
14	362.7	398.454218295084	28.0506694377768	298.895112267139	35.7542182950843
15	413.6	480.482629954056	33.4738882689606	313.243481776984	66.8826299540556
16	407.1	464.896422499133	27.0883237348385	322.215253766029	57.7964224991326
17	383.2	423.050201882548	12.1627723623776	331.187025755074	39.8502018825484
18	347.7	363.424140541716	0.926110161893321	331.049749296391	15.7241405417158
19	333.8	347.178082703691	-10.4905555413983	330.912472837708	13.3780827036906
20	312.3	324.508822999452	-21.1284565884058	321.219633588954	12.2088229994523
21	295.4	303.739569647317	-24.4663639875165	311.526794340199	8.33956964731715
22	283.3	297.243278878601	-28.3649926793949	297.721713800794	13.9432788786013
23	287.6	312.66700507497	-21.3836383363578	283.916633261388	25.0670050749699
24	265.7	273.11393180399	-13.1151675322482	271.401235728258	7.41393180398978
25	250.2	224.26669543565	17.2474663692209	258.885838195129	-25.9333045643498
26	234.7	192.389104208222	28.0506694377768	248.960226354001	-42.3108957917777
27	244	215.491497218167	33.4738882689606	239.034614512873	-28.5085027818334
28	231.2	203.190447049224	27.0883237348385	232.121229215937	-28.0095529507759
29	223.8	210.229383718620	12.1627723623776	225.207843919002	-13.5706162813798
30	223.5	224.748357194022	0.926110161893321	221.325532644085	1.24835719402213
31	210.5	214.047334172231	-10.4905555413983	217.443221369167	3.54733417223144
32	201.6	208.589345788736	-21.1284565884058	215.739110799669	6.9893457887365
33	190.7	191.831363757345	-24.4663639875165	214.035000230172	1.13136375734476
34	207.5	230.650575211749	-28.3649926793949	212.714417467646	23.1505752117492
35	198.8	207.589803631238	-21.3836383363578	211.393834705120	8.78980363123813
36	196.6	196.190359570189	-13.1151675322482	210.124807962059	-0.40964042981102
37	204.2	182.296752411780	17.2474663692209	208.855781218999	-21.9032475882196
38	227.4	218.822601280841	28.0506694377768	207.926729281382	-8.57739871915928
39	229.7	218.928434387273	33.4738882689606	206.997677343766	-10.7715656127268
40	217.9	202.405619879017	27.0883237348385	206.306056386144	-15.4943801209827
41	221.4	225.0227922091	12.1627723623776	205.614435428522	3.62279220910008
42	216.3	227.009264137520	0.926110161893321	204.664625700586	10.7092641375205
43	197	200.775739568748	-10.4905555413983	203.71481597265	3.77573956874824
44	193.8	207.477066636024	-21.1284565884058	201.251389952382	13.6770666360238
45	196.8	219.278400055403	-24.4663639875165	198.787963932114	22.4784000554025
46	180.5	194.991430120663	-28.3649926793949	194.373562558732	14.4914301206632
47	174.8	181.024477151008	-21.3836383363578	189.959161185349	6.22447715100836
48	181.6	191.161073098852	-13.1151675322482	185.154094433396	9.56107309885203
49	190	182.403505949336	17.2474663692209	180.349027681443	-7.59649405066375
50	190.6	176.790001192541	28.0506694377768	176.359329369682	-13.809998807459
51	179	152.156480673118	33.4738882689606	172.369631057922	-26.8435193268821
52	174.1	150.128099799827	27.0883237348385	170.983576465335	-23.9719002001733
53	161.1	140.439705764874	12.1627723623776	169.597521872748	-20.6602942351256
54	168.6	167.943856644458	0.926110161893321	168.330033193649	-0.656143355541872
55	169.4	182.228011026849	-10.4905555413983	167.062544514549	12.8280110268493
56	152.2	159.287389871123	-21.1284565884058	166.241066717282	7.08738987112346
57	148.3	155.646775067501	-24.4663639875165	165.419588920016	7.34677506750086
58	137.7	138.763268515524	-28.3649926793949	165.001724163870	1.06326851552447
59	145	146.799778928633	-21.3836383363578	164.583859407725	1.79977892863263
60	153.4	155.530534624521	-13.1151675322482	164.384632907728	2.13053462452052

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260004605jyvu6kp43lqbca2/1jmex1260004534.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260004605jyvu6kp43lqbca2/1jmex1260004534.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260004605jyvu6kp43lqbca2/28dzk1260004534.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260004605jyvu6kp43lqbca2/28dzk1260004534.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260004605jyvu6kp43lqbca2/3moqs1260004534.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260004605jyvu6kp43lqbca2/3moqs1260004534.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260004605jyvu6kp43lqbca2/4iky31260004534.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260004605jyvu6kp43lqbca2/4iky31260004534.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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