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Paper: Nijverheid

*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 18:35:04 +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/t1293388394giaj0h7wz92udvn.htm/, Retrieved Sun, 26 Dec 2010 19:33:15 +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/t1293388394giaj0h7wz92udvn.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 «

105,0 104,0 109,8 98,6 93,5 98,2 88,0 85,3 96,8 98,8 110,3 111,6 111,2 106,9 117,6 97,0 97,3 98,4 87,6 87,4 94,7 101,5 110,4 108,4 109,7 105,2 111,1 96,2 97,3 98,9 91,7 90,9 98,8 111,5 119,0 115,3 116,3 113,6 115,1 109,7 97,6 100,8 94,0 87,2 102,9 111,3 106,6 108,9 108,2 100,2 104,0 90,0 87,4 91,9 89,3 81,3 94,9 102,6 107,2 114,0

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	'RServer@AstonUniversity' @ vre.aston.ac.uk

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	105	102.422855015785	8.59240626220917	98.9847387220055	-2.57714498421473
2	104	104.314116871312	4.4843638633032	99.2015192653845	0.314116871312336
3	109.8	110.165378360875	10.0163218303621	99.4182998087634	0.365378360874558
4	98.6	100.861794703048	-3.30663188332245	99.644837180275	2.26179470304751
5	93.5	94.2182095813604	-7.08958413314692	99.8713745517865	0.718209581360426
6	98.2	100.379982558607	-4.10597766714644	100.125995108539	2.17998255860736
7	88	87.2817549476964	-11.6623706129881	100.380615665292	-0.718245052303573
8	85.3	85.2906310988278	-15.3354154880396	100.644784389212	-0.00936890117222333
9	96.8	96.7995104129409	-4.10846352607281	100.908953113132	-0.000489587059121277
10	98.8	93.0333117473989	3.45572269637396	101.110965556227	-5.76668825260113
11	110.3	110.227117551855	9.05990444882288	101.312977999322	-0.072882448145279
12	111.6	111.70718596393	9.99972722221843	101.493086813851	0.107185963930149
13	111.2	112.13439810941	8.59240626220917	101.67319562838	0.93439810941041
14	106.9	107.530783762247	4.4843638633032	101.784852374449	0.630783762247376
15	117.6	123.287169049119	10.0163218303621	101.896509120518	5.68716904911949
16	97	95.4313417586927	-3.30663188332245	101.87529012463	-1.56865824130728
17	97.3	99.835513004406	-7.08958413314692	101.854071128741	2.5355130044059
18	98.4	99.2568394417539	-4.10597766714644	101.649138225393	0.856839441753863
19	87.6	85.418165290944	-11.6623706129881	101.444205322044	-2.18183470905606
20	87.4	88.9706038322114	-15.3354154880396	101.164811655828	1.57060383221138
21	94.7	92.6230455364606	-4.10846352607281	100.885417989612	-2.07695446353944
22	101.5	98.8112698739006	3.45572269637396	100.733007429725	-2.68873012609936
23	110.4	111.159498681339	9.05990444882288	100.580596869839	0.75949868133857
24	108.4	106.102723937606	9.99972722221843	100.697548840176	-2.2972760623942
25	109.7	109.993092927278	8.59240626220917	100.814500810513	0.29309292727784
26	105.2	104.724703749069	4.4843638633032	101.190932387628	-0.475296250931294
27	111.1	110.616314204895	10.0163218303621	101.567363964743	-0.483685795105274
28	96.2	93.5444726641784	-3.30663188332245	102.162159219144	-2.65552733582163
29	97.3	98.932629659602	-7.08958413314692	102.756954473545	1.63262965960195
30	98.9	98.4864064156746	-4.10597766714644	103.419571251472	-0.413593584325383
31	91.7	90.9801825835894	-11.6623706129881	104.082188029399	-0.719817416410592
32	90.9	92.4134486245034	-15.3354154880396	104.721966863536	1.51344862450341
33	98.8	96.3467178283992	-4.10846352607281	105.361745697674	-2.45328217160085
34	111.5	113.660120514254	3.45572269637396	105.884156789372	2.1601205142542
35	119	122.533527670107	9.05990444882288	106.40656788107	3.53352767010711
36	115.3	113.95148209176	9.99972722221843	106.648790686022	-1.3485179082405
37	116.3	117.116580246817	8.59240626220917	106.891013490974	0.816580246816727
38	113.6	115.830193210144	4.4843638633032	106.885442926553	2.2301932101438
39	115.1	113.303805807506	10.0163218303621	106.879872362132	-1.79619419249397
40	109.7	116.11353978577	-3.30663188332245	106.593092097552	6.41353978577013
41	97.6	95.9832723001741	-7.08958413314692	106.306311832973	-1.61672769982586
42	100.8	100.038932458112	-4.10597766714644	105.667045209034	-0.761067541887627
43	94	94.6345920278927	-11.6623706129881	105.027778585095	0.634592027892722
44	87.2	85.6776801722042	-15.3354154880396	104.057735315835	-1.52231982779583
45	102.9	106.820771479497	-4.10846352607281	103.087692046575	3.92077147949738
46	111.3	117.167303017432	3.45572269637396	101.976974286194	5.86730301743164
47	106.6	103.273839025364	9.05990444882288	100.866256525813	-3.32616097463625
48	108.9	107.846483204795	9.99972722221843	99.9537895729869	-1.0535167952053
49	108.2	108.76627111763	8.59240626220917	99.0413226201604	0.56627111763045
50	100.2	97.5159985023988	4.4843638633032	98.399637634298	-2.68400149760117
51	104	100.225725521202	10.0163218303621	97.7579526484356	-3.77427447879762
52	90	85.513796329858	-3.30663188332245	97.7928355534645	-4.48620367014202
53	87.4	84.0618656746535	-7.08958413314692	97.8277184584934	-3.33813432534647
54	91.9	89.9396794564597	-4.10597766714644	97.9662982106868	-1.96032054354033
55	89.3	92.1574926501079	-11.6623706129881	98.1048779628802	2.85749265010791
56	81.3	79.6063419004749	-15.3354154880396	98.3290735875647	-1.69365809952511
57	94.9	95.3551943138236	-4.10846352607281	98.5532692122492	0.455194313823625
58	102.6	102.884569657467	3.45572269637396	98.8597076461593	0.284569657466733
59	107.2	106.173949471108	9.05990444882288	99.1661460800694	-1.0260505288923
60	114	118.470269076017	9.99972722221843	99.5300037017649	4.47026907601669

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293388394giaj0h7wz92udvn/11v6r1293388500.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293388394giaj0h7wz92udvn/11v6r1293388500.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293388394giaj0h7wz92udvn/21v6r1293388500.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293388394giaj0h7wz92udvn/21v6r1293388500.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293388394giaj0h7wz92udvn/3u55c1293388500.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293388394giaj0h7wz92udvn/3u55c1293388500.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293388394giaj0h7wz92udvn/4u55c1293388500.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293388394giaj0h7wz92udvn/4u55c1293388500.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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