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ws 8: productie

*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, 27 Nov 2010 18:26:46 +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/27/t1290882317l0yab3l57glatke.htm/, Retrieved Sat, 27 Nov 2010 19:25:18 +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/27/t1290882317l0yab3l57glatke.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 «

94,6 95,9 104,7 102,8 98,1 113,9 80,9 95,7 113,2 105,9 108,8 102,3 99 100,7 115,5 100,7 109,9 114,6 85,4 100,5 114,8 116,5 112,9 102 106 105,3 118,8 106,1 109,3 117,2 92,5 104,2 112,5 122,4 113,3 100 110,7 112,8 109,8 117,3 109,1 115,9 96 99,8 116,8 115,7 99,4 94,3 91 93,2 103,1 94,1 91,8 102,7 82,6 89,1 104,5 105,1 95,1 88,7 86,3 91,8 111,5 99,7 97,5 111,7 86,2 95,4

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	3 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Seasonal Decomposition by Loess - Parameters
Component	Window	Degree	Jump
Seasonal	681	0	69
Trend	19	1	2
Low-pass	13	1	2

Seasonal Decomposition by Loess - Time Series Components
t	Observed	Fitted	Seasonal	Trend	Remainder
1	94.6	95.7196452563907	-5.01795612935602	98.4983108729653	1.11964525639068
2	95.9	95.922353197718	-3.10377270183268	98.9814195041147	0.0223531977179476
3	104.7	102.525064345157	7.41040751957922	99.4645281352642	-2.17493565484338
4	102.8	105.416584330781	0.224179392069346	99.9592362771494	2.61658433078127
5	98.1	96.4247711393204	-0.678715558355041	100.453944419035	-1.6752288606796
6	113.9	117.514164817833	9.32622876514923	100.959606417017	3.61416481783343
7	80.9	76.4535497217298	-16.1188181367298	101.465268415	-4.44645027827023
8	95.7	95.446792202734	-6.01812676901854	101.971334566285	-0.253207797266057
9	113.2	115.264488297278	8.65811098515259	102.477400717569	2.06448829727823
10	105.9	99.4165593472809	9.41562234245817	102.967818310261	-6.48344065271912
11	108.8	111.948635590061	2.19312850698615	103.458235902953	3.1486355900611
12	102.3	107.02067678298	-6.2902874220305	103.869610639051	4.72067678297954
13	99	98.7369707542069	-5.01795612935602	104.280985375149	-0.26302924579312
14	100.7	99.8686562745178	-3.10377270183268	104.635116427315	-0.831343725482242
15	115.5	118.60034500094	7.41040751957922	104.989247479481	3.10034500094008
16	100.7	95.8680462257486	0.224179392069346	105.307774382182	-4.83195377425143
17	109.9	114.852414273472	-0.678715558355041	105.626301284883	4.95241427347158
18	114.6	113.912567815633	9.32622876514923	105.961203419218	-0.687432184366912
19	85.4	80.622712583178	-16.1188181367298	106.296105553552	-4.77728741682206
20	100.5	100.333479362707	-6.01812676901854	106.684647406312	-0.16652063729299
21	114.8	113.868699755776	8.65811098515259	107.073189259071	-0.931300244223792
22	116.5	116.16256317088	9.41562234245817	107.421814486661	-0.337436829119582
23	112.9	115.836431778762	2.19312850698615	107.770439714252	2.93643177876223
24	102	102.217369742014	-6.2902874220305	108.072917680016	0.217369742014114
25	106	108.642560483575	-5.01795612935602	108.375395645781	2.64256048357491
26	105.3	105.119696334622	-3.10377270183268	108.58407636721	-0.180303665377735
27	118.8	121.396835391781	7.41040751957922	108.79275708864	2.59683539178106
28	106.1	103.105022350576	0.224179392069346	108.870798257355	-2.9949776494241
29	109.3	110.329876132285	-0.678715558355041	108.94883942607	1.02987613228527
30	117.2	116.012820557749	9.32622876514923	109.060950677102	-1.18717944225074
31	92.5	91.9457562085965	-16.1188181367298	109.173061928133	-0.554243791403479
32	104.2	105.044866947619	-6.01812676901854	109.3732598214	0.844866947618613
33	112.5	106.768431300181	8.65811098515259	109.573457714667	-5.73156869981919
34	122.4	125.583136392051	9.41562234245817	109.801241265491	3.18313639205132
35	113.3	114.377846676699	2.19312850698615	110.029024816314	1.07784667669941
36	100	96.1222193090731	-6.2902874220305	110.168068112957	-3.87778069092688
37	110.7	116.110844719756	-5.01795612935602	110.3071114096	5.41084471975572
38	112.8	118.437760880382	-3.10377270183268	110.266011821451	5.63776088038215
39	109.8	101.96468024712	7.41040751957922	110.224912233301	-7.83531975287998
40	117.3	124.609631059052	0.224179392069346	109.766189548879	7.30963105905205
41	109.1	109.571248693899	-0.678715558355041	109.307466864456	0.471248693898602
42	115.9	114.152895039911	9.32622876514923	108.32087619494	-1.74710496008926
43	96	100.784532611306	-16.1188181367298	107.334285525424	4.78453261130618
44	99.8	99.6166988675608	-6.01812676901854	106.001427901458	-0.183301132439155
45	116.8	120.273318737356	8.65811098515259	104.668570277492	3.47331873735564
46	115.7	118.760859112566	9.41562234245817	103.223518544976	3.06085911256577
47	99.4	94.8284046805535	2.19312850698615	101.77846681246	-4.57159531944652
48	94.3	94.4627470024499	-6.2902874220305	100.427540419581	0.16274700244989
49	91	87.9413421026552	-5.01795612935602	99.0766140267008	-3.0586578973448
50	93.2	91.461420101592	-3.10377270183268	98.0423526002407	-1.73857989840806
51	103.1	101.78150130664	7.41040751957922	97.0080911737807	-1.3184986933599
52	94.1	91.6252710902236	0.224179392069346	96.350549517707	-2.47472890977636
53	91.8	88.5857076967217	-0.678715558355041	95.6930078616333	-3.2142923032783
54	102.7	100.724613421198	9.32622876514923	95.3491578136528	-1.97538657880199
55	82.6	86.3135103710576	-16.1188181367298	95.0053077656722	3.71351037105761
56	89.1	89.1805898900068	-6.01812676901854	95.0375368790118	0.0805898900067632
57	104.5	105.272123022496	8.65811098515259	95.0697659923514	0.772123022496046
58	105.1	105.338448585249	9.41562234245817	95.4459290722928	0.238448585249046
59	95.1	92.1847793407796	2.19312850698615	95.8220921522342	-2.91522065922035
60	88.7	87.2136738915749	-6.2902874220305	96.4766135304556	-1.4863261084251
61	86.3	80.486821220679	-5.01795612935602	97.131134908677	-5.81317877932096
62	91.8	88.9386565017545	-3.10377270183268	97.7651162000781	-2.86134349824546
63	111.5	117.190494988941	7.41040751957922	98.3990974914793	5.69049498894148
64	99.7	100.100137503503	0.224179392069346	99.0756831044275	0.40013750350316
65	97.5	95.9264468409793	-0.678715558355041	99.7522687173757	-1.57355315902066
66	111.7	113.597601844838	9.32622876514923	100.476169390013	1.89760184483805
67	86.2	87.31874807408	-16.1188181367298	101.20007006265	1.11874807408007
68	95.4	94.8648436888571	-6.01812676901854	101.953283080161	-0.535156311142927

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290882317l0yab3l57glatke/1qyid1290882401.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290882317l0yab3l57glatke/1qyid1290882401.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290882317l0yab3l57glatke/2qyid1290882401.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290882317l0yab3l57glatke/2qyid1290882401.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290882317l0yab3l57glatke/30phy1290882401.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290882317l0yab3l57glatke/30phy1290882401.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290882317l0yab3l57glatke/40phy1290882401.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290882317l0yab3l57glatke/40phy1290882401.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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