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workshop 9 - review link 2

*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: Fri, 11 Dec 2009 05:08:29 -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/11/t1260533344hfwz9p96b16ezm6.htm/, Retrieved Fri, 11 Dec 2009 13:09:09 +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/2009/Dec/11/t1260533344hfwz9p96b16ezm6.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 «

5.4 5.4 5.6 5.7 5.8 5.8 5.8 5.9 6.1 6.4 6.4 6.3 6.2 6.2 6.3 6.4 6.5 6.6 6.6 6.6 6.8 7 7.2 7.3 7.5 7.6 7.6 7.7 7.7 7.7 7.7 7.6 7.7 7.9 7.9 7.9 7.8 7.6 7.4 7 7 7.2 7.5 7.8 7.8 7.7 7.6 7.6 7.5 7.5 7.6 7.6 7.9 7.6 7.5 7.5 7.6 7.7 7.8 7.9 7.9

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	611	0	62
Trend	19	1	2
Low-pass	13	1	2

Seasonal Decomposition by Loess - Time Series Components
t	Observed	Fitted	Seasonal	Trend	Remainder
1	5.4	5.27075555016359	0.0387322216114774	5.49051222822493	-0.129244449836411
2	5.4	5.30552276302457	-0.0669073505785131	5.56138458755395	-0.0944772369754334
3	5.6	5.6357710796415	-0.0680280265244615	5.63225694688296	0.0357710796415001
4	5.7	5.82041221618889	-0.122116581690210	5.70170436550132	0.120412216188889
5	5.8	5.88505340250394	-0.0562051866236181	5.77115178411968	0.0850534025039371
6	5.8	5.8463446205719	-0.0862175198512663	5.83987289927937	0.0463446205719009
7	5.8	5.76763588840749	-0.076229902846541	5.90859401443905	-0.0323641115925088
8	5.9	5.87140361933342	-0.0473525944149266	5.97594897508151	-0.0285963806665812
9	6.1	6.11517136270123	0.0415247015748089	6.04330393572396	0.015171362701226
10	6.4	6.54857739916545	0.147900306704964	6.10352229412958	0.148577399165453
11	6.4	6.48198341074589	0.154275936718910	6.1637406525352	0.0819834107458881
12	6.3	6.23416292931727	0.140623968733699	6.22521310194903	-0.0658370706827283
13	6.2	6.07458222702567	0.0387322216114774	6.28668555136286	-0.125417772974332
14	6.2	6.11990880383026	-0.0669073505785131	6.34699854674825	-0.080091196169736
15	6.3	6.26071648439082	-0.0680280265244615	6.40731154213364	-0.0392835156091831
16	6.4	6.45156671078234	-0.122116581690210	6.47054987090787	0.0515667107823417
17	6.5	6.52241698694152	-0.0562051866236181	6.5337881996821	0.0224169869415247
18	6.6	6.67007833702601	-0.0862175198512663	6.61613918282525	0.0700783370260147
19	6.6	6.57773973687813	-0.076229902846541	6.69849016596841	-0.0222602631218685
20	6.6	6.4462365153384	-0.0473525944149266	6.80111607907653	-0.153763484661603
21	6.8	6.65473330624054	0.0415247015748089	6.90374199218465	-0.145266693759458
22	7	6.83977449675676	0.147900306704964	7.01232519653828	-0.160225503243245
23	7.2	7.12481566238918	0.154275936718910	7.12090840089191	-0.0751843376108221
24	7.3	7.2344044991531	0.140623968733699	7.2249715321132	-0.0655955008469045
25	7.5	7.63223311505402	0.0387322216114774	7.3290346633345	0.132233115054024
26	7.6	7.84809630197954	-0.0669073505785131	7.41881104859897	0.248096301979541
27	7.6	7.75944059266102	-0.0680280265244615	7.50858743386345	0.159440592661015
28	7.7	7.94725143822806	-0.122116581690210	7.57486514346215	0.247251438228062
29	7.7	7.81506233356277	-0.0562051866236181	7.64114285306085	0.115062333562768
30	7.7	7.80919209134572	-0.0862175198512663	7.67702542850555	0.109192091345720
31	7.7	7.7633218988963	-0.076229902846541	7.71290800395024	0.063321898896298
32	7.6	7.53418840060835	-0.0473525944149266	7.71316419380658	-0.0658115993916546
33	7.7	7.64505491476227	0.0415247015748089	7.71342038366292	-0.0549450852377262
34	7.9	7.97182987533218	0.147900306704964	7.68026981796285	0.0718298753321838
35	7.9	7.9986048110183	0.154275936718910	7.64711925226279	0.0986048110183022
36	7.9	8.04950455226961	0.140623968733699	7.60987147899669	0.149504552269613
37	7.8	7.98864407265794	0.0387322216114774	7.57262370573059	0.188644072657936
38	7.6	7.71196162406456	-0.0669073505785131	7.55494572651395	0.111961624064562
39	7.4	7.33076027922715	-0.0680280265244615	7.53726774729732	-0.0692397207728535
40	7	6.59666835364326	-0.122116581690210	7.52544822804695	-0.403331646356741
41	7	6.54257647782703	-0.0562051866236181	7.51362870879659	-0.457423522172967
42	7.2	6.98505328620969	-0.0862175198512663	7.50116423364157	-0.214946713790305
43	7.5	7.58753014435998	-0.076229902846541	7.48869975848656	0.0875301443599827
44	7.8	8.15150027743632	-0.0473525944149266	7.49585231697860	0.351500277436322
45	7.8	8.05547042295454	0.0415247015748089	7.50300487547065	0.255470422954541
46	7.7	7.71666274708089	0.147900306704964	7.53543694621415	0.0166627470808889
47	7.6	7.47785504632345	0.154275936718910	7.56786901695764	-0.122144953676554
48	7.6	7.46751549645492	0.140623968733699	7.59186053481139	-0.132484503545085
49	7.5	7.3454157257234	0.0387322216114774	7.61585205266513	-0.154584274276603
50	7.5	7.44951883867078	-0.0669073505785131	7.61738851190773	-0.0504811613292206
51	7.6	7.64910305537412	-0.0680280265244615	7.61892497115034	0.0491030553741183
52	7.6	7.70016446242637	-0.122116581690210	7.62195211926384	0.100164462426371
53	7.9	8.23122591924629	-0.0562051866236181	7.62497926737733	0.331225919246286
54	7.6	7.64833441027575	-0.0862175198512663	7.63788310957551	0.0483344102757517
55	7.5	7.42544295107284	-0.076229902846541	7.6507869517737	-0.0745570489271552
56	7.5	7.3840951294973	-0.0473525944149266	7.66325746491762	-0.115904870502694
57	7.6	7.48274732036365	0.0415247015748089	7.67572797806154	-0.117252679636353
58	7.7	7.56586298618555	0.147900306704964	7.68623670710949	-0.134137013814452
59	7.8	7.74897862712366	0.154275936718910	7.69674543615743	-0.0510213728763418
60	7.9	7.95272998148495	0.140623968733699	7.70664604978136	0.052729981484946
61	7.9	8.04472111498325	0.0387322216114774	7.71654666340528	0.144721114983245

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/11/t1260533344hfwz9p96b16ezm6/10izm1260533306.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t1260533344hfwz9p96b16ezm6/10izm1260533306.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t1260533344hfwz9p96b16ezm6/28dkg1260533306.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t1260533344hfwz9p96b16ezm6/28dkg1260533306.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t1260533344hfwz9p96b16ezm6/3682m1260533306.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t1260533344hfwz9p96b16ezm6/3682m1260533306.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t1260533344hfwz9p96b16ezm6/4lqrk1260533306.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t1260533344hfwz9p96b16ezm6/4lqrk1260533306.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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