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review workshop 9

*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, 04 Dec 2009 03:31:35 -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/04/t1259922846qa4kl908q825sb7.htm/, Retrieved Fri, 04 Dec 2009 11:34:11 +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/04/t1259922846qa4kl908q825sb7.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 «

12.610 10.862 52.929 56.902 81.776 87.876 82.103 72.846 60.632 33.521 15.342 7.758 8.668 13.082 38.157 58.263 81.153 88.476 72.329 75.845 61.108 37.665 12.755 2.793 12.935 19.533 33.404 52.074 70.735 69.702 61.656 82.993 53.990 32.283 15.686 2.713 12.842 19.244 48.488 54.464 84.192 84.458 85.793 75.163 68.212 49.233 24.302 5.402 15.058 33.559 70.358 85.934 94.452 129.305 113.882 107.256 94.274 57.842 26.611 14.521

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	12.61	14.0584286999257	-37.3654401908273	48.5270114909016	1.44842869992573
2	10.862	4.36898896743465	-30.9835137067524	48.3385247393178	-6.49301103256535
3	52.929	59.7317578843913	-2.02379587212517	48.1500379877339	6.80275788439126
4	56.902	55.1252470074015	10.7051891670515	47.973563825547	-1.77675299259854
5	81.776	84.2467362830729	31.508174053567	47.7970896633601	2.47073628307286
6	87.876	87.2618269882088	40.8537031125849	47.6364698992063	-0.614173011791202
7	82.103	84.8435062084558	31.8866436564918	47.4758501350524	2.74050620845576
8	72.846	67.1979329631758	31.1901033238247	47.3039637129995	-5.64806703682424
9	60.632	58.4837723473442	15.6481503617092	47.1320772909466	-2.1482276526558
10	33.521	30.5473503066624	-10.3989773779415	46.8936270712792	-2.97364969333763
11	15.342	18.1101315942980	-34.0813084459098	46.6551768516117	2.76813159429803
12	7.758	15.9346298368019	-46.9389334761924	46.5203036393906	8.17662983680187
13	8.668	8.31600976365794	-37.3654401908273	46.3854304271694	-0.351990236342061
14	13.082	10.8461546352834	-30.9835137067524	46.301359071469	-2.23584536471656
15	38.157	32.1205081563566	-2.02379587212517	46.2172877157685	-6.03649184364338
16	58.263	59.6915765229133	10.7051891670515	46.1292343100352	1.42857652291329
17	81.153	84.7566450421312	31.508174053567	46.0411809043018	3.60364504213117
18	88.476	90.0066900773342	40.8537031125849	46.0916068100808	1.53069007733423
19	72.329	66.6293236276483	31.8866436564918	46.1420327158598	-5.69967637235167
20	75.845	74.284341505331	31.1901033238247	46.2155551708443	-1.56065849466903
21	61.108	60.2787720124621	15.6481503617092	46.2890776258287	-0.82922798753792
22	37.665	39.8179275478828	-10.3989773779415	45.9110498300587	2.15292754788280
23	12.755	14.0582864116210	-34.0813084459098	45.5330220342888	1.30328641162102
24	2.793	7.80808403188616	-46.9389334761924	44.7168494443063	5.01508403188616
25	12.935	19.3347633365036	-37.3654401908273	43.9006768543238	6.39976333650355
26	19.533	26.8212045742047	-30.9835137067524	43.2283091325477	7.2882045742047
27	33.404	26.2758544613535	-2.02379587212517	42.5559414107717	-7.12814553864649
28	52.074	51.3226180338142	10.7051891670515	42.1201927991343	-0.751381966185811
29	70.735	68.2773817589361	31.508174053567	41.6844441874969	-2.45761824106393
30	69.702	56.8600379721069	40.8537031125849	41.6902589153082	-12.8419620278931
31	61.656	49.7292827003887	31.8866436564918	41.6960736431195	-11.9267172996113
32	82.993	92.5300875213702	31.1901033238247	42.265809154805	9.53708752137024
33	53.99	49.4963049718003	15.6481503617092	42.8355446664906	-4.49369502819974
34	32.283	31.0921842084374	-10.3989773779415	43.8727931695042	-1.19081579156263
35	15.686	20.5432667733920	-34.0813084459098	44.9100416725178	4.85726677339196
36	2.713	6.40367270169268	-46.9389334761924	45.9612607744998	3.69067270169268
37	12.842	16.0369603143456	-37.3654401908273	47.0124798764817	3.19496031434562
38	19.244	21.6992090315541	-30.9835137067524	47.7723046751983	2.4552090315541
39	48.488	50.4676663982103	-2.02379587212517	48.5321294739149	1.97966639821026
40	54.464	49.0318241235195	10.7051891670515	49.190986709429	-5.43217587648054
41	84.192	87.0259820014899	31.508174053567	49.8498439449431	2.83398200148987
42	84.458	77.5871878575259	40.8537031125849	50.4751090298891	-6.87081214247407
43	85.793	88.598982228673	31.8866436564918	51.1003741148351	2.80598222867302
44	75.163	66.8994314848926	31.1901033238247	52.2364651912827	-8.2635685151074
45	68.212	67.4032933705606	15.6481503617092	53.3725562677302	-0.808706629439364
46	49.233	53.6027070269077	-10.3989773779415	55.2622703510338	4.36970702690769
47	24.302	25.5333240115723	-34.0813084459098	57.1519844343375	1.23132401157226
48	5.402	-1.91091190368085	-46.9389334761924	59.6538453798733	-7.31291190368085
49	15.058	5.32573386541827	-37.3654401908273	62.1557063254091	-9.73226613458173
50	33.559	33.5116158043584	-30.9835137067524	64.589897902394	-0.0473841956415981
51	70.358	75.7157063927462	-2.02379587212517	67.024089479379	5.35770639274622
52	85.934	93.2000490215971	10.7051891670515	67.9627618113514	7.26604902159713
53	94.452	88.4943918031092	31.508174053567	68.9014341433238	-5.95760819689077
54	129.305	148.189140153249	40.8537031125849	69.567156734166	18.8841401532490
55	113.882	125.6444770185	31.8866436564918	70.2328793250082	11.7624770184999
56	107.256	112.478090080801	31.1901033238247	70.8438065953747	5.22209008080056
57	94.274	101.445115772550	15.6481503617092	71.4547338657412	7.17111577254965
58	57.842	54.2025935876946	-10.3989773779415	71.8803837902469	-3.63940641230538
59	26.611	14.9972747311571	-34.0813084459098	72.3060337147527	-11.6137252688429
60	14.521	3.45342772496797	-46.9389334761924	72.5275057512245	-11.0675722750320

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259922846qa4kl908q825sb7/1c6u51259922693.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259922846qa4kl908q825sb7/1c6u51259922693.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259922846qa4kl908q825sb7/2nbkd1259922693.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259922846qa4kl908q825sb7/2nbkd1259922693.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259922846qa4kl908q825sb7/3fl3c1259922693.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259922846qa4kl908q825sb7/3fl3c1259922693.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259922846qa4kl908q825sb7/41v0r1259922693.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259922846qa4kl908q825sb7/41v0r1259922693.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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