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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 10:40:41 -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/t1259948535s1fz2rx5hybb1xj.htm/, Retrieved Fri, 04 Dec 2009 18:42:21 +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/t1259948535s1fz2rx5hybb1xj.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 «

95.5 76.7 79.4 55.2 60 64.8 82.3 210.5 106 80.8 97.3 189.5 90 69.3 87.3 57.4 56.2 61.6 77.7 177.2 97.6 81.6 96.8 191.3 106 75.1 72 63.5 57.4 62.3 79.4 178.1 109.3 85.2 102.7 193.7 108.4 73.4 85.9 58.5 58.6 62.7 77.5 180.5 102.2 82.6 97.8 197.8 93.8 72.4 77.7 58.7 53.1 64.3 76.4 188.4 105.5 79.8 96.1 202.5

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	95.5	91.0650048888439	0.635264192194359	99.2997309189617	-4.43499511115608
2	76.7	78.5864713421847	-24.6313681838695	99.4448968416848	1.88647134218468
3	79.4	76.6679318616705	-17.4579946260784	99.590062764408	-2.73206813832951
4	55.2	49.9983840867836	-39.2178694160115	99.619485329228	-5.20161591321636
5	60	61.1288394226003	-40.7777473166481	99.6489078940479	1.12883942260025
6	64.8	64.7657421336396	-34.7552436604617	99.5895015268221	-0.034257866360349
7	82.3	84.3626424136274	-19.2927375732237	99.5300951595963	2.06264241362737
8	210.5	232.588292483841	88.9194803631598	99.4922271529995	22.0882924838407
9	106	106.513921667922	6.03171918567537	99.4543591464026	0.513921667921991
10	80.8	78.4518303221718	-16.1448987196711	99.2930683974993	-2.34816967782822
11	97.3	95.5297413551913	-0.0615190037872699	99.131777648596	-1.77025864480871
12	189.5	183.747817963913	96.752914799708	98.4992672363794	-5.75218203608739
13	90	81.4979789836428	0.635264192194359	97.8667568241629	-8.50202101635722
14	69.3	66.237904852841	-24.6313681838695	96.9934633310285	-3.06209514715897
15	87.3	95.9378247881843	-17.4579946260784	96.120169837894	8.6378247881843
16	57.4	58.2250786431168	-39.2178694160115	95.7927907728947	0.825078643116825
17	56.2	57.7123356087528	-40.7777473166481	95.4654117078953	1.51233560875281
18	61.6	62.2891918988192	-34.7552436604617	95.6660517616425	0.689191898819246
19	77.7	78.826045757834	-19.2927375732237	95.8666918153897	1.12604575783402
20	177.2	169.446301747438	88.9194803631598	96.0342178894019	-7.75369825256168
21	97.6	92.9665368509106	6.03171918567537	96.201743963414	-4.63346314908944
22	81.6	83.0604376484498	-16.1448987196711	96.2844610712213	1.46043764844981
23	96.8	97.2943408247588	-0.0615190037872699	96.3671781790285	0.494340824758808
24	191.3	189.244090769862	96.752914799708	96.6029944304299	-2.05590923013784
25	106	114.525925125974	0.635264192194359	96.8388106818313	8.52592512597435
26	75.1	77.6760869372284	-24.6313681838695	97.155281246641	2.57608693722840
27	72	63.9862428146275	-17.4579946260784	97.4717518114509	-8.01375718537248
28	63.5	68.4485551138434	-39.2178694160115	97.7693143021682	4.94855511384336
29	57.4	57.5108705237627	-40.7777473166481	98.0668767928854	0.110870523762685
30	62.3	61.0085139968748	-34.7552436604617	98.346729663587	-1.29148600312524
31	79.4	79.4661550389352	-19.2927375732237	98.6265825342885	0.0661550389351788
32	178.1	168.348234163148	88.9194803631598	98.9322854736918	-9.75176583685163
33	109.3	113.330292401229	6.03171918567537	99.2379884130952	4.03029240122947
34	85.2	87.0358382818304	-16.1448987196711	99.5090604378407	1.83583828183038
35	102.7	105.681386541201	-0.0615190037872699	99.7801324625863	2.98138654120102
36	193.7	190.812452911922	96.752914799708	99.8346322883704	-2.88754708807838
37	108.4	116.275603693651	0.635264192194359	99.8891321141545	7.87560369365114
38	73.4	71.7463179541934	-24.6313681838695	99.685050229676	-1.65368204580659
39	85.9	89.7770262808807	-17.4579946260784	99.4809683451977	3.87702628088073
40	58.5	57.1277199111168	-39.2178694160115	99.0901495048947	-1.37228008888322
41	58.6	59.2784166520563	-40.7777473166481	98.6993306645918	0.678416652056342
42	62.7	61.8529933859437	-34.7552436604617	98.3022502745181	-0.84700661405634
43	77.5	76.3875676887793	-19.2927375732237	97.9051698844444	-1.1124323112207
44	180.5	174.553687793783	88.9194803631598	97.5268318430577	-5.94631220621751
45	102.2	101.219787012654	6.03171918567537	97.148493801671	-0.98021298734642
46	82.6	84.423596417403	-16.1448987196711	96.9213023022682	1.82359641740291
47	97.8	98.967408200922	-0.0615190037872699	96.6941108028653	1.16740820092200
48	197.8	202.154777307055	96.752914799708	96.6923078932369	4.35477730705514
49	93.8	90.2742308241971	0.635264192194359	96.6905049836085	-3.52576917580285
50	72.4	72.6116600459814	-24.6313681838695	96.8197081378881	0.211660045981361
51	77.7	75.9090833339106	-17.4579946260784	96.9489112921678	-1.79091666608942
52	58.7	59.4722157583038	-39.2178694160115	97.1456536577077	0.772215758303844
53	53.1	49.6353512934005	-40.7777473166481	97.3423960232476	-3.46464870659946
54	64.3	65.7196018836136	-34.7552436604617	97.6356417768481	1.41960188361362
55	76.4	74.163850042775	-19.2927375732237	97.9288875304486	-2.23614995722494
56	188.4	189.62658350866	88.9194803631598	98.2539361281801	1.22658350866008
57	105.5	106.389296088413	6.03171918567537	98.5789847259116	0.88929608841299
58	79.8	76.815214093752	-16.1448987196711	98.9296846259192	-2.98478590624809
59	96.1	92.9811344778606	-0.0615190037872699	99.2803845259267	-3.11886552213943
60	202.5	208.593887671421	96.752914799708	99.6531975288714	6.09388767142065

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259948535s1fz2rx5hybb1xj/1c83i1259948439.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259948535s1fz2rx5hybb1xj/1c83i1259948439.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259948535s1fz2rx5hybb1xj/2egtd1259948439.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259948535s1fz2rx5hybb1xj/2egtd1259948439.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259948535s1fz2rx5hybb1xj/3gtrp1259948439.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259948535s1fz2rx5hybb1xj/3gtrp1259948439.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259948535s1fz2rx5hybb1xj/4byo61259948439.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259948535s1fz2rx5hybb1xj/4byo61259948439.ps (open in new window)

Parameters (Session):

par1 = FALSE ; par2 = -0.4 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;

Parameters (R input):

par1 = 12 ; par2 = periodic ; par3 = 0 ; par5 = 1 ; par7 = 1 ; par8 = FALSE ; par9 = 1 ;

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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