Home » date » 2009 » Dec » 04 »

WS 9, Populair model 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, 04 Dec 2009 08:58:57 -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/t1259942438cr63t88s1bczdhd.htm/, Retrieved Fri, 04 Dec 2009 17:00:43 +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/t1259942438cr63t88s1bczdhd.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 «

95.1 97 112.7 102.9 97.4 111.4 87.4 96.8 114.1 110.3 103.9 101.6 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

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	1 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	95.1	92.8878530528455	-4.24367339882487	101.555820345979	-2.21214694715448
2	97	95.3963663468978	-3.07619862842305	101.679832281525	-1.60363365310221
3	112.7	116.804875654548	6.79128012838068	101.803844217071	4.10487565454818
4	102.9	103.405116775952	0.497032285268324	101.897850938779	0.505116775952288
5	97.4	93.4653469314822	-0.657204591969873	101.991857660488	-3.93465306851778
6	111.4	111.552291561930	9.19941189240246	102.048296545667	0.152291561930312
7	87.4	89.639238780271	-16.9439742111179	102.104735430847	2.23923878027105
8	96.8	97.4932760190589	-6.01593238948192	102.122656370423	0.693276019058871
9	114.1	117.227319074072	8.83210361592867	102.140577309999	3.12731907407208
10	110.3	109.848659600143	8.71892977778947	102.032410622067	-0.451340399856647
11	103.9	103.649998008847	2.22575805701757	101.924243934135	-0.250001991152686
12	101.6	106.772018171012	-5.32753759771041	101.755519426699	5.17201817101156
13	94.6	91.8568784795623	-4.24367339882487	101.586794919263	-2.74312152043771
14	95.9	93.4875038089862	-3.07619862842305	101.388694819437	-2.41249619101379
15	104.7	101.418125152008	6.79128012838068	101.190594719611	-3.28187484799177
16	102.8	103.956459970716	0.497032285268324	101.146507744015	1.15645997071630
17	98.1	95.7547838235502	-0.657204591969873	101.102420768420	-2.34521617644980
18	113.9	117.255040449628	9.19941189240246	101.345547657969	3.3550404496284
19	80.9	77.1552996635993	-16.9439742111179	101.588674547519	-3.74470033640074
20	95.7	95.375260234472	-6.01593238948192	102.040672155010	-0.324739765527951
21	113.2	115.075226621570	8.83210361592867	102.492669762501	1.87522662157021
22	105.9	100.116510652309	8.71892977778947	102.964559569902	-5.78348934769102
23	108.8	111.937792565680	2.22575805701757	103.436449377302	3.13779256568044
24	102.3	106.083403293726	-5.32753759771041	103.844134303985	3.78340329372575
25	99	97.9918541681576	-4.24367339882487	104.251819230667	-1.00814583184243
26	100.7	99.8651265729173	-3.07619862842305	104.611072055506	-0.834873427082712
27	115.5	119.238394991275	6.79128012838068	104.970324880344	3.7383949912751
28	100.7	95.592876056631	0.497032285268324	105.310091658101	-5.1071239433689
29	109.9	114.807346156113	-0.657204591969873	105.649858435857	4.90734615611291
30	114.6	114.010834204930	9.19941189240246	105.989753902667	-0.58916579506969
31	85.4	81.4143248416404	-16.9439742111179	106.329649369477	-3.98567515835961
32	100.5	100.308325084281	-6.01593238948192	106.707607305201	-0.191674915718636
33	114.8	113.682331143148	8.83210361592867	107.085565240924	-1.11766885685229
34	116.5	116.863960993884	8.71892977778947	107.417109228327	0.36396099388395
35	112.9	115.825588727253	2.22575805701757	107.748653215730	2.92558872725287
36	102	101.280096262271	-5.32753759771041	108.047441335439	-0.71990373772897
37	106	107.897443943676	-4.24367339882487	108.346229455149	1.89744394367567
38	105.3	105.116166711282	-3.07619862842305	108.560031917141	-0.183833288717821
39	118.8	122.034885492487	6.79128012838068	108.773834379133	3.23488549248681
40	106.1	102.829852241030	0.497032285268324	108.873115473701	-3.27014775896961
41	109.3	110.284808023700	-0.657204591969873	108.97239656827	0.984808023699799
42	117.2	116.111086915627	9.19941189240246	109.089501191971	-1.08891308437299
43	92.5	92.7373683954469	-16.9439742111179	109.206605815671	0.237368395446865
44	104.2	105.019712574705	-6.01593238948192	109.396219814777	0.819712574705022
45	112.5	106.582062570189	8.83210361592867	109.585833813883	-5.91793742981146
46	122.4	126.284534142304	8.71892977778947	109.796536079906	3.88453414230432
47	113.3	114.367003597053	2.22575805701757	110.007238345930	1.06700359705277
48	100	95.1849458248002	-5.32753759771041	110.142591772910	-4.81505417519976
49	110.7	115.365728198934	-4.24367339882487	110.277945199891	4.66572819893419
50	112.8	118.434231291714	-3.07619862842305	110.241967336709	5.63423129171413
51	109.8	102.602730398092	6.79128012838068	110.205989473527	-7.19726960190782
52	117.3	124.611369335157	0.497032285268324	109.491598379574	7.31136933515744
53	109.1	110.079997306349	-0.657204591969873	108.777207285621	0.979997306348523
54	115.9	114.663022112178	9.19941189240246	107.937565995419	-1.23697788782169
55	96	101.846049505901	-16.9439742111179	107.097924705217	5.84604950590072
56	99.8	99.3996202253004	-6.01593238948192	106.216312164181	-0.400379774699573
57	116.8	119.433196760925	8.83210361592867	105.334699623146	2.63319676092549
58	115.7	118.288961991147	8.71892977778947	104.392108231064	2.58896199114703
59	99.4	93.1247251040013	2.22575805701757	103.449516838981	-6.27527489599875
60	94.3	91.4823370935319	-5.32753759771041	102.445200504179	-2.81766290646812

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259942438cr63t88s1bczdhd/10u3k1259942334.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259942438cr63t88s1bczdhd/10u3k1259942334.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259942438cr63t88s1bczdhd/22xvs1259942335.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259942438cr63t88s1bczdhd/22xvs1259942335.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259942438cr63t88s1bczdhd/359rt1259942335.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259942438cr63t88s1bczdhd/359rt1259942335.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259942438cr63t88s1bczdhd/4hgqz1259942335.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259942438cr63t88s1bczdhd/4hgqz1259942335.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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