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SHWWS9klesmeth2

*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: Tue, 01 Dec 2009 13:00:11 -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/01/t1259697670n0rvw45bw9uo17b.htm/, Retrieved Tue, 01 Dec 2009 21:01:15 +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/01/t1259697670n0rvw45bw9uo17b.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 «

161 149 139 135 130 127 122 117 112 113 149 157 157 147 137 132 125 123 117 114 111 112 144 150 149 134 123 116 117 111 105 102 95 93 124 130 124 115 106 105 105 101 95 93 84 87 116 120 117 109 105 107 109 109 108 107 99 103 131 137

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	161	164.29632802765	20.6473097193903	137.05636225296	3.29632802764985
2	149	151.304789194063	10.1731780480802	136.522032757857	2.30478919406261
3	139	140.313250883282	1.69904585396374	135.987703262755	1.31325088328163
4	135	135.524253373633	-1.06634083294633	135.542087459313	0.524253373633115
5	130	127.535253642055	-2.63172529792659	135.096471655872	-2.46474635794522
6	127	124.651001426636	-5.36075591477991	134.709754488144	-2.34899857336373
7	122	119.566749211217	-9.88978653163286	134.323037320415	-2.43325078878257
8	117	112.489962804393	-12.4386052858600	133.948642481467	-4.51003719560703
9	112	109.013176528270	-18.5874241707890	133.574247642519	-2.98682347172974
10	113	109.678342806877	-17.0608040840480	133.382461277171	-3.32165719312295
11	149	150.543508431975	14.2658166562019	133.190674911823	1.54350843197489
12	157	160.751471980194	20.2500950202546	132.998432999551	3.75147198019431
13	157	160.546499193331	20.6473097193903	132.806191087279	3.54649919333085
14	147	151.334200640699	10.1731780480802	132.492621311221	4.33420064069929
15	137	140.121902610874	1.69904585396374	132.179051535162	3.12190261087403
16	132	133.333349815498	-1.06634083294633	131.732991017448	1.33334981549788
17	125	121.344794798192	-2.63172529792659	131.286930499735	-3.65520520180807
18	123	120.712345533623	-5.36075591477991	130.648410381157	-2.28765446637732
19	117	113.879896269053	-9.88978653163286	130.009890262580	-3.12010373094691
20	114	111.243065555655	-12.4386052858600	129.195539730205	-2.75693444434464
21	111	112.206234972959	-18.5874241707890	128.381189197830	1.20623497295941
22	112	113.581938155142	-17.0608040840480	127.478865928906	1.58193815514173
23	144	147.157640683815	14.2658166562019	126.576542659983	3.15764068381506
24	150	154.172650504003	20.2500950202546	125.577254475742	4.17265050400349
25	149	152.774723989109	20.6473097193903	124.577966291501	3.77472398910902
26	134	134.511930855911	10.1731780480802	123.314891096009	0.51193085591062
27	123	122.249138245518	1.69904585396374	122.051815900518	-0.750861754481505
28	116	112.584430578826	-1.06634083294633	120.481910254121	-3.41556942117444
29	117	117.719720690203	-2.63172529792659	118.912004607724	0.719720690202792
30	111	110.143826082345	-5.36075591477991	117.216929832434	-0.856173917654587
31	105	104.367931474488	-9.88978653163286	115.521855057145	-0.63206852551231
32	102	102.468690405776	-12.4386052858600	113.969914880084	0.468690405775675
33	95	96.1694494677654	-18.5874241707890	112.417974703024	1.16944946776542
34	93	91.8566756822555	-17.0608040840480	111.204128401793	-1.14332431774451
35	124	123.743901243237	14.2658166562019	109.990282100562	-0.25609875676345
36	130	130.698049902048	20.2500950202546	109.051855077698	0.698049902047558
37	124	119.239262225776	20.6473097193903	108.113428054834	-4.76073777422435
38	115	112.462349593310	10.1731780480802	107.36447235861	-2.53765040669022
39	106	103.685437483650	1.69904585396374	106.615516662386	-2.31456251634981
40	105	105.063159523894	-1.06634083294633	106.003181309052	0.0631595238943419
41	105	107.240879342209	-2.63172529792659	105.390845955718	2.24087934220869
42	101	102.540062404281	-5.36075591477991	104.820693510499	1.54006240428134
43	95	95.6392454663537	-9.88978653163286	104.250541065279	0.639245466353671
44	93	94.6484780649082	-12.4386052858600	103.790127220952	1.64847806490823
45	84	83.2577107941645	-18.5874241707890	103.329713376624	-0.742289205835462
46	87	87.8084946213283	-17.0608040840480	103.252309462720	0.80849462132825
47	116	114.559277794983	14.2658166562019	103.174905548815	-1.44072220501704
48	120	115.996165051234	20.2500950202546	103.753739928512	-4.00383494876617
49	117	109.020115972402	20.6473097193903	104.332574308208	-7.9798840275982
50	109	102.293314970123	10.1731780480802	105.533506981797	-6.70668502987736
51	105	101.566514490650	1.69904585396374	106.734439655387	-3.43348550935026
52	107	106.680458642266	-1.06634083294633	108.38588219068	-0.319541357733641
53	109	110.594400571953	-2.63172529792659	110.037324725973	1.59440057195314
54	109	111.719856346339	-5.36075591477991	111.640899568440	2.71985634633944
55	108	112.645312120725	-9.88978653163286	113.244474410907	4.64531212072539
56	107	111.526131071247	-12.4386052858600	114.912474214613	4.52613107124738
57	99	100.006950152471	-18.5874241707890	116.580474018318	1.00695015247112
58	103	104.804175735012	-17.0608040840480	118.256628349036	1.80417573501177
59	131	127.801400664043	14.2658166562019	119.932782679755	-3.19859933595659
60	137	132.173516791046	20.2500950202546	121.576388188700	-4.82648320895441

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/01/t1259697670n0rvw45bw9uo17b/1o1md1259697609.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/01/t1259697670n0rvw45bw9uo17b/1o1md1259697609.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/01/t1259697670n0rvw45bw9uo17b/2ss221259697609.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/01/t1259697670n0rvw45bw9uo17b/2ss221259697609.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/01/t1259697670n0rvw45bw9uo17b/39f6s1259697609.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/01/t1259697670n0rvw45bw9uo17b/39f6s1259697609.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/01/t1259697670n0rvw45bw9uo17b/42rgc1259697609.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/01/t1259697670n0rvw45bw9uo17b/42rgc1259697609.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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