Home » date » 2009 » Jun » 02 »

Opgave 9, oefening 1, Stap 1, Sara Vandenberghe, de juiste versie

*Unverified author*

R Software Module: rwasp_decomposeloess.wasp (opens new window with default values)

Title produced by software: Decomposition by Loess

Date of computation: Tue, 02 Jun 2009 01:26:59 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Jun/02/t1243927666ll5uuz0j4affhhe.htm/, Retrieved Tue, 02 Jun 2009 09:27:52 +0200

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/Jun/02/t1243927666ll5uuz0j4affhhe.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 «

41086 39690 43129 37863 35953 29133 24693 22205 21725 27192 21790 13253 37702 30364 32609 30212 29965 28352 25814 22414 20506 28806 22228 13971 36845 35338 35022 34777 26887 23970 22780 17351 21382 24561 17409 11514 31514 27071 29462 26105 22397 23843 21705 18089 20764 25316 17704 15548 28029 29383 36438 32034 22679 24319 18004 17537 20366 22782 19169 13807 29743 25591 29096 26482 22405 27044 17970 18730 19684 19785 18479 10698

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	721	0	73
Trend	19	1	2
Low-pass	13	1	2

Seasonal Decomposition by Loess - Time Series Components
t	Observed	Fitted	Seasonal	Trend	Remainder
1	41086	39765.5877520622	8016.04177882911	34390.3704691087	-1320.41224793783
2	39690	40483.6159166965	5307.45671236408	33588.9273709394	793.615916696486
3	43129	44904.8115425091	8565.70418472071	32787.4842727702	1775.81154250913
4	37863	38041.8885082853	5665.29615310634	32018.8153386084	178.888508285268
5	35953	39374.9678060536	1280.88578949974	31250.1464044466	3421.96780605364
6	29133	26945.6209875880	803.419222166538	30516.9597902454	-2187.37901241197
7	24693	22954.6053756259	-3352.37855167013	29783.7731760442	-1738.39462437410
8	22205	21012.5446188337	-5652.56678712428	29050.0221682906	-1192.45538116634
9	21725	19296.3142029408	-4162.58536347779	28316.2711605370	-2428.68579705923
10	27192	26702.3207129158	-2.76958531050464	27684.4488723947	-489.679287084222
11	21790	21649.9964985952	-5122.6230828476	27052.6265842524	-140.003501404826
12	13253	11005.7402153704	-11345.8784375805	26846.1382222102	-2247.25978462961
13	37702	40748.308361003	8016.04177882911	26639.6498601679	3046.30836100299
14	30364	28755.9675613027	5307.45671236408	26664.5757263332	-1608.03243869726
15	32609	29962.7942227808	8565.70418472071	26689.5015924984	-2646.20577721916
16	30212	28007.0165879138	5665.29615310634	26751.6872589799	-2204.9834120862
17	29965	31835.241285039	1280.88578949974	26813.8729254613	1870.241285039
18	28352	28984.8872877684	803.419222166538	26915.6934900651	632.887287768386
19	25814	27962.8644970013	-3352.37855167013	27017.5140546689	2148.86449700125
20	22414	23289.939069944	-5652.56678712428	27190.6277171803	875.939069944005
21	20506	17810.8439837861	-4162.58536347779	27363.7413796917	-2695.15601621388
22	28806	30170.7731880218	-2.76958531050464	27443.9963972887	1364.77318802182
23	22228	22054.3716679619	-5122.6230828476	27524.2514148857	-173.628332038101
24	13971	11928.2861887096	-11345.8784375805	27359.5922488709	-2042.71381129037
25	36845	38479.0251383148	8016.04177882911	27194.9330828561	1634.02513831475
26	35338	38422.2093830700	5307.45671236408	26946.3339045659	3084.20938307004
27	35022	34780.5610890037	8565.70418472071	26697.7347262756	-241.438910996323
28	34777	37487.7864545794	5665.29615310634	26400.9173923142	2710.78645457942
29	26887	26389.0141521474	1280.88578949974	26104.1000583529	-497.9858478526
30	23970	21444.3240703537	803.419222166538	25692.2567074797	-2525.67592964626
31	22780	23631.9651950636	-3352.37855167013	25280.4133566066	851.965195063556
32	17351	15586.6767269591	-5652.56678712428	24767.8900601652	-1764.32327304094
33	21382	22671.2185997539	-4162.58536347779	24255.3667637239	1289.21859975392
34	24561	25328.0179503678	-2.76958531050464	23796.7516349427	767.01795036778
35	17409	16602.4865766860	-5122.6230828476	23338.1365061616	-806.513423313969
36	11514	11284.6111677483	-11345.8784375805	23089.2672698323	-229.388832251727
37	31514	32171.5601876679	8016.04177882911	22840.398033503	657.560187667907
38	27071	26049.5941221377	5307.45671236408	22784.9491654982	-1021.40587786227
39	29462	27628.7955177859	8565.70418472071	22729.5002974934	-1833.20448221411
40	26105	23738.83918743	5665.29615310634	22805.8646594637	-2366.16081257002
41	22397	20630.8851890663	1280.88578949974	22882.2290214340	-1766.11481093370
42	23843	23853.5358689037	803.419222166538	23029.0449089298	10.5358689036875
43	21705	23586.5177552445	-3352.37855167013	23175.8607964256	1881.51775524455
44	18089	18405.6288275960	-5652.56678712428	23424.9379595283	316.628827595952
45	20764	22016.5702408467	-4162.58536347779	23674.0151226311	1252.57024084672
46	25316	26712.4449074679	-2.76958531050464	23922.3246778426	1396.44490746794
47	17704	16359.9888497936	-5122.6230828476	24170.6342330540	-1344.01115020644
48	15548	18246.044990641	-11345.8784375805	24195.8334469395	2698.04499064101
49	28029	23820.9255603458	8016.04177882911	24221.0326608250	-4208.07443965416
50	29383	29357.6619172478	5307.45671236408	24100.8813703881	-25.3380827521505
51	36438	40329.5657353282	8565.70418472071	23980.7300799511	3891.5657353282
52	32034	34500.6376160078	5665.29615310634	23902.0662308859	2466.63761600781
53	22679	20253.7118286797	1280.88578949974	23823.4023818206	-2425.28817132035
54	24319	24089.7381878047	803.419222166538	23744.8425900287	-229.261812195284
55	18004	15694.0957534333	-3352.37855167013	23666.2827982369	-2309.90424656674
56	17537	17284.0365297475	-5652.56678712428	23442.5302573768	-252.963470252518
57	20366	21675.8076469611	-4162.58536347779	23218.7777165167	1309.80764696106
58	22782	22601.6558894239	-2.76958531050464	22965.1136958866	-180.344110576123
59	19169	20749.1734075911	-5122.6230828476	22711.4496752565	1580.17340759108
60	13807	16311.7423297352	-11345.8784375805	22648.1361078453	2504.74232973524
61	29743	28885.1356807368	8016.04177882911	22584.8225404341	-857.864319263212
62	25591	23315.3181746960	5307.45671236408	22559.2251129399	-2275.68182530402
63	29096	27092.6681298335	8565.70418472071	22533.6276854458	-2003.33187016649
64	26482	24788.7295232435	5665.29615310634	22509.9743236502	-1693.27047675652
65	22405	21042.7932486457	1280.88578949974	22486.3209618546	-1362.20675135432
66	27044	30815.0152992919	803.419222166538	22469.5654785415	3771.01529929193
67	17970	16839.5685564417	-3352.37855167013	22452.8099952285	-1130.43144355834
68	18730	20648.9163639743	-5652.56678712428	22463.65042315	1918.91636397429
69	19684	21056.0945124063	-4162.58536347779	22474.4908510715	1372.09451240629
70	19785	17073.1635633569	-2.76958531050464	22499.6060219537	-2711.83643664315
71	18479	19555.9018900118	-5122.6230828476	22524.7211928358	1076.9018900118
72	10698	10189.5107696766	-11345.8784375805	22552.3676679039	-508.489230323401

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t1243927666ll5uuz0j4affhhe/1d6oq1243927617.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t1243927666ll5uuz0j4affhhe/1d6oq1243927617.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t1243927666ll5uuz0j4affhhe/2xjav1243927617.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t1243927666ll5uuz0j4affhhe/2xjav1243927617.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t1243927666ll5uuz0j4affhhe/3tiaf1243927617.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t1243927666ll5uuz0j4affhhe/3tiaf1243927617.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t1243927666ll5uuz0j4affhhe/4rphp1243927617.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t1243927666ll5uuz0j4affhhe/4rphp1243927617.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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