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*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: Sun, 26 Dec 2010 13:10:58 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/26/t1293369048fab6hbwac7xfk6b.htm/, Retrieved Sun, 26 Dec 2010 14:10:48 +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/2010/Dec/26/t1293369048fab6hbwac7xfk6b.htm/},
    year = {2010},
}
@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 = {2010},
    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:

Decomposition by Loess bouwgrondprijzen: gemiddelde prijs(€/m²)

Dataseries X:

» Textbox « » Textfile « » CSV «

26 26 27 28 27 29 27 30 27 30 32 30 32 33 34 32 34 37 37 36 34 38 41 41 44 42 45 45 49 54 52 53 51 55 60 60 63 60 64 65 75 70 72 69 75 74 74 75 79 79 85 78 84 85 85 82 91 90 98 98

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	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Seasonal Decomposition by Loess - Parameters
Component	Window	Degree	Jump
Seasonal	601	0	61
Trend	7	1	1
Low-pass	5	1	1

Seasonal Decomposition by Loess - Time Series Components
t	Observed	Fitted	Seasonal	Trend	Remainder
1	26	26.1175380073450	0.313949820128525	25.5685121725265	0.117538007344969
2	26	25.9399375111189	-0.151532450571166	26.2115949394523	-0.0600624888811474
3	27	26.245134284286	0.928632273477323	26.8262334422367	-0.754865715714011
4	28	29.6788777536470	-1.09105790998472	27.4121801563378	1.67887775364696
5	27	25.8742815646295	0.313949820128525	27.811768615242	-1.12571843537053
6	29	30.2338155088116	-0.151532450571166	27.9177169417595	1.23381550881163
7	27	24.8003157936680	0.928632273477323	28.2710519328547	-2.19968420633203
8	30	32.7044201929765	-1.09105790998472	28.3866377170082	2.70442019297654
9	27	24.6048551733869	0.313949820128525	29.0811950064846	-2.39514482661313
10	30	30.4646593476744	-0.151532450571166	29.6868731028968	0.464659347674374
11	32	32.60805525649	0.928632273477323	30.4633124700327	0.608055256489969
12	30	29.7403485271169	-1.09105790998472	31.3507093828678	-0.259651472883093
13	32	31.6997161717479	0.313949820128525	31.9863340081236	-0.300283828252095
14	33	33.6084901907635	-0.151532450571166	32.5430422598077	0.608490190763483
15	34	34.0515072294560	0.928632273477323	33.0198604970666	0.0515072294560355
16	32	31.4401865369826	-1.09105790998472	33.6508713730021	-0.559813463017363
17	34	33.0868911461567	0.313949820128525	34.5991590337148	-0.913108853843333
18	37	38.5777553693303	-0.151532450571166	35.5737770812409	1.57775536933026
19	37	36.9389514420032	0.928632273477323	36.1324162845195	-0.0610485579968483
20	36	37.022276235323	-1.09105790998472	36.0687816746617	1.02227623532298
21	34	31.1045855726682	0.313949820128525	36.5814646072033	-2.89541442733181
22	38	38.3646069409068	-0.151532450571166	37.7869255096643	0.364606940906818
23	41	41.2516140986738	0.928632273477323	39.8197536278489	0.251614098673798
24	41	41.4970037467422	-1.09105790998472	41.5940541632425	0.497003746742244
25	44	45.1254746077308	0.313949820128525	42.5605755721407	1.12547460773075
26	42	40.7262400683599	-0.151532450571166	43.4252923822113	-1.27375993164014
27	45	44.5512357505744	0.928632273477323	44.5201319759483	-0.448764249425587
28	45	44.4401863221266	-1.09105790998472	46.6508715878581	-0.559813677873386
29	49	48.4871073009848	0.313949820128525	49.1989428788867	-0.512892699015211
30	54	57.0035151079101	-0.151532450571166	51.1480173426611	3.00351510791010
31	52	50.7132996004824	0.928632273477323	52.3580681260403	-1.28670039951758
32	53	54.5350471610443	-1.09105790998472	52.5560107489404	1.53504716104432
33	51	48.1045858361422	0.313949820128525	53.5814643437293	-2.89541416385783
34	55	54.6517294207234	-0.151532450571166	55.4998030298478	-0.348270579276637
35	60	60.9644915702393	0.928632273477323	58.1068761562834	0.964491570239282
36	60	60.7971654504018	-1.09105790998472	60.2938924595829	0.797165450401828
37	63	64.3818604414934	0.313949820128525	61.304189738378	1.38186044149342
38	60	57.9133085753379	-0.151532450571166	62.2382238752333	-2.08669142466213
39	64	62.8508563260157	0.928632273477323	64.220511400507	-1.14914367398428
40	65	63.7585820928584	-1.09105790998472	67.3324758171263	-1.24141790714161
41	75	79.8899783359786	0.313949820128525	69.7960718438929	4.88997833597861
42	70	69.0139027985854	-0.151532450571166	71.1376296519858	-0.986097201414594
43	72	71.7638428197176	0.928632273477323	71.3075249068051	-0.236157180282405
44	69	67.1582581842085	-1.09105790998472	71.9327997257762	-1.84174181579148
45	75	76.8383541331085	0.313949820128525	72.847696046763	1.83835413310848
46	74	74.3521087966641	-0.151532450571166	73.7994236539071	0.352108796664083
47	74	72.2255288597558	0.928632273477323	74.8458388667668	-1.77447114024416
48	75	74.9967334528375	-1.09105790998472	76.0943244571472	-0.003266547162454
49	79	79.5689276458744	0.313949820128525	78.117122533997	0.568927645874439
50	79	78.1702354400829	-0.151532450571166	79.9812970104883	-0.829764559917137
51	85	88.1156279524687	0.928632273477323	80.955739774054	3.11562795246869
52	78	74.9326078141394	-1.09105790998472	82.1584500958453	-3.06739218586061
53	84	84.7252593311492	0.313949820128525	82.9607908487222	0.725259331149246
54	85	86.4652048130338	-0.151532450571166	83.6863276375373	1.46520481303385
55	85	84.3511240016062	0.928632273477323	84.7202437249165	-0.648875998393791
56	82	78.8888312672925	-1.09105790998472	86.2022266426923	-3.11116873270753
57	91	93.1072409303075	0.313949820128525	88.578809249564	2.10724093030753
58	90	87.9123115226084	-0.151532450571166	92.2392209279627	-2.08768847739155
59	98	99.2690503724982	0.928632273477323	95.8023173540244	1.26905037249824
60	98	97.68975943274	-1.09105790998472	99.4012984772446	-0.310240567259910

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293369048fab6hbwac7xfk6b/1mag51293369054.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293369048fab6hbwac7xfk6b/1mag51293369054.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293369048fab6hbwac7xfk6b/2mag51293369054.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293369048fab6hbwac7xfk6b/2mag51293369054.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293369048fab6hbwac7xfk6b/3f1x81293369054.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293369048fab6hbwac7xfk6b/3f1x81293369054.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293369048fab6hbwac7xfk6b/48sxt1293369054.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293369048fab6hbwac7xfk6b/48sxt1293369054.ps (open in new window)

Parameters (Session):

par1 = 4 ; par2 = periodic ; par3 = 0 ; par5 = 1 ; par7 = 1 ; par8 = FALSE ;

Parameters (R input):

par1 = 4 ; 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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