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*The author of this computation has been verified*

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

Title produced by software: Classical Decomposition

Date of computation: Thu, 03 Dec 2009 09:24:13 -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/03/t1259858020f2t92j8gjp6qh6k.htm/, Retrieved Thu, 03 Dec 2009 17:33:45 +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/03/t1259858020f2t92j8gjp6qh6k.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 «

22 22 20 21 20 21 21 21 19 21 21 22 19 24 22 22 22 24 22 23 24 21 20 22 23 23 22 20 21 21 20 20 17 18 19 19 20 21 20 21 19 22 20 18 16 17 18 19 18 20 21 18 19 19 19 21 19 19 17 16 16 17 16 15 16 16 16 18 19 16 16 16

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	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	22	NA	NA	-0.493055555555556	NA
2	22	NA	NA	1.37361111111111	NA
3	20	NA	NA	0.598611111111111	NA
4	21	NA	NA	-0.359722222222221	NA
5	20	NA	NA	-0.0763888888888878	NA
6	21	NA	NA	1.01527777777778	NA
7	21	21.0069444444444	20.7916666666667	0.215277777777778	-0.00694444444444642
8	21	21.2569444444444	20.75	0.506944444444444	-0.256944444444446
9	19	19.8986111111111	20.9166666666667	-1.01805555555556	-0.898611111111109
10	21	20.3069444444444	21.0416666666667	-0.734722222222221	0.693055555555553
11	21	20.3152777777778	21.1666666666667	-0.85138888888889	0.68472222222222
12	22	21.1986111111111	21.375	-0.176388888888889	0.80138888888889
13	19	21.0486111111111	21.5416666666667	-0.493055555555556	-2.04861111111111
14	24	23.0402777777778	21.6666666666667	1.37361111111111	0.959722222222222
15	22	22.5569444444444	21.9583333333333	0.598611111111111	-0.556944444444447
16	22	21.8069444444444	22.1666666666667	-0.359722222222221	0.193055555555556
17	22	22.0486111111111	22.125	-0.0763888888888878	-0.0486111111111107
18	24	23.0986111111111	22.0833333333333	1.01527777777778	0.901388888888889
19	22	22.4652777777778	22.25	0.215277777777778	-0.465277777777779
20	23	22.8819444444444	22.375	0.506944444444444	0.118055555555561
21	24	21.3152777777778	22.3333333333333	-1.01805555555556	2.68472222222222
22	21	21.5152777777778	22.25	-0.734722222222221	-0.515277777777779
23	20	21.2736111111111	22.125	-0.85138888888889	-1.27361111111111
24	22	21.7819444444444	21.9583333333333	-0.176388888888889	0.218055555555559
25	23	21.2569444444444	21.75	-0.493055555555556	1.74305555555556
26	23	22.9152777777778	21.5416666666667	1.37361111111111	0.0847222222222257
27	22	21.7236111111111	21.125	0.598611111111111	0.276388888888889
28	20	20.3486111111111	20.7083333333333	-0.359722222222221	-0.348611111111111
29	21	20.4652777777778	20.5416666666667	-0.0763888888888878	0.534722222222225
30	21	21.3902777777778	20.375	1.01527777777778	-0.390277777777776
31	20	20.3402777777778	20.125	0.215277777777778	-0.340277777777775
32	20	20.4236111111111	19.9166666666667	0.506944444444444	-0.423611111111107
33	17	18.7319444444444	19.75	-1.01805555555556	-1.73194444444444
34	18	18.9736111111111	19.7083333333333	-0.734722222222221	-0.973611111111108
35	19	18.8152777777778	19.6666666666667	-0.85138888888889	0.18472222222222
36	19	19.4486111111111	19.625	-0.176388888888889	-0.448611111111109
37	20	19.1736111111111	19.6666666666667	-0.493055555555556	0.82638888888889
38	21	20.9569444444444	19.5833333333333	1.37361111111111	0.0430555555555543
39	20	20.0569444444444	19.4583333333333	0.598611111111111	-0.0569444444444436
40	21	19.0152777777778	19.375	-0.359722222222221	1.98472222222222
41	19	19.2152777777778	19.2916666666667	-0.0763888888888878	-0.215277777777779
42	22	20.2652777777778	19.25	1.01527777777778	1.73472222222222
43	20	19.3819444444444	19.1666666666667	0.215277777777778	0.618055555555557
44	18	19.5486111111111	19.0416666666667	0.506944444444444	-1.54861111111111
45	16	18.0236111111111	19.0416666666667	-1.01805555555556	-2.02361111111111
46	17	18.2236111111111	18.9583333333333	-0.734722222222221	-1.22361111111111
47	18	17.9819444444444	18.8333333333333	-0.85138888888889	0.0180555555555557
48	19	18.5319444444444	18.7083333333333	-0.176388888888889	0.468055555555555
49	18	18.0486111111111	18.5416666666667	-0.493055555555556	-0.0486111111111072
50	20	19.9986111111111	18.625	1.37361111111111	0.00138888888888999
51	21	19.4736111111111	18.875	0.598611111111111	1.52638888888889
52	18	18.7236111111111	19.0833333333333	-0.359722222222221	-0.723611111111111
53	19	19.0486111111111	19.125	-0.0763888888888878	-0.0486111111111072
54	19	19.9736111111111	18.9583333333333	1.01527777777778	-0.973611111111111
55	19	18.9652777777778	18.75	0.215277777777778	0.0347222222222179
56	21	19.0486111111111	18.5416666666667	0.506944444444444	1.95138888888889
57	19	17.1902777777778	18.2083333333333	-1.01805555555556	1.80972222222222
58	19	17.1402777777778	17.875	-0.734722222222221	1.85972222222222
59	17	16.7736111111111	17.625	-0.85138888888889	0.226388888888888
60	16	17.1986111111111	17.375	-0.176388888888889	-1.19861111111111
61	16	16.6319444444444	17.125	-0.493055555555556	-0.631944444444446
62	17	18.2486111111111	16.875	1.37361111111111	-1.24861111111111
63	16	17.3486111111111	16.75	0.598611111111111	-1.34861111111111
64	15	16.2652777777778	16.625	-0.359722222222221	-1.26527777777778
65	16	16.3819444444444	16.4583333333333	-0.0763888888888878	-0.381944444444443
66	16	17.4319444444444	16.4166666666667	1.01527777777778	-1.43194444444444
67	16	NA	NA	0.215277777777778	NA
68	18	NA	NA	0.506944444444444	NA
69	19	NA	NA	-1.01805555555556	NA
70	16	NA	NA	-0.734722222222221	NA
71	16	NA	NA	-0.85138888888889	NA
72	16	NA	NA	-0.176388888888889	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/03/t1259858020f2t92j8gjp6qh6k/18l2k1259857449.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/03/t1259858020f2t92j8gjp6qh6k/18l2k1259857449.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/03/t1259858020f2t92j8gjp6qh6k/2xl3i1259857449.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/03/t1259858020f2t92j8gjp6qh6k/2xl3i1259857449.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/03/t1259858020f2t92j8gjp6qh6k/38qro1259857449.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/03/t1259858020f2t92j8gjp6qh6k/38qro1259857449.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/03/t1259858020f2t92j8gjp6qh6k/47t0d1259857449.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/03/t1259858020f2t92j8gjp6qh6k/47t0d1259857449.ps (open in new window)

Parameters (Session):

par1 = additive ; par2 = 12 ;

Parameters (R input):

par1 = additive ; par2 = 12 ;

R code (references can be found in the software module):

par2 <- as.numeric(par2)

x <- ts(x,freq=par2)

m <- decompose(x,type=par1)

m$figure

bitmap(file='test1.png')

plot(m)

dev.off()

mylagmax <- length(x)/2

bitmap(file='test2.png')

op <- par(mfrow = c(2,2))

acf(as.numeric(x),lag.max = mylagmax,main='Observed')

acf(as.numeric(m$trend),na.action=na.pass,lag.max = mylagmax,main='Trend')

acf(as.numeric(m$seasonal),na.action=na.pass,lag.max = mylagmax,main='Seasonal')

acf(as.numeric(m$random),na.action=na.pass,lag.max = mylagmax,main='Random')

par(op)

dev.off()

bitmap(file='test3.png')

op <- par(mfrow = c(2,2))

spectrum(as.numeric(x),main='Observed')

spectrum(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')

spectrum(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')

spectrum(as.numeric(m$random[!is.na(m$random)]),main='Random')

par(op)

dev.off()

bitmap(file='test4.png')

op <- par(mfrow = c(2,2))

cpgram(as.numeric(x),main='Observed')

cpgram(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')

cpgram(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')

cpgram(as.numeric(m$random[!is.na(m$random)]),main='Random')

par(op)

dev.off()

load(file='createtable')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Classical Decomposition by Moving Averages',6,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'t',header=TRUE)

a<-table.element(a,'Observations',header=TRUE)

a<-table.element(a,'Fit',header=TRUE)

a<-table.element(a,'Trend',header=TRUE)

a<-table.element(a,'Seasonal',header=TRUE)

a<-table.element(a,'Random',header=TRUE)

a<-table.row.end(a)

for (i in 1:length(m$trend)) {

a<-table.row.start(a)

a<-table.element(a,i,header=TRUE)

a<-table.element(a,x[i])

if (par1 == 'additive') a<-table.element(a,m$trend[i]+m$seasonal[i]) else a<-table.element(a,m$trend[i]*m$seasonal[i])

a<-table.element(a,m$trend[i])

a<-table.element(a,m$seasonal[i])

a<-table.element(a,m$random[i])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable.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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