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

Title produced by software: Classical Decomposition

Date of computation: Wed, 15 Dec 2010 12:10:10 +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/15/t1292414909dly4l6bgldafnaw.htm/, Retrieved Wed, 15 Dec 2010 13:08:29 +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/15/t1292414909dly4l6bgldafnaw.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:

KDGP2W92

Dataseries X:

» Textbox « » Textfile « » CSV «

13,2 13,8 16,2 14,7 13,9 16,0 14,4 12,3 15,9 15,9 15,5 15,1 14,5 15,1 17,4 16,2 15,6 17,2 14,9 13,8 17,5 16,2 17,5 16,6 16,2 16,6 19,6 15,9 18,0 18,3 16,3 14,9 18,2 18,4 18,5 16,0 17,4 17,2 19,6 17,2 18,3 19,3 18,1 16,2 18,4 20,5 19,0 16,5 18,7 19,0 19,2 20,5 19,3 20,6 20,1 16,1 20,4 19,7 15,6 14,4 13,9 14,3 15,3 14,4 13,8 15,7 14,7 12,5 16,2 16,1 16 15,8 15,2

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

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	13.2	NA	NA	-0.803125	NA
2	13.8	NA	NA	-0.507291666666666	NA
3	16.2	NA	NA	1.26854166666667	NA
4	14.7	NA	NA	-0.115625000000000	NA
5	13.9	NA	NA	0.0385416666666663	NA
6	16	NA	NA	1.24854166666667	NA
7	14.4	14.6327083333333	14.7958333333333	-0.163125	-0.232708333333333
8	12.3	13.0427083333333	14.9041666666667	-1.86145833333333	-0.742708333333331
9	15.9	15.429375	15.0083333333333	0.421041666666666	0.470625000000002
10	15.9	16.3335416666667	15.1208333333333	1.21270833333333	-0.433541666666663
11	15.5	15.634375	15.2541666666667	0.380208333333333	-0.134374999999997
12	15.1	14.2560416666667	15.375	-1.11895833333333	0.843958333333337
13	14.5	14.6427083333333	15.4458333333333	-0.803125	-0.142708333333330
14	15.1	15.021875	15.5291666666667	-0.507291666666666	0.0781250000000018
15	17.4	16.926875	15.6583333333333	1.26854166666667	0.473125000000001
16	16.2	15.621875	15.7375	-0.115625000000000	0.578125000000002
17	15.6	15.871875	15.8333333333333	0.0385416666666663	-0.271875000000001
18	17.2	17.2277083333333	15.9791666666667	1.24854166666667	-0.027708333333333
19	14.9	15.949375	16.1125	-0.163125	-1.04937500000000
20	13.8	14.384375	16.2458333333333	-1.86145833333333	-0.584375
21	17.5	16.8210416666667	16.4	0.421041666666666	0.678958333333334
22	16.2	17.691875	16.4791666666667	1.21270833333333	-1.491875
23	17.5	16.946875	16.5666666666667	0.380208333333333	0.553125000000001
24	16.6	15.5935416666667	16.7125	-1.11895833333333	1.00645833333333
25	16.2	16.0135416666667	16.8166666666667	-0.803125	0.186458333333334
26	16.6	16.4135416666667	16.9208333333333	-0.507291666666666	0.186458333333338
27	19.6	18.264375	16.9958333333333	1.26854166666667	1.335625
28	15.9	17.0010416666667	17.1166666666667	-0.115625000000000	-1.10104166666666
29	18	17.2885416666667	17.25	0.0385416666666663	0.711458333333333
30	18.3	18.5152083333333	17.2666666666667	1.24854166666667	-0.215208333333333
31	16.3	17.1285416666667	17.2916666666667	-0.163125	-0.828541666666666
32	14.9	15.5052083333333	17.3666666666667	-1.86145833333333	-0.605208333333334
33	18.2	17.8127083333333	17.3916666666667	0.421041666666666	0.387291666666663
34	18.4	18.6585416666667	17.4458333333333	1.21270833333333	-0.258541666666666
35	18.5	17.8927083333333	17.5125	0.380208333333333	0.607291666666669
36	16	16.4477083333333	17.5666666666667	-1.11895833333333	-0.447708333333335
37	17.4	16.8802083333333	17.6833333333333	-0.803125	0.519791666666666
38	17.2	17.3052083333333	17.8125	-0.507291666666666	-0.105208333333334
39	19.6	19.1435416666667	17.875	1.26854166666667	0.456458333333334
40	17.2	17.8552083333333	17.9708333333333	-0.115625000000000	-0.655208333333331
41	18.3	18.1177083333333	18.0791666666667	0.0385416666666663	0.182291666666668
42	19.3	19.369375	18.1208333333333	1.24854166666667	-0.0693749999999973
43	18.1	18.0327083333333	18.1958333333333	-0.163125	0.0672916666666659
44	16.2	16.4635416666667	18.325	-1.86145833333333	-0.263541666666665
45	18.4	18.804375	18.3833333333333	0.421041666666666	-0.404374999999998
46	20.5	19.716875	18.5041666666667	1.21270833333333	0.783125000000005
47	19	19.0635416666667	18.6833333333333	0.380208333333333	-0.0635416666666657
48	16.5	17.6602083333333	18.7791666666667	-1.11895833333333	-1.16020833333334
49	18.7	18.1135416666667	18.9166666666667	-0.803125	0.586458333333333
50	19	18.4885416666667	18.9958333333333	-0.507291666666666	0.511458333333334
51	19.2	20.3435416666667	19.075	1.26854166666667	-1.14354166666667
52	20.5	19.009375	19.125	-0.115625000000000	1.490625
53	19.3	18.9885416666667	18.95	0.0385416666666663	0.311458333333334
54	20.6	19.969375	18.7208333333333	1.24854166666667	0.630625000000002
55	20.1	18.2702083333333	18.4333333333333	-0.163125	1.82979166666667
56	16.1	16.1760416666667	18.0375	-1.86145833333333	-0.076041666666665
57	20.4	18.1002083333333	17.6791666666667	0.421041666666666	2.29979166666666
58	19.7	18.4752083333333	17.2625	1.21270833333333	1.22479166666666
59	15.6	17.159375	16.7791666666667	0.380208333333333	-1.559375
60	14.4	15.226875	16.3458333333333	-1.11895833333333	-0.826874999999998
61	13.9	15.1135416666667	15.9166666666667	-0.803125	-1.21354166666666
62	14.3	15.034375	15.5416666666667	-0.507291666666666	-0.734374999999998
63	15.3	16.4852083333333	15.2166666666667	1.26854166666667	-1.18520833333333
64	14.4	14.7760416666667	14.8916666666667	-0.115625000000000	-0.376041666666666
65	13.8	14.796875	14.7583333333333	0.0385416666666663	-0.996875
66	15.7	16.081875	14.8333333333333	1.24854166666667	-0.381875000000001
67	14.7	14.7827083333333	14.9458333333333	-0.163125	-0.0827083333333345
68	12.5	NA	NA	-1.86145833333333	NA
69	16.2	NA	NA	0.421041666666666	NA
70	16.1	NA	NA	1.21270833333333	NA
71	16	NA	NA	0.380208333333333	NA
72	15.8	NA	NA	-1.11895833333333	NA
73	15.2	NA	NA	NA	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292414909dly4l6bgldafnaw/1n0ff1292415007.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292414909dly4l6bgldafnaw/1n0ff1292415007.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292414909dly4l6bgldafnaw/2n0ff1292415007.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292414909dly4l6bgldafnaw/2n0ff1292415007.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292414909dly4l6bgldafnaw/3x9ei1292415007.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292414909dly4l6bgldafnaw/3x9ei1292415007.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292414909dly4l6bgldafnaw/4x9ei1292415007.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292414909dly4l6bgldafnaw/4x9ei1292415007.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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