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KDGP2W52

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Wed, 13 Jan 2010 15:25:04 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Jan/13/t1263421551fcjc4l3dolape8w.htm/, Retrieved Wed, 13 Jan 2010 23:25:56 +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/Jan/13/t1263421551fcjc4l3dolape8w.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:

KDGP2W52

Dataseries X:

» Textbox « » Textfile « » CSV «

1 4 -3 -3 0 6 -1 0 -1 1 -4 -1 -1 0 3 0 8 8 8 8 11 13 5 12 13 9 11 7 12 11 10 13 14 10 13 12 13 17 15 6 9 6 11 12 13 11 16 16 19 14 15 12 14 16 13 13 15 12 13 12 15 10 8 11 8 13 9 8 8 6 8 6 12 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	2 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	-331	NA	NA	0.214409722222223	NA
2	3	NA	NA	-0.00434027777777812	NA
3	0	NA	NA	-3.15017361111111	NA
4	8	NA	NA	-1.35850694444444	NA
5	8	NA	NA	-0.577256944444445	NA
6	8	NA	NA	-1.25434027777778	NA
7	8	-6.56684027777778	-6	-0.566840277777777	14.5668402777778
8	11	12.7456597222222	8.5	4.24565972222222	-1.74565972222222
9	13	8.23524305555556	9.125	-0.889756944444444	4.76475694444444
10	5	11.2873263888889	9.58333333333333	1.70399305555556	-6.28732638888889
11	12	8.98524305555555	9.875	-0.889756944444445	3.01475694444445
12	13	12.6102430555556	10.0833333333333	2.52690972222222	0.389756944444446
13	9	10.5894097222222	10.375	0.214409722222223	-1.58940972222222
14	11	10.7039930555556	10.7083333333333	-0.00434027777777812	0.296006944444445
15	7	7.55815972222222	10.7083333333333	-3.15017361111111	-0.558159722222221
16	12	9.55815972222222	10.9166666666667	-1.35850694444444	2.44184027777778
17	11	10.6727430555556	11.25	-0.577256944444445	0.327256944444445
18	10	9.99565972222222	11.25	-1.25434027777778	0.00434027777777857
19	13	11.0164930555556	11.5833333333333	-0.566840277777777	1.98350694444444
20	14	16.3289930555556	12.0833333333333	4.24565972222222	-2.32899305555556
21	10	11.3185763888889	12.2083333333333	-0.889756944444444	-1.31857638888889
22	13	13.7456597222222	12.0416666666667	1.70399305555556	-0.745659722222221
23	12	10.8185763888889	11.7083333333333	-0.889756944444445	1.18142361111111
24	13	14.0685763888889	11.5416666666667	2.52690972222222	-1.06857638888889
25	17	11.7560763888889	11.5416666666667	0.214409722222223	5.24392361111111
26	15	11.4539930555556	11.4583333333333	-0.00434027777777812	3.54600694444444
27	6	8.30815972222222	11.4583333333333	-3.15017361111111	-2.30815972222222
28	9	10.2664930555556	11.625	-1.35850694444444	-1.26649305555556
29	6	11.3394097222222	11.9166666666667	-0.577256944444445	-5.33940972222222
30	11	11.0789930555556	12.3333333333333	-1.25434027777778	-0.0789930555555554
31	12	11.8914930555556	12.4583333333333	-0.566840277777777	0.108506944444443
32	13	16.5789930555556	12.3333333333333	4.24565972222222	-3.57899305555555
33	11	11.6935763888889	12.5833333333333	-0.889756944444444	-0.693576388888888
34	16	14.7456597222222	13.0416666666667	1.70399305555556	1.25434027777778
35	16	12.7769097222222	13.6666666666667	-0.889756944444445	3.22309027777778
36	19	16.6935763888889	14.1666666666667	2.52690972222222	2.30642361111111
37	14	14.5060763888889	14.2916666666667	0.214409722222223	-0.506076388888891
38	15	14.4123263888889	14.4166666666667	-0.00434027777777812	0.587673611111112
39	12	11.3914930555556	14.5416666666667	-3.15017361111111	0.608506944444443
40	14	13.0998263888889	14.4583333333333	-1.35850694444444	0.900173611111112
41	16	13.5894097222222	14.1666666666667	-0.577256944444445	2.41059027777778
42	13	12.5789930555556	13.8333333333333	-1.25434027777778	0.421006944444446
43	13	12.9331597222222	13.5	-0.566840277777777	0.0668402777777768
44	15	17.2873263888889	13.0416666666667	4.24565972222222	-2.28732638888889
45	12	11.8185763888889	12.7083333333333	-0.889756944444444	0.181423611111111
46	13	14.1206597222222	12.4166666666667	1.70399305555556	-1.12065972222222
47	12	11.1519097222222	12.0416666666667	-0.889756944444445	0.848090277777779
48	15	14.2769097222222	11.75	2.52690972222222	0.723090277777779
49	10	11.5894097222222	11.375	0.214409722222223	-1.58940972222222
50	8	10.8706597222222	10.875	-0.00434027777777812	-2.87065972222222
51	11	7.18315972222222	10.3333333333333	-3.15017361111111	3.81684027777778
52	8	8.51649305555556	9.875	-1.35850694444444	-0.516493055555555
53	13	8.83940972222222	9.41666666666667	-0.577256944444445	4.16059027777778
54	9	7.78732638888889	9.04166666666667	-1.25434027777778	1.21267361111111
55	8	8.59982638888889	9.16666666666666	-0.566840277777777	-0.599826388888888
56	8	NA	NA	4.24565972222222	NA
57	6	NA	NA	-0.889756944444444	NA
58	8	NA	NA	1.70399305555556	NA
59	6	NA	NA	-0.889756944444445	NA
60	12	NA	NA	2.52690972222222	NA
61	16	NA	NA	NA	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jan/13/t1263421551fcjc4l3dolape8w/1uvcu1263421501.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/13/t1263421551fcjc4l3dolape8w/1uvcu1263421501.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/13/t1263421551fcjc4l3dolape8w/2iebb1263421501.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/13/t1263421551fcjc4l3dolape8w/2iebb1263421501.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/13/t1263421551fcjc4l3dolape8w/3lsia1263421501.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/13/t1263421551fcjc4l3dolape8w/3lsia1263421501.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/13/t1263421551fcjc4l3dolape8w/4uc351263421501.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/13/t1263421551fcjc4l3dolape8w/4uc351263421501.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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