Home » date » 2010 » Dec » 03 »

ws 9

*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: Fri, 03 Dec 2010 14:45:00 +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/03/t1291387391k9u6i57i3dm18ca.htm/, Retrieved Fri, 03 Dec 2010 15:43:11 +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/03/t1291387391k9u6i57i3dm18ca.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:

Dataseries X:

» Textbox « » Textfile « » CSV «

46 62 66 59 58 61 41 27 58 70 49 59 44 36 72 45 56 54 53 35 61 52 47 51 52 63 74 45 51 64 36 30 55 64 39 40 63 45 59 55 40 64 27 28 45 57 45 69 60 56 58 50 51 53 37 22 55 70 62 58 39 49 58 47 42 62 39 40 72 70 54 65

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	46	NA	NA	1.15902777777777	NA
2	62	NA	NA	-0.732638888888892	NA
3	66	NA	NA	13.4423611111111	NA
4	59	NA	NA	-2.47430555555555	NA
5	58	NA	NA	-2.91597222222222	NA
6	61	NA	NA	8.39236111111111	NA
7	41	42.0673611111111	54.5833333333333	-12.5159722222222	-1.06736111111111
8	27	30.6673611111111	53.4166666666667	-22.7493055555556	-3.66736111111111
9	58	56.4090277777778	52.5833333333333	3.82569444444445	1.59097222222223
10	70	64.0423611111111	52.25	11.7923611111111	5.9576388888889
11	49	49.4090277777778	51.5833333333333	-2.17430555555556	-0.409027777777773
12	59	56.1590277777778	51.2083333333333	4.95069444444444	2.84097222222222
13	44	52.5756944444445	51.4166666666667	1.15902777777777	-8.57569444444445
14	36	51.5173611111111	52.25	-0.732638888888892	-15.5173611111111
15	72	66.1506944444444	52.7083333333333	13.4423611111111	5.84930555555556
16	45	49.6090277777778	52.0833333333333	-2.47430555555555	-4.60902777777778
17	56	48.3340277777778	51.25	-2.91597222222222	7.66597222222223
18	54	59.2256944444444	50.8333333333333	8.39236111111111	-5.22569444444444
19	53	38.3173611111111	50.8333333333333	-12.5159722222222	14.6826388888889
20	35	29.5423611111111	52.2916666666667	-22.7493055555556	5.45763888888889
21	61	57.3256944444444	53.5	3.82569444444445	3.67430555555556
22	52	65.3756944444444	53.5833333333333	11.7923611111111	-13.3756944444444
23	47	51.2006944444444	53.375	-2.17430555555556	-4.20069444444444
24	51	58.5340277777778	53.5833333333333	4.95069444444444	-7.53402777777777
25	52	54.4506944444444	53.2916666666667	1.15902777777777	-2.45069444444444
26	63	51.6423611111111	52.375	-0.732638888888892	11.3576388888889
27	74	65.3590277777778	51.9166666666667	13.4423611111111	8.64097222222222
28	45	49.6923611111111	52.1666666666667	-2.47430555555555	-4.69236111111111
29	51	49.4173611111111	52.3333333333333	-2.91597222222222	1.58263888888889
30	64	59.9340277777778	51.5416666666667	8.39236111111111	4.06597222222222
31	36	39.0256944444444	51.5416666666667	-12.5159722222222	-3.02569444444444
32	30	28.5006944444444	51.25	-22.7493055555556	1.49930555555556
33	55	53.7006944444444	49.875	3.82569444444445	1.29930555555556
34	64	61.4590277777778	49.6666666666667	11.7923611111111	2.54097222222223
35	39	47.4506944444444	49.625	-2.17430555555556	-8.45069444444444
36	40	54.1173611111111	49.1666666666667	4.95069444444444	-14.1173611111111
37	63	49.9506944444444	48.7916666666667	1.15902777777777	13.0493055555556
38	45	47.6006944444444	48.3333333333333	-0.732638888888892	-2.60069444444444
39	59	61.2756944444444	47.8333333333333	13.4423611111111	-2.27569444444445
40	55	44.6506944444444	47.125	-2.47430555555555	10.3493055555556
41	40	44.1673611111111	47.0833333333333	-2.91597222222222	-4.16736111111111
42	64	56.9340277777778	48.5416666666667	8.39236111111111	7.06597222222223
43	27	37.1090277777778	49.625	-12.5159722222222	-10.1090277777778
44	28	27.2090277777778	49.9583333333333	-22.7493055555556	0.790972222222216
45	45	54.2006944444445	50.375	3.82569444444445	-9.20069444444445
46	57	61.9173611111111	50.125	11.7923611111111	-4.91736111111111
47	45	48.2006944444444	50.375	-2.17430555555556	-3.20069444444444
48	69	55.3256944444444	50.375	4.95069444444444	13.6743055555556
49	60	51.4923611111111	50.3333333333333	1.15902777777777	8.50763888888889
50	56	49.7673611111111	50.5	-0.732638888888892	6.2326388888889
51	58	64.1090277777778	50.6666666666667	13.4423611111111	-6.10902777777778
52	50	49.1506944444444	51.625	-2.47430555555555	0.849305555555553
53	51	49.9590277777778	52.875	-2.91597222222222	1.04097222222222
54	53	61.5173611111111	53.125	8.39236111111111	-8.5173611111111
55	37	39.2756944444444	51.7916666666667	-12.5159722222222	-2.27569444444444
56	22	27.8756944444444	50.625	-22.7493055555556	-5.87569444444445
57	55	54.1590277777778	50.3333333333333	3.82569444444445	0.840972222222234
58	70	62.0006944444444	50.2083333333333	11.7923611111111	7.99930555555556
59	62	47.5340277777778	49.7083333333333	-2.17430555555556	14.4659722222222
60	58	54.6590277777778	49.7083333333333	4.95069444444444	3.34097222222221
61	39	51.3256944444444	50.1666666666667	1.15902777777777	-12.3256944444444
62	49	50.2673611111111	51	-0.732638888888892	-1.26736111111111
63	58	65.9006944444444	52.4583333333333	13.4423611111111	-7.90069444444444
64	47	50.6923611111111	53.1666666666667	-2.47430555555555	-3.69236111111111
65	42	49.9173611111111	52.8333333333333	-2.91597222222222	-7.91736111111111
66	62	61.1840277777778	52.7916666666667	8.39236111111111	0.815972222222221
67	39	NA	NA	-12.5159722222222	NA
68	40	NA	NA	-22.7493055555556	NA
69	72	NA	NA	3.82569444444445	NA
70	70	NA	NA	11.7923611111111	NA
71	54	NA	NA	-2.17430555555556	NA
72	65	NA	NA	4.95069444444444	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291387391k9u6i57i3dm18ca/1jegm1291387496.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291387391k9u6i57i3dm18ca/1jegm1291387496.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291387391k9u6i57i3dm18ca/2jegm1291387496.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291387391k9u6i57i3dm18ca/2jegm1291387496.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291387391k9u6i57i3dm18ca/3b5g71291387496.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291387391k9u6i57i3dm18ca/3b5g71291387496.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291387391k9u6i57i3dm18ca/4b5g71291387496.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291387391k9u6i57i3dm18ca/4b5g71291387496.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])

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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