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ws 8: productie

*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: Sat, 27 Nov 2010 18:10:23 +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/Nov/27/t1290881348y2vbaqfkpciykin.htm/, Retrieved Sat, 27 Nov 2010 19:09:08 +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/Nov/27/t1290881348y2vbaqfkpciykin.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 «

94,6 95,9 104,7 102,8 98,1 113,9 80,9 95,7 113,2 105,9 108,8 102,3 99 100,7 115,5 100,7 109,9 114,6 85,4 100,5 114,8 116,5 112,9 102 106 105,3 118,8 106,1 109,3 117,2 92,5 104,2 112,5 122,4 113,3 100 110,7 112,8 109,8 117,3 109,1 115,9 96 99,8 116,8 115,7 99,4 94,3 91 93,2 103,1 94,1 91,8 102,7 82,6 89,1 104,5 105,1 95,1 88,7 86,3 91,8 111,5 99,7 97,5 111,7 86,2 95,4

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	94.6	NA	NA	-3.72673611111111	NA
2	95.9	NA	NA	-2.35069444444444	NA
3	104.7	NA	NA	6.60868055555556	NA
4	102.8	NA	NA	-0.542361111111103	NA
5	98.1	NA	NA	0.0836805555555561	NA
6	113.9	NA	NA	7.94305555555555	NA
7	80.9	86.3253472222222	101.583333333333	-15.2579861111111	-5.42534722222219
8	95.7	96.2086805555555	101.966666666667	-5.75798611111111	-0.508680555555543
9	113.2	110.743055555556	102.616666666667	8.12638888888889	2.45694444444445
10	105.9	113.932638888889	102.979166666667	10.9534722222222	-8.03263888888888
11	108.8	104.726388888889	103.383333333333	1.34305555555556	4.07361111111109
12	102.3	96.4815972222222	103.904166666667	-7.42256944444444	5.81840277777778
13	99	100.394097222222	104.120833333333	-3.72673611111111	-1.39409722222221
14	100.7	102.157638888889	104.508333333333	-2.35069444444444	-1.45763888888888
15	115.5	111.383680555556	104.775	6.60868055555556	4.11631944444444
16	100.7	104.740972222222	105.283333333333	-0.542361111111103	-4.04097222222219
17	109.9	105.979513888889	105.895833333333	0.0836805555555561	3.92048611111113
18	114.6	113.997222222222	106.054166666667	7.94305555555555	0.602777777777774
19	85.4	91.0753472222222	106.333333333333	-15.2579861111111	-5.6753472222222
20	100.5	101.058680555556	106.816666666667	-5.75798611111111	-0.558680555555526
21	114.8	115.272222222222	107.145833333333	8.12638888888889	-0.4722222222222
22	116.5	118.461805555556	107.508333333333	10.9534722222222	-1.96180555555554
23	112.9	109.051388888889	107.708333333333	1.34305555555556	3.84861111111114
24	102	100.369097222222	107.791666666667	-7.42256944444444	1.63090277777778
25	106	104.469097222222	108.195833333333	-3.72673611111111	1.53090277777778
26	105.3	106.295138888889	108.645833333333	-2.35069444444444	-0.995138888888874
27	118.8	115.312847222222	108.704166666667	6.60868055555556	3.48715277777778
28	106.1	108.311805555556	108.854166666667	-0.542361111111103	-2.21180555555556
29	109.3	109.200347222222	109.116666666667	0.0836805555555561	0.099652777777763
30	117.2	116.993055555556	109.05	7.94305555555555	0.20694444444446
31	92.5	93.9045138888889	109.1625	-15.2579861111111	-1.40451388888889
32	104.2	103.912847222222	109.670833333333	-5.75798611111111	0.287152777777777
33	112.5	117.734722222222	109.608333333333	8.12638888888889	-5.23472222222222
34	122.4	120.653472222222	109.7	10.9534722222222	1.7465277777778
35	113.3	111.501388888889	110.158333333333	1.34305555555556	1.79861111111113
36	100	102.673263888889	110.095833333333	-7.42256944444444	-2.67326388888890
37	110.7	106.460763888889	110.1875	-3.72673611111111	4.23923611111111
38	112.8	107.799305555556	110.15	-2.35069444444444	5.00069444444445
39	109.8	116.754513888889	110.145833333333	6.60868055555556	-6.95451388888888
40	117.3	109.503472222222	110.045833333333	-0.542361111111103	7.7965277777778
41	109.1	109.271180555556	109.1875	0.0836805555555561	-0.171180555555551
42	115.9	116.313888888889	108.370833333333	7.94305555555555	-0.413888888888891
43	96	92.0545138888889	107.3125	-15.2579861111111	3.94548611111111
44	99.8	99.9170138888889	105.675	-5.75798611111111	-0.117013888888891
45	116.8	112.705555555556	104.579166666667	8.12638888888889	4.09444444444445
46	115.7	114.286805555556	103.333333333333	10.9534722222222	1.41319444444446
47	99.4	102.988888888889	101.645833333333	1.34305555555556	-3.58888888888887
48	94.3	92.9524305555556	100.375	-7.42256944444444	1.34756944444445
49	91	95.5399305555556	99.2666666666667	-3.72673611111111	-4.53993055555556
50	93.2	95.9118055555555	98.2625	-2.35069444444444	-2.71180555555554
51	103.1	103.912847222222	97.3041666666666	6.60868055555556	-0.812847222222217
52	94.1	95.8076388888889	96.35	-0.542361111111103	-1.70763888888889
53	91.8	95.8128472222222	95.7291666666667	0.0836805555555561	-4.01284722222222
54	102.7	103.259722222222	95.3166666666666	7.94305555555555	-0.559722222222192
55	82.6	79.6295138888889	94.8875	-15.2579861111111	2.97048611111111
56	89.1	88.8753472222222	94.6333333333333	-5.75798611111111	0.224652777777763
57	104.5	103.051388888889	94.925	8.12638888888889	1.44861111111112
58	105.1	106.461805555556	95.5083333333333	10.9534722222222	-1.36180555555556
59	95.1	97.3222222222222	95.9791666666667	1.34305555555556	-2.22222222222223
60	88.7	89.1690972222222	96.5916666666666	-7.42256944444444	-0.469097222222189
61	86.3	NA	97.1166666666666	NA	NA
62	91.8	NA	97.5291666666667	NA	NA
63	111.5	NA	NA	NA	NA
64	99.7	NA	NA	NA	NA
65	97.5	NA	NA	NA	NA
66	111.7	NA	NA	NA	NA
67	86.2	NA	NA	NA	NA
68	95.4	NA	NA	NA	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290881348y2vbaqfkpciykin/1lhli1290881420.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290881348y2vbaqfkpciykin/1lhli1290881420.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290881348y2vbaqfkpciykin/2lhli1290881420.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290881348y2vbaqfkpciykin/2lhli1290881420.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290881348y2vbaqfkpciykin/3w9ll1290881420.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290881348y2vbaqfkpciykin/3w9ll1290881420.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290881348y2vbaqfkpciykin/4w9ll1290881420.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290881348y2vbaqfkpciykin/4w9ll1290881420.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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