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additief model eigen reeks

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Mon, 13 Dec 2010 13:11:59 +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/13/t1292245883mas1tapeqwpl3ax.htm/, Retrieved Mon, 13 Dec 2010 14:11:27 +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/13/t1292245883mas1tapeqwpl3ax.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 «

96,92 99,06 99,65 99,82 99,99 100,33 99,31 101,1 101,1 100,93 100,85 100,93 99,6 101,88 101,81 102,38 102,74 102,82 101,72 103,47 102,98 102,68 102,9 103,03 101,29 103,69 103,68 104,2 104,08 104,16 103,05 104,66 104,46 104,95 105,85 106,23 104,86 107,44 108,23 108,45 109,39 110,15 109,13 110,28 110,17 109,99 109,26 109,11 107,06 109,53 108,92 109,24 109,12 109 107,23 109,49 109,04 109,02 109,23 109,46

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	'George Udny Yule' @ 72.249.76.132

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	96.92	NA	NA	-1.95264756944444	NA
2	99.06	NA	NA	0.309956597222212	NA
3	99.65	NA	NA	0.164852430555552	NA
4	99.82	NA	NA	0.405373263888894	NA
5	99.99	NA	NA	0.498810763888895	NA
6	100.33	NA	NA	0.522664930555561	NA
7	99.31	99.4246440972222	100.110833333333	-0.686189236111111	-0.114644097222211
8	101.1	101.014123263889	100.34	0.674123263888895	0.0858767361111177
9	101.1	100.815998263889	100.5475	0.268498263888880	0.284001736111122
10	100.93	100.777977430556	100.744166666667	0.0338107638888877	0.152022569444455
11	100.85	100.883498263889	100.965416666667	-0.0819184027777874	-0.0334982638888874
12	100.93	101.026414930556	101.18375	-0.157335069444439	-0.0964149305555253
13	99.6	99.4352690972222	101.387916666667	-1.95264756944444	0.164730902777777
14	101.88	101.897039930556	101.587083333333	0.309956597222212	-0.0170399305555406
15	101.81	101.929019097222	101.764166666667	0.164852430555552	-0.119019097222207
16	102.38	102.320789930556	101.915416666667	0.405373263888894	0.059210069444461
17	102.74	102.572560763889	102.07375	0.498810763888895	0.167439236111107
18	102.82	102.769331597222	102.246666666667	0.522664930555561	0.0506684027777737
19	101.72	101.718394097222	102.404583333333	-0.686189236111111	0.00160590277776862
20	103.47	103.224539930556	102.550416666667	0.674123263888895	0.245460069444434
21	102.98	102.972248263889	102.70375	0.268498263888880	0.00775173611110347
22	102.68	102.891310763889	102.8575	0.0338107638888877	-0.211310763888889
23	102.9	102.907248263889	102.989166666667	-0.0819184027777874	-0.00724826388888289
24	103.03	102.943498263889	103.100833333333	-0.157335069444439	0.0865017361111171
25	101.29	101.259435763889	103.212083333333	-1.95264756944444	0.0305642361111182
26	103.69	103.627039930556	103.317083333333	0.309956597222212	0.0629600694444576
27	103.68	103.593185763889	103.428333333333	0.164852430555552	0.0868142361111097
28	104.2	103.989956597222	103.584583333333	0.405373263888894	0.210043402777785
29	104.08	104.300894097222	103.802083333333	0.498810763888895	-0.220894097222214
30	104.16	104.580998263889	104.058333333333	0.522664930555561	-0.42099826388889
31	103.05	103.654227430556	104.340416666667	-0.686189236111111	-0.604227430555554
32	104.66	105.319539930556	104.645416666667	0.674123263888895	-0.659539930555553
33	104.46	105.259748263889	104.99125	0.268498263888880	-0.799748263888901
34	104.95	105.391727430556	105.357916666667	0.0338107638888877	-0.441727430555559
35	105.85	105.674331597222	105.75625	-0.0819184027777874	0.175668402777788
36	106.23	106.069748263889	106.227083333333	-0.157335069444439	0.160251736111121
37	104.86	104.777352430556	106.73	-1.95264756944444	0.0826475694444468
38	107.44	107.527456597222	107.2175	0.309956597222212	-0.0874565972222143
39	108.23	107.854435763889	107.689583333333	0.164852430555552	0.375564236111117
40	108.45	108.542873263889	108.1375	0.405373263888894	-0.092873263888876
41	109.39	108.988394097222	108.489583333333	0.498810763888895	0.401605902777789
42	110.15	109.274331597222	108.751666666667	0.522664930555561	0.87566840277779
43	109.13	108.277144097222	108.963333333333	-0.686189236111111	0.852855902777776
44	110.28	109.816206597222	109.142083333333	0.674123263888895	0.463793402777782
45	110.17	109.526414930556	109.257916666667	0.268498263888880	0.643585069444455
46	109.99	109.353394097222	109.319583333333	0.0338107638888877	0.636605902777774
47	109.26	109.259331597222	109.34125	-0.0819184027777874	0.000668402777790789
48	109.11	109.124748263889	109.282083333333	-0.157335069444439	-0.0147482638888903
49	107.06	107.202352430556	109.155	-1.95264756944444	-0.142352430555547
50	109.53	109.352873263889	109.042916666667	0.309956597222212	0.177126736111106
51	108.92	109.127769097222	108.962916666667	0.164852430555552	-0.207769097222226
52	109.24	109.280789930556	108.875416666667	0.405373263888894	-0.0407899305555475
53	109.12	109.332560763889	108.83375	0.498810763888895	-0.212560763888888
54	109	109.369748263889	108.847083333333	0.522664930555561	-0.36974826388888
55	107.23	NA	NA	-0.686189236111111	NA
56	109.49	NA	NA	0.674123263888895	NA
57	109.04	NA	NA	0.268498263888880	NA
58	109.02	NA	NA	0.0338107638888877	NA
59	109.23	NA	NA	-0.0819184027777874	NA
60	109.46	NA	NA	-0.157335069444439	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292245883mas1tapeqwpl3ax/1c7ec1292245916.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292245883mas1tapeqwpl3ax/1c7ec1292245916.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292245883mas1tapeqwpl3ax/25hvy1292245916.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292245883mas1tapeqwpl3ax/25hvy1292245916.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292245883mas1tapeqwpl3ax/35hvy1292245916.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292245883mas1tapeqwpl3ax/35hvy1292245916.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292245883mas1tapeqwpl3ax/4x8ui1292245916.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292245883mas1tapeqwpl3ax/4x8ui1292245916.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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