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*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, 26 Nov 2010 10:00:41 +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/26/t1290765557cyuh85edxaz3wwt.htm/, Retrieved Fri, 26 Nov 2010 10:59:22 +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/26/t1290765557cyuh85edxaz3wwt.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 «

101,76 102,37 102,38 102,86 102,87 102,92 102,95 103,02 104,08 104,16 104,24 104,33 104,73 104,86 105,03 105,62 105,63 105,63 105,94 106,61 107,69 107,78 107,93 108,48 108,14 108,48 108,48 108,89 108,93 109,21 109,47 109,80 111,73 111,85 112,12 112,15 112,17 112,67 112,80 113,44 113,53 114,53 114,51 115,05 116,67 117,07 116,92 117,00 117,02 117,35 117,36 117,82 117,88 118,24 118,50 118,80 119,76 120,09

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	'Gwilym Jenkins' @ 72.249.127.135

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	101.76	NA	NA	-0.0784606481481486	NA
2	102.37	NA	NA	-0.0827662037037097	NA
3	102.38	NA	NA	-0.324710648148157	NA
4	102.86	NA	NA	-0.132210648148147	NA
5	102.87	NA	NA	-0.440960648148146	NA
6	102.92	NA	NA	-0.366377314814809	NA
7	102.95	102.755706018519	103.285416666667	-0.529710648148136	0.194293981481493
8	103.02	103.152372685185	103.512916666667	-0.360543981481481	-0.132372685185189
9	104.08	104.565150462963	103.727083333333	0.838067129629629	-0.485150462962977
10	104.16	104.653206018519	103.9525	0.700706018518515	-0.493206018518507
11	104.24	104.633622685185	104.1825	0.451122685185191	-0.393622685185193
12	104.33	104.736261574074	104.410416666667	0.325844907407399	-0.406261574074051
13	104.73	104.569456018518	104.647916666667	-0.0784606481481486	0.16054398148151
14	104.86	104.839317129630	104.922083333333	-0.0827662037037097	0.0206828703703934
15	105.03	104.897372685185	105.222083333333	-0.324710648148157	0.132627314814826
16	105.62	105.391122685185	105.523333333333	-0.132210648148147	0.228877314814838
17	105.63	105.386956018518	105.827916666667	-0.440960648148146	0.24304398148152
18	105.63	105.788206018518	106.154583333333	-0.366377314814809	-0.158206018518499
19	105.94	105.939872685185	106.469583333333	-0.529710648148136	0.000127314814832857
20	106.61	106.401956018518	106.7625	-0.360543981481481	0.208043981481509
21	107.69	107.895150462963	107.057083333333	0.838067129629629	-0.205150462962948
22	107.78	108.037789351852	107.337083333333	0.700706018518515	-0.257789351851841
23	107.93	108.061956018519	107.610833333333	0.451122685185191	-0.131956018518508
24	108.48	108.223344907407	107.8975	0.325844907407399	0.256655092592609
25	108.14	108.115289351852	108.19375	-0.0784606481481486	0.0247106481481580
26	108.48	108.390983796296	108.47375	-0.0827662037037097	0.0890162037037072
27	108.48	108.450289351852	108.775	-0.324710648148157	0.0297106481481535
28	108.89	108.980706018519	109.112916666667	-0.132210648148147	-0.0907060185185173
29	108.93	109.016122685185	109.457083333333	-0.440960648148146	-0.0861226851851882
30	109.21	109.418206018519	109.784583333333	-0.366377314814809	-0.208206018518524
31	109.47	109.575706018519	110.105416666667	-0.529710648148136	-0.105706018518518
32	109.8	110.087372685185	110.447916666667	-0.360543981481481	-0.28737268518519
33	111.73	111.640567129630	110.8025	0.838067129629629	0.0894328703703735
34	111.85	111.872789351852	111.172083333333	0.700706018518515	-0.0227893518518272
35	112.12	112.004456018519	111.553333333333	0.451122685185191	0.115543981481494
36	112.15	112.292511574074	111.966666666667	0.325844907407399	-0.142511574074078
37	112.17	112.319872685185	112.398333333333	-0.0784606481481486	-0.149872685185187
38	112.67	112.744317129630	112.827083333333	-0.0827662037037097	-0.0743171296296197
39	112.8	112.926956018519	113.251666666667	-0.324710648148157	-0.126956018518513
40	113.44	113.542789351852	113.675	-0.132210648148147	-0.102789351851854
41	113.53	113.651539351852	114.0925	-0.440960648148146	-0.121539351851851
42	114.53	114.128206018518	114.494583333333	-0.366377314814809	0.401793981481504
43	114.51	114.369039351852	114.89875	-0.529710648148136	0.140960648148166
44	115.05	114.935289351852	115.295833333333	-0.360543981481481	0.114710648148161
45	116.67	116.518900462963	115.680833333333	0.838067129629629	0.151099537037027
46	117.07	116.754039351852	116.053333333333	0.700706018518515	0.315960648148135
47	116.92	116.868206018519	116.417083333333	0.451122685185191	0.0517939814814667
48	117	117.078761574074	116.752916666667	0.325844907407399	-0.0787615740740648
49	117.02	NA	117.07375	NA	NA
50	117.35	NA	117.39625	NA	NA
51	117.36	NA	117.68125	NA	NA
52	117.82	NA	117.935833333333	NA	NA
53	117.88	NA	NA	NA	NA
54	118.24	NA	NA	NA	NA
55	118.5	NA	NA	NA	NA
56	118.8	NA	NA	NA	NA
57	119.76	NA	NA	NA	NA
58	120.09	NA	NA	NA	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/26/t1290765557cyuh85edxaz3wwt/1ooa71290765638.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t1290765557cyuh85edxaz3wwt/1ooa71290765638.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t1290765557cyuh85edxaz3wwt/2hy9a1290765638.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t1290765557cyuh85edxaz3wwt/2hy9a1290765638.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t1290765557cyuh85edxaz3wwt/3hy9a1290765638.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t1290765557cyuh85edxaz3wwt/3hy9a1290765638.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t1290765557cyuh85edxaz3wwt/4979d1290765638.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t1290765557cyuh85edxaz3wwt/4979d1290765638.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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