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Workshop 8

*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: Tue, 07 Dec 2010 13:33:40 +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/07/t12917287659hplrbujagv481p.htm/, Retrieved Tue, 07 Dec 2010 14:32:47 +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/07/t12917287659hplrbujagv481p.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 «

103,48 103,93 103,89 104,4 104,79 104,77 105,13 105,26 104,96 104,75 105,01 105,15 105,2 105,77 105,78 106,26 106,13 106,12 106,57 106,44 106,54 107,1 108,1 108,4 108,84 109,62 110,42 110,67 111,66 112,28 112,87 112,18 112,36 112,16 111,49 111,25 111,36 111,74 111,1 111,33 111,25 111,04 110,97 111,31 111,02 111,07 111,36 111,54 112,05 112,52 112,94 113,33 113,78 113,77 113,82 113,89 114,25 114,41

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	'RServer@AstonUniversity' @ vre.aston.ac.uk

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	103.48	NA	NA	-0.25884259259259	NA
2	103.93	NA	NA	0.152685185185183	NA
3	103.89	NA	NA	0.0411574074074023	NA
4	104.4	NA	NA	0.189212962962956	NA
5	104.79	NA	NA	0.273240740740743	NA
6	104.77	NA	NA	0.229629629629633	NA
7	105.13	105.067407407407	104.698333333333	0.369074074074079	0.0625925925925799
8	105.26	104.866851851852	104.846666666667	0.020185185185185	0.393148148148157
9	104.96	104.825740740741	105.002083333333	-0.176342592592589	0.134259259259252
10	104.75	104.921018518519	105.158333333333	-0.237314814814813	-0.171018518518508
11	105.01	105.056574074074	105.291666666667	-0.235092592592602	-0.0465740740740586
12	105.15	105.036157407407	105.40375	-0.367592592592586	0.11384259259259
13	105.2	105.261157407407	105.52	-0.25884259259259	-0.061157407407407
14	105.77	105.781851851852	105.629166666667	0.152685185185183	-0.0118518518518584
15	105.78	105.785324074074	105.744166666667	0.0411574074074023	-0.00532407407406765
16	106.26	106.09712962963	105.907916666667	0.189212962962956	0.162870370370385
17	106.13	106.407824074074	106.134583333333	0.273240740740743	-0.277824074074076
18	106.12	106.62837962963	106.39875	0.229629629629633	-0.508379629629616
19	106.57	107.054907407407	106.685833333333	0.369074074074079	-0.48490740740742
20	106.44	107.018101851852	106.997916666667	0.020185185185185	-0.578101851851855
21	106.54	107.175324074074	107.351666666667	-0.176342592592589	-0.635324074074063
22	107.1	107.491435185185	107.72875	-0.237314814814813	-0.391435185185188
23	108.1	107.907824074074	108.142916666667	-0.235092592592602	0.192175925925937
24	108.4	108.262407407407	108.63	-0.367592592592586	0.137592592592597
25	108.84	108.890324074074	109.149166666667	-0.25884259259259	-0.0503240740740694
26	109.62	109.803518518519	109.650833333333	0.152685185185183	-0.183518518518497
27	110.42	110.173657407407	110.1325	0.0411574074074023	0.246342592592597
28	110.67	110.775046296296	110.585833333333	0.189212962962956	-0.105046296296294
29	111.66	111.211157407407	110.937916666667	0.273240740740743	0.448842592592584
30	112.28	111.427546296296	111.197916666667	0.229629629629633	0.852453703703702
31	112.87	111.790740740741	111.421666666667	0.369074074074079	1.07925925925927
32	112.18	111.635185185185	111.615	0.020185185185185	0.544814814814828
33	112.36	111.555324074074	111.731666666667	-0.176342592592589	0.804675925925935
34	112.16	111.550185185185	111.7875	-0.237314814814813	0.609814814814825
35	111.49	111.562824074074	111.797916666667	-0.235092592592602	-0.0728240740740631
36	111.25	111.361574074074	111.729166666667	-0.367592592592586	-0.11157407407407
37	111.36	111.339490740741	111.598333333333	-0.25884259259259	0.0205092592592564
38	111.74	111.635601851852	111.482916666667	0.152685185185183	0.104398148148164
39	111.1	111.431990740741	111.390833333333	0.0411574074074023	-0.331990740740721
40	111.33	111.478796296296	111.289583333333	0.189212962962956	-0.148796296296297
41	111.25	111.511990740741	111.23875	0.273240740740743	-0.261990740740728
42	111.04	111.475046296296	111.245416666667	0.229629629629633	-0.435046296296278
43	110.97	111.655324074074	111.28625	0.369074074074079	-0.68532407407406
44	111.31	111.367685185185	111.3475	0.020185185185185	-0.0576851851851785
45	111.02	111.280324074074	111.456666666667	-0.176342592592589	-0.260324074074077
46	111.07	111.379351851852	111.616666666667	-0.237314814814813	-0.309351851851858
47	111.36	111.570324074074	111.805416666667	-0.235092592592602	-0.210324074074066
48	111.54	111.656990740741	112.024583333333	-0.367592592592586	-0.116990740740732
49	112.05	NA	112.257083333333	NA	NA
50	112.52	NA	112.483333333333	NA	NA
51	112.94	NA	112.725416666667	NA	NA
52	113.33	NA	112.999166666667	NA	NA
53	113.78	NA	NA	NA	NA
54	113.77	NA	NA	NA	NA
55	113.82	NA	NA	NA	NA
56	113.89	NA	NA	NA	NA
57	114.25	NA	NA	NA	NA
58	114.41	NA	NA	NA	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/07/t12917287659hplrbujagv481p/1uf5c1291728816.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/07/t12917287659hplrbujagv481p/1uf5c1291728816.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/07/t12917287659hplrbujagv481p/2uf5c1291728816.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/07/t12917287659hplrbujagv481p/2uf5c1291728816.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/07/t12917287659hplrbujagv481p/3uf5c1291728816.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/07/t12917287659hplrbujagv481p/3uf5c1291728816.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/07/t12917287659hplrbujagv481p/4n64x1291728816.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/07/t12917287659hplrbujagv481p/4n64x1291728816.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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