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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: Tue, 29 Dec 2009 03:53:42 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/29/t1262084074cschpc2xs6zqv8k.htm/, Retrieved Tue, 29 Dec 2009 11:54:39 +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/2009/Dec/29/t1262084074cschpc2xs6zqv8k.htm/},
    year = {2009},
}
@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 = {2009},
    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 «

8 8,1 7,7 7,5 7,6 7,8 7,8 7,8 7,5 7,5 7,1 7,5 7,5 7,6 7,7 7,7 7,9 8,1 8,2 8,2 8,2 7,9 7,3 6,9 6,6 6,7 6,9 7 7,1 7,2 7,1 6,9 7 6,8 6,4 6,7 6,6 6,4 6,3 6,2 6,5 6,8 6,8 6,4 6,1 5,8 6,1 7,2 7,3 6,9 6,1 5,8 6,2 7,1 7,7 7,9 7,7 7,4 7,5 8 8,1

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	'Gwilym Jenkins' @ 72.249.127.135

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	8	NA	NA	1.00360812691921	NA
2	8.1	NA	NA	0.986680333147035	NA
3	7.7	NA	NA	0.962527812296396	NA
4	7.5	NA	NA	0.951722152838305	NA
5	7.6	NA	NA	0.988356900924954	NA
6	7.8	NA	NA	1.04107780348833	NA
7	7.8	8.09361634691506	7.6375	1.05972063462063	0.96372247777386
8	7.8	7.78339396428436	7.59583333333333	1.02469256798039	1.00213352115951
9	7.5	7.73914654240461	7.575	1.02166951054846	0.96909910658827
10	7.5	7.49834165486022	7.58333333333333	0.988792306135414	1.00022116158694
11	7.1	7.37904166705374	7.60416666666667	0.970394520598848	0.962184565470118
12	7.5	7.63494446728838	7.62916666666667	1.00075733050203	0.98232541600446
13	7.5	7.6859655719896	7.65833333333333	1.00360812691921	0.975804527063285
14	7.6	7.58921622912261	7.69166666666667	0.986680333147035	1.00142093340759
15	7.7	7.44755894764337	7.7375	0.962527812296396	1.03389581124920
16	7.7	7.40757075625814	7.78333333333333	0.951722152838305	1.03947707735290
17	7.9	7.71742013472235	7.80833333333333	0.988356900924954	1.02365814768282
18	8.1	8.11173121884659	7.79166666666667	1.04107780348833	0.998553795912353
19	8.2	8.19075740508862	7.72916666666667	1.05972063462063	1.00112841761198
20	8.2	7.84316769741655	7.65416666666667	1.02469256798039	1.04549594199050
21	8.2	7.74766045499251	7.58333333333333	1.02166951054846	1.05838401768317
22	7.9	7.43654213572676	7.52083333333333	0.988792306135414	1.06232168873846
23	7.3	7.23752579946641	7.45833333333333	0.970394520598848	1.00863198312028
24	6.9	7.39309477908372	7.3875	1.00075733050203	0.933303333202387
25	6.6	7.33052102703905	7.30416666666667	1.00360812691921	0.900345279094831
26	6.7	7.10820956671343	7.20416666666667	0.986680333147035	0.942572097392146
27	6.9	6.83394746730441	7.1	0.962527812296396	1.00966535563986
28	7	6.66602057883829	7.00416666666667	0.951722152838305	1.05010176869570
29	7.1	6.84025338515145	6.92083333333333	0.988356900924954	1.03797324458951
30	7.2	7.15740989898229	6.875	1.04107780348833	1.00595049069689
31	7.1	7.27674835772833	6.86666666666667	1.05972063462063	0.975710530440343
32	6.9	7.02341364303224	6.85416666666667	1.02469256798039	0.982428253652029
33	7	6.96438049690536	6.81666666666667	1.02166951054846	1.00511452570842
34	6.8	6.6825880022985	6.75833333333333	0.988792306135414	1.01756983935881
35	6.4	6.50164328801228	6.7	0.970394520598848	0.984366523429593
36	6.7	6.66337589225932	6.65833333333333	1.00075733050203	1.00549632923804
37	6.6	6.65308554136858	6.62916666666667	1.00360812691921	0.992020914049805
38	6.4	6.50797903071565	6.59583333333333	0.986680333147035	0.983408208568893
39	6.3	6.29252557288769	6.5375	0.962527812296396	1.00118782625922
40	6.2	6.14653890374738	6.45833333333333	0.951722152838305	1.00869775610141
41	6.5	6.32960231967356	6.40416666666667	0.988356900924954	1.02692075611082
42	6.8	6.67591141486893	6.4125	1.04107780348833	1.01858751223911
43	6.8	6.84844460123583	6.4625	1.05972063462063	0.992926189221552
44	6.4	6.67331034897228	6.5125	1.02469256798039	0.959044262190748
45	6.1	6.66639355632872	6.525	1.02166951054846	0.915037485929552
46	5.8	6.42714998988019	6.5	0.988792306135414	0.902421759120658
47	6.1	6.27926121037505	6.47083333333333	0.970394520598848	0.971451862827611
48	7.2	6.4757338927902	6.47083333333333	1.00075733050203	1.11184309287572
49	7.3	6.544361327619	6.52083333333333	1.00360812691921	1.11546408191003
50	6.9	6.53264603904433	6.62083333333333	0.986680333147035	1.05623356274932
51	6.1	6.49706273300067	6.75	0.962527812296396	0.938885808969665
52	5.8	6.55102081870366	6.88333333333333	0.951722152838305	0.885358199967944
53	6.2	6.92673461398238	7.00833333333333	0.988356900924954	0.895082653734793
54	7.1	7.39165240476716	7.1	1.04107780348833	0.960543003269599
55	7.7	7.59466454811452	7.16666666666667	1.05972063462063	1.01386966484407
56	7.9	NA	NA	1.02469256798039	NA
57	7.7	NA	NA	1.02166951054846	NA
58	7.4	NA	NA	0.988792306135414	NA
59	7.5	NA	NA	0.970394520598848	NA
60	8	NA	NA	1.00075733050203	NA
61	8.1	NA	NA	NA	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/29/t1262084074cschpc2xs6zqv8k/1jza21262084019.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/29/t1262084074cschpc2xs6zqv8k/1jza21262084019.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/29/t1262084074cschpc2xs6zqv8k/2srrb1262084019.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/29/t1262084074cschpc2xs6zqv8k/2srrb1262084019.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/29/t1262084074cschpc2xs6zqv8k/3ou681262084019.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/29/t1262084074cschpc2xs6zqv8k/3ou681262084019.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/29/t1262084074cschpc2xs6zqv8k/4zur81262084019.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/29/t1262084074cschpc2xs6zqv8k/4zur81262084019.ps (open in new window)

Parameters (Session):

par1 = multiplicative ; par2 = 12 ;

Parameters (R input):

par1 = multiplicative ; 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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