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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, 04 Dec 2009 05:22:47 -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/04/t1259929403c3yt5n0b2qy2sp0.htm/, Retrieved Fri, 04 Dec 2009 13:23:29 +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/04/t1259929403c3yt5n0b2qy2sp0.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 «

20366 22782 19169 13807 29743 25591 29096 26482 22405 27044 17970 18730 19684 19785 18479 10698 31956 29506 34506 27165 26736 23691 18157 17328 18205 20995 17382 9367 31124 26551 30651 25859 25100 25778 20418 18688 20424 24776 19814 12738 31566 30111 30019 31934 25826 26835 20205 17789 20520 22518 15572 11509 25447 24090 27786 26195 20516 22759 19028 16971 20036

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	1 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	20366	NA	NA	0.853002771004918	NA
2	22782	NA	NA	0.953397249096139	NA
3	19169	NA	NA	0.770986372116776	NA
4	13807	NA	NA	0.481554533693397	NA
5	29743	NA	NA	1.30591786898977	NA
6	25591	NA	NA	1.19862145741049	NA
7	29096	30486.9800724671	22737	1.34085323800269	0.954374619291228
8	26482	27488.4415147028	22583.7083333333	1.21718015079615	0.963386737870734
9	22405	25012.7096803423	22430.0833333333	1.11514118376774	0.895744614891054
10	27044	23562.0166471622	22271.7916666667	1.05793090200402	1.14777951331501
11	17970	20621.4822642113	22234.4583333333	0.927456021417716	0.871421354185922
12	18730	17496.1190061018	22489.7916666667	0.777958251700203	1.07052312535528
13	19684	19515.2817293075	22878.3333333333	0.853002771004918	1.00864544376211
14	19785	22054.1837905188	23132.2083333333	0.953397249096139	0.897108693204311
15	18479	17995.6892848742	23341.125	0.770986372116776	1.02685702711771
16	10698	11259.6479125023	23381.875	0.481554533693397	0.950118519080987
17	31956	30362.5360407677	23249.9583333333	1.30591786898977	1.05248125377580
18	29506	27807.2187309518	23199.3333333333	1.19862145741049	1.06109137650496
19	34506	30945.9429620584	23079.2916666667	1.34085323800269	1.11504115555006
20	27165	28078.0131502447	23068.0833333333	1.21718015079615	0.967482985873709
21	26736	25729.4202119931	23072.7916666667	1.11514118376774	1.03912174389137
22	23691	24302.391956748	22971.625	1.05793090200402	0.974842313553491
23	18157	21221.5849540695	22881.5	0.927456021417716	0.85559113700969
24	17328	17678.0964071453	22723.7083333333	0.777958251700203	0.980196034738003
25	18205	19141.3466395682	22439.9583333333	0.853002771004918	0.95108250964785
26	20995	21189.1744113909	22224.9166666667	0.953397249096139	0.990836150214208
27	17382	17040.5977919824	22102.3333333333	0.770986372116776	1.02003463799716
28	9367	10652.5280341483	22121.125	0.481554533693397	0.879321788215213
29	31124	29124.9612069217	22302.2916666667	1.30591786898977	1.06863661650486
30	26551	26912.8473534806	22453.1666666667	1.19862145741049	0.98655484688305
31	30651	30306.3559675311	22602.2916666667	1.34085323800269	1.01137200502885
32	25859	27815.3558168708	22852.2916666667	1.21718015079615	0.929666338631403
33	25100	25772.2137549202	23111.1666666667	1.11514118376774	0.97391711238652
34	25778	24705.8162740456	23352.9583333333	1.05793090200402	1.04339802879052
35	20418	21806.1913995698	23511.8333333333	0.927456021417716	0.9363395755759
36	18688	18420.9492927376	23678.5833333333	0.777958251700203	1.01449711972052
37	20424	20301.9635348668	23800.5833333333	0.853002771004918	1.00601106710312
38	24776	22907.6332280013	24027.375	0.953397249096139	1.08156088206069
39	19814	18743.2569459379	24310.75	0.770986372116776	1.05712684071666
40	12738	11742.7273688857	24385.0416666667	0.481554533693397	1.08475651352951
41	31566	31890.786426953	24420.2083333333	1.30591786898977	0.98981566579749
42	30111	29215.0495752411	24373.875	1.19862145741049	1.03066742784233
43	30019	32636.9265018345	24340.4166666667	1.34085323800269	0.919786365248352
44	31934	29517.0243835234	24250.3333333333	1.21718015079615	1.08188412168761
45	25826	26740.5280161585	23979.5	1.11514118376774	0.965799926777592
46	26835	25127.4898994027	23751.5416666667	1.05793090200402	1.06795386675841
47	20205	21744.5542181464	23445.375	0.927456021417716	0.92919817059935
48	17789	17846.0057298040	22939.5416666667	0.777958251700203	0.996805686904563
49	20520	19274.130737588	22595.625	0.853002771004918	1.06463945271380
50	22518	21225.9199303665	22263.4583333333	0.953397249096139	1.06087274774767
51	15572	16809.8801201264	21803.0833333333	0.770986372116776	0.926359967395347
52	11509	10311.045675443	21412	0.481554533693397	1.11618165240118
53	25447	27676.4806372339	21193.125	1.30591786898977	0.919444937148746
54	24090	25302.8989659355	21110	1.19862145741049	0.952064821996549
55	27786	28232.6705660751	21055.75	1.34085323800269	0.98417894739962
56	26195	NA	NA	1.21718015079615	NA
57	20516	NA	NA	1.11514118376774	NA
58	22759	NA	NA	1.05793090200402	NA
59	19028	NA	NA	0.927456021417716	NA
60	16971	NA	NA	0.777958251700203	NA
61	20036	NA	NA	NA	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259929403c3yt5n0b2qy2sp0/1fewn1259929365.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259929403c3yt5n0b2qy2sp0/1fewn1259929365.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259929403c3yt5n0b2qy2sp0/2z57t1259929365.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259929403c3yt5n0b2qy2sp0/2z57t1259929365.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259929403c3yt5n0b2qy2sp0/3wsee1259929365.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259929403c3yt5n0b2qy2sp0/3wsee1259929365.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259929403c3yt5n0b2qy2sp0/418841259929365.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259929403c3yt5n0b2qy2sp0/418841259929365.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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