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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 05:19:05 -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/t1259929209lh7p9vmb3g2ce8d.htm/, Retrieved Fri, 04 Dec 2009 13:20:14 +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/t1259929209lh7p9vmb3g2ce8d.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 «

1,4816 1,4562 1,4268 1,4088 1,4016 1,3650 1,3190 1,3050 1,2785 1,3239 1,3449 1,2732 1,3322 1,4369 1,4975 1,5770 1,5553 1,5557 1,5750 1,5527 1,4748 1,4718 1,4570 1,4684 1,4227 1,3896 1,3622 1,3716 1,3419 1,3511 1,3516 1,3242 1,3074 1,2999 1,3213 1,2881 1,2611 1,2727 1,2811 1,2684 1,2650 1,2770 1,2271 1,2020 1,1938 1,2103 1,1856 1,1786 1,2015 1,2256 1,2292 1,2037 1,2165 1,2694 1,2938 1,3201 1,3014 1,3119 1,3408 1,2991

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	1.4816	NA	NA	0.981860655178136	NA
2	1.4562	NA	NA	1.00184099483241	NA
3	1.4268	NA	NA	1.00952760851050	NA
4	1.4088	NA	NA	1.01710725137427	NA
5	1.4016	NA	NA	1.01001172492984	NA
6	1.365	NA	NA	1.02474042705716	NA
7	1.319	1.37440241909337	1.35915	1.01122202780662	0.959689812587847
8	1.305	1.35029162149220	1.35212083333333	0.998647153570865	0.966457896374898
9	1.2785	1.32594739111901	1.3542625	0.97909186078697	0.964216234040047
10	1.3239	1.35328362355783	1.36421666666667	0.99198584552148	0.978287165346332
11	1.3449	1.37115790912445	1.37762916666667	0.995302612851992	0.980849828491882
12	1.2732	1.36227688912274	1.39197916666667	0.97866183757976	0.934611759302395
13	1.3322	1.38500445802215	1.41059166666667	0.981860655178136	0.961874160248149
14	1.4369	1.43421469651468	1.43157916666667	1.00184099483241	1.00187231625212
15	1.4975	1.46389495327590	1.45007916666667	1.00952760851050	1.02295591404895
16	1.577	1.48947304864688	1.46442083333333	1.01710725137427	1.05876370266158
17	1.5553	1.49002400558494	1.47525416666667	1.01001172492984	1.04380868641740
18	1.5557	1.52487353198596	1.48805833333333	1.02474042705716	1.02021575387559
19	1.575	1.51679512088389	1.4999625	1.01122202780662	1.03837359331839
20	1.5527	1.49973084596447	1.5017625	0.998647153570865	1.03531910687712
21	1.4748	1.46291418334427	1.49415416666667	0.97909186078697	1.00812475317490
22	1.4718	1.46809771862823	1.47995833333333	0.99198584552148	1.0025218221681
23	1.457	1.45563836548448	1.46250833333333	0.995302612851992	1.00093542087637
24	1.4684	1.41425606597120	1.44509166666667	0.97866183757976	1.03828439229046
25	1.4227	1.40136880227512	1.42725833333333	0.981860655178136	1.01522168731761
26	1.3896	1.41102207748431	1.40842916666667	1.00184099483241	0.984818042306961
27	1.3622	1.40519512920605	1.39193333333333	1.00952760851050	0.96940273396027
28	1.3716	1.40136613299658	1.37779583333333	1.01710725137427	0.97875920339752
29	1.3419	1.3786449626183	1.36497916666667	1.01001172492984	0.97334704466006
30	1.3511	1.38525691855121	1.3518125	1.02474042705716	0.975342538922723
31	1.3516	1.35257687699321	1.33756666666667	1.01122202780662	0.999277766011066
32	1.3242	1.32416867636671	1.3259625	0.998647153570865	1.00002365531964
33	1.3074	1.29016158360725	1.3177125	0.97909186078697	1.01336143984737
34	1.2999	1.29953452382799	1.31003333333333	0.99198584552148	1.00028123621598
35	1.3213	1.29641068289926	1.30252916666667	0.995302612851992	1.01919863622620
36	1.2881	1.26857817368979	1.2962375	0.97866183757976	1.01538874522287
37	1.2611	1.26459970409487	1.2879625	0.981860655178136	0.99723255977086
38	1.2727	1.28003554174745	1.27768333333333	1.00184099483241	0.994269267135007
39	1.2811	1.27993799118011	1.26785833333333	1.00952760851050	1.00090786337143
40	1.2684	1.28093639648699	1.25939166666667	1.01710725137427	0.99021309994675
41	1.265	1.26251886454449	1.25000416666667	1.01001172492984	1.00196522644151
42	1.277	1.27046037221013	1.2397875	1.02474042705716	1.00514744728204
43	1.2271	1.24657552792838	1.23274166666667	1.01122202780662	0.984376776623598
44	1.202	1.22663413770129	1.22829583333333	0.998647153570865	0.97991728996924
45	1.1938	1.19857569912947	1.22417083333333	0.97909186078697	0.996015521478587
46	1.2103	1.20954074126741	1.2193125	0.99198584552148	1.00062772481049
47	1.1856	1.20889040647581	1.21459583333333	0.995302612851992	0.980734062946445
48	1.1786	1.18639096812138	1.21225833333333	0.97866183757976	0.993433051725171
49	1.2015	1.19268659327520	1.21472083333333	0.981860655178136	1.00738954120428
50	1.2256	1.22467130377053	1.22242083333333	1.00184099483241	1.00075832284680
51	1.2292	1.24356134635345	1.231825	1.00952760851050	0.988451437160248
52	1.2037	1.26176392479858	1.24054166666667	1.01710725137427	0.953981942535048
53	1.2165	1.26376875405409	1.25124166666667	1.01001172492984	0.962596991021929
54	1.2694	1.29396962550753	1.26272916666667	1.02474042705716	0.98101220846054
55	1.2938	NA	NA	1.01122202780662	NA
56	1.3201	NA	NA	0.998647153570865	NA
57	1.3014	NA	NA	0.97909186078697	NA
58	1.3119	NA	NA	0.99198584552148	NA
59	1.3408	NA	NA	0.995302612851992	NA
60	1.2991	NA	NA	0.97866183757976	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259929209lh7p9vmb3g2ce8d/1rxev1259929144.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259929209lh7p9vmb3g2ce8d/1rxev1259929144.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259929209lh7p9vmb3g2ce8d/2qqdz1259929144.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259929209lh7p9vmb3g2ce8d/2qqdz1259929144.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259929209lh7p9vmb3g2ce8d/377b71259929144.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259929209lh7p9vmb3g2ce8d/377b71259929144.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259929209lh7p9vmb3g2ce8d/4qvci1259929144.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259929209lh7p9vmb3g2ce8d/4qvci1259929144.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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