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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 19:10:31 +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/t1291749722p71a5rxpxwn3rqc.htm/, Retrieved Tue, 07 Dec 2010 20:22:03 +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/t1291749722p71a5rxpxwn3rqc.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:

KDGP2W92 - Sofie Baert

Dataseries X:

» Textbox « » Textfile « » CSV «

6,59 6,59 6,59 6,59 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,79 6,79 6,79 6,81 6,80 6,80 6,85 6,85 6,85 6,85 6,85 6,85 6,86 6,86 6,88 6,88 6,88 6,91 6,91 6,91 6,91 6,99 6,99 6,99 7,02 7,02 7,05 7,05 7,05 7,05 7,10 7,10 7,10 7,10 7,12 7,13 7,18 7,24 7,24 7,24 7,27 7,27 7,27 7,27 7,30 7,30 7,57 7,76 7,94 7,94 7,96

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	6.59	NA	NA	1.00234327714714	NA
2	6.59	NA	NA	1.00096284469815	NA
3	6.59	NA	NA	0.999400711893269	NA
4	6.59	NA	NA	0.99944231821516	NA
5	6.63	NA	NA	0.996415066966497	NA
6	6.63	NA	NA	0.994194739876087	NA
7	6.63	6.61922168594642	6.61833333333333	1.00013422603069	1.00162833556043
8	6.63	6.61565926612042	6.62166666666667	0.999092766089164	1.00216769535774
9	6.63	6.61002841240303	6.625	0.997740137721212	1.00302140722414
10	6.63	6.65099788383146	6.62833333333333	1.00341934380158	0.99684289723163
11	6.63	6.65317913898154	6.63	1.0034960993939	0.996516080734136
12	6.63	6.65226664394826	6.63	1.00335846816716	0.99665277338687
13	6.63	6.64553592748556	6.63	1.00234327714714	0.997662200963912
14	6.63	6.63638366034872	6.63	1.00096284469815	0.99903808148
15	6.63	6.62602671985237	6.63	0.999400711893269	1.00059964746833
16	6.63	6.63296551855462	6.63666666666667	0.99944231821516	0.99955291211053
17	6.63	6.62616019532721	6.65	0.996415066966497	1.00057949167536
18	6.63	6.624650950041	6.66333333333333	0.994194739876087	1.00080744630915
19	6.63	6.6783962943199	6.6775	1.00013422603069	0.992753306005355
20	6.63	6.6860120483992	6.69208333333333	0.999092766089164	0.991622502622829
21	6.63	6.69109479859288	6.70625	0.997740137721212	0.990869237332323
22	6.79	6.74548653870611	6.7225	1.00341934380158	1.00659899935141
23	6.79	6.76439995666437	6.74083333333333	1.0034960993939	1.00378452538283
24	6.79	6.78186711275318	6.75916666666667	1.00335846816716	1.0011992106468
25	6.81	6.79338156086476	6.7775	1.00234327714714	1.00244626906149
26	6.8	6.80237666542783	6.79583333333333	1.00096284469815	0.999650612492556
27	6.8	6.81008301762605	6.81416666666667	0.999400711893269	0.998519398720992
28	6.85	6.82244312471624	6.82625	0.99944231821516	1.00403915060632
29	6.85	6.80759077210402	6.83208333333333	0.996415066966497	1.0062296970126
30	6.85	6.79904927732759	6.83875	0.994194739876087	1.00749380105867
31	6.85	6.84633549977422	6.84541666666667	1.00013422603069	1.00053524987578
32	6.85	6.84545060232092	6.85166666666666	0.999092766089164	1.00066458702916
33	6.85	6.84408161971013	6.85958333333333	0.997740137721212	1.00086474425916
34	6.86	6.89014616077084	6.86666666666667	1.00341934380158	0.99562474292019
35	6.86	6.89569069633507	6.87166666666666	1.0034960993939	0.994824202838153
36	6.88	6.89976173276282	6.87666666666667	1.00335846816716	0.997135881856763
37	6.88	6.90113346315808	6.885	1.00234327714714	0.996937682299451
38	6.88	6.90330708560156	6.89666666666666	1.00096284469815	0.99662377968812
39	6.91	6.90419325132933	6.90833333333333	0.999400711893269	1.00084104665951
40	6.91	6.91697371064742	6.92083333333333	0.99944231821516	0.99899179743351
41	6.91	6.90930814352352	6.93416666666667	0.996415066966497	1.0001001339732
42	6.91	6.9075822030974	6.94791666666667	0.994194739876087	1.00035002072093
43	6.99	6.96301782614447	6.96208333333333	1.00013422603069	1.00387506890392
44	6.99	6.96992090942954	6.97625	0.999092766089164	1.00288082043274
45	6.99	6.9733721125565	6.98916666666667	0.997740137721212	1.00238448302702
46	7.02	7.0268620463638	7.00291666666667	1.00341934380158	0.99902345509012
47	7.02	7.04328824762092	7.01875	1.0034960993939	0.996693554657686
48	7.05	7.05820875752755	7.03458333333333	1.00335846816716	0.998836991394057
49	7.05	7.06359660266235	7.04708333333333	1.00234327714714	0.998075116201112
50	7.05	7.06387820860522	7.05708333333333	1.00096284469815	0.998035327309535
51	7.05	7.06409736523226	7.06833333333333	0.999400711893269	0.998004364251597
52	7.1	7.07688448156185	7.08083333333333	0.99944231821516	1.00326634107118
53	7.1	7.07122559190558	7.09666666666667	0.996415066966497	1.00406922501912
54	7.1	7.07245283079351	7.11375	0.994194739876087	1.0038949951121
55	7.1	7.13054030900461	7.12958333333333	1.00013422603069	0.9957169712699
56	7.12	7.14018296831723	7.14666666666667	0.999092766089164	0.997173326172903
57	7.13	7.14880808677248	7.165	0.997740137721212	0.997369059772736
58	7.18	7.20580516267508	7.18125	1.00341934380158	0.996418837022023
59	7.24	7.22057255851384	7.19541666666667	1.0034960993939	1.00269056800257
60	7.24	7.23505068754201	7.21083333333333	1.00335846816716	1.00068407433088
61	7.24	7.24443603558098	7.2275	1.00234327714714	0.999387663089412
62	7.27	7.2615683704331	7.25458333333333	1.00096284469815	1.00116113064517
63	7.27	7.29520877985757	7.29958333333333	0.999400711893269	0.996544474514948
64	7.27	7.35339685626804	7.3575	0.99944231821516	0.988658730393838
65	7.27	7.39173910511313	7.41833333333333	0.996415066966497	0.983530383935098
66	7.3	7.43409116742344	7.4775	0.994194739876087	0.98196266841453
67	7.3	NA	NA	1.00013422603069	NA
68	7.57	NA	NA	0.999092766089164	NA
69	7.76	NA	NA	0.997740137721212	NA
70	7.94	NA	NA	1.00341934380158	NA
71	7.94	NA	NA	1.0034960993939	NA
72	7.96	NA	NA	1.00335846816716	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/07/t1291749722p71a5rxpxwn3rqc/1vn5i1291749027.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/07/t1291749722p71a5rxpxwn3rqc/1vn5i1291749027.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/07/t1291749722p71a5rxpxwn3rqc/2vn5i1291749027.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/07/t1291749722p71a5rxpxwn3rqc/2vn5i1291749027.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/07/t1291749722p71a5rxpxwn3rqc/36em31291749027.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/07/t1291749722p71a5rxpxwn3rqc/36em31291749027.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/07/t1291749722p71a5rxpxwn3rqc/46em31291749027.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/07/t1291749722p71a5rxpxwn3rqc/46em31291749027.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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