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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: Sun, 07 Jun 2009 05:07:22 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Jun/07/t1244372871pzzjlpow510nunm.htm/, Retrieved Sun, 07 Jun 2009 13:07:56 +0200

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/Jun/07/t1244372871pzzjlpow510nunm.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 «

0 1 0 0 1 3 9 2 3 5 3 5 2 2 0 1 0 1 3 3 2 1 1 5 0 3 1 0 1 4 0 0 1 6 14 1 1 0 0 1 1 1 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 2 0 1 0 1 0 0 1 2 0 0 1 2 0 3 1 1 0 2 0 4 0 2 1 1 1 1 0 1 1 0 2 1 3 1 2 4 0 0 0 1 0 1 0 2 2 4 2 3 3 0 0 2 7 8 2 4 1 1 2 4

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	0	NA	NA	0.56795571354298	NA
2	1	NA	NA	0.885600553738695	NA
3	0	NA	NA	0.384391498719661	NA
4	0	NA	NA	0.640831635999864	NA
5	1	NA	NA	0.744814376862322	NA
6	3	NA	NA	0.956322404732267	NA
7	9	3.4574071891276	2.75	1.25723897786458	2.60310675245369
8	2	3.28026206844348	2.875	1.14096071945860	0.609707382602214
9	3	3.10117337974230	2.91666666666667	1.06325944448308	0.967375774471948
10	5	3.22338475893459	2.95833333333333	1.08959484809056	1.55116449754904
11	3	4.43213528471168	2.95833333333333	1.49818657511381	0.676874645579587
12	5	5.0173892122818	2.83333333333333	1.77084325139358	0.996534211011728
13	2	1.41988928385745	2.5	0.56795571354298	1.40856052844244
14	2	2.02950126898451	2.29166666666667	0.885600553738695	0.98546378391807
15	0	0.880897184565889	2.29166666666667	0.384391498719661	0
16	1	1.33506590833305	2.08333333333333	0.640831635999864	0.749026691310386
17	0	1.36549302424759	1.83333333333333	0.744814376862322	0
18	1	1.67356420828147	1.75	0.956322404732267	0.59752711909803
19	3	2.09539829644097	1.66666666666667	1.25723897786458	1.43170871384953
20	3	1.85406116912023	1.625	1.14096071945860	1.61806959229049
21	2	1.81640155099192	1.70833333333333	1.06325944448308	1.10107811728514
22	1	1.86139119882138	1.70833333333333	1.08959484809056	0.53723258207796
23	1	2.55940206581942	1.70833333333333	1.49818657511381	0.390716258830493
24	5	3.32033109636296	1.875	1.77084325139358	1.50587391886217
25	0	1.06491696289309	1.875	0.56795571354298	0
26	3	1.43910089982538	1.625	0.885600553738695	2.08463492751899
27	1	0.560570935632838	1.45833333333333	0.384391498719661	1.78389555439774
28	0	1.04135140849978	1.625	0.640831635999864	0
29	1	1.76893414504801	2.375	0.744814376862322	0.565312169929795
30	4	2.62988661301373	2.75	0.956322404732267	1.52097812134044
31	0	3.30025231689453	2.625	1.25723897786458	0
32	0	2.89994182862395	2.54166666666667	1.14096071945860	0
33	1	2.52524118064731	2.375	1.06325944448308	0.396001779023604
34	6	2.58778776421509	2.375	1.08959484809056	2.31858272265225
35	14	3.62061755652504	2.41666666666667	1.49818657511381	3.86674366497764
36	1	4.05818245111028	2.29166666666667	1.77084325139358	0.246415732177445
37	1	1.25423553407408	2.20833333333333	0.56795571354298	0.797298412325907
38	0	1.99260124591206	2.25	0.885600553738695	0
39	0	0.864880872119236	2.25	0.384391498719661	0
40	1	1.28166327199973	2	0.640831635999864	0.780236136781652
41	1	0.899984038708639	1.20833333333333	0.744814376862322	1.11113081675856
42	1	0.597701502957667	0.625	0.956322404732267	1.67307593347448
43	1	0.73338940375434	0.583333333333333	1.25723897786458	1.36353210842812
44	0	0.665560419684184	0.583333333333333	1.14096071945860	0
45	1	0.664537152801923	0.625	1.06325944448308	1.50480676028970
46	0	0.680996780056603	0.625	1.08959484809056	0
47	1	0.873942168816388	0.583333333333333	1.49818657511381	1.14424047228930
48	0	0.959206761171521	0.541666666666667	1.77084325139358	0
49	1	0.28397785677149	0.5	0.56795571354298	3.52140132110609
50	0	0.442800276869348	0.5	0.885600553738695	0
51	1	0.192195749359830	0.5	0.384391498719661	5.20302870032673
52	0	0.320415817999932	0.5	0.640831635999864	0
53	1	0.341373256061897	0.458333333333333	0.744814376862322	2.92934488054530
54	0	0.478161202366133	0.5	0.956322404732267	0
55	1	0.681004446343316	0.541666666666667	1.25723897786458	1.46841919369183
56	0	0.618020389706742	0.541666666666667	1.14096071945860	0
57	1	0.575932199095	0.541666666666667	1.06325944448308	1.73631549264196
58	0	0.590197209382389	0.541666666666667	1.08959484809056	0
59	0	0.811517728186646	0.541666666666667	1.49818657511381	0
60	2	0.885421625696789	0.5	1.77084325139358	2.25881087829325
61	0	0.28397785677149	0.5	0.56795571354298	0
62	1	0.516600323014239	0.583333333333333	0.885600553738695	1.93573243269621
63	0	0.240244686699788	0.625	0.384391498719661	0
64	1	0.373818454333254	0.583333333333333	0.640831635999864	2.67509532610852
65	0	0.465508985538951	0.625	0.744814376862322	0
66	0	0.637548269821511	0.666666666666667	0.956322404732267	0
67	1	0.838159318576389	0.666666666666667	1.25723897786458	1.19309059487461
68	2	0.855720539593951	0.75	1.14096071945860	2.33721163330849
69	0	0.930352013922692	0.875	1.06325944448308	0
70	0	0.998795277416351	0.916666666666667	1.08959484809056	0
71	1	1.37333769385432	0.916666666666667	1.49818657511381	0.728153027820464
72	2	1.77084325139358	1	1.77084325139358	1.12940543914662
73	0	0.591620534940604	1.04166666666667	0.56795571354298	0
74	3	0.959400599883587	1.08333333333333	0.885600553738695	3.12695239127849
75	1	0.448456748506271	1.16666666666667	0.384391498719661	2.22986944299717
76	1	0.80103954499983	1.25	0.640831635999864	1.24837781885064
77	0	0.993085835816429	1.33333333333333	0.744814376862322	0
78	2	1.23524977277918	1.29166666666667	0.956322404732267	1.61910574207208
79	0	1.62393367974175	1.29166666666667	1.25723897786458	0
80	4	1.42620089932325	1.25	1.14096071945860	2.80465395997019
81	0	1.19616687504346	1.125	1.06325944448308	0
82	2	1.18039441876478	1.08333333333333	1.08959484809056	1.69434891270741
83	1	1.68545989700303	1.125	1.49818657511381	0.593309874520378
84	1	1.91841352234304	1.08333333333333	1.77084325139358	0.521264048836904
85	1	0.615285356338228	1.08333333333333	0.56795571354298	1.62526214820281
86	1	0.92250057681114	1.04166666666667	0.885600553738695	1.08401016230988
87	0	0.400407811166313	1.04166666666667	0.384391498719661	0
88	1	0.720935590499848	1.125	0.640831635999864	1.38708646538960
89	1	0.837916173970112	1.125	0.744814376862322	1.19343680318512
90	0	1.23524977277918	1.29166666666667	0.956322404732267	0
91	2	1.7287035945638	1.375	1.25723897786458	1.15693633442386
92	1	1.47374092930069	1.29166666666667	1.14096071945860	0.678545312896013
93	3	1.32907430560385	1.25	1.06325944448308	2.25721014043454
94	1	1.36199356011321	1.25	1.08959484809056	0.734217862173212
95	2	1.81030877826252	1.20833333333333	1.49818657511381	1.10478390427933
96	4	2.13976892876724	1.20833333333333	1.77084325139358	1.86936072686338
97	0	0.662614999133476	1.16666666666667	0.56795571354298	0
98	0	0.996300622956032	1.125	0.885600553738695	0
99	0	0.432440436059618	1.125	0.384391498719661	0
100	1	0.77433822683317	1.20833333333333	0.640831635999864	1.29142532984549
101	0	0.993085835816429	1.33333333333333	0.744814376862322	0
102	1	1.23524977277918	1.29166666666667	0.956322404732267	0.80955287103604
103	0	1.7287035945638	1.375	1.25723897786458	0
104	2	1.71144107918790	1.5	1.14096071945860	1.16860581665424
105	2	1.59488916672461	1.5	1.06325944448308	1.25400563357475
106	4	1.67979205747295	1.54166666666667	1.08959484809056	2.38124712056177
107	2	2.80909982833839	1.875	1.49818657511381	0.711971849424454
108	3	4.35332299300921	2.45833333333333	1.77084325139358	0.689128742530144
109	3	1.60920785503844	2.83333333333333	0.56795571354298	1.8642712876444
110	0	2.65680166121609	3	0.885600553738695	0
111	0	1.16919080860563	3.04166666666667	0.384391498719661	0
112	2	1.84239095349961	2.875	0.640831635999864	1.08554592943534
113	7	2.04823953637138	2.75	0.744814376862322	3.41756902730285
114	8	2.66973337987758	2.79166666666667	0.956322404732267	2.99655391070057
115	2	NA	NA	1.25723897786458	NA
116	4	NA	NA	1.14096071945860	NA
117	1	NA	NA	1.06325944448308	NA
118	1	NA	NA	1.08959484809056	NA
119	2	NA	NA	1.49818657511381	NA
120	4	NA	NA	1.77084325139358	NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/07/t1244372871pzzjlpow510nunm/1iya51244372840.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/07/t1244372871pzzjlpow510nunm/1iya51244372840.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/07/t1244372871pzzjlpow510nunm/2yt7f1244372840.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/07/t1244372871pzzjlpow510nunm/2yt7f1244372840.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/07/t1244372871pzzjlpow510nunm/31twu1244372840.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/07/t1244372871pzzjlpow510nunm/31twu1244372840.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/07/t1244372871pzzjlpow510nunm/4h4ly1244372840.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/07/t1244372871pzzjlpow510nunm/4h4ly1244372840.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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