Home » date » 2010 » Dec » 26 »

CD Yuan

*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: Sun, 26 Dec 2010 12:41:34 +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/26/t1293367169shs69rc5i1wukqg.htm/, Retrieved Sun, 26 Dec 2010 13:39:29 +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/2010/Dec/26/t1293367169shs69rc5i1wukqg.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:

Dataseries X:

» Textbox « » Textfile « » CSV «

7,4271 7,7662 7,6289 7,5281 7,3831 7,2355 7,0617 7,1237 7,4533 7,5411 7,4978 7,3525 7,3862 7,311 7,2013 7,249 7,3321 7,59 7,9082 8,2123 8,0929 8,118 8,1206 8,2883 8,4281 8,7917 8,9168 8,9446 8,9786 9,5862 9,6533 9,4125 9,2195 9,2882 9,6774 9,6857 10,1688 10,4399 10,4675 10,149 9,9163 9,9268 10,0529 10,1622 10,083 10,1134 10,3423 10,7536 11,0967 10,8588 10,7719 10,9262 10,708 10,5062 10,0683 9,8954 9,9589 9,9177 9,7189 9,5273 9,5746 9,763 9,6117 9,6581 9,8361 10,2353 10,1285 10,1347 10,2141 10,0971 9,9651 10,1286 10,3356 10,1238 10,1326 10,2467 10,44 10,3689 10,2415 10,3899 10,3162 10,4533 10,6741 10,8957 10,7404 10,6568 10,5682 10,9833 11,0237 10,8462 10,7287 10,7809 10,2609 9,8252 9,1071 8,695 9,2205 9,0496 8,7406 8,921 9,011 9,3157 9,5786 9,6246 9,7485 9,9431 10,1152 10,1827 9,9777 9,7436 9,3462 9,2623 9,1505 8,5794 8,3245 8,6538 8,752 8,8104 9,2665 9,0895

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	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	7.4271	NA	NA	0.0968546296296301	NA
2	7.7662	NA	NA	0.0627689814814819	NA
3	7.6289	NA	NA	-0.0593717592592592	NA
4	7.5281	NA	NA	-0.00643842592592586	NA
5	7.3831	NA	NA	-0.0142699074074085	NA
6	7.2355	NA	NA	0.0315444444444437	NA
7	7.0617	7.44103101851852	7.41487916666667	0.0261518518518516	-0.379331018518517
8	7.1237	7.43434166666667	7.39420833333333	0.0401333333333336	-0.310641666666665
9	7.4533	7.33724212962963	7.357425	-0.0201828703703704	0.116057870370372
10	7.5411	7.28623935185185	7.32797916666667	-0.0417398148148143	0.254860648148149
11	7.4978	7.24754074074074	7.314225	-0.0666842592592594	0.25025925925926
12	7.3525	7.27810462962963	7.32687083333333	-0.0487662037037033	0.0743953703703708
13	7.3862	7.47376712962963	7.3769125	0.0968546296296301	-0.0875671296296296
14	7.311	7.52031064814815	7.45754166666667	0.0627689814814819	-0.209310648148148
15	7.2013	7.47017824074074	7.52955	-0.0593717592592592	-0.26887824074074
16	7.249	7.57379907407407	7.5802375	-0.00643842592592586	-0.324799074074074
17	7.3321	7.61595509259259	7.630225	-0.0142699074074085	-0.283855092592590
18	7.59	7.72671111111111	7.69516666666667	0.0315444444444437	-0.136711111111111
19	7.9082	7.80372268518519	7.77757083333333	0.0261518518518516	0.104477314814814
20	8.2123	7.9228125	7.88267916666666	0.0401333333333336	0.289487500000003
21	8.0929	7.9956712962963	8.01585416666667	-0.0201828703703704	0.0972287037037045
22	8.118	8.11624351851852	8.15798333333333	-0.0417398148148143	0.00175648148148255
23	8.1206	8.23055324074074	8.2972375	-0.0666842592592594	-0.109953240740740
24	8.2883	8.40025046296296	8.44901666666667	-0.0487662037037033	-0.111950462962962
25	8.4281	8.7017587962963	8.60490416666667	0.0968546296296301	-0.273658796296296
26	8.7917	8.79039398148148	8.727625	0.0627689814814819	0.00130601851851964
27	8.9168	8.76520324074074	8.824575	-0.0593717592592592	0.151596759259260
28	8.9446	8.91383657407407	8.920275	-0.00643842592592586	0.0307634259259277
29	8.9786	9.0196300925926	9.0339	-0.0142699074074085	-0.0410300925925924
30	9.5862	9.18853611111111	9.15699166666667	0.0315444444444437	0.397663888888889
31	9.6533	9.31389768518518	9.28774583333333	0.0261518518518516	0.339402314814816
32	9.4125	9.46908333333333	9.42895	0.0401333333333336	-0.0565833333333323
33	9.2195	9.54205462962963	9.5622375	-0.0201828703703704	-0.322554629629629
34	9.2882	9.63529351851852	9.67703333333333	-0.0417398148148143	-0.347093518518518
35	9.6774	9.69960324074074	9.7662875	-0.0666842592592594	-0.0222032407407387
36	9.6857	9.7707837962963	9.81955	-0.0487662037037033	-0.0850837962962956
37	10.1688	9.9472462962963	9.85039166666667	0.0968546296296301	0.221553703703703
38	10.4399	9.96104814814815	9.89827916666666	0.0627689814814819	0.478851851851854
39	10.4675	9.90612407407407	9.96549583333333	-0.0593717592592592	0.561375925925926
40	10.149	10.0294199074074	10.0358583333333	-0.00643842592592586	0.119580092592592
41	9.9163	10.0836759259259	10.0979458333333	-0.0142699074074085	-0.167375925925926
42	9.9268	10.2016902777778	10.1701458333333	0.0315444444444437	-0.274890277777779
43	10.0529	10.2794560185185	10.2533041666667	0.0261518518518516	-0.226556018518520
44	10.1622	10.3495541666667	10.3094208333333	0.0401333333333336	-0.187354166666669
45	10.083	10.3193754629630	10.3395583333333	-0.0201828703703704	-0.236375462962965
46	10.1134	10.3428851851852	10.384625	-0.0417398148148143	-0.229485185185185
47	10.3423	10.3833115740741	10.4499958333333	-0.0666842592592594	-0.0410115740740729
48	10.7536	10.4583587962963	10.507125	-0.0487662037037033	0.295241203703705
49	11.0967	10.6287629629630	10.5319083333333	0.0968546296296301	0.467937037037037
50	10.8588	10.5842023148148	10.5214333333333	0.0627689814814819	0.274597685185185
51	10.7719	10.4457740740741	10.5051458333333	-0.0593717592592592	0.326125925925924
52	10.9262	10.4853824074074	10.4918208333333	-0.00643842592592586	0.440817592592593
53	10.708	10.4434217592593	10.4576916666667	-0.0142699074074085	0.264578240740741
54	10.5062	10.4121652777778	10.3806208333333	0.0315444444444437	0.0940347222222204
55	10.0683	10.2922560185185	10.2661041666667	0.0261518518518516	-0.223956018518518
56	9.8954	10.1971583333333	10.157025	0.0401333333333336	-0.301758333333334
57	9.9589	10.0428421296296	10.063025	-0.0201828703703704	-0.0839421296296301
58	9.9177	9.92010601851852	9.96184583333333	-0.0417398148148143	-0.00240601851851707
59	9.7189	9.8059949074074	9.87267916666667	-0.0666842592592594	-0.0870949074074066
60	9.5273	9.7762962962963	9.8250625	-0.0487662037037033	-0.248996296296296
61	9.5746	9.91313796296297	9.81628333333333	0.0968546296296301	-0.338537962962965
62	9.763	9.89153148148148	9.8287625	0.0627689814814819	-0.128531481481481
63	9.6117	9.7899949074074	9.84936666666666	-0.0593717592592592	-0.178294907407405
64	9.6581	9.86103657407407	9.867475	-0.00643842592592586	-0.202936574074073
65	9.8361	9.87093842592592	9.88520833333333	-0.0142699074074085	-0.0348384259259245
66	10.2353	9.95206527777778	9.92052083333333	0.0315444444444437	0.283234722222225
67	10.1285	10.0034351851852	9.97728333333333	0.0261518518518516	0.125064814814815
68	10.1347	10.0641583333333	10.024025	0.0401333333333336	0.0705416666666672
69	10.2141	10.0405796296296	10.0607625	-0.0201828703703704	0.173520370370369
70	10.0971	10.0652518518519	10.1069916666667	-0.0417398148148143	0.0318481481481463
71	9.9651	10.0899949074074	10.1566791666667	-0.0666842592592594	-0.124894907407407
72	10.1286	10.1386421296296	10.1874083333333	-0.0487662037037033	-0.0100421296296300
73	10.3356	10.2945379629630	10.1976833333333	0.0968546296296301	0.0410620370370367
74	10.1238	10.2757939814815	10.213025	0.0627689814814819	-0.15199398148148
75	10.1326	10.1685407407407	10.2279125	-0.0593717592592592	-0.0359407407407417
76	10.2467	10.2405699074074	10.2470083333333	-0.00643842592592586	0.00613009259259556
77	10.44	10.2771217592593	10.2913916666667	-0.0142699074074085	0.162878240740742
78	10.3689	10.3844402777778	10.3528958333333	0.0315444444444437	-0.0155402777777773
79	10.2415	10.4278768518519	10.401725	0.0261518518518516	-0.186376851851850
80	10.3899	10.4809333333333	10.4408	0.0401333333333336	-0.0910333333333302
81	10.3162	10.4609754629630	10.4811583333333	-0.0201828703703704	-0.144775462962963
82	10.4533	10.4882601851852	10.53	-0.0417398148148143	-0.0349601851851826
83	10.6741	10.5183282407407	10.5850125	-0.0666842592592594	0.155771759259258
84	10.8957	10.5804546296296	10.6292208333333	-0.0487662037037033	0.31524537037037
85	10.7404	10.7662629629630	10.6694083333333	0.0968546296296301	-0.0258629629629628
86	10.6568	10.7687689814815	10.706	0.0627689814814819	-0.111968981481482
87	10.5682	10.6606157407407	10.7199875	-0.0593717592592592	-0.0924157407407424
88	10.9833	10.6850740740741	10.6915125	-0.00643842592592586	0.298225925925927
89	11.0237	10.5857800925926	10.60005	-0.0142699074074085	0.437919907407407
90	10.8462	10.4746069444444	10.4430625	0.0315444444444437	0.371593055555554
91	10.7287	10.3141893518519	10.2880375	0.0261518518518516	0.414510648148147
92	10.7809	10.197875	10.1577416666667	0.0401333333333336	0.583024999999999
93	10.2609	9.99444212962963	10.014625	-0.0201828703703704	0.266457870370369
94	9.8252	9.81080601851852	9.85254583333333	-0.0417398148148143	0.0143939814814811
95	9.1071	9.6160699074074	9.68275416666667	-0.0666842592592594	-0.508969907407407
96	8.695	9.48635462962963	9.53512083333333	-0.0487662037037033	-0.79135462962963
97	9.2205	9.5202837962963	9.42342916666667	0.0968546296296301	-0.299783796296298
98	9.0496	9.39009814814815	9.32732916666667	0.0627689814814819	-0.340498148148146
99	8.7406	9.19842824074074	9.2578	-0.0593717592592592	-0.45782824074074
100	8.921	9.23492407407407	9.2413625	-0.00643842592592586	-0.313924074074071
101	9.011	9.27400925925926	9.28827916666667	-0.0142699074074085	-0.263009259259261
102	9.3157	9.42381527777778	9.39227083333333	0.0315444444444437	-0.108115277777779
103	9.5786	9.51196018518518	9.48580833333333	0.0261518518518516	0.0666398148148151
104	9.6246	9.58640833333333	9.546275	0.0401333333333336	0.0381916666666662
105	9.7485	9.58024212962963	9.600425	-0.0201828703703704	0.16825787037037
106	9.9431	9.59813935185185	9.63987916666667	-0.0417398148148143	0.344960648148147
107	10.1152	9.59322824074074	9.6599125	-0.0666842592592594	0.521971759259261
108	10.1827	9.58627962962963	9.63504583333333	-0.0487662037037033	0.596420370370371
109	9.9777	9.64896712962963	9.5521125	0.0968546296296301	0.328732870370372
110	9.7436	9.52217731481481	9.45940833333333	0.0627689814814819	0.221422685185187
111	9.3462	9.31806574074074	9.3774375	-0.0593717592592592	0.0281342592592591
112	9.2623	9.2822824074074	9.28872083333333	-0.00643842592592586	-0.0199824074074080
113	9.1505	9.1918925925926	9.2061625	-0.0142699074074085	-0.0413925925925938
114	8.5794	9.15679444444444	9.12525	0.0315444444444437	-0.577394444444444
115	8.3245	NA	NA	0.0261518518518516	NA
116	8.6538	NA	NA	0.0401333333333336	NA
117	8.752	NA	NA	-0.0201828703703704	NA
118	8.8104	NA	NA	-0.0417398148148143	NA
119	9.2665	NA	NA	-0.0666842592592594	NA
120	9.0895	NA	NA	-0.0487662037037033	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293367169shs69rc5i1wukqg/1s81n1293367291.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293367169shs69rc5i1wukqg/1s81n1293367291.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293367169shs69rc5i1wukqg/2s81n1293367291.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293367169shs69rc5i1wukqg/2s81n1293367291.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293367169shs69rc5i1wukqg/3lh071293367291.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293367169shs69rc5i1wukqg/3lh071293367291.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293367169shs69rc5i1wukqg/4lh071293367291.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293367169shs69rc5i1wukqg/4lh071293367291.ps (open in new window)

Parameters (Session):

par1 = additive ; par2 = 12 ;

Parameters (R input):

par1 = additive ; 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])

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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