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TUM

*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: Thu, 09 Dec 2010 17:41:47 +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/09/t1291916692scxk40n76ovqf57.htm/, Retrieved Thu, 09 Dec 2010 18:44:57 +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/09/t1291916692scxk40n76ovqf57.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 «

10 10 10 10 10 9,94 10,06 10,06 10,06 10,06 10,06 10,06 10,06 10,06 10,06 10,06 10,06 10,06 10,06 10,06 9,94 9,94 9,94 9,94 9,94 9,94 10,06 10,06 9,94 10,06 10,06 10,06 10,18 10,28 10,28 10,18 10,28 10,28 10,28 10,18 10,28 10,28 10,18 10,18 10,18 10,28 10,28 10,18 10,18 10,18 10,18 10,18 10,18 10,28 10,28 10,28 10,18 10,18 10,18 10,28 10,18 10,18 10,28 10,18 10,18 10,18 10,28 10,28 10,28 10,28 10,28 10,28 10,18 10,18 10,18 10,18 10,18 10,18 10,18 10,18 10,18 10,28 10,28 10,28 10,28 10,28 10,28 10,28 10,28 10,18 10,28 10,28 10,28 10,28 10,18 10,28 10,28 10,28 10,18 10,18 10,28 10,28 10,28 10,28 10,28 10,28 10,28 10,18 10,28 10,28 10,28 10,42 10,42 10,42 10,42 10,42 10,42 10,42 10,42 10,42 10,42 10,34 10,34 10,34 10,42 10,42 10,42 10,42 10,34 10,42 10,42 10,42 10,42 10,42 10,42 10,42 10,42 10,42 10,42 10,42 10,42 10,42 10,42 10,42 10,42 10,42 10,34 10,34 10,34 10 etc...

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	9 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	10	NA	NA	-0.00525252525252532	NA
2	10	NA	NA	-0.0152525252525256	NA
3	10	NA	NA	-0.00707070707070742	NA
4	10	NA	NA	-0.0152525252525251	NA
5	10	NA	NA	-0.00343434343434338	NA
6	9.94	NA	NA	0.00474747474747511	NA
7	10.06	10.0386111111111	10.0275	0.0111111111111117	0.0213888888888896
8	10.06	10.0408838383838	10.0325	0.00838383838383883	0.0191161616161626
9	10.06	10.0270959595960	10.0375	-0.0104040404040401	0.0329040404040413
10	10.06	10.0645202020202	10.0425	0.0220202020202017	-0.00452020202019909
11	10.06	10.0583080808081	10.0475	0.0108080808080807	0.00169191919192180
12	10.06	10.0545959595960	10.055	-0.000404040404041006	0.00540404040404319
13	10.06	10.0547474747475	10.06	-0.00525252525252532	0.0052525252525264
14	10.06	10.0447474747475	10.06	-0.0152525252525256	0.0152525252525280
15	10.06	10.0479292929293	10.055	-0.00707070707070742	0.0120707070707091
16	10.06	10.0297474747475	10.045	-0.0152525252525251	0.0302525252525268
17	10.06	10.0315656565657	10.035	-0.00343434343434338	0.0284343434343448
18	10.06	10.0297474747475	10.025	0.00474747474747511	0.0302525252525268
19	10.06	10.0261111111111	10.015	0.0111111111111117	0.0338888888888906
20	10.06	10.0133838383838	10.005	0.00838383838383883	0.0466161616161624
21	9.94	9.98959595959596	10	-0.0104040404040401	-0.0495959595959583
22	9.94	10.0220202020202	10	0.0220202020202017	-0.0820202020201997
23	9.94	10.0058080808081	9.995	0.0108080808080807	-0.0658080808080808
24	9.94	9.98959595959596	9.99	-0.000404040404041006	-0.0495959595959601
25	9.94	9.98474747474748	9.99	-0.00525252525252532	-0.0447474747474761
26	9.94	9.97474747474747	9.99	-0.0152525252525256	-0.0347474747474745
27	10.06	9.9929292929293	10	-0.00707070707070742	0.0670707070707088
28	10.06	10.0089141414141	10.0241666666667	-0.0152525252525251	0.0510858585858589
29	9.94	10.0490656565657	10.0525	-0.00343434343434338	-0.109065656565656
30	10.06	10.0814141414141	10.0766666666667	0.00474747474747511	-0.0214141414141391
31	10.06	10.1119444444444	10.1008333333333	0.0111111111111117	-0.051944444444441
32	10.06	10.1375505050505	10.1291666666667	0.00838383838383883	-0.0775505050505032
33	10.18	10.1420959595960	10.1525	-0.0104040404040401	0.0379040404040403
34	10.28	10.1886868686869	10.1666666666667	0.0220202020202017	0.0913131313131323
35	10.28	10.1966414141414	10.1858333333333	0.0108080808080807	0.083358585858587
36	10.18	10.2087626262626	10.2091666666667	-0.000404040404041006	-0.0287626262626262
37	10.28	10.2180808080808	10.2233333333333	-0.00525252525252532	0.0619191919191913
38	10.28	10.2180808080808	10.2333333333333	-0.0152525252525256	0.0619191919191913
39	10.28	10.2312626262626	10.2383333333333	-0.00707070707070742	0.0487373737373726
40	10.18	10.2230808080808	10.2383333333333	-0.0152525252525251	-0.0430808080808092
41	10.28	10.2348989898990	10.2383333333333	-0.00343434343434338	0.0451010101010088
42	10.28	10.2430808080808	10.2383333333333	0.00474747474747511	0.0369191919191909
43	10.18	10.2452777777778	10.2341666666667	0.0111111111111117	-0.0652777777777782
44	10.18	10.2342171717172	10.2258333333333	0.00838383838383883	-0.0542171717171716
45	10.18	10.2070959595960	10.2175	-0.0104040404040401	-0.0270959595959592
46	10.28	10.2353535353535	10.2133333333333	0.0220202020202017	0.0446464646464637
47	10.28	10.2199747474747	10.2091666666667	0.0108080808080807	0.0600252525252536
48	10.18	10.2045959595960	10.205	-0.000404040404041006	-0.0245959595959580
49	10.18	10.2039141414141	10.2091666666667	-0.00525252525252532	-0.0239141414141404
50	10.18	10.2022474747475	10.2175	-0.0152525252525256	-0.0222474747474752
51	10.18	10.2145959595960	10.2216666666667	-0.00707070707070742	-0.0345959595959613
52	10.18	10.2022474747475	10.2175	-0.0152525252525251	-0.0222474747474752
53	10.18	10.2057323232323	10.2091666666667	-0.00343434343434338	-0.0257323232323223
54	10.28	10.2139141414141	10.2091666666667	0.00474747474747511	0.0660858585858595
55	10.28	10.2244444444444	10.2133333333333	0.0111111111111117	0.0555555555555571
56	10.28	10.2217171717172	10.2133333333333	0.00838383838383883	0.0582828282828292
57	10.18	10.2070959595960	10.2175	-0.0104040404040401	-0.0270959595959575
58	10.18	10.2436868686869	10.2216666666667	0.0220202020202017	-0.063686868686867
59	10.18	10.2324747474747	10.2216666666667	0.0108080808080807	-0.0524747474747489
60	10.28	10.2170959595960	10.2175	-0.000404040404041006	0.0629040404040389
61	10.18	10.2080808080808	10.2133333333333	-0.00525252525252532	-0.0280808080808086
62	10.18	10.1980808080808	10.2133333333333	-0.0152525252525256	-0.0180808080808053
63	10.28	10.2104292929293	10.2175	-0.00707070707070742	0.0695707070707066
64	10.18	10.2105808080808	10.2258333333333	-0.0152525252525251	-0.0305808080808081
65	10.18	10.2307323232323	10.2341666666667	-0.00343434343434338	-0.0507323232323245
66	10.18	10.2430808080808	10.2383333333333	0.00474747474747511	-0.063080808080807
67	10.28	10.2494444444444	10.2383333333333	0.0111111111111117	0.030555555555555
68	10.28	10.2467171717172	10.2383333333333	0.00838383838383883	0.0332828282828288
69	10.28	10.2237626262626	10.2341666666667	-0.0104040404040401	0.0562373737373765
70	10.28	10.2520202020202	10.23	0.0220202020202017	0.0279797979798015
71	10.28	10.2408080808081	10.23	0.0108080808080807	0.0391919191919197
72	10.28	10.2295959595960	10.23	-0.000404040404041006	0.0504040404040396
73	10.18	10.2205808080808	10.2258333333333	-0.00525252525252532	-0.0405808080808097
74	10.18	10.2022474747475	10.2175	-0.0152525252525256	-0.0222474747474752
75	10.18	10.2020959595960	10.2091666666667	-0.00707070707070742	-0.022095959595962
76	10.18	10.1897474747475	10.205	-0.0152525252525251	-0.00974747474747772
77	10.18	10.2015656565657	10.205	-0.00343434343434338	-0.0215656565656577
78	10.18	10.2097474747475	10.205	0.00474747474747511	-0.0297474747474737
79	10.18	10.2202777777778	10.2091666666667	0.0111111111111117	-0.0402777777777761
80	10.18	10.2258838383838	10.2175	0.00838383838383883	-0.0458838383838387
81	10.18	10.2154292929293	10.2258333333333	-0.0104040404040401	-0.0354292929292921
82	10.28	10.2561868686869	10.2341666666667	0.0220202020202017	0.0238131313131333
83	10.28	10.2533080808081	10.2425	0.0108080808080807	0.0266919191919204
84	10.28	10.2462626262626	10.2466666666667	-0.000404040404041006	0.0337373737373756
85	10.28	10.2455808080808	10.2508333333333	-0.00525252525252532	0.0344191919191932
86	10.28	10.2439141414141	10.2591666666667	-0.0152525252525256	0.0360858585858601
87	10.28	10.2604292929293	10.2675	-0.00707070707070742	0.0195707070707059
88	10.28	10.2564141414141	10.2716666666667	-0.0152525252525251	0.0235858585858573
89	10.28	10.2640656565657	10.2675	-0.00343434343434338	0.0159343434343420
90	10.18	10.2680808080808	10.2633333333333	0.00474747474747511	-0.0880808080808091
91	10.28	10.2744444444444	10.2633333333333	0.0111111111111117	0.00555555555555465
92	10.28	10.2717171717172	10.2633333333333	0.00838383838383883	0.00828282828282667
93	10.28	10.2487626262626	10.2591666666667	-0.0104040404040401	0.0312373737373743
94	10.28	10.2728535353535	10.2508333333333	0.0220202020202017	0.00714646464646584
95	10.18	10.2574747474747	10.2466666666667	0.0108080808080807	-0.0774747474747475
96	10.28	10.2504292929293	10.2508333333333	-0.000404040404041006	0.0295707070707056
97	10.28	10.2497474747475	10.255	-0.00525252525252532	0.0302525252525232
98	10.28	10.2397474747475	10.255	-0.0152525252525256	0.0402525252525265
99	10.18	10.2479292929293	10.255	-0.00707070707070742	-0.0679292929292927
100	10.18	10.2397474747475	10.255	-0.0152525252525251	-0.0597474747474749
101	10.28	10.2557323232323	10.2591666666667	-0.00343434343434338	0.0242676767676748
102	10.28	10.2639141414141	10.2591666666667	0.00474747474747511	0.0160858585858570
103	10.28	10.2661111111111	10.255	0.0111111111111117	0.0138888888888893
104	10.28	10.2633838383838	10.255	0.00838383838383883	0.0166161616161631
105	10.28	10.2487626262626	10.2591666666667	-0.0104040404040401	0.0312373737373743
106	10.28	10.2953535353535	10.2733333333333	0.0220202020202017	-0.015353535353535
107	10.28	10.2999747474747	10.2891666666667	0.0108080808080807	-0.0199747474747483
108	10.18	10.3004292929293	10.3008333333333	-0.000404040404041006	-0.120429292929293
109	10.28	10.3072474747475	10.3125	-0.00525252525252532	-0.027247474747476
110	10.28	10.3089141414141	10.3241666666667	-0.0152525252525256	-0.0289141414141412
111	10.28	10.3287626262626	10.3358333333333	-0.00707070707070742	-0.0487626262626275
112	10.42	10.3322474747475	10.3475	-0.0152525252525251	0.087752525252526
113	10.42	10.3557323232323	10.3591666666667	-0.00343434343434338	0.0642676767676775
114	10.42	10.3797474747475	10.375	0.00474747474747511	0.0402525252525248
115	10.42	10.4019444444444	10.3908333333333	0.0111111111111117	0.0180555555555557
116	10.42	10.4075505050505	10.3991666666667	0.00838383838383883	0.0124494949494931
117	10.42	10.3937626262626	10.4041666666667	-0.0104040404040401	0.0262373737373736
118	10.42	10.4253535353535	10.4033333333333	0.0220202020202017	-0.00535353535353522
119	10.42	10.4108080808081	10.4	0.0108080808080807	0.00919191919191853
120	10.42	10.3995959595960	10.4	-0.000404040404041006	0.0204040404040402
121	10.42	10.3947474747475	10.4	-0.00525252525252532	0.0252525252525242
122	10.34	10.3847474747475	10.4	-0.0152525252525256	-0.0447474747474743
123	10.34	10.3895959595960	10.3966666666667	-0.00707070707070742	-0.0495959595959601
124	10.34	10.3780808080808	10.3933333333333	-0.0152525252525251	-0.0380808080808084
125	10.42	10.3898989898990	10.3933333333333	-0.00343434343434338	0.0301010101010100
126	10.42	10.3980808080808	10.3933333333333	0.00474747474747511	0.0219191919191921
127	10.42	10.4044444444444	10.3933333333333	0.0111111111111117	0.015555555555558
128	10.42	10.4050505050505	10.3966666666667	0.00838383838383883	0.0149494949494944
129	10.34	10.3929292929293	10.4033333333333	-0.0104040404040401	-0.0529292929292939
130	10.42	10.4320202020202	10.41	0.0220202020202017	-0.0120202020202029
131	10.42	10.4241414141414	10.4133333333333	0.0108080808080807	-0.00414141414141511
132	10.42	10.4129292929293	10.4133333333333	-0.000404040404041006	0.00707070707070656
133	10.42	10.4080808080808	10.4133333333333	-0.00525252525252532	0.0119191919191923
134	10.42	10.3980808080808	10.4133333333333	-0.0152525252525256	0.0219191919191939
135	10.42	10.4095959595960	10.4166666666667	-0.00707070707070742	0.0104040404040386
136	10.42	10.4047474747475	10.42	-0.0152525252525251	0.0152525252525244
137	10.42	10.4165656565657	10.42	-0.00343434343434338	0.00343434343434268
138	10.42	10.4247474747475	10.42	0.00474747474747511	-0.00474747474747517
139	10.42	10.4311111111111	10.42	0.0111111111111117	-0.0111111111111111
140	10.42	10.4283838383838	10.42	0.00838383838383883	-0.00838383838383905
141	10.42	10.4062626262626	10.4166666666667	-0.0104040404040401	0.0137373737373725
142	10.42	10.4320202020202	10.41	0.0220202020202017	-0.0120202020202012
143	10.42	10.4141414141414	10.4033333333333	0.0108080808080807	0.00585858585858468
144	10.42	10.3962626262626	10.3966666666667	-0.000404040404041006	0.0237373737373741
145	10.42	NA	NA	NA	NA
146	10.42	NA	NA	NA	NA
147	10.34	NA	NA	NA	NA
148	10.34	NA	NA	NA	NA
149	10.34	NA	NA	NA	NA
150	10.34	NA	NA	NA	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/09/t1291916692scxk40n76ovqf57/1qutm1291916497.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/09/t1291916692scxk40n76ovqf57/1qutm1291916497.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/09/t1291916692scxk40n76ovqf57/2qutm1291916497.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/09/t1291916692scxk40n76ovqf57/2qutm1291916497.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/09/t1291916692scxk40n76ovqf57/313a71291916497.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/09/t1291916692scxk40n76ovqf57/313a71291916497.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/09/t1291916692scxk40n76ovqf57/4uu9s1291916497.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/09/t1291916692scxk40n76ovqf57/4uu9s1291916497.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])

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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