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Opgave9; Opdracht 2; Stap 1

*Unverified author*

R Software Module: /rwasp_decompose.wasp (opens new window with default values)

Title produced by software: Classical Decomposition

Date of computation: Sat, 11 Dec 2010 12:17:32 +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/11/t1292070092e1muzusocnn62l2.htm/, Retrieved Sat, 11 Dec 2010 13:21:36 +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/11/t1292070092e1muzusocnn62l2.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

Dataseries X:

» Textbox « » Textfile « » CSV «

13,81 13,9 13,91 13,94 13,96 14,01 14,01 14,06 14,09 14,13 14,12 14,13 14,14 14,16 14,21 14,26 14,29 14,32 14,33 14,39 14,48 14,44 14,46 14,48 14,53 14,58 14,62 14,62 14,61 14,65 14,68 14,7 14,78 14,84 14,89 14,89 15,13 15,25 15,33 15,36 15,4 15,4 15,41 15,47 15,54 15,55 15,59 15,65 15,75 15,86 15,89 15,94 15,93 15,95 15,99 15,99 16,06 16,08 16,07 16,11 16,15 16,18 16,3 16,42 16,49 16,5 16,58 16,64 16,66 16,81 16,91 16,92 16,95 17,11 17,16 17,16 17,27 17,34 17,39 17,43 17,45 17,5 17,56 17,65

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	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	13.81	NA	NA	-0.0121412037037041	NA
2	13.9	NA	NA	0.0226504629629629	NA
3	13.91	NA	NA	0.0375810185185185	NA
4	13.94	NA	NA	0.0325115740740751	NA
5	13.96	NA	NA	0.0235532407407401	NA
6	14.01	NA	NA	0.00355324074074146	NA
7	14.01	14.0043865740741	14.0195833333333	-0.0151967592592610	0.00561342592592418
8	14.06	14.0265393518519	14.0441666666667	-0.0176273148148141	0.0334606481481483
9	14.09	14.0650115740741	14.0675	-0.00248842592592593	0.0249884259259225
10	14.13	14.0859143518519	14.0933333333333	-0.00741898148148204	0.0440856481481475
11	14.12	14.0993171296296	14.1204166666667	-0.021099537037037	0.0206828703703685
12	14.13	14.1032060185185	14.1470833333333	-0.0438773148148139	0.0267939814814824
13	14.14	14.1611921296296	14.1733333333333	-0.0121412037037041	-0.0211921296296271
14	14.16	14.2230671296296	14.2004166666667	0.0226504629629629	-0.0630671296296281
15	14.21	14.2679976851852	14.2304166666667	0.0375810185185185	-0.0579976851851818
16	14.26	14.2920949074074	14.2595833333333	0.0325115740740751	-0.0320949074074051
17	14.29	14.3102199074074	14.2866666666667	0.0235532407407401	-0.0202199074074052
18	14.32	14.3189699074074	14.3154166666667	0.00355324074074146	0.00103009259259501
19	14.33	14.3310532407407	14.34625	-0.0151967592592610	-0.00105324074073998
20	14.39	14.3623726851852	14.38	-0.0176273148148141	0.0276273148148167
21	14.48	14.4120949074074	14.4145833333333	-0.00248842592592593	0.0679050925925946
22	14.44	14.4392476851852	14.4466666666667	-0.00741898148148204	0.000752314814816302
23	14.46	14.4539004629630	14.475	-0.021099537037037	0.00609953703703781
24	14.48	14.4582060185185	14.5020833333333	-0.0438773148148139	0.0217939814814834
25	14.53	14.5182754629630	14.5304166666667	-0.0121412037037041	0.0117245370370362
26	14.58	14.5805671296296	14.5579166666667	0.0226504629629629	-0.000567129629629903
27	14.62	14.6209143518519	14.5833333333333	0.0375810185185185	-0.000914351851852402
28	14.62	14.6450115740741	14.6125	0.0325115740740751	-0.0250115740740746
29	14.61	14.6706365740741	14.6470833333333	0.0235532407407401	-0.060636574074076
30	14.65	14.6856365740741	14.6820833333333	0.00355324074074146	-0.0356365740740738
31	14.68	14.7089699074074	14.7241666666667	-0.0151967592592610	-0.0289699074074043
32	14.7	14.7594560185185	14.7770833333333	-0.0176273148148141	-0.0594560185185191
33	14.78	14.8320949074074	14.8345833333333	-0.00248842592592593	-0.0520949074074046
34	14.84	14.8875810185185	14.895	-0.00741898148148204	-0.0475810185185157
35	14.89	14.9376504629630	14.95875	-0.021099537037037	-0.0476504629629613
36	14.89	14.9790393518518	15.0229166666667	-0.0438773148148139	-0.0890393518518486
37	15.13	15.0724421296296	15.0845833333333	-0.0121412037037041	0.0575578703703741
38	15.25	15.1697337962963	15.1470833333333	0.0226504629629629	0.0802662037037027
39	15.33	15.2484143518518	15.2108333333333	0.0375810185185185	0.0815856481481525
40	15.36	15.3045949074074	15.2720833333333	0.0325115740740751	0.0554050925925917
41	15.4	15.3543865740741	15.3308333333333	0.0235532407407401	0.0456134259259269
42	15.4	15.3952199074074	15.3916666666667	0.00355324074074146	0.00478009259259338
43	15.41	15.4339699074074	15.4491666666667	-0.0151967592592610	-0.0239699074074053
44	15.47	15.4827893518519	15.5004166666667	-0.0176273148148141	-0.0127893518518523
45	15.54	15.5466782407407	15.5491666666667	-0.00248842592592593	-0.0066782407407402
46	15.55	15.5892476851852	15.5966666666667	-0.00741898148148204	-0.0392476851851811
47	15.59	15.6218171296296	15.6429166666667	-0.021099537037037	-0.0318171296296281
48	15.65	15.6440393518518	15.6879166666667	-0.0438773148148139	0.005960648148152
49	15.75	15.7228587962963	15.735	-0.0121412037037041	0.0271412037037049
50	15.86	15.8034837962963	15.7808333333333	0.0226504629629629	0.0565162037037048
51	15.89	15.8617476851852	15.8241666666667	0.0375810185185185	0.0282523148148162
52	15.94	15.9004282407407	15.8679166666667	0.0325115740740751	0.0395717592592586
53	15.93	15.9335532407407	15.91	0.0235532407407401	-0.00355324074074126
54	15.95	15.9527199074074	15.9491666666667	0.00355324074074146	-0.00271990740741046
55	15.99	15.9698032407407	15.985	-0.0151967592592610	0.0201967592592585
56	15.99	15.9973726851852	16.015	-0.0176273148148141	-0.00737268518518164
57	16.06	16.0429282407407	16.0454166666667	-0.00248842592592593	0.0170717592592560
58	16.08	16.0750810185185	16.0825	-0.00741898148148204	0.00491898148148096
59	16.07	16.1047337962963	16.1258333333333	-0.021099537037037	-0.0347337962962939
60	16.11	16.1282060185185	16.1720833333333	-0.0438773148148139	-0.0182060185185193
61	16.15	16.2074421296296	16.2195833333333	-0.0121412037037041	-0.0574421296296315
62	16.18	16.2939004629630	16.27125	0.0226504629629629	-0.113900462962963
63	16.3	16.3609143518519	16.3233333333333	0.0375810185185185	-0.0609143518518493
64	16.42	16.4112615740741	16.37875	0.0325115740740751	0.00873842592593022
65	16.49	16.4677199074074	16.4441666666667	0.0235532407407401	0.0222800925925952
66	16.5	16.5164699074074	16.5129166666667	0.00355324074074146	-0.0164699074074051
67	16.58	16.5648032407407	16.58	-0.0151967592592610	0.0151967592592612
68	16.64	16.6344560185185	16.6520833333333	-0.0176273148148141	0.00554398148148039
69	16.66	16.7241782407407	16.7266666666667	-0.00248842592592593	-0.0641782407407376
70	16.81	16.7859143518519	16.7933333333333	-0.00741898148148204	0.0240856481481480
71	16.91	16.8355671296296	16.8566666666667	-0.021099537037037	0.074432870370373
72	16.92	16.8802893518519	16.9241666666667	-0.0438773148148139	0.0397106481481480
73	16.95	16.9807754629630	16.9929166666667	-0.0121412037037041	-0.0307754629629642
74	17.11	17.0822337962963	17.0595833333333	0.0226504629629629	0.0277662037037061
75	17.16	17.1629976851852	17.1254166666667	0.0375810185185185	-0.00299768518518917
76	17.16	17.2195949074074	17.1870833333333	0.0325115740740751	-0.0595949074074049
77	17.27	17.2664699074074	17.2429166666667	0.0235532407407401	0.00353009259259451
78	17.34	17.3039699074074	17.3004166666667	0.00355324074074146	0.0360300925925934
79	17.39	NA	NA	-0.0151967592592610	NA
80	17.43	NA	NA	-0.0176273148148141	NA
81	17.45	NA	NA	-0.00248842592592593	NA
82	17.5	NA	NA	-0.00741898148148204	NA
83	17.56	NA	NA	-0.021099537037037	NA
84	17.65	NA	NA	-0.0438773148148139	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292070092e1muzusocnn62l2/1g9mw1292069848.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292070092e1muzusocnn62l2/1g9mw1292069848.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292070092e1muzusocnn62l2/2g9mw1292069848.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292070092e1muzusocnn62l2/2g9mw1292069848.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292070092e1muzusocnn62l2/3q13h1292069848.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292070092e1muzusocnn62l2/3q13h1292069848.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292070092e1muzusocnn62l2/4q13h1292069848.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292070092e1muzusocnn62l2/4q13h1292069848.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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