Home » date » 2010 » Dec » 15 »

Decomposition van reeks 'fruistindex denemarken'

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Wed, 15 Dec 2010 21:27:25 +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/15/t1292448356mteaziea1riambh.htm/, Retrieved Wed, 15 Dec 2010 22:25:58 +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/15/t1292448356mteaziea1riambh.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 «

98,6 100,1 98,8 98,3 102,8 103,6 105,2 100,1 98,2 98,4 97,4 98,4 100,3 101,1 104,1 107,3 110,1 112,6 114,3 115,3 109,9 108,2 103,2 101,8 105,6 108,2 109,8 114,6 117,2 116,5 116,1 112,1 106,8 106,9 104,5 103 105,9 107,7 107,1 112,5 114,5 114,6 113,1 112,8 111,9 112 112,4 110 112,3 109,6 111,9 110,8 110,4 110,8 114 108,4 110,5 105,1 102,3 104,3 103,4 102,4 104,5 107,3 110,1 111,8 111,8 105,7 106 106,4 107,1 111,5 109,6 109,9 109,3 111,4 112,9 115,5 118,4 116,2 113,3 113,8 114,1 117,1 115,5 115,2 114,2 115,3 118,8 118 118,1 111,8 112 114,3 115 118,5 117,6 119,1 120,6 123,6 122,7 123,8 123,1 124,5 120,7 118,7 119 122,3 118,6 118,1 118,2 120,8 119,7 119,7 117,1 114,5 116,5 116,4 114,9 115,5

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	'RServer@AstonUniversity' @ vre.aston.ac.uk

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	98.6	NA	NA	-1.88900462962963	NA
2	100.1	NA	NA	-1.73298611111111	NA
3	98.8	NA	NA	-0.951041666666671	NA
4	98.3	NA	NA	1.53645833333333	NA
5	102.8	NA	NA	2.7943287037037	NA
6	103.6	NA	NA	3.40081018518519	NA
7	105.2	104.214699074074	100.0625	4.15219907407407	0.985300925925927
8	100.1	101.129050925926	100.175	0.954050925925927	-1.02905092592592
9	98.2	99.2628472222222	100.4375	-1.17465277777778	-1.0628472222222
10	98.4	99.053587962963	101.033333333333	-1.97974537037037	-0.653587962962959
11	97.4	98.5725694444444	101.7125	-3.13993055555556	-1.17256944444443
12	98.4	100.421180555556	102.391666666667	-1.97048611111111	-2.02118055555553
13	100.3	101.256828703704	103.145833333333	-1.88900462962963	-0.956828703703707
14	101.1	102.425347222222	104.158333333333	-1.73298611111111	-1.32534722222222
15	104.1	104.328125	105.279166666667	-0.951041666666671	-0.228124999999991
16	107.3	107.711458333333	106.175	1.53645833333333	-0.411458333333314
17	110.1	109.619328703704	106.825	2.7943287037037	0.480671296296293
18	112.6	110.609143518519	107.208333333333	3.40081018518519	1.99085648148149
19	114.3	111.723032407407	107.570833333333	4.15219907407407	2.57696759259261
20	115.3	109.041550925926	108.0875	0.954050925925927	6.25844907407409
21	109.9	107.446180555556	108.620833333333	-1.17465277777778	2.45381944444446
22	108.2	107.18275462963	109.1625	-1.97974537037037	1.01724537037039
23	103.2	106.622569444444	109.7625	-3.13993055555556	-3.42256944444443
24	101.8	108.250347222222	110.220833333333	-1.97048611111111	-6.45034722222219
25	105.6	108.569328703704	110.458333333333	-1.88900462962963	-2.96932870370368
26	108.2	108.667013888889	110.4	-1.73298611111111	-0.467013888888857
27	109.8	109.186458333333	110.1375	-0.951041666666671	0.613541666666677
28	114.6	111.490625	109.954166666667	1.53645833333333	3.10937500000001
29	117.2	112.74849537037	109.954166666667	2.7943287037037	4.45150462962965
30	116.5	113.459143518518	110.058333333333	3.40081018518519	3.04085648148151
31	116.1	114.273032407407	110.120833333333	4.15219907407407	1.8269675925926
32	112.1	111.066550925926	110.1125	0.954050925925927	1.03344907407408
33	106.8	108.804513888889	109.979166666667	-1.17465277777778	-2.00451388888887
34	106.9	107.799421296296	109.779166666667	-1.97974537037037	-0.899421296296282
35	104.5	106.439236111111	109.579166666667	-3.13993055555556	-1.93923611111109
36	103	107.417013888889	109.3875	-1.97048611111111	-4.41701388888887
37	105.9	107.294328703704	109.183333333333	-1.88900462962963	-1.39432870370369
38	107.7	107.354513888889	109.0875	-1.73298611111111	0.345486111111128
39	107.1	108.378125	109.329166666667	-0.951041666666671	-1.27812499999999
40	112.5	111.290625	109.754166666667	1.53645833333333	1.20937500000002
41	114.5	113.090162037037	110.295833333333	2.7943287037037	1.40983796296298
42	114.6	114.317476851852	110.916666666667	3.40081018518519	0.282523148148144
43	113.1	115.627199074074	111.475	4.15219907407407	-2.52719907407408
44	112.8	112.774884259259	111.820833333333	0.954050925925927	0.0251157407407447
45	111.9	110.925347222222	112.1	-1.17465277777778	0.974652777777777
46	112	110.249421296296	112.229166666667	-1.97974537037037	1.7505787037037
47	112.4	108.847569444444	111.9875	-3.13993055555556	3.55243055555557
48	110	109.687847222222	111.658333333333	-1.97048611111111	0.312152777777769
49	112.3	109.64849537037	111.5375	-1.88900462962963	2.65150462962964
50	109.6	109.658680555556	111.391666666667	-1.73298611111111	-0.0586805555555543
51	111.9	110.198958333333	111.15	-0.951041666666671	1.70104166666668
52	110.8	112.340625	110.804166666667	1.53645833333333	-1.54062500000002
53	110.4	112.890162037037	110.095833333333	2.7943287037037	-2.49016203703702
54	110.8	112.838310185185	109.4375	3.40081018518519	-2.0383101851852
55	114	112.981365740741	108.829166666667	4.15219907407407	1.01863425925926
56	108.4	109.112384259259	108.158333333333	0.954050925925927	-0.712384259259252
57	110.5	106.375347222222	107.55	-1.17465277777778	4.12465277777778
58	105.1	105.116087962963	107.095833333333	-1.97974537037037	-0.0160879629629562
59	102.3	103.797569444444	106.9375	-3.13993055555556	-1.49756944444444
60	104.3	104.996180555556	106.966666666667	-1.97048611111111	-0.696180555555529
61	103.4	105.027662037037	106.916666666667	-1.88900462962963	-1.627662037037
62	102.4	104.979513888889	106.7125	-1.73298611111111	-2.57951388888885
63	104.5	105.461458333333	106.4125	-0.951041666666671	-0.961458333333312
64	107.3	107.815625	106.279166666667	1.53645833333333	-0.515624999999972
65	110.1	109.327662037037	106.533333333333	2.7943287037037	0.772337962962979
66	111.8	110.434143518518	107.033333333333	3.40081018518519	1.3658564814815
67	111.8	111.743865740741	107.591666666667	4.15219907407407	0.0561342592592666
68	105.7	109.116550925926	108.1625	0.954050925925927	-3.4165509259259
69	106	107.500347222222	108.675	-1.17465277777778	-1.5003472222222
70	106.4	107.066087962963	109.045833333333	-1.97974537037037	-0.666087962962948
71	107.1	106.193402777778	109.333333333333	-3.13993055555556	0.906597222222231
72	111.5	107.633680555556	109.604166666667	-1.97048611111111	3.86631944444447
73	109.6	108.144328703704	110.033333333333	-1.88900462962963	1.45567129629632
74	109.9	109.012847222222	110.745833333333	-1.73298611111111	0.8871527777778
75	109.3	110.536458333333	111.4875	-0.951041666666671	-1.23645833333336
76	111.4	113.636458333333	112.1	1.53645833333333	-2.23645833333332
77	112.9	115.494328703704	112.7	2.7943287037037	-2.5943287037037
78	115.5	116.625810185185	113.225	3.40081018518519	-1.1258101851852
79	118.4	117.856365740741	113.704166666667	4.15219907407407	0.543634259259264
80	116.2	115.124884259259	114.170833333333	0.954050925925927	1.07511574074076
81	113.3	113.421180555556	114.595833333333	-1.17465277777778	-0.12118055555554
82	113.8	112.98275462963	114.9625	-1.97974537037037	0.817245370370372
83	114.1	112.230902777778	115.370833333333	-3.13993055555556	1.86909722222224
84	117.1	113.750347222222	115.720833333333	-1.97048611111111	3.34965277777778
85	115.5	113.92349537037	115.8125	-1.88900462962963	1.57650462962965
86	115.2	113.883680555556	115.616666666667	-1.73298611111111	1.31631944444446
87	114.2	114.428125	115.379166666667	-0.951041666666671	-0.228124999999977
88	115.3	116.882291666667	115.345833333333	1.53645833333333	-1.58229166666662
89	118.8	118.19849537037	115.404166666667	2.7943287037037	0.601504629629659
90	118	118.900810185185	115.5	3.40081018518519	-0.900810185185165
91	118.1	119.798032407407	115.645833333333	4.15219907407407	-1.6980324074074
92	111.8	116.849884259259	115.895833333333	0.954050925925927	-5.04988425925926
93	112	115.150347222222	116.325	-1.17465277777778	-3.15034722222221
94	114.3	114.95775462963	116.9375	-1.97974537037037	-0.657754629629622
95	115	114.305902777778	117.445833333333	-3.13993055555556	0.694097222222254
96	118.5	115.879513888889	117.85	-1.97048611111111	2.62048611111115
97	117.6	116.41099537037	118.3	-1.88900462962963	1.18900462962965
98	119.1	117.304513888889	119.0375	-1.73298611111111	1.79548611111113
99	120.6	118.978125	119.929166666667	-0.951041666666671	1.621875
100	123.6	122.011458333333	120.475	1.53645833333333	1.58854166666667
101	122.7	123.619328703704	120.825	2.7943287037037	-0.919328703703684
102	123.8	124.550810185185	121.15	3.40081018518519	-0.750810185185173
103	123.1	125.502199074074	121.35	4.15219907407407	-2.40219907407406
104	124.5	122.304050925926	121.35	0.954050925925927	2.19594907407408
105	120.7	120.033680555556	121.208333333333	-1.17465277777778	0.666319444444454
106	118.7	119.011921296296	120.991666666667	-1.97974537037037	-0.311921296296276
107	119	117.610069444444	120.75	-3.13993055555556	1.38993055555555
108	122.3	118.483680555556	120.454166666667	-1.97048611111111	3.81631944444443
109	118.6	118.144328703704	120.033333333333	-1.88900462962963	0.455671296296302
110	118.1	117.633680555556	119.366666666667	-1.73298611111111	0.466319444444437
111	118.2	117.823958333333	118.775	-0.951041666666671	0.376041666666666
112	120.8	120.040625	118.504166666667	1.53645833333333	0.759375000000006
113	119.7	121.031828703704	118.2375	2.7943287037037	-1.33182870370369
114	119.7	121.184143518519	117.783333333333	3.40081018518519	-1.48414351851852
115	117.1	NA	NA	4.15219907407407	NA
116	114.5	NA	NA	0.954050925925927	NA
117	116.5	NA	NA	-1.17465277777778	NA
118	116.4	NA	NA	-1.97974537037037	NA
119	114.9	NA	NA	-3.13993055555556	NA
120	115.5	NA	NA	-1.97048611111111	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292448356mteaziea1riambh/1dq6h1292448439.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292448356mteaziea1riambh/1dq6h1292448439.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292448356mteaziea1riambh/2dq6h1292448439.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292448356mteaziea1riambh/2dq6h1292448439.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292448356mteaziea1riambh/36zn21292448439.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292448356mteaziea1riambh/36zn21292448439.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292448356mteaziea1riambh/4mbtt1292448439.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292448356mteaziea1riambh/4mbtt1292448439.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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