Home » date » 2010 » Dec » 14 »

Index aantal personen luchtvervoer Denemarken 2000-2009

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Tue, 14 Dec 2010 14:05:31 +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/14/t1292335596si9yryqz7otid5r.htm/, Retrieved Tue, 14 Dec 2010 15:06:37 +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/14/t1292335596si9yryqz7otid5r.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 «

100,4 97,7 97 96,5 98,4 106,3 103,1 102,4 95 98,1 106,1 99,1 101,2 95,5 99,8 97,1 97,5 96,8 97,7 100,9 94,3 99,5 100,8 97 99,2 101 102,3 97 91,2 97,6 95,7 100,5 94,4 102,9 105,1 98,8 100,7 99,6 107,7 102,9 101,6 102,7 110,5 109,8 94,3 102,5 105 102,3 107,7 100,3 99,5 95 97,7 96,3 97,8 106,4 96,1 106,2 114,7 111,9 121 117,7 115,4 114,3 109,5 108,1 108,2 99,1 101,2 98,1 95,5 97,9 98,2 98,7 95,6 95,8 94,4 96,5 103,3 104,3 104,5 102,3 103,8 103,1 102,2 106,3 102,1 94 102,6 102,6 106,7 107,9 109,3 105,9 109,1 108,5 111,7 109,8 109,1 108,5 108,5 106,2 117,1 109,8 115,2 115,9 119,2 121 118,6 117,6 114,6 110,6 102,5 101,6 107,4 105,8 102,8 104 100,4 100,6

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	100.4	NA	NA	2.97723765432099	NA
2	97.7	NA	NA	1.38603395061728	NA
3	97	NA	NA	1.28973765432098	NA
4	96.5	NA	NA	-2.20702160493827	NA
5	98.4	NA	NA	-3.28572530864197	NA
6	106.3	NA	NA	-2.94405864197531	NA
7	103.1	101.32862654321	100.041666666667	1.2869598765432	1.77137345679013
8	102.4	101.205015432099	99.9833333333333	1.22168209876543	1.19498456790126
9	95	96.9675154320988	100.008333333333	-3.04081790123457	-1.96751543209878
10	98.1	99.9735339506173	100.15	-0.17646604938271	-1.8735339506173
11	106.1	102.976774691358	100.1375	2.83927469135803	3.12322530864198
12	99.1	100.357330246914	99.7041666666667	0.653163580246918	-1.25733024691357
13	101.2	102.060570987654	99.0833333333333	2.97723765432099	-0.860570987654313
14	95.5	100.181867283951	98.7958333333333	1.38603395061728	-4.68186728395062
15	99.8	99.9939043209876	98.7041666666667	1.28973765432098	-0.193904320987642
16	97.1	96.526311728395	98.7333333333333	-2.20702160493827	0.573688271604908
17	97.5	95.2851080246914	98.5708333333333	-3.28572530864197	2.21489197530863
18	96.8	95.3184413580247	98.2625	-2.94405864197531	1.48155864197533
19	97.7	99.3786265432099	98.0916666666667	1.2869598765432	-1.67862654320986
20	100.9	99.4591820987654	98.2375	1.22168209876543	1.44081790123458
21	94.3	95.5300154320988	98.5708333333333	-3.04081790123457	-1.23001543209875
22	99.5	98.4943672839506	98.6708333333333	-0.17646604938271	1.00563271604939
23	100.8	101.243441358025	98.4041666666667	2.83927469135803	-0.443441358024685
24	97	98.8281635802469	98.175	0.653163580246918	-1.82816358024689
25	99.2	101.102237654321	98.125	2.97723765432099	-1.90223765432098
26	101	99.4110339506173	98.025	1.38603395061728	1.58896604938272
27	102.3	99.302237654321	98.0125	1.28973765432098	2.99776234567902
28	97	95.951311728395	98.1583333333333	-2.20702160493827	1.04868827160495
29	91.2	95.1934413580247	98.4791666666667	-3.28572530864197	-3.99344135802468
30	97.6	95.789274691358	98.7333333333333	-2.94405864197531	1.81072530864198
31	95.7	100.157793209877	98.8708333333333	1.2869598765432	-4.45779320987654
32	100.5	100.096682098765	98.875	1.22168209876543	0.403317901234587
33	94.4	96.0008487654321	99.0416666666667	-3.04081790123457	-1.60084876543209
34	102.9	99.3360339506173	99.5125	-0.17646604938271	3.56396604938274
35	105.1	103.030941358025	100.191666666667	2.83927469135803	2.06905864197532
36	98.8	101.490663580247	100.8375	0.653163580246918	-2.69066358024689
37	100.7	104.643904320988	101.666666666667	2.97723765432099	-3.94390432098763
38	99.6	104.056867283951	102.670833333333	1.38603395061728	-4.45686728395061
39	107.7	104.343904320988	103.054166666667	1.28973765432098	3.35609567901233
40	102.9	100.826311728395	103.033333333333	-2.20702160493827	2.07368827160494
41	101.6	99.726774691358	103.0125	-3.28572530864197	1.87322530864198
42	102.7	100.210108024691	103.154166666667	-2.94405864197531	2.48989197530867
43	110.5	104.87862654321	103.591666666667	1.2869598765432	5.62137345679012
44	109.8	105.134182098765	103.9125	1.22168209876543	4.66581790123456
45	94.3	100.559182098765	103.6	-3.04081790123457	-6.25918209876542
46	102.5	102.752700617284	102.929166666667	-0.17646604938271	-0.252700617283949
47	105	105.276774691358	102.4375	2.83927469135803	-0.276774691358028
48	102.3	102.66149691358	102.008333333333	0.653163580246918	-0.361496913580254
49	107.7	104.189737654321	101.2125	2.97723765432099	3.510262345679
50	100.3	101.927700617284	100.541666666667	1.38603395061728	-1.62770061728396
51	99.5	101.764737654321	100.475	1.28973765432098	-2.26473765432098
52	95	98.4971450617284	100.704166666667	-2.20702160493827	-3.49714506172838
53	97.7	97.976774691358	101.2625	-3.28572530864197	-0.276774691358014
54	96.3	99.1226080246913	102.066666666667	-2.94405864197531	-2.82260802469135
55	97.8	104.307793209877	103.020833333333	1.2869598765432	-6.50779320987654
56	106.4	105.521682098765	104.3	1.22168209876543	0.878317901234567
57	96.1	102.646682098765	105.6875	-3.04081790123457	-6.54668209876546
58	106.2	106.977700617284	107.154166666667	-0.17646604938271	-0.777700617283955
59	114.7	111.289274691358	108.45	2.83927469135803	3.41072530864196
60	111.9	110.08649691358	109.433333333333	0.653163580246918	1.81350308641974
61	121	113.335570987654	110.358333333333	2.97723765432099	7.66442901234568
62	117.7	111.873533950617	110.4875	1.38603395061728	5.82646604938273
63	115.4	111.685570987654	110.395833333333	1.28973765432098	3.71442901234569
64	114.3	108.063811728395	110.270833333333	-2.20702160493827	6.23618827160495
65	109.5	105.847608024691	109.133333333333	-3.28572530864197	3.65239197530865
66	108.1	104.805941358025	107.75	-2.94405864197531	3.2940586419753
67	108.2	107.50362654321	106.216666666667	1.2869598765432	0.696373456790113
68	99.1	105.696682098765	104.475	1.22168209876543	-6.59668209876543
69	101.2	99.8175154320988	102.858333333333	-3.04081790123457	1.38248456790126
70	98.1	101.086033950617	101.2625	-0.17646604938271	-2.9860339506173
71	95.5	102.701774691358	99.8625	2.83927469135803	-7.20177469135804
72	97.9	99.4031635802469	98.75	0.653163580246918	-1.50316358024689
73	98.2	101.039737654321	98.0625	2.97723765432099	-2.83973765432098
74	98.7	99.4610339506173	98.075	1.38603395061728	-0.761033950617275
75	95.6	99.7189043209876	98.4291666666667	1.28973765432098	-4.11890432098765
76	95.8	96.5346450617284	98.7416666666667	-2.20702160493827	-0.734645061728386
77	94.4	95.976774691358	99.2625	-3.28572530864197	-1.57677469135801
78	96.5	96.8809413580247	99.825	-2.94405864197531	-0.380941358024671
79	103.3	101.495293209877	100.208333333333	1.2869598765432	1.80470679012346
80	104.3	101.913348765432	100.691666666667	1.22168209876543	2.38665123456791
81	104.5	98.2383487654321	101.279166666667	-3.04081790123457	6.26165123456789
82	102.3	101.298533950617	101.475	-0.17646604938271	1.00146604938273
83	103.8	104.580941358025	101.741666666667	2.83927469135803	-0.780941358024691
84	103.1	102.990663580247	102.3375	0.653163580246918	0.109336419753106
85	102.2	105.710570987654	102.733333333333	2.97723765432099	-3.5105709876543
86	106.3	104.411033950617	103.025	1.38603395061728	1.88896604938272
87	102.1	104.664737654321	103.375	1.28973765432098	-2.56473765432101
88	94	101.517978395062	103.725	-2.20702160493827	-7.51797839506175
89	102.6	100.810108024691	104.095833333333	-3.28572530864197	1.78989197530865
90	102.6	101.597608024691	104.541666666667	-2.94405864197531	1.00239197530867
91	106.7	106.449459876543	105.1625	1.2869598765432	0.250540123456801
92	107.9	106.925848765432	105.704166666667	1.22168209876543	0.974151234567913
93	109.3	103.100848765432	106.141666666667	-3.04081790123457	6.19915123456792
94	105.9	106.861033950617	107.0375	-0.17646604938271	-0.961033950617278
95	109.1	110.726774691358	107.8875	2.83927469135803	-1.62677469135802
96	108.5	108.93649691358	108.283333333333	0.653163580246918	-0.436496913580257
97	111.7	111.843904320988	108.866666666667	2.97723765432099	-0.143904320987659
98	109.8	110.765200617284	109.379166666667	1.38603395061728	-0.965200617283955
99	109.1	110.993904320988	109.704166666667	1.28973765432098	-1.89390432098766
100	108.5	108.159645061728	110.366666666667	-2.20702160493827	0.340354938271602
101	108.5	107.918441358025	111.204166666667	-3.28572530864197	0.581558641975292
102	106.2	109.201774691358	112.145833333333	-2.94405864197531	-3.00177469135801
103	117.1	114.24112654321	112.954166666667	1.2869598765432	2.85887345679011
104	109.8	114.788348765432	113.566666666667	1.22168209876543	-4.98834876543211
105	115.2	111.080015432099	114.120833333333	-3.04081790123457	4.11998456790126
106	115.9	114.261033950617	114.4375	-0.17646604938271	1.63896604938274
107	119.2	117.114274691358	114.275	2.83927469135803	2.08572530864201
108	121	114.48649691358	113.833333333333	0.653163580246918	6.51350308641979
109	118.6	116.214737654321	113.2375	2.97723765432099	2.38526234567902
110	117.6	114.052700617284	112.666666666667	1.38603395061728	3.54729938271605
111	114.6	113.273070987654	111.983333333333	1.28973765432098	1.32692901234569
112	110.6	108.763811728395	110.970833333333	-2.20702160493827	1.83618827160495
113	102.5	106.405941358025	109.691666666667	-3.28572530864197	-3.90594135802468
114	101.6	105.114274691358	108.058333333333	-2.94405864197531	-3.51427469135804
115	107.4	NA	NA	1.2869598765432	NA
116	105.8	NA	NA	1.22168209876543	NA
117	102.8	NA	NA	-3.04081790123457	NA
118	104	NA	NA	-0.17646604938271	NA
119	100.4	NA	NA	2.83927469135803	NA
120	100.6	NA	NA	0.653163580246918	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292335596si9yryqz7otid5r/1hu9m1292335526.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292335596si9yryqz7otid5r/1hu9m1292335526.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292335596si9yryqz7otid5r/2hu9m1292335526.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292335596si9yryqz7otid5r/2hu9m1292335526.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292335596si9yryqz7otid5r/3am871292335526.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292335596si9yryqz7otid5r/3am871292335526.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292335596si9yryqz7otid5r/4am871292335526.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292335596si9yryqz7otid5r/4am871292335526.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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