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Paper - Ontleden tijdreeks 25-50 jaar

*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, 28 Nov 2010 19:03:27 +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/Nov/28/t1290971294rsqkg1igt7nnmzr.htm/, Retrieved Sun, 28 Nov 2010 20:08:14 +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/Nov/28/t1290971294rsqkg1igt7nnmzr.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 «

376.974 377.632 378.205 370.861 369.167 371.551 382.842 381.903 384.502 392.058 384.359 388.884 386.586 387.495 385.705 378.67 377.367 376.911 389.827 387.82 387.267 380.575 372.402 376.74 377.795 376.126 370.804 367.98 367.866 366.121 379.421 378.519 372.423 355.072 344.693 342.892 344.178 337.606 327.103 323.953 316.532 306.307 327.225 329.573 313.761 307.836 300.074 304.198 306.122 300.414 292.133 290.616 280.244 285.179 305.486 305.957 293.886 289.441 288.776 299.149 306.532 309.914 313.468 314.901 309.16 316.15 336.544 339.196 326.738 320.838 318.62 331.533 335.378

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	376.974	NA	NA	2.16510555555555	NA
2	377.632	NA	NA	0.975213888888889	NA
3	378.205	NA	NA	-2.65592777777778	NA
4	370.861	NA	NA	-4.19966111111111	NA
5	369.167	NA	NA	-8.0485361111111	NA
6	371.551	NA	NA	-7.12298611111111	NA
7	382.842	391.660672222222	380.312	11.3486722222222	-8.81867222222223
8	381.903	391.265238888889	381.123458333333	10.1417805555555	-9.3622388888889
9	384.502	385.980688888889	381.846916666667	4.13377222222222	-1.47868888888877
10	392.058	381.244455555556	382.484791666667	-1.24033611111108	10.8135444444445
11	384.359	378.999122222222	383.151833333333	-4.15271111111110	5.3598777777778
12	388.884	382.372447222222	383.716833333333	-1.34438611111110	6.51155277777781
13	386.586	386.396313888889	384.231208333333	2.16510555555555	0.189686111111143
14	387.495	385.744005555556	384.768791666667	0.975213888888889	1.75099444444447
15	385.705	382.474613888889	385.130541666667	-2.65592777777778	3.2303861111111
16	378.67	380.567630555556	384.767291666667	-4.19966111111111	-1.89763055555562
17	377.367	375.742088888889	383.790625	-8.0485361111111	1.62491111111115
18	376.911	375.663430555556	382.786416666667	-7.12298611111111	1.24756944444442
19	389.827	393.262797222222	381.914125	11.3486722222222	-3.43579722222222
20	387.82	391.215905555556	381.074125	10.1417805555555	-3.39590555555554
21	387.267	384.113313888889	379.979541666667	4.13377222222222	3.15368611111114
22	380.575	377.672913888889	378.91325	-1.24033611111108	2.90208611111109
23	372.402	373.919247222222	378.071958333333	-4.15271111111110	-1.51724722222230
24	376.74	375.882113888889	377.2265	-1.34438611111110	0.8578861111111
25	377.795	378.508438888889	376.343333333333	2.16510555555555	-0.713438888888845
26	376.126	376.497422222222	375.522208333333	0.975213888888889	-0.371422222222236
27	370.804	371.860238888889	374.516166666667	-2.65592777777778	-1.05623888888886
28	367.98	368.635380555556	372.835041666667	-4.19966111111111	-0.655380555555496
29	367.866	362.569338888889	370.617875	-8.0485361111111	5.29666111111112
30	366.121	360.930013888889	368.053	-7.12298611111111	5.1909861111111
31	379.421	376.590630555556	365.241958333333	11.3486722222222	2.83036944444439
32	378.519	372.378030555556	362.23625	10.1417805555555	6.14096944444441
33	372.423	362.944147222222	358.810375	4.13377222222222	9.47885277777777
34	355.072	353.914705555556	355.155041666667	-1.24033611111108	1.15729444444446
35	344.693	347.028955555556	351.181666666667	-4.15271111111110	-2.33595555555559
36	342.892	345.206113888889	346.5505	-1.34438611111110	-2.31411388888893
37	344.178	344.048522222222	341.883416666667	2.16510555555555	0.129477777777765
38	337.606	338.644380555556	337.669166666667	0.975213888888889	-1.03838055555559
39	327.103	330.529572222222	333.1855	-2.65592777777778	-3.42657222222226
40	323.953	324.573422222222	328.773083333333	-4.19966111111111	-0.620422222222317
41	316.532	316.897255555556	324.945791666667	-8.0485361111111	-0.365255555555621
42	306.307	314.351430555556	321.474416666667	-7.12298611111111	-8.0444305555555
43	327.225	329.625172222222	318.2765	11.3486722222222	-2.40017222222218
44	329.573	325.282947222222	315.141166666667	10.1417805555555	4.29005277777776
45	313.761	316.268188888889	312.134416666667	4.13377222222222	-2.50718888888889
46	307.836	308.047955555556	309.288291666667	-1.24033611111108	-0.211955555555505
47	300.074	302.234538888889	306.38725	-4.15271111111110	-2.16053888888882
48	304.198	302.650530555556	303.994916666667	-1.34438611111110	1.54746944444440
49	306.122	304.373897222222	302.208791666667	2.16510555555555	1.74810277777777
50	300.414	301.294213888889	300.319	0.975213888888889	-0.880213888888932
51	292.133	295.850947222222	298.506875	-2.65592777777778	-3.71794722222222
52	290.616	292.712630555556	296.912291666667	-4.19966111111111	-2.09663055555558
53	280.244	287.626547222222	295.675083333333	-8.0485361111111	-7.38254722222217
54	285.179	287.870972222222	294.993958333333	-7.12298611111111	-2.69197222222220
55	305.486	306.149338888889	294.800666666667	11.3486722222222	-0.663338888888859
56	305.957	305.355363888889	295.213583333333	10.1417805555555	0.601636111111077
57	293.886	300.632147222222	296.498375	4.13377222222222	-6.74614722222219
58	289.441	297.158872222222	298.399208333333	-1.24033611111108	-7.71787222222224
59	288.776	296.463205555556	300.615916666667	-4.15271111111110	-7.68720555555552
60	299.149	301.766822222222	303.111208333333	-1.34438611111110	-2.61782222222223
61	306.532	307.860855555556	305.69575	2.16510555555555	-1.32885555555561
62	309.914	309.350005555556	308.374791666667	0.975213888888889	0.563994444444461
63	313.468	308.472655555556	311.128583333333	-2.65592777777778	4.99534444444447
64	314.901	309.605963888889	313.805625	-4.19966111111111	5.29503611111113
65	309.16	308.308797222222	316.357333333333	-8.0485361111111	0.851202777777814
66	316.15	311.827180555556	318.950166666667	-7.12298611111111	4.32281944444446
67	336.544	332.850088888889	321.501416666667	11.3486722222222	3.69391111111105
68	339.196	NA	NA	10.1417805555555	NA
69	326.738	NA	NA	4.13377222222222	NA
70	320.838	NA	NA	-1.24033611111108	NA
71	318.62	NA	NA	-4.15271111111110	NA
72	331.533	NA	NA	-1.34438611111110	NA
73	335.378	NA	NA	NA	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290971294rsqkg1igt7nnmzr/10cbe1290971004.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290971294rsqkg1igt7nnmzr/10cbe1290971004.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290971294rsqkg1igt7nnmzr/20cbe1290971004.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290971294rsqkg1igt7nnmzr/20cbe1290971004.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290971294rsqkg1igt7nnmzr/3slsz1290971004.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290971294rsqkg1igt7nnmzr/3slsz1290971004.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290971294rsqkg1igt7nnmzr/4slsz1290971004.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290971294rsqkg1igt7nnmzr/4slsz1290971004.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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