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additief decompositiemodel jonge werkzoekenden <25j

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Fri, 05 Jun 2009 02:32:00 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Jun/05/t124419077738y8gzp2ely3fof.htm/, Retrieved Fri, 05 Jun 2009 10:33:02 +0200

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/2009/Jun/05/t124419077738y8gzp2ely3fof.htm/},
    year = {2009},
}
@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 = {2009},
    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:

additief decompositiemodel datareeks aantal jonge werkzoekenden onder de 25 jaar

Dataseries X:

» Textbox « » Textfile « » CSV «

51772 48439 45716 43851 41622 45180 72550 77681 71177 63390 57386 56765 55772 53605 50338 47314 44596 47029 72490 78086 71058 63276 56918 55170 52980 50466 48553 46307 43796 45642 70765 75685 69220 62898 56011 54148 46626 46018 42408 42483 40113 41381 62348 63611 58389 46175 40555 37909 37866 34418 31736 29533 27604 30575 51345 52455 43367 37077 33016 33117 32279 30369 28983 27864 24591 29528 46549 47932 41584 37295 34666 36773 39591 39833

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	'George Udny Yule' @ 72.249.76.132

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	51772	NA	NA	-4612.77013888889	NA
2	48439	NA	NA	-6277.58680555555	NA
3	45716	NA	NA	-8354.67013888889	NA
4	43851	NA	NA	-9594.00347222222	NA
5	41622	NA	NA	-11747.4118055556	NA
6	45180	NA	NA	-8700.47847222222	NA
7	72550	69930.1131944444	56460.75	13469.3631944444	2619.88680555556
8	77681	73416.0381944444	56842.6666666667	16573.3715277778	4264.96180555556
9	71177	69440.5965277778	57250.5	12190.0965277778	1736.40347222222
10	63390	64545.7298611111	57587.375	6958.35486111111	-1155.72986111110
11	57386	59175.1631944444	57855.5833333333	1319.57986111111	-1789.16319444443
12	56765	56832.6965277778	58056.5416666667	-1223.84513888889	-67.6965277777708
13	55772	53518.3131944444	58131.0833333333	-4612.77013888889	2253.68680555558
14	53605	51867.8715277778	58145.4583333333	-6277.58680555555	1737.12847222223
15	50338	49802.7048611111	58157.375	-8354.67013888889	535.295138888898
16	47314	48553.6631944444	58147.6666666667	-9594.00347222222	-1239.66319444444
17	44596	46376.0048611111	58123.4166666667	-11747.4118055556	-1780.00486111111
18	47029	49336.9798611111	58037.4583333333	-8700.47847222222	-2307.97986111111
19	72490	71324.0298611111	57854.6666666667	13469.3631944444	1165.97013888888
20	78086	74180.9131944445	57607.5416666667	16573.3715277778	3905.08680555555
21	71058	69592.4715277778	57402.375	12190.0965277778	1465.52847222221
22	63276	64244.3965277778	57286.0416666667	6958.35486111111	-968.396527777775
23	56918	58530.3298611111	57210.75	1319.57986111111	-1612.32986111111
24	55170	55895.7798611111	57119.625	-1223.84513888889	-725.779861111107
25	52980	52377.1881944444	56989.9583333333	-4612.77013888889	602.81180555556
26	50466	50540.4548611111	56818.0416666667	-6277.58680555555	-74.4548611111168
27	48553	48286.7465277778	56641.4166666667	-8354.67013888889	266.253472222226
28	46307	46955.0798611111	56549.0833333333	-9594.00347222222	-648.07986111111
29	43796	44748.1298611111	56495.5416666667	-11747.4118055556	-952.129861111105
30	45642	47714.6881944444	56415.1666666667	-8700.47847222222	-2072.68819444444
31	70765	69577.1965277778	56107.8333333333	13469.3631944444	1187.80347222223
32	75685	72231.1215277778	55657.75	16573.3715277778	3453.87847222223
33	69220	67406.4715277778	55216.375	12190.0965277778	1813.52847222223
34	62898	61759.3548611111	54801	6958.35486111111	1138.64513888889
35	56011	55807.7881944444	54488.2083333333	1319.57986111111	203.211805555555
36	54148	52933.3631944444	54157.2083333333	-1223.84513888889	1214.63680555555
37	46626	49016.1881944444	53628.9583333333	-4612.77013888889	-2390.18819444445
38	46018	46497.5798611111	52775.1666666667	-6277.58680555555	-479.57986111111
39	42408	43466.1215277778	51820.7916666667	-8354.67013888889	-1058.12152777777
40	42483	41078.7048611111	50672.7083333333	-9594.00347222222	1404.29513888888
41	40113	37584.5048611111	49331.9166666667	-11747.4118055556	2528.49513888889
42	41381	39310.8131944444	48011.2916666667	-8700.47847222222	2070.18680555555
43	62348	60439.0298611111	46969.6666666667	13469.3631944444	1908.97013888889
44	63611	62694.7048611111	46121.3333333333	16573.3715277778	916.295138888898
45	58389	57383.4298611111	45193.3333333333	12190.0965277778	1005.57013888889
46	46175	51167.4381944444	44209.0833333333	6958.35486111111	-4992.43819444444
47	40555	44467.8715277778	43148.2916666667	1319.57986111111	-3912.87152777777
48	37909	40952.9881944444	42176.8333333333	-1223.84513888889	-3043.98819444444
49	37866	36655.3548611111	41268.125	-4612.77013888889	1210.64513888889
50	34418	34067.2465277778	40344.8333333333	-6277.58680555555	350.753472222219
51	31736	30899.4131944444	39254.0833333333	-8354.67013888889	836.586805555562
52	29533	28655.0798611111	38249.0833333333	-9594.00347222222	877.920138888883
53	27604	25808.4631944444	37555.875	-11747.4118055556	1795.53680555556
54	30575	28341.6048611111	37042.0833333333	-8700.47847222222	2233.39513888889
55	51345	50078.9881944444	36609.625	13469.3631944444	1266.01180555556
56	52455	52781.4965277778	36208.125	16573.3715277778	-326.496527777781
57	43367	48114.8048611111	35924.7083333333	12190.0965277778	-4747.80486111112
58	37077	42698.8131944444	35740.4583333333	6958.35486111111	-5621.81319444445
59	33016	36864.9548611111	35545.375	1319.57986111111	-3848.95486111111
60	33117	34152.3631944444	35376.2083333333	-1223.84513888889	-1035.36319444445
61	32279	30519.9798611111	35132.75	-4612.77013888889	1759.02013888889
62	30369	28466.8715277778	34744.4583333333	-6277.58680555555	1902.12847222223
63	28983	26127.0381944444	34481.7083333333	-8354.67013888889	2855.96180555555
64	27864	24822.4965277778	34416.5	-9594.00347222222	3041.50347222222
65	24591	22746.9215277778	34494.3333333333	-11747.4118055556	1844.07847222222
66	29528	26014.9381944444	34715.4166666667	-8700.47847222222	3513.06180555556
67	46549	48641.7798611111	35172.4166666667	13469.3631944444	-2092.77986111111
68	47932	52444.7881944444	35871.4166666667	16573.3715277778	-4512.78819444444
69	41584	NA	NA	12190.0965277778	NA
70	37295	NA	NA	6958.35486111111	NA
71	34666	NA	NA	1319.57986111111	NA
72	36773	NA	NA	-1223.84513888889	NA
73	39591	NA	NA	NA	NA
74	39833	NA	NA	NA	NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t124419077738y8gzp2ely3fof/12gks1244190718.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t124419077738y8gzp2ely3fof/12gks1244190718.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t124419077738y8gzp2ely3fof/2jio31244190718.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t124419077738y8gzp2ely3fof/2jio31244190718.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t124419077738y8gzp2ely3fof/3rm421244190718.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t124419077738y8gzp2ely3fof/3rm421244190718.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t124419077738y8gzp2ely3fof/4gqg41244190718.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t124419077738y8gzp2ely3fof/4gqg41244190718.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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