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Aantal openstaande VDAB-vacatures: Klassieke Decompositie

*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: Sat, 18 Dec 2010 17:36:19 +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/18/t1292693883g7hkpsxfyjmpdgd.htm/, Retrieved Sat, 18 Dec 2010 18:38:08 +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/18/t1292693883g7hkpsxfyjmpdgd.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 «

27.951 29.781 32.914 33.488 35.652 36.488 35.387 35.676 34.844 32.447 31.068 29.010 29.812 30.951 32.974 32.936 34.012 32.946 31.948 30.599 27.691 25.073 23.406 22.248 22.896 25.317 26.558 26.471 27.543 26.198 24.725 25.005 23.462 20.780 19.815 19.761 21.454 23.899 24.939 23.580 24.562 24.696 23.785 23.812 21.917 19.713 19.282 18.788 21.453 24.482 27.474 27.264 27.349 30.632 29.429 30.084 26.290 24.379 23.335 21.346 21.106 24.514 28.353 30.805 31.348 34.556 33.855 34.787 32.529 29.998 29.257 28.155 30.466 35.704 39.327 39.351 42.234 43.630 43.722 43.121 37.985 37.135 34.646 33.026 35.087 38.846 42.013 43.908 42.868 44.423 44.167 43.636 44.382 42.142 43.452 36.912 42.413 45.344 44.873 47.510 49.554 47.369 45.998 48.140 48.441 44.928 40.454 38.661 37.246 36.843 36.424 37.594 38.144 38.737 34.560 36.080 33.508 35.462 33.374 32.110 35.533 35.532 37.903 36.763 40.399 44.164 44.496 43.110 etc...

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	10 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	27.951	NA	NA	-3.3988375771605	NA
2	29.781	NA	NA	-0.733879243827162	NA
3	32.914	NA	NA	1.16321334876543	NA
4	33.488	NA	NA	1.87588464506173	NA
5	35.652	NA	NA	2.76180594135803	NA
6	36.488	NA	NA	3.35600038580247	NA
7	35.387	35.0628707561728	32.9697083333333	2.09316242283950	0.324129243827166
8	35.676	35.4831346450617	33.096	2.38713464506173	0.192865354938277
9	34.844	33.3726902006173	33.14725	0.225440200617287	1.47130979938272
10	32.447	31.4677642746914	33.12675	-1.65898572530864	0.97923572530864
11	31.068	29.9303661265432	33.0354166666667	-3.10505054012346	1.13763387345679
12	29.01	27.8536114969136	32.8195	-4.96588850308642	1.15638850308643
13	29.812	29.1297874228395	32.528625	-3.3988375771605	0.682212577160499
14	30.951	31.4399124228395	32.1737916666667	-0.733879243827162	-0.488912422839505
15	32.974	32.8274216820988	31.6642083333333	1.16321334876543	0.146578317901238
16	32.936	32.9348013117284	31.0589166666667	1.87588464506173	0.00119868827161085
17	34.012	33.1942226080247	30.4324166666667	2.76180594135803	0.817777391975309
18	32.946	33.1874170524691	29.8314166666667	3.35600038580247	-0.241417052469139
19	31.948	31.3546624228395	29.2615	2.09316242283950	0.593337577160494
20	30.599	31.1257179783951	28.7385833333333	2.38713464506173	-0.526717978395062
21	27.691	28.4619402006173	28.2365	0.225440200617287	-0.770940200617286
22	25.073	26.0408059413580	27.6997916666667	-1.65898572530864	-0.967805941358023
23	23.406	24.0558244598765	27.160875	-3.10505054012346	-0.649824459876545
24	22.248	21.6442781635802	26.6101666666667	-4.96588850308642	0.603721836419755
25	22.896	22.6292040895062	26.0280416666667	-3.3988375771605	0.26679591049383
26	25.317	24.7601207561728	25.494	-0.733879243827162	0.556879243827165
27	26.558	26.2479216820988	25.0847083333333	1.16321334876543	0.31007831790124
28	26.471	26.6055096450617	24.729625	1.87588464506173	-0.134509645061726
29	27.543	27.1629309413580	24.401125	2.76180594135803	0.380069058641975
30	26.198	27.5038753858025	24.147875	3.35600038580247	-1.30587538580247
31	24.725	26.0773290895062	23.9841666666667	2.09316242283950	-1.35232908950617
32	25.005	26.2521346450617	23.865	2.38713464506173	-1.24713464506173
33	23.462	23.9638985339506	23.7384583333333	0.225440200617287	-0.50189853395062
34	20.78	21.891555941358	23.5505416666667	-1.65898572530864	-1.11155594135802
35	19.815	20.2008244598765	23.305875	-3.10505054012346	-0.385824459876545
36	19.761	18.1531948302469	23.1190833333333	-4.96588850308642	1.60780516975309
37	21.454	19.6184957561728	23.0173333333333	-3.3988375771605	1.83550424382716
38	23.899	22.1945790895062	22.9284583333333	-0.733879243827162	1.70442091049383
39	24.939	23.9775883487654	22.814375	1.16321334876543	0.96141165123457
40	23.58	24.5814263117284	22.7055416666667	1.87588464506173	-1.00142631172840
41	24.562	25.4006809413580	22.638875	2.76180594135803	-0.838680941358021
42	24.696	25.9321253858025	22.576125	3.35600038580247	-1.23612538580247
43	23.785	24.6287040895062	22.5355416666667	2.09316242283950	-0.843704089506176
44	23.812	24.9469263117284	22.5597916666667	2.38713464506173	-1.13492631172839
45	21.917	22.9151485339506	22.6897083333333	0.225440200617287	-0.998148533950612
46	19.713	21.2898476080247	22.9488333333333	-1.65898572530864	-1.57684760802469
47	19.282	20.1134077932099	23.2184583333333	-3.10505054012346	-0.83140779320987
48	18.788	18.6160281635802	23.5819166666667	-4.96588850308642	0.171971836419758
49	21.453	20.6655790895062	24.0644166666667	-3.3988375771605	0.787420910493829
50	24.482	23.8270374228395	24.5609166666667	-0.733879243827162	0.654962577160497
51	27.474	26.1676716820988	25.0044583333333	1.16321334876543	1.30632831790124
52	27.264	27.2569679783951	25.3810833333333	1.87588464506173	0.00703202160494243
53	27.349	28.506180941358	25.744375	2.76180594135803	-1.15718094135802
54	30.632	29.3758337191358	26.0198333333333	3.35600038580247	1.2561662808642
55	29.429	28.2051207561728	26.1119583333333	2.09316242283950	1.22387924382716
56	30.084	28.4859679783951	26.0988333333333	2.38713464506173	1.59803202160494
57	26.29	26.3622318672839	26.1367916666667	0.225440200617287	-0.0722318672839499
58	24.379	24.6619726080247	26.3209583333333	-1.65898572530864	-0.282972608024693
59	23.335	23.5300744598765	26.635125	-3.10505054012346	-0.195074459876537
60	21.346	21.9993614969136	26.96525	-4.96588850308642	-0.653361496913579
61	21.106	23.9143290895062	27.3131666666667	-3.3988375771605	-2.80832908950617
62	24.514	26.9596624228395	27.6935416666667	-0.733879243827162	-2.44566242283951
63	28.353	29.3126716820988	28.1494583333333	1.16321334876543	-0.959671682098765
64	30.805	30.5194263117284	28.6435416666667	1.87588464506173	0.285573688271604
65	31.348	31.8862226080247	29.1244166666667	2.76180594135803	-0.538222608024697
66	34.556	33.0108753858025	29.654875	3.35600038580247	1.54512461419753
67	33.855	32.4217457561728	30.3285833333333	2.09316242283950	1.43325424382716
68	34.787	33.5719679783951	31.1848333333333	2.38713464506173	1.21503202160494
69	32.529	32.3337735339506	32.1083333333333	0.225440200617287	0.195226466049391
70	29.998	31.262680941358	32.9216666666667	-1.65898572530864	-1.26468094135802
71	29.257	30.6262827932099	33.7313333333333	-3.10505054012346	-1.36928279320988
72	28.155	29.5971114969136	34.563	-4.96588850308642	-1.44211149691358
73	30.466	31.9533707561728	35.3522083333333	-3.3988375771605	-1.48737075617284
74	35.704	35.3767040895062	36.1105833333333	-0.733879243827162	0.327295910493831
75	39.327	37.8483800154321	36.6851666666667	1.16321334876543	1.4786199845679
76	39.351	39.0857596450617	37.209875	1.87588464506173	0.265240354938271
77	42.234	40.4935976080247	37.7317916666667	2.76180594135803	1.74040239197531
78	43.63	41.5152920524691	38.1592916666667	3.35600038580247	2.11470794753087
79	43.722	40.6479540895062	38.5547916666667	2.09316242283950	3.07404591049383
80	43.121	41.2653846450617	38.87825	2.38713464506173	1.85561535493827
81	37.985	39.3465235339506	39.1210833333333	0.225440200617287	-1.36152353395061
82	37.135	37.7638892746914	39.422875	-1.65898572530864	-0.62888927469136
83	34.646	36.5341161265432	39.6391666666667	-3.10505054012346	-1.88811612654320
84	33.026	34.7327364969136	39.698625	-4.96588850308642	-1.70673649691359
85	35.087	36.3513707561728	39.7502083333333	-3.3988375771605	-1.26437075617284
86	38.846	39.0563290895062	39.7902083333333	-0.733879243827162	-0.210329089506175
87	42.013	41.2414216820988	40.0782083333333	1.16321334876543	0.77157831790123
88	43.908	42.4292596450617	40.553375	1.87588464506173	1.47874035493827
89	42.868	43.8907226080247	41.1289166666667	2.76180594135803	-1.02272260802469
90	44.423	45.0137503858025	41.65775	3.35600038580247	-0.590750385802465
91	44.167	44.2180790895062	42.1249166666667	2.09316242283950	-0.0510790895061746
92	43.636	45.0880513117284	42.7009166666667	2.38713464506173	-1.45205131172839
93	44.382	43.3162735339506	43.0908333333333	0.225440200617287	1.06572646604938
94	42.142	41.7010976080247	43.3600833333333	-1.65898572530864	0.440902391975307
95	43.452	40.6836994598765	43.78875	-3.10505054012346	2.76830054012346
96	36.912	39.2241948302469	44.1900833333333	-4.96588850308642	-2.31219483024691
97	42.413	40.9902874228395	44.389125	-3.3988375771605	1.42271257716050
98	45.344	43.9192040895062	44.6530833333333	-0.733879243827162	1.42479591049382
99	44.873	46.1730883487654	45.009875	1.16321334876543	-1.30008834876543
100	47.51	47.1709679783951	45.2950833333333	1.87588464506173	0.339032021604936
101	49.554	48.048055941358	45.28625	2.76180594135803	1.50594405864199
102	47.369	48.5902087191358	45.2342083333333	3.35600038580247	-1.22120871913580
103	45.998	47.1849540895062	45.0917916666667	2.09316242283950	-1.18695408950617
104	48.14	46.9094263117284	44.5222916666667	2.38713464506173	1.23057368827160
105	48.441	44.0414818672840	43.8160416666667	0.225440200617287	4.39951813271605
106	44.928	41.3918476080247	43.0508333333333	-1.65898572530864	3.53615239197531
107	40.454	39.0571994598765	42.16225	-3.10505054012346	1.39680054012346
108	38.661	36.3612781635802	41.3271666666667	-4.96588850308642	2.29972183641976
109	37.246	37.0920790895062	40.4909166666667	-3.3988375771605	0.153920910493824
110	36.843	38.7779540895062	39.5118333333333	-0.733879243827162	-1.93495408950617
111	36.424	39.5503383487654	38.387125	1.16321334876543	-3.12633834876543
112	37.594	39.2463846450617	37.3705	1.87588464506173	-1.65238464506174
113	38.144	39.4428892746914	36.6810833333333	2.76180594135803	-1.29888927469137
114	38.737	39.4691253858025	36.113125	3.35600038580247	-0.732125385802469
115	34.56	37.8619540895062	35.7687916666667	2.09316242283950	-3.30195408950617
116	36.08	38.0299263117284	35.6427916666667	2.38713464506173	-1.94992631172840
117	33.508	35.8752318672839	35.6497916666667	0.225440200617287	-2.36723186728394
118	35.462	34.017805941358	35.6767916666667	-1.65898572530864	1.44419405864198
119	33.374	32.6310744598765	35.736125	-3.10505054012346	0.742925540123458
120	32.11	31.0903198302469	36.0562083333333	-4.96588850308642	1.01968016975308
121	35.533	NA	36.6963333333333	NA	NA
122	35.532	NA	37.40325	NA	NA
123	37.903	NA	38.1283333333333	NA	NA
124	36.763	NA	38.9133333333333	NA	NA
125	40.399	NA	39.7225416666667	NA	NA
126	44.164	NA	NA	NA	NA
127	44.496	NA	NA	NA	NA
128	43.11	NA	NA	NA	NA
129	43.88	NA	NA	NA	NA
130	43.93	NA	NA	NA	NA
131	44.327	NA	NA	NA	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292693883g7hkpsxfyjmpdgd/1q39b1292693768.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292693883g7hkpsxfyjmpdgd/1q39b1292693768.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292693883g7hkpsxfyjmpdgd/20c8w1292693768.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292693883g7hkpsxfyjmpdgd/20c8w1292693768.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292693883g7hkpsxfyjmpdgd/30c8w1292693768.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292693883g7hkpsxfyjmpdgd/30c8w1292693768.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292693883g7hkpsxfyjmpdgd/4bl7h1292693768.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292693883g7hkpsxfyjmpdgd/4bl7h1292693768.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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