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Paper Decomposition - Inschrijvingen voertuigen

*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 19:04:54 +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/t1292699076wr8o852fklb8tz6.htm/, Retrieved Sat, 18 Dec 2010 20:04:37 +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/18/t1292699076wr8o852fklb8tz6.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 «

130.678 120.877 137.114 134.406 120.262 130.846 120.343 98.881 115.678 120.796 94.261 89.151 119.880 131.468 155.089 149.581 122.788 143.900 112.115 109.600 117.446 118.456 101.901 89.940 129.143 126.102 143.048 142.258 131.011 146.471 114.073 114.642 118.226 111.338 108.701 80.512 146.865 137.179 166.536 137.070 127.090 139.966 122.243 109.097 116.591 111.964 109.754 77.609 138.445 127.901 156.615 133.264 143.521 152.139 131.523 113.925 86.495 127.877 107.017 78.716 138.278 144.238 143.679 159.932 136.781 148.173 125.673 105.573 122.405 128.045 94.467 85.573 121.501 125.074 144.979 142.120 124.213 144.407 125.170 109.267 122.354 122.589 104.982 90.542

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	130.678	NA	NA	8.5238420138889	NA
2	120.877	NA	NA	8.05986284722222	NA
3	137.114	NA	NA	27.6053767361111	NA
4	134.406	NA	NA	19.9263975694444	NA
5	120.262	NA	NA	6.70266145833333	NA
6	130.846	NA	NA	21.5605503472222	NA
7	120.343	114.979022569444	117.3245	-2.34547743055556	5.36397743055556
8	98.881	102.629647569444	117.315875	-14.6862274305556	-3.74864756944444
9	115.678	107.923300347222	118.506125	-10.5828246527778	7.75469965277779
10	120.796	116.135529513889	119.887375	-3.75184548611111	4.66047048611112
11	94.261	99.729564236111	120.624916666667	-20.8953524305556	-5.46856423611109
12	89.151	81.1571197916666	121.274083333333	-40.1169635416667	7.99388020833335
13	119.88	129.999008680556	121.475166666667	8.5238420138889	-10.1190086805556
14	131.468	129.638821180556	121.578958333333	8.05986284722222	1.82917881944445
15	155.089	149.704626736111	122.09925	27.6053767361111	5.38437326388889
16	149.581	142.001814236111	122.075416666667	19.9263975694444	7.57918576388889
17	122.788	128.998911458333	122.29625	6.70266145833333	-6.21091145833333
18	143.9	144.208008680556	122.647458333333	21.5605503472222	-0.30800868055556
19	112.115	120.720814236111	123.066291666667	-2.34547743055556	-8.60581423611112
20	109.6	108.542439236111	123.228666666667	-14.6862274305556	1.05756076388889
21	117.446	111.920550347222	122.503375	-10.5828246527778	5.52544965277778
22	118.456	117.944696180556	121.696541666667	-3.75184548611111	0.511303819444436
23	101.901	100.838689236111	121.734041666667	-20.8953524305556	1.06231076388887
24	89.94	82.066828125	122.183791666667	-40.1169635416667	7.873171875
25	129.143	130.896342013889	122.3725	8.5238420138889	-1.7533420138889
26	126.102	130.724029513889	122.664166666667	8.05986284722222	-4.62202951388886
27	143.048	150.512126736111	122.90675	27.6053767361111	-7.4641267361111
28	142.258	142.569064236111	122.642666666667	19.9263975694444	-0.311064236111093
29	131.011	129.332078125	122.629416666667	6.70266145833333	1.67892187500001
30	146.471	144.080467013889	122.519916666667	21.5605503472222	2.39053298611113
31	114.073	120.520022569444	122.8655	-2.34547743055556	-6.44702256944443
32	114.642	109.379230902778	124.065458333333	-14.6862274305556	5.26276909722223
33	118.226	114.922842013889	125.505666666667	-10.5828246527778	3.30315798611113
34	111.338	122.516321180556	126.268166666667	-3.75184548611111	-11.1783211805556
35	108.701	104.993272569444	125.888625	-20.8953524305556	3.70772743055556
36	80.512	85.3372447916666	125.454208333333	-40.1169635416667	-4.82524479166665
37	146.865	134.047425347222	125.523583333333	8.5238420138889	12.8175746527778
38	137.179	133.692821180556	125.632958333333	8.05986284722222	3.48617881944443
39	166.536	152.939168402778	125.333791666667	27.6053767361111	13.5968315972222
40	137.07	145.218147569444	125.29175	19.9263975694444	-8.14814756944443
41	127.09	132.064369791667	125.361708333333	6.70266145833333	-4.97436979166665
42	139.966	146.845175347222	125.284625	21.5605503472222	-6.8791753472222
43	122.243	122.467355902778	124.812833333333	-2.34547743055556	-0.224355902777774
44	109.097	109.389189236111	124.075416666667	-14.6862274305556	-0.292189236111085
45	116.591	112.692633680556	123.275458333333	-10.5828246527778	3.89836631944446
46	111.964	118.951654513889	122.7035	-3.75184548611111	-6.98765451388888
47	109.754	102.334189236111	123.229541666667	-20.8953524305556	7.41981076388892
48	77.609	84.3044114583333	124.421375	-40.1169635416667	-6.69541145833331
49	138.445	133.839092013889	125.31525	8.5238420138889	4.60590798611112
50	127.901	133.962946180556	125.903083333333	8.05986284722222	-6.06194618055552
51	156.615	152.455626736111	124.85025	27.6053767361111	4.15937326388891
52	133.264	144.185689236111	124.259291666667	19.9263975694444	-10.9216892361111
53	143.521	131.510953125	124.808291666667	6.70266145833333	12.010046875
54	152.139	146.300925347222	124.740375	21.5605503472222	5.83807465277778
55	131.523	122.434064236111	124.779541666667	-2.34547743055556	9.08893576388891
56	113.925	110.767064236111	125.453291666667	-14.6862274305556	3.15793576388893
57	86.495	115.012175347222	125.595	-10.5828246527778	-28.5171753472222
58	127.877	122.415321180556	126.167166666667	-3.75184548611111	5.46167881944447
59	107.017	106.102147569444	126.9975	-20.8953524305556	0.91485243055557
60	78.716	86.434453125	126.551416666667	-40.1169635416667	-7.718453125
61	138.278	134.666258680556	126.142416666667	8.5238420138889	3.61174131944445
62	144.238	133.610529513889	125.550666666667	8.05986284722222	10.6274704861111
63	143.679	154.304293402778	126.698916666667	27.6053767361111	-10.6252934027778
64	159.932	148.128564236111	128.202166666667	19.9263975694444	11.8034357638889
65	136.781	134.388911458333	127.68625	6.70266145833333	2.39208854166668
66	148.173	149.009592013889	127.449041666667	21.5605503472222	-0.83659201388889
67	125.673	124.690230902778	127.035708333333	-2.34547743055556	0.982769097222246
68	105.573	110.851939236111	125.538166666667	-14.6862274305556	-5.27893923611113
69	122.405	114.211008680556	124.793833333333	-10.5828246527778	8.19399131944445
70	128.045	120.353987847222	124.105833333333	-3.75184548611111	7.69101215277777
71	94.467	101.944647569444	122.84	-20.8953524305556	-7.47764756944446
72	85.573	82.042453125	122.159416666667	-40.1169635416667	3.530546875
73	121.501	130.505383680556	121.981541666667	8.5238420138889	-9.00438368055555
74	125.074	130.174362847222	122.1145	8.05986284722222	-5.10036284722223
75	144.979	149.871668402778	122.266291666667	27.6053767361111	-4.89266840277774
76	142.12	141.963230902778	122.036833333333	19.9263975694444	0.156769097222238
77	124.213	128.950286458333	122.247625	6.70266145833333	-4.73728645833333
78	144.407	144.453342013889	122.892791666667	21.5605503472222	-0.0463420138888893
79	125.17	NA	NA	-2.34547743055556	NA
80	109.267	NA	NA	-14.6862274305556	NA
81	122.354	NA	NA	-10.5828246527778	NA
82	122.589	NA	NA	-3.75184548611111	NA
83	104.982	NA	NA	-20.8953524305556	NA
84	90.542	NA	NA	-40.1169635416667	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292699076wr8o852fklb8tz6/1dcwq1292699090.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292699076wr8o852fklb8tz6/1dcwq1292699090.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292699076wr8o852fklb8tz6/2dcwq1292699090.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292699076wr8o852fklb8tz6/2dcwq1292699090.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292699076wr8o852fklb8tz6/3dcwq1292699090.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292699076wr8o852fklb8tz6/3dcwq1292699090.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292699076wr8o852fklb8tz6/4n3va1292699090.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292699076wr8o852fklb8tz6/4n3va1292699090.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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