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Opgave 9 2

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Tue, 12 Jan 2010 13:22:07 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Jan/12/t1263327775lhmblpoco5p2oce.htm/, Retrieved Tue, 12 Jan 2010 21:23:02 +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/Jan/12/t1263327775lhmblpoco5p2oce.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:

KDGP2W52

Dataseries X:

» Textbox « » Textfile « » CSV «

161,88 162,05 162,16 162,61 162,53 162,53 162,53 162,53 162,83 161,61 161,79 161,79 161,79 161,79 161,85 161,77 161,86 161,89 161,89 161,89 162,18 162,43 162,58 162,57 162,57 162,57 162,44 162,79 163,15 163,23 163,23 163,23 163,38 163,71 163,73 163,73 163,73 163,73 163,93 164,27 164,57 164,73 164,73 164,76 165,75 165,86 165,99 166,13 166,13 166,13 166,15 166,45 166,48 166,51 166,51 166,51 166,58 166,82 167,35 167,5 167,5 167,6 167,72 167,29 166,98 166,98 166,98 166,98 167,63 167,83 167,85 167,87 167,87 167,96 167,7 169,25 168,79 168,77 168,77 169 168,92 169,23 169,28 169,29 169,29 170,29 170,59 171,98 172,31 172,28 172,28 172,45 172,27 172,65 172,08 172,2 172,2 172,2 172,36 172,53 173,18 173,17 173,17 173,17 173,4 174,47 174,56 174,59 174,59 175,22 175,3 175,25 175,54 175,58 175,58 175,68 176,05 176,4 176,58 176,49

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	'Gwilym Jenkins' @ 72.249.127.135

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	161.88	NA	NA	-0.143094135802472	NA
2	162.05	NA	NA	-0.062168209876544	NA
3	162.16	NA	NA	-0.123140432098758	NA
4	162.61	NA	NA	0.140516975308634	NA
5	162.53	NA	NA	0.145794753086419	NA
6	162.53	NA	NA	0.0403780864197411	NA
7	162.53	162.191026234568	162.232916666667	-0.0418904320987721	0.338973765432115
8	162.53	162.104405864198	162.218333333333	-0.113927469135807	0.42559413580247
9	162.83	162.227739197531	162.194583333333	0.0331558641975349	0.602260802469146
10	161.61	162.246026234568	162.146666666667	0.0993595679012479	-0.636026234567879
11	161.79	162.131026234568	162.08375	0.0472762345679114	-0.341026234567892
12	161.79	162.006905864198	162.029166666667	-0.0222608024691350	-0.216905864197543
13	161.79	161.832739197531	161.975833333333	-0.143094135802472	-0.0427391975308637
14	161.79	161.860331790123	161.9225	-0.062168209876544	-0.070331790123447
15	161.85	161.745609567901	161.86875	-0.123140432098758	0.104390432098768
16	161.77	162.016350308642	161.875833333333	0.140516975308634	-0.246350308641894
17	161.86	162.088711419753	161.942916666667	0.145794753086419	-0.22871141975304
18	161.89	162.048711419753	162.008333333333	0.0403780864197411	-0.158711419753047
19	161.89	162.031442901235	162.073333333333	-0.0418904320987721	-0.141442901234569
20	161.89	162.024405864198	162.138333333333	-0.113927469135807	-0.134405864197532
21	162.18	162.228572530864	162.195416666667	0.0331558641975349	-0.0485725308641918
22	162.43	162.361859567901	162.2625	0.0993595679012479	0.068140432098744
23	162.58	162.406026234568	162.35875	0.0472762345679114	0.173973765432095
24	162.57	162.446072530864	162.468333333333	-0.0222608024691350	0.123927469135793
25	162.57	162.436905864198	162.58	-0.143094135802472	0.133094135802452
26	162.57	162.62949845679	162.691666666667	-0.062168209876544	-0.0594984567900951
27	162.44	162.674359567901	162.7975	-0.123140432098758	-0.234359567901237
28	162.79	163.041350308642	162.900833333333	0.140516975308634	-0.251350308642003
29	163.15	163.147878086420	163.002083333333	0.145794753086419	0.00212191358025393
30	163.23	163.138711419753	163.098333333333	0.0403780864197411	0.0912885802468963
31	163.23	163.153109567901	163.195	-0.0418904320987721	0.0768904320987644
32	163.23	163.177739197531	163.291666666667	-0.113927469135807	0.0522608024691635
33	163.38	163.435239197531	163.402083333333	0.0331558641975349	-0.0552391975308524
34	163.71	163.625192901235	163.525833333333	0.0993595679012479	0.0848070987654523
35	163.73	163.693942901235	163.646666666667	0.0472762345679114	0.0360570987654398
36	163.73	163.746072530864	163.768333333333	-0.0222608024691350	-0.0160725308641929
37	163.73	163.750239197531	163.893333333333	-0.143094135802472	-0.0202391975308842
38	163.73	163.957415123457	164.019583333333	-0.062168209876544	-0.227415123456808
39	163.93	164.058942901235	164.182083333333	-0.123140432098758	-0.128942901234581
40	164.27	164.510933641975	164.370416666667	0.140516975308634	-0.240933641975289
41	164.57	164.699961419753	164.554166666667	0.145794753086419	-0.129961419753101
42	164.73	164.788711419753	164.748333333333	0.0403780864197411	-0.0587114197530809
43	164.73	164.906442901235	164.948333333333	-0.0418904320987721	-0.176442901234566
44	164.76	165.034405864198	165.148333333333	-0.113927469135807	-0.274405864197519
45	165.75	165.373989197531	165.340833333333	0.0331558641975349	0.376010802469125
46	165.86	165.623526234568	165.524166666667	0.0993595679012479	0.236473765432095
47	165.99	165.741859567901	165.694583333333	0.0472762345679114	0.248140432098808
48	166.13	165.826072530864	165.848333333333	-0.0222608024691350	0.3039274691358
49	166.13	165.853572530864	165.996666666667	-0.143094135802472	0.276427469135797
50	166.13	166.081581790123	166.14375	-0.062168209876544	0.0484182098765302
51	166.15	166.128109567901	166.25125	-0.123140432098758	0.0218904320987576
52	166.45	166.466350308642	166.325833333333	0.140516975308634	-0.0163503086419894
53	166.48	166.568294753086	166.4225	0.145794753086419	-0.0882947530864158
54	166.51	166.576628086420	166.53625	0.0403780864197411	-0.0666280864197404
55	166.51	166.608526234568	166.650416666667	-0.0418904320987721	-0.0985262345679132
56	166.51	166.654822530864	166.76875	-0.113927469135807	-0.144822530864218
57	166.58	166.928572530864	166.895416666667	0.0331558641975349	-0.348572530864203
58	166.82	167.095192901235	166.995833333333	0.0993595679012479	-0.275192901234561
59	167.35	167.098942901235	167.051666666667	0.0472762345679114	0.251057098765443
60	167.5	167.069822530864	167.092083333333	-0.0222608024691350	0.430177469135828
61	167.5	166.988155864197	167.13125	-0.143094135802472	0.511844135802505
62	167.6	167.10824845679	167.170416666667	-0.062168209876544	0.491751543209887
63	167.72	167.110609567901	167.23375	-0.123140432098758	0.609390432098763
64	167.29	167.460100308642	167.319583333333	0.140516975308634	-0.170100308641963
65	166.98	167.528294753086	167.3825	0.145794753086419	-0.548294753086424
66	166.98	167.459128086420	167.41875	0.0403780864197411	-0.479128086419735
67	166.98	167.407692901235	167.449583333333	-0.0418904320987721	-0.427692901234536
68	166.98	167.366072530864	167.48	-0.113927469135807	-0.386072530864141
69	167.63	167.527322530864	167.494166666667	0.0331558641975349	0.102677469135813
70	167.83	167.674359567901	167.575	0.0993595679012479	0.155640432098807
71	167.85	167.779359567901	167.732083333333	0.0472762345679114	0.0706404320987701
72	167.87	167.859822530864	167.882083333333	-0.0222608024691350	0.0101774691358116
73	167.87	167.888155864198	168.03125	-0.143094135802472	-0.0181558641975244
74	167.96	168.127831790123	168.19	-0.062168209876544	-0.167831790123472
75	167.7	168.204776234568	168.327916666667	-0.123140432098758	-0.504776234567942
76	169.25	168.580516975309	168.44	0.140516975308634	0.669483024691345
77	168.79	168.703711419753	168.557916666667	0.145794753086419	0.0862885802469009
78	168.77	168.717044753086	168.676666666667	0.0403780864197411	0.0529552469135979
79	168.77	168.753109567901	168.795	-0.0418904320987721	0.0168904320987906
80	169	168.837322530864	168.95125	-0.113927469135807	0.162677469135843
81	168.92	169.201905864198	169.16875	0.0331558641975349	-0.28190586419754
82	169.23	169.502276234568	169.402916666667	0.0993595679012479	-0.272276234567926
83	169.28	169.710609567901	169.663333333333	0.0472762345679114	-0.430609567901229
84	169.29	169.933989197531	169.95625	-0.0222608024691350	-0.643989197530857
85	169.29	170.105655864198	170.24875	-0.143094135802472	-0.815655864197538
86	170.29	170.476581790123	170.53875	-0.062168209876544	-0.186581790123427
87	170.59	170.698942901235	170.822083333333	-0.123140432098758	-0.108942901234542
88	171.98	171.244683641975	171.104166666667	0.140516975308634	0.735316358024733
89	172.31	171.509128086420	171.363333333333	0.145794753086419	0.800871913580266
90	172.28	171.641628086420	171.60125	0.0403780864197411	0.638371913580272
91	172.28	171.801859567901	171.84375	-0.0418904320987721	0.478140432098741
92	172.45	171.930655864198	172.044583333333	-0.113927469135807	0.519344135802413
93	172.27	172.231072530864	172.197916666667	0.0331558641975349	0.0389274691358139
94	172.65	172.393942901235	172.294583333333	0.0993595679012479	0.256057098765467
95	172.08	172.401026234568	172.35375	0.0472762345679114	-0.321026234567853
96	172.2	172.404822530864	172.427083333333	-0.0222608024691350	-0.204822530864192
97	172.2	172.358155864197	172.50125	-0.143094135802472	-0.158155864197511
98	172.2	172.506165123457	172.568333333333	-0.062168209876544	-0.306165123456765
99	172.36	172.522276234568	172.645416666667	-0.123140432098758	-0.162276234567884
100	172.53	172.908850308642	172.768333333333	0.140516975308634	-0.378850308641944
101	173.18	173.093294753086	172.9475	0.145794753086419	0.0867052469135956
102	173.17	173.190794753086	173.150416666667	0.0403780864197411	-0.0207947530864487
103	173.17	173.307692901235	173.349583333333	-0.0418904320987721	-0.137692901234601
104	173.17	173.461072530864	173.575	-0.113927469135807	-0.291072530864255
105	173.4	173.856489197531	173.823333333333	0.0331558641975349	-0.456489197530885
106	174.47	174.158526234568	174.059166666667	0.0993595679012479	0.311473765432083
107	174.56	174.318109567901	174.270833333333	0.0472762345679114	0.241890432098756
108	174.59	174.447322530864	174.469583333333	-0.0222608024691350	0.142677469135805
109	174.59	174.527322530864	174.670416666667	-0.143094135802472	0.0626774691358207
110	175.22	174.81324845679	174.875416666667	-0.062168209876544	0.406751543209879
111	175.3	174.967276234568	175.090416666667	-0.123140432098758	0.332723765432092
112	175.25	175.421766975309	175.28125	0.140516975308634	-0.171766975308685
113	175.54	175.591628086420	175.445833333333	0.145794753086419	-0.0516280864197824
114	175.58	175.649544753086	175.609166666667	0.0403780864197411	-0.0695447530864044
115	175.58	NA	NA	-0.0418904320987721	NA
116	175.68	NA	NA	-0.113927469135807	NA
117	176.05	NA	NA	0.0331558641975349	NA
118	176.4	NA	NA	0.0993595679012479	NA
119	176.58	NA	NA	0.0472762345679114	NA
120	176.49	NA	NA	-0.0222608024691350	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jan/12/t1263327775lhmblpoco5p2oce/1g41z1263327724.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/12/t1263327775lhmblpoco5p2oce/1g41z1263327724.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/12/t1263327775lhmblpoco5p2oce/2bbbx1263327724.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/12/t1263327775lhmblpoco5p2oce/2bbbx1263327724.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/12/t1263327775lhmblpoco5p2oce/355cb1263327724.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/12/t1263327775lhmblpoco5p2oce/355cb1263327724.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/12/t1263327775lhmblpoco5p2oce/4j5n01263327724.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/12/t1263327775lhmblpoco5p2oce/4j5n01263327724.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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