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opgave 6 oef 2

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

Title produced by software: Classical Decomposition

Date of computation: Fri, 08 Jan 2010 09:08:46 -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/08/t12629670172wcnb5ldlk646ht.htm/, Retrieved Fri, 08 Jan 2010 17:10:23 +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/08/t12629670172wcnb5ldlk646ht.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 «

102,5 77,7 82,8 77,3 103,1 99,7 99,5 107,2 96,7 97,1 105,2 151,2 102,7 75,4 87,2 83,7 105,8 111,5 99,7 111,2 101,5 110,9 116,3 164,9 118,1 83,7 84 107,2 113,7 120,7 111,2 112,4 112,5 130,4 130,7 174,3 132,2 91,8 104,2 104,8 131,4 141,2 132,7 135,7 136,9 151,2 144 201,5 149,6 108,7 122,8 126,7 139,9 162,5 142,7 151,6 148,1 159 157,8 226,7 153,7 122,3 117,6 166 154,5 183,9 164,4 173,3 160,2 166,4 170,3 238,4 166,8 122,5 141,8 140,5 173,8 188,8 168 187,4 177,7 183,8 196,1 264,6 193,7 141,3 170,1 163,7 190,1 230,7 195,9 210,3 204,7 210,3 221,2 288,2 203,2 162,4 149,2 195,3 213,7 227,9 212,1 226,8 212,6 220,9 228,1 311,6

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	'Gwilym Jenkins' @ 72.249.127.135

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	102.5	NA	NA	1.01860237749486	NA
2	77.7	NA	NA	0.747205802621793	NA
3	82.8	NA	NA	0.80155869200008	NA
4	77.3	NA	NA	0.879634543888747	NA
5	103.1	NA	NA	0.985080885490468	NA
6	99.7	NA	NA	1.08499349602838	NA
7	99.5	96.6987437028958	100.008333333333	0.96690686145717	1.0289689006272
8	107.2	102.344416497326	99.9208333333333	1.02425503354148	1.04744356037049
9	96.7	97.2849567865602	100.008333333333	0.972768503823616	0.993987181514162
10	97.1	103.499312270431	100.458333333333	1.0302710470719	0.938170485097422
11	105.2	105.787899960649	100.8375	1.04909284701276	0.994442654019337
12	151.2	146.038457409837	101.441666666667	1.43962990956875	1.03534372165873
13	102.7	103.838024032456	101.941666666667	1.01860237749486	0.98904039206197
14	75.4	76.3021658777288	102.116666666667	0.747205802621793	0.988176405383112
15	87.2	82.1464066184748	102.483333333333	0.80155869200008	1.06151934807077
16	83.7	90.8295969443789	103.258333333333	0.879634543888747	0.921505795641207
17	105.8	102.739831852966	104.295833333333	0.985080885490468	1.02978560595090
18	111.5	114.281460775422	105.329166666667	1.08499349602838	0.975661312372547
19	99.7	103.015868531083	106.541666666667	0.96690686145717	0.96781206062363
20	111.2	110.137290210854	107.529166666667	1.02425503354148	1.00964895529127
21	101.5	104.807699882796	107.741666666667	0.972768503823616	0.968440296977273
22	110.9	111.874557323920	108.5875	1.0302710470719	0.99128883861325
23	116.3	115.290932666506	109.895833333333	1.04909284701276	1.00875235640961
24	164.9	159.235064914217	110.608333333333	1.43962990956875	1.03557592725468
25	118.1	113.544455854667	111.470833333333	1.01860237749486	1.04012123807405
26	83.7	83.6870498936408	112	0.747205802621793	1.00015474444822
27	84	90.1820325057757	112.508333333333	0.80155869200008	0.931449399242807
28	107.2	100.084085374875	113.779166666667	0.879634543888747	1.07109936208611
29	113.7	113.473109001123	115.191666666667	0.985080885490468	1.00199951337259
30	120.7	126.058161013563	116.183333333333	1.08499349602838	0.957494532916541
31	111.2	113.285225155476	117.1625	0.96690686145717	0.981593141094845
32	112.4	120.951716273330	118.0875	1.02425503354148	0.929296445417903
33	112.5	116.018856889363	119.266666666667	0.972768503823616	0.969669957249114
34	130.4	123.641111240687	120.008333333333	1.0302710470719	1.05466538347553
35	130.7	126.568680771893	120.645833333333	1.04909284701276	1.03264092825264
36	174.3	175.97676107091	122.2375	1.43962990956875	0.990471690348736
37	132.2	126.293962279644	123.9875	1.01860237749486	1.04676421274422
38	91.8	94.0389636174636	125.854166666667	0.747205802621793	0.97619110705461
39	104.2	102.472599116444	127.841666666667	0.80155869200008	1.01685719790901
40	104.8	114.110591205968	129.725	0.879634543888747	0.918407300255221
41	131.4	129.189253628385	131.145833333333	0.985080885490468	1.01711246337852
42	141.2	144.123302722436	132.833333333333	1.08499349602838	0.979716654647682
43	132.7	130.234296681102	134.691666666667	0.96690686145717	1.01893282631176
44	135.7	139.422448711528	136.120833333333	1.02425503354148	0.973300937216864
45	136.9	133.852946126130	137.6	0.972768503823616	1.02276418982216
46	151.2	143.503878469027	139.2875	1.0302710470719	1.05363005943170
47	144	147.454370867839	140.554166666667	1.04909284701276	0.976573289435176
48	201.5	204.133522718892	141.795833333333	1.43962990956875	0.987099018897945
49	149.6	145.762000219515	143.1	1.01860237749486	1.02633059216192
50	108.7	107.731509950508	144.179166666667	0.747205802621793	1.00898984939445
51	122.8	116.473157603378	145.308333333333	0.80155869200008	1.05432017579678
52	126.7	128.514606862146	146.1	0.879634543888747	0.985880150852483
53	139.9	144.806890167099	147	0.985080885490468	0.96611424938802
54	162.5	161.257158347217	148.625	1.08499349602838	1.00770720298882
55	142.7	144.886964410767	149.845833333333	0.96690686145717	0.984905719988948
56	151.6	154.235737134121	150.583333333333	1.02425503354148	0.982910982998517
57	148.1	146.823192843778	150.933333333333	0.972768503823616	1.00869622252106
58	159	156.966086817433	152.354166666667	1.0302710470719	1.01295765998761
59	157.8	162.189754148172	154.6	1.04909284701276	0.972934454637855
60	226.7	224.726228883682	156.1	1.43962990956875	1.00878300288365
61	153.7	160.833071229866	157.895833333333	1.01860237749486	0.955649225776014
62	122.3	119.331880036211	159.704166666667	0.747205802621793	1.02487281657582
63	117.6	129.141124764863	161.1125	0.80155869200008	0.910631684632787
64	166	142.434823519185	161.925	0.879634543888747	1.16544533070342
65	154.5	160.326018617263	162.754166666667	0.985080885490468	0.963661427711423
66	183.9	177.681247393347	163.7625	1.08499349602838	1.03499948755361
67	164.4	159.342221989552	164.795833333333	0.96690686145717	1.03174160588008
68	173.3	169.360569796084	165.35	1.02425503354148	1.02326061023920
69	160.2	161.836253419456	166.366666666667	0.972768503823616	0.989889450695484
70	166.4	171.346953516145	166.3125	1.0302710470719	0.97112902555528
71	170.3	174.206238466664	166.054166666667	1.04909284701276	0.977576931222174
72	238.4	240.508171767329	167.0625	1.43962990956875	0.991234510861573
73	166.8	170.531014698932	167.416666666667	1.01860237749486	0.97812119569265
74	122.5	125.645769068365	168.154166666667	0.747205802621793	0.97496319142546
75	141.8	135.840819498830	169.470833333333	0.80155869200008	1.04386884975485
76	140.5	150.351534414184	170.925	0.879634543888747	0.93447666196046
77	173.8	170.148095946341	172.725	0.985080885490468	1.02146309092293
78	188.8	189.756320842896	174.891666666667	1.08499349602838	0.994960268840332
79	168	171.243233942654	177.104166666667	0.96690686145717	0.981060659344122
80	187.4	183.350186462538	179.008333333333	1.02425503354148	1.02208786157024
81	177.7	176.042726777380	180.970833333333	0.972768503823616	1.00941403972182
82	183.8	188.659799902983	183.116666666667	1.0302710470719	0.97424040571716
83	196.1	193.833017146195	184.7625	1.04909284701276	1.01169554540904
84	264.6	269.480723697401	187.1875	1.43962990956875	0.981888412534913
85	193.7	193.632067785201	190.095833333333	1.01860237749486	1.00035083142775
86	141.3	143.622295336441	192.2125	0.747205802621793	0.98383053737582
87	170.1	155.736174199849	194.291666666667	0.80155869200008	1.09223178798343
88	163.7	172.866513593803	196.520833333333	0.879634543888747	0.946973457130382
89	190.1	195.706840421129	198.670833333333	0.985080885490468	0.971350820395117
90	230.7	217.758194652895	200.7	1.08499349602838	1.05943200148097
91	195.9	195.391732807547	202.079166666667	0.96690686145717	1.00260127276190
92	210.3	208.286528799967	203.354166666667	1.02425503354148	1.00966683352800
93	204.7	197.82463485883	203.3625	0.972768503823616	1.03475484813141
94	210.3	209.977824985312	203.808333333333	1.0302710470719	1.00153432875453
95	221.2	216.226778209721	206.108333333333	1.04909284701276	1.02300002724665
96	288.2	297.967400532992	206.975	1.43962990956875	0.967219902192251
97	203.2	211.393946742767	207.533333333333	1.01860237749486	0.96123849869392
98	162.4	156.088178810182	208.895833333333	0.747205802621793	1.04043753497498
99	149.2	168.257188934467	209.9125	0.80155869200008	0.886737743242048
100	195.3	185.324337821628	210.683333333333	0.879634543888747	1.05382812800321
101	213.7	208.258412703753	211.4125	0.985080885490468	1.02612901551299
102	227.9	230.750991767835	212.675	1.08499349602838	0.987644725831978
103	212.1	NA	NA	0.96690686145717	NA
104	226.8	NA	NA	1.02425503354148	NA
105	212.6	NA	NA	0.972768503823616	NA
106	220.9	NA	NA	1.0302710470719	NA
107	228.1	NA	NA	1.04909284701276	NA
108	311.6	NA	NA	1.43962990956875	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jan/08/t12629670172wcnb5ldlk646ht/15cx71262966924.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/08/t12629670172wcnb5ldlk646ht/15cx71262966924.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/08/t12629670172wcnb5ldlk646ht/2m5go1262966924.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/08/t12629670172wcnb5ldlk646ht/2m5go1262966924.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/08/t12629670172wcnb5ldlk646ht/3qmp41262966924.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/08/t12629670172wcnb5ldlk646ht/3qmp41262966924.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/08/t12629670172wcnb5ldlk646ht/4uaav1262966924.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/08/t12629670172wcnb5ldlk646ht/4uaav1262966924.ps (open in new window)

Parameters (Session):

par1 = multiplicative ; par2 = 12 ;

Parameters (R input):

par1 = multiplicative ; 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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