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opgave 9 oef 2

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Mon, 06 Dec 2010 18:40:58 +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/06/t1291660786jh7vzdb7exo4ltf.htm/, Retrieved Mon, 06 Dec 2010 19:39:51 +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/06/t1291660786jh7vzdb7exo4ltf.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:

KDGP2W92

Dataseries X:

» Textbox « » Textfile « » CSV «

102,8 106,3 103,7 106,9 104,3 105,4 96,2 95,7 95,9 93,6 94,7 94,5 96,6 96,7 98,9 102 105,2 106,4 99,3 96,4 93,1 95,6 93,3 96,7 105,6 105,2 107 104,9 104,5 105,2 99,7 100,2 98,5 98,4 97,1 98,4 100,6 111,3 119 117,8 108,8 109,3 103,5 103,7 110 105,5 110,4 106,7 110,2 105,2 108 108,1 107,2 106 99,4 100,2 100,3 100,8 99,5 100,2 103 111 120,5 109,5 106,6 105,5 103,9 104,9 104,8 99,6 97 95,4 99,3 103,9 107,4 107,4 111 113,2 108,5 113,3 113,8 105,3 107,5 109,4 118,9 119 115 124,1 120,5 117,7 117,1 118,1 119,6 118,8 124,9 124 124,9 121,7 121,6 125,1 127,9 129 130,1 130,3 127,9 124,1 125,7 129,2 129,2 132,6 131,5 131 125,8 127,2 127,3 127,5 122 118,4 118,3 115,5

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	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	102.8	NA	NA	0.279359567901239	NA
2	106.3	NA	NA	2.02148919753087	NA
3	103.7	NA	NA	4.23121141975309	NA
4	106.9	NA	NA	4.10667438271605	NA
5	104.3	NA	NA	2.5048225308642	NA
6	105.4	NA	NA	2.52056327160494	NA
7	96.2	98.0122299382716	99.7416666666667	-1.72943672839506	-1.8122299382716
8	95.7	97.6765817901234	99.0833333333333	-1.40675154320988	-1.97658179012343
9	95.9	96.9483410493827	98.4833333333333	-1.53499228395062	-1.0483410493827
10	93.6	93.8372299382716	98.0791666666667	-4.24193672839506	-0.237229938271597
11	94.7	94.3927854938271	97.9125	-3.51971450617284	0.307214506172869
12	94.5	94.7603780864197	97.9916666666666	-3.23128858024691	-0.260378086419735
13	96.6	98.4418595679012	98.1625	0.279359567901239	-1.84185956790124
14	96.7	100.342322530864	98.3208333333333	2.02148919753087	-3.64232253086418
15	98.9	102.464544753086	98.2333333333333	4.23121141975309	-3.56454475308642
16	102	102.306674382716	98.2	4.10667438271605	-0.306674382716054
17	105.2	100.729822530864	98.225	2.5048225308642	4.4701774691358
18	106.4	100.778896604938	98.2583333333333	2.52056327160494	5.62110339506172
19	99.3	96.995563271605	98.725	-1.72943672839506	2.30443672839506
20	96.4	98.0474151234568	99.4541666666667	-1.40675154320988	-1.64741512345677
21	93.1	98.6108410493827	100.145833333333	-1.53499228395062	-5.51084104938272
22	95.6	96.3622299382716	100.604166666667	-4.24193672839506	-0.762229938271616
23	93.3	97.1761188271605	100.695833333333	-3.51971450617284	-3.87611882716048
24	96.7	97.3853780864198	100.616666666667	-3.23128858024691	-0.68537808641976
25	105.6	100.862692901235	100.583333333333	0.279359567901239	4.73730709876543
26	105.2	102.779822530864	100.758333333333	2.02148919753087	2.42017746913579
27	107	105.372878086420	101.141666666667	4.23121141975309	1.62712191358024
28	104.9	105.590007716049	101.483333333333	4.10667438271605	-0.690007716049379
29	104.5	104.263155864198	101.758333333333	2.5048225308642	0.236844135802471
30	105.2	104.508063271605	101.9875	2.52056327160494	0.691936728395063
31	99.7	100.120563271605	101.85	-1.72943672839506	-0.420563271604934
32	100.2	100.489081790123	101.895833333333	-1.40675154320988	-0.289081790123461
33	98.5	101.115007716049	102.65	-1.53499228395062	-2.61500771604938
34	98.4	99.445563271605	103.6875	-4.24193672839506	-1.04556327160493
35	97.1	100.884452160494	104.404166666667	-3.51971450617284	-3.78445216049383
36	98.4	101.522878086420	104.754166666667	-3.23128858024691	-3.12287808641975
37	100.6	105.362692901235	105.083333333333	0.279359567901239	-4.76269290123456
38	111.3	107.408989197531	105.3875	2.02148919753087	3.89101080246914
39	119	110.243711419753	106.0125	4.23121141975309	8.7562885802469
40	117.8	110.894174382716	106.7875	4.10667438271605	6.90582561728397
41	108.8	110.142322530864	107.6375	2.5048225308642	-1.34232253086419
42	109.3	111.058063271605	108.5375	2.52056327160494	-1.75806327160494
43	103.5	107.553896604938	109.283333333333	-1.72943672839506	-4.05389660493825
44	103.7	108.022415123457	109.429166666667	-1.40675154320988	-4.32241512345678
45	110	107.181674382716	108.716666666667	-1.53499228395062	2.81832561728397
46	105.5	103.612229938272	107.854166666667	-4.24193672839506	1.88777006172840
47	110.4	103.863618827160	107.383333333333	-3.51971450617284	6.53638117283951
48	106.7	103.947878086420	107.179166666667	-3.23128858024691	2.75212191358024
49	110.2	107.150192901235	106.870833333333	0.279359567901239	3.04980709876543
50	105.2	108.575655864198	106.554166666667	2.02148919753087	-3.37565586419754
51	108	110.235378086420	106.004166666667	4.23121141975309	-2.23537808641976
52	108.1	109.510841049383	105.404166666667	4.10667438271605	-1.41084104938273
53	107.2	107.258989197531	104.754166666667	2.5048225308642	-0.0589891975308632
54	106	106.549729938272	104.029166666667	2.52056327160494	-0.549729938271611
55	99.4	101.728896604938	103.458333333333	-1.72943672839506	-2.32889660493827
56	100.2	101.99324845679	103.4	-1.40675154320988	-1.79324845679010
57	100.3	102.627507716049	104.1625	-1.53499228395062	-2.32750771604938
58	100.8	100.499729938272	104.741666666667	-4.24193672839506	0.300270061728412
59	99.5	101.255285493827	104.775	-3.51971450617284	-1.75528549382713
60	100.2	101.497878086420	104.729166666667	-3.23128858024691	-1.29787808641974
61	103	105.175192901235	104.895833333333	0.279359567901239	-2.17519290123455
62	111	107.300655864198	105.279166666667	2.02148919753087	3.69934413580246
63	120.5	109.893711419753	105.6625	4.23121141975309	10.6062885802469
64	109.5	109.906674382716	105.8	4.10667438271605	-0.406674382716048
65	106.6	108.150655864198	105.645833333333	2.5048225308642	-1.55065586419752
66	105.5	107.862229938272	105.341666666667	2.52056327160494	-2.36222993827160
67	103.9	103.258063271605	104.9875	-1.72943672839506	0.641936728395052
68	104.9	103.130748456790	104.5375	-1.40675154320988	1.76925154320988
69	104.8	102.160841049383	103.695833333333	-1.53499228395062	2.63915895061727
70	99.6	98.820563271605	103.0625	-4.24193672839506	0.779436728395055
71	97	99.6386188271605	103.158333333333	-3.51971450617284	-2.63861882716047
72	95.4	100.431211419753	103.6625	-3.23128858024691	-5.03121141975306
73	99.3	104.454359567901	104.175	0.279359567901239	-5.15435956790124
74	103.9	106.738155864198	104.716666666667	2.02148919753087	-2.83815586419753
75	107.4	109.672878086420	105.441666666667	4.23121141975309	-2.27287808641975
76	107.4	110.160841049383	106.054166666667	4.10667438271605	-2.76084104938272
77	111	109.233989197531	106.729166666667	2.5048225308642	1.76601080246914
78	113.2	110.270563271605	107.75	2.52056327160494	2.92943672839506
79	108.5	107.420563271605	109.15	-1.72943672839506	1.07943672839507
80	113.3	109.189081790123	110.595833333333	-1.40675154320988	4.11091820987653
81	113.8	110.006674382716	111.541666666667	-1.53499228395062	3.79332561728394
82	105.3	108.312229938272	112.554166666667	-4.24193672839506	-3.0122299382716
83	107.5	110.126118827160	113.645833333333	-3.51971450617284	-2.62611882716050
84	109.4	110.997878086420	114.229166666667	-3.23128858024691	-1.59787808641974
85	118.9	115.054359567901	114.775	0.279359567901239	3.84564043209879
86	119	117.354822530864	115.333333333333	2.02148919753087	1.64517746913583
87	115	120.006211419753	115.775	4.23121141975309	-5.00621141975306
88	124.1	120.685841049383	116.579166666667	4.10667438271605	3.41415895061729
89	120.5	120.371489197531	117.866666666667	2.5048225308642	0.128510802469137
90	117.7	121.720563271605	119.2	2.52056327160494	-4.02056327160494
91	117.1	118.328896604938	120.058333333333	-1.72943672839506	-1.22889660493829
92	118.1	119.014081790123	120.420833333333	-1.40675154320988	-0.914081790123461
93	119.6	119.273341049383	120.808333333333	-1.53499228395062	0.326658950617272
94	118.8	116.883063271605	121.125	-4.24193672839506	1.91693672839506
95	124.9	117.955285493827	121.475	-3.51971450617284	6.94471450617287
96	124	119.022878086420	122.254166666667	-3.23128858024691	4.97712191358025
97	124.9	123.546026234568	123.266666666667	0.279359567901239	1.35397376543212
98	121.7	126.338155864198	124.316666666667	2.02148919753087	-4.63815586419751
99	121.6	129.402044753086	125.170833333333	4.23121141975309	-7.80204475308643
100	125.1	129.844174382716	125.7375	4.10667438271605	-4.74417438271605
101	127.9	128.496489197531	125.991666666667	2.5048225308642	-0.596489197530857
102	129	128.762229938272	126.241666666667	2.52056327160494	0.237770061728412
103	130.1	124.908063271605	126.6375	-1.72943672839506	5.19193672839506
104	130.3	125.864081790123	127.270833333333	-1.40675154320988	4.43591820987658
105	127.9	126.602507716049	128.1375	-1.53499228395062	1.29749228395065
106	124.1	124.553896604938	128.795833333333	-4.24193672839506	-0.453896604938251
107	125.7	125.434452160494	128.954166666667	-3.51971450617284	0.265547839506155
108	129.2	125.560378086420	128.791666666667	-3.23128858024691	3.63962191358024
109	129.2	128.879359567901	128.6	0.279359567901239	0.32064043209877
110	132.6	130.388155864198	128.366666666667	2.02148919753087	2.21184413580249
111	131.5	132.235378086420	128.004166666667	4.23121141975309	-0.735378086419757
112	131	131.627507716049	127.520833333333	4.10667438271605	-0.62750771604938
113	125.8	129.479822530864	126.975	2.5048225308642	-3.6798225308642
114	127.2	128.616396604938	126.095833333333	2.52056327160494	-1.41639660493827
115	127.3	NA	NA	-1.72943672839506	NA
116	127.5	NA	NA	-1.40675154320988	NA
117	122	NA	NA	-1.53499228395062	NA
118	118.4	NA	NA	-4.24193672839506	NA
119	118.3	NA	NA	-3.51971450617284	NA
120	115.5	NA	NA	-3.23128858024691	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660786jh7vzdb7exo4ltf/1gm0b1291660854.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660786jh7vzdb7exo4ltf/1gm0b1291660854.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660786jh7vzdb7exo4ltf/2gm0b1291660854.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660786jh7vzdb7exo4ltf/2gm0b1291660854.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660786jh7vzdb7exo4ltf/39v0e1291660854.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660786jh7vzdb7exo4ltf/39v0e1291660854.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660786jh7vzdb7exo4ltf/414zz1291660854.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660786jh7vzdb7exo4ltf/414zz1291660854.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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