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Opgave 9 oefening 2

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Sun, 12 Dec 2010 14:28:49 +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/12/t1292164052an28ncxc0md197g.htm/, Retrieved Sun, 12 Dec 2010 15:27:33 +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/12/t1292164052an28ncxc0md197g.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 «

132.1 125 127.1 101.5 85.7 79.3 70.9 77.1 83.9 96.2 111.7 127.2 143.6 134.9 135.6 105.3 86.4 74.6 67.6 73.4 78.5 98.2 118.6 136.9 137.9 115.6 119.3 98.5 84.3 73.5 60.7 69.5 77.9 113.9 126.3 135.1 130.5 113.1 110 90.8 85.4 72.5 64.7 67.2 77.9 105.2 107.2 120.7 121.3 107.9 105.6 81.3 71.7 64.8 57.3 61.9 70.1 88.8 106.8 110.7 114.1 108 111.5 86.8 78.4 68 57.3 65.3 73.3 88.6 101.3 122.9 126.6 114.1 124.7 93.3 77.2 66.5 57.9 63.7 65.8 85 101 105.3 121 117.9 106 86.6 79.9 65.2 61.2 67.6 78.9 95.5 113.1 124.4 122 110.3 114 93.3 75.5 65.4 59.2 63.8 74.2 91.7 107 120.7 127.4 119.7 112.7 84.4 75.6 66.5 59.9 64.8 74.3 100.4 105.9 131.1

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	132.1	NA	NA	32.9709104938272	NA
2	125	NA	NA	21.6454475308642	NA
3	127.1	NA	NA	21.5135030864198	NA
4	101.5	NA	NA	-2.8059413580247	NA
5	85.7	NA	NA	-14.5652006172839	NA
6	79.3	NA	NA	-25.3786265432099	NA
7	70.9	69.010725308642	101.954166666667	-32.9434413580247	1.88927469135803
8	77.1	75.8042438271605	102.845833333333	-27.0415895061728	1.2957561728395
9	83.9	84.5510030864197	103.6125	-19.0614969135802	-0.651003086419749
10	96.2	105.498225308642	104.125	1.37322530864197	-9.29822530864197
11	111.7	120.244984567901	104.3125	15.9324845679012	-8.54498456790121
12	127.2	132.506558641975	104.145833333333	28.360725308642	-5.3065586419753
13	143.6	136.783410493827	103.8125	32.9709104938272	6.81658950617283
14	134.9	125.166280864198	103.520833333333	21.6454475308642	9.73371913580246
15	135.6	124.655169753086	103.141666666667	21.5135030864198	10.9448302469136
16	105.3	100.194058641975	103	-2.8059413580247	5.10594135802469
17	86.4	88.8056327160494	103.370833333333	-14.5652006172839	-2.40563271604938
18	74.6	78.6838734567901	104.0625	-25.3786265432099	-4.08387345679012
19	67.6	71.285725308642	104.229166666667	-32.9434413580247	-3.68572530864196
20	73.4	76.1459104938271	103.1875	-27.0415895061728	-2.74591049382714
21	78.5	82.6426697530864	101.704166666667	-19.0614969135802	-4.14266975308641
22	98.2	102.114891975309	100.741666666667	1.37322530864197	-3.91489197530865
23	118.6	116.303317901235	100.370833333333	15.9324845679012	2.29668209876544
24	136.9	128.598225308642	100.2375	28.360725308642	8.30177469135803
25	137.9	132.875077160494	99.9041666666667	32.9709104938272	5.02492283950617
26	115.6	121.099614197531	99.4541666666667	21.6454475308642	-5.49961419753087
27	119.3	120.780169753086	99.2666666666667	21.5135030864198	-1.48016975308643
28	98.5	97.0898919753086	99.8958333333333	-2.8059413580247	1.41010802469135
29	84.3	86.3056327160494	100.870833333333	-14.5652006172839	-2.00563271604939
30	73.5	75.7380401234568	101.116666666667	-25.3786265432099	-2.23804012345678
31	60.7	67.7898919753086	100.733333333333	-32.9434413580247	-7.08989197530863
32	69.5	73.2792438271605	100.320833333333	-27.0415895061728	-3.77924382716049
33	77.9	80.7676697530864	99.8291666666667	-19.0614969135802	-2.86766975308642
34	113.9	100.494058641975	99.1208333333334	1.37322530864197	13.4059413580247
35	126.3	114.778317901235	98.8458333333333	15.9324845679012	11.5216820987654
36	135.1	127.210725308642	98.85	28.360725308642	7.88927469135801
37	130.5	131.945910493827	98.975	32.9709104938272	-1.44591049382717
38	113.1	120.691280864198	99.0458333333333	21.6454475308642	-7.59128086419754
39	110	120.46350308642	98.95	21.5135030864198	-10.4635030864198
40	90.8	95.7815586419753	98.5875	-2.8059413580247	-4.98155864197531
41	85.4	82.8639660493827	97.4291666666667	-14.5652006172839	2.53603395061729
42	72.5	70.6547067901235	96.0333333333333	-25.3786265432099	1.84529320987654
43	64.7	62.1065586419753	95.05	-32.9434413580247	2.59344135802469
44	67.2	67.4084104938272	94.45	-27.0415895061728	-0.208410493827159
45	77.9	74.9885030864197	94.05	-19.0614969135802	2.91149691358025
46	105.2	94.8440586419753	93.4708333333333	1.37322530864197	10.3559413580247
47	107.2	108.436651234568	92.5041666666667	15.9324845679012	-1.23665123456789
48	120.7	119.973225308642	91.6125	28.360725308642	0.726774691358031
49	121.3	123.95424382716	90.9833333333333	32.9709104938272	-2.6542438271605
50	107.9	112.099614197531	90.4541666666667	21.6454475308642	-4.19961419753086
51	105.6	111.421836419753	89.9083333333333	21.5135030864198	-5.82183641975308
52	81.3	86.0940586419753	88.9	-2.8059413580247	-4.79405864197531
53	71.7	73.634799382716	88.2	-14.5652006172839	-1.93479938271604
54	64.8	62.3880401234568	87.7666666666667	-25.3786265432099	2.41195987654321
55	57.3	54.1065586419753	87.05	-32.9434413580247	3.19344135802469
56	61.9	59.7125771604938	86.7541666666667	-27.0415895061728	2.18742283950618
57	70.1	67.9426697530864	87.0041666666667	-19.0614969135802	2.15733024691356
58	88.8	88.8523919753086	87.4791666666667	1.37322530864197	-0.0523919753086517
59	106.8	103.919984567901	87.9875	15.9324845679012	2.88001543209877
60	110.7	116.760725308642	88.4	28.360725308642	-6.06072530864199
61	114.1	121.504243827161	88.5333333333333	32.9709104938272	-7.40424382716051
62	108	110.320447530864	88.675	21.6454475308642	-2.3204475308642
63	111.5	110.46350308642	88.95	21.5135030864198	1.03649691358025
64	86.8	86.2690586419753	89.075	-2.8059413580247	0.530941358024691
65	78.4	74.272299382716	88.8375	-14.5652006172839	4.12770061728396
66	68	63.7380401234568	89.1166666666667	-25.3786265432099	4.26195987654322
67	57.3	57.2023919753086	90.1458333333333	-32.9434413580247	0.097608024691354
68	65.3	63.8792438271605	90.9208333333333	-27.0415895061728	1.4207561728395
69	73.3	72.6635030864198	91.725	-19.0614969135802	0.636496913580245
70	88.6	93.9190586419753	92.5458333333333	1.37322530864197	-5.3190586419753
71	101.3	108.699151234568	92.7666666666667	15.9324845679012	-7.3991512345679
72	122.9	121.014891975309	92.6541666666667	28.360725308642	1.88510802469136
73	126.6	125.587577160494	92.6166666666666	32.9709104938272	1.01242283950619
74	114.1	114.220447530864	92.575	21.6454475308642	-0.120447530864183
75	124.7	113.709336419753	92.1958333333333	21.5135030864198	10.9906635802469
76	93.3	88.9273919753086	91.7333333333333	-2.8059413580247	4.37260802469137
77	77.2	77.0056327160494	91.5708333333333	-14.5652006172839	0.194367283950626
78	66.5	65.4463734567901	90.825	-25.3786265432099	1.0536265432099
79	57.9	56.9148919753087	89.8583333333333	-32.9434413580247	0.985108024691328
80	63.7	62.7417438271605	89.7833333333333	-27.0415895061728	0.958256172839512
81	65.8	70.1010030864198	89.1625	-19.0614969135802	-4.30100308641977
82	85	89.4773919753086	88.1041666666667	1.37322530864197	-4.47739197530865
83	101	103.869984567901	87.9375	15.9324845679012	-2.86998456790124
84	105.3	116.356558641975	87.9958333333333	28.360725308642	-11.0565586419753
85	121	121.050077160494	88.0791666666667	32.9709104938272	-0.0500771604938137
86	117.9	110.024614197531	88.3791666666666	21.6454475308642	7.87538580246915
87	106	110.60100308642	89.0875	21.5135030864198	-4.60100308641974
88	86.6	87.2648919753087	90.0708333333334	-2.8059413580247	-0.664891975308663
89	79.9	76.447299382716	91.0125	-14.5652006172839	3.45270061728398
90	65.2	66.9338734567901	92.3125	-25.3786265432099	-1.73387345679012
91	61.2	60.2065586419753	93.15	-32.9434413580247	0.993441358024683
92	67.6	65.8334104938272	92.875	-27.0415895061728	1.76658950617283
93	78.9	73.8301697530864	92.8916666666667	-19.0614969135802	5.06983024691358
94	95.5	94.8773919753086	93.5041666666666	1.37322530864197	0.622608024691374
95	113.1	109.532484567901	93.6	15.9324845679012	3.56751543209874
96	124.4	121.785725308642	93.425	28.360725308642	2.61427469135805
97	122	126.320910493827	93.35	32.9709104938272	-4.32091049382714
98	110.3	114.753780864198	93.1083333333333	21.6454475308642	-4.45378086419753
99	114	114.267669753086	92.7541666666666	21.5135030864198	-0.267669753086395
100	93.3	89.5940586419753	92.4	-2.8059413580247	3.70594135802469
101	75.5	77.422299382716	91.9875	-14.5652006172839	-1.92229938271606
102	65.4	66.2005401234568	91.5791666666667	-25.3786265432099	-0.800540123456784
103	59.2	58.7065586419753	91.65	-32.9434413580247	0.493441358024697
104	63.8	65.2250771604938	92.2666666666667	-27.0415895061728	-1.42507716049384
105	74.2	73.5426697530864	92.6041666666667	-19.0614969135802	0.657330246913588
106	91.7	93.5523919753087	92.1791666666667	1.37322530864197	-1.85239197530865
107	107	107.744984567901	91.8125	15.9324845679012	-0.744984567901241
108	120.7	120.223225308642	91.8625	28.360725308642	0.476774691358017
109	127.4	124.908410493827	91.9375	32.9709104938272	2.49158950617284
110	119.7	113.653780864198	92.0083333333333	21.6454475308642	6.04621913580246
111	112.7	113.567669753086	92.0541666666667	21.5135030864198	-0.867669753086432
112	84.4	89.6148919753087	92.4208333333333	-2.8059413580247	-5.21489197530865
113	75.6	78.172299382716	92.7375	-14.5652006172839	-2.57229938271605
114	66.5	67.7463734567901	93.125	-25.3786265432099	-1.24637345679012
115	59.9	NA	NA	-32.9434413580247	NA
116	64.8	NA	NA	-27.0415895061728	NA
117	74.3	NA	NA	-19.0614969135802	NA
118	100.4	NA	NA	1.37322530864197	NA
119	105.9	NA	NA	15.9324845679012	NA
120	131.1	NA	NA	28.360725308642	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292164052an28ncxc0md197g/1ybmo1292164124.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292164052an28ncxc0md197g/1ybmo1292164124.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292164052an28ncxc0md197g/2ybmo1292164124.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292164052an28ncxc0md197g/2ybmo1292164124.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292164052an28ncxc0md197g/3r23r1292164124.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292164052an28ncxc0md197g/3r23r1292164124.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292164052an28ncxc0md197g/4r23r1292164124.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292164052an28ncxc0md197g/4r23r1292164124.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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