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TIJDREEKS B - STAP 24

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Thu, 19 Aug 2010 22:57:48 +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/Aug/20/t1282258698fg7bm9dw7ofwmg4.htm/, Retrieved Fri, 20 Aug 2010 00:58:23 +0200

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/Aug/20/t1282258698fg7bm9dw7ofwmg4.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:

Mertens Jeroen

Dataseries X:

» Textbox « » Textfile « » CSV «

76 75 74 72 70 69 70 72 73 73 74 76 74 67 66 58 55 58 64 68 66 76 75 88 85 83 77 66 65 65 63 62 57 68 69 79 74 76 82 75 75 76 78 77 67 74 68 87 76 88 95 96 96 105 108 113 101 107 102 116 105 121 134 140 131 141 131 128 123 129 125 144 135 141 156 159 146 154 145 133 126 127 122 148

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	76	NA	NA	1.00269559664826	NA
2	75	NA	NA	1.02875183023750	NA
3	74	NA	NA	1.06663268214420	NA
4	72	NA	NA	1.01144741289536	NA
5	70	NA	NA	0.967346733496809	NA
6	69	NA	NA	1.00684596782950	NA
7	70	71.4904889265399	72.75	0.982687133010857	0.979151227681889
8	72	71.4816874883731	72.3333333333333	0.988226094309306	1.00725098315161
9	73	65.7222981414952	71.6666666666667	0.917055322904584	1.11073413535900
10	73	69.8929934359808	70.75	0.987886833017397	1.04445376297790
11	74	66.4411154277832	69.5416666666667	0.955414481885438	1.11376817688187
12	76	74.277970199706	68.4583333333333	1.08500991162078	1.02318358721521
13	74	67.9326266729194	67.75	1.00269559664826	1.08931456980596
14	67	69.2692899026584	67.3333333333333	1.02875183023750	0.967239596279284
15	66	71.3310606183935	66.875	1.06663268214420	0.925263124196154
16	58	67.4719711685616	66.7083333333333	1.01144741289536	0.859616207967331
17	55	64.6913128025991	66.875	0.967346733496809	0.850191434015701
18	58	67.8781989978391	67.4166666666667	1.00684596782950	0.854471698664934
19	64	67.1912327196174	68.375	0.982687133010857	0.952505221433664
20	68	68.6817135544968	69.5	0.988226094309306	0.99007430771284
21	66	64.7670321801363	70.625	0.917055322904584	1.01903696646829
22	76	70.5515846579924	71.4166666666667	0.987886833017397	1.07722598108064
23	75	68.9490784427324	72.1666666666667	0.955414481885438	1.08775928110908
24	88	79.0700973093645	72.875	1.08500991162078	1.11293653346216
25	85	73.3221155049038	73.125	1.00269559664826	1.15926824280343
26	83	74.9274249689647	72.8333333333333	1.02875183023750	1.10773858883285
27	77	77.0197682564959	72.2083333333333	1.06663268214420	0.999743335289843
28	66	72.3184900220186	71.5	1.01144741289536	0.912629674373804
29	65	68.601005850482	70.9166666666667	0.967346733496809	0.947507973012371
30	65	70.7728811553489	70.2916666666667	1.00684596782950	0.91843088678731
31	63	68.2558104470458	69.4583333333333	0.982687133010857	0.922998343838825
32	62	67.8993678965019	68.7083333333333	0.988226094309306	0.913116011543816
33	57	62.9329215343271	68.625	0.917055322904584	0.905726265527162
34	68	68.370001235079	69.2083333333333	0.987886833017397	0.994588251742064
35	69	66.8790137319806	70	0.955414481885438	1.03171377910146
36	79	76.9000774861229	70.875	1.08500991162078	1.02730715732056
37	74	72.1523039754808	71.9583333333333	1.00269559664826	1.02560827475651
38	76	75.3132069053037	73.2083333333333	1.02875183023750	1.00911915881579
39	82	79.197476649207	74.25	1.06663268214420	1.03538652327531
40	75	75.7742686827444	74.9166666666667	1.01144741289536	0.98978190491041
41	75	72.6719233539477	75.125	0.967346733496809	1.03203543457510
42	76	75.9329667404751	75.4166666666667	1.00684596782950	1.00088279521270
43	78	74.52044091999	75.8333333333333	0.982687133010857	1.04669267971382
44	77	75.5169440401361	76.4166666666667	0.988226094309306	1.01963871788927
45	67	71.0335768866509	77.4583333333333	0.917055322904584	0.943215911918847
46	74	77.9195739542472	78.875	0.987886833017397	0.949697184477053
47	68	77.0302926020134	80.625	0.955414481885438	0.882769592364532
48	87	89.7393614403022	82.7083333333333	1.08500991162078	0.969474248575699
49	76	85.3962416478765	85.1666666666667	1.00269559664826	0.889968908858764
50	88	90.4444317417136	87.9166666666667	1.02875183023750	0.972973109624987
51	95	96.8858019614317	90.8333333333333	1.06663268214420	0.980535827507705
52	96	94.6967640323285	93.625	1.01144741289536	1.01376220170762
53	96	93.2683475546506	96.4166666666667	0.967346733496809	1.02928809737675
54	105	99.7197027304471	99.0416666666667	1.00684596782950	1.05295139400712
55	108	99.7017987033932	101.458333333333	0.982687133010857	1.08323020652108
56	113	102.816689895431	104.041666666667	0.988226094309306	1.09904335682199
57	101	98.1631301892449	107.041666666667	0.917055322904584	1.02889954512744
58	107	109.161495048422	110.5	0.987886833017397	0.98019910731835
59	102	108.718206251214	113.791666666667	0.955414481885438	0.938205324730155
60	116	126.674907181726	116.75	1.08500991162078	0.915729899320848
61	105	119.529670917111	119.208333333333	1.00269559664826	0.878442977332493
62	121	124.264648160772	120.791666666667	1.02875183023750	0.973728262952568
63	134	130.484731448974	122.333333333333	1.06663268214420	1.02694007576205
64	140	125.588053767841	124.166666666667	1.01144741289536	1.11475570963780
65	131	121.925994534494	126.041666666667	0.967346733496809	1.07442223867150
66	141	129.044091543481	128.166666666667	1.00684596782950	1.09264979367529
67	131	128.322561452334	130.583333333333	0.982687133010857	1.02086490884660
68	128	131.104661845035	132.666666666667	0.988226094309306	0.976319210916357
69	123	123.267519653758	134.416666666667	0.917055322904584	0.997829763635147
70	129	134.476095144493	136.125	0.987886833017397	0.959278300439872
71	125	131.409300195993	137.541666666667	0.955414481885438	0.951226433848794
72	144	150.499916491066	138.708333333333	1.08500991162078	0.956811162141397
73	135	140.210267597981	139.833333333333	1.00269559664826	0.962839614478731
74	141	144.668226127149	140.625	1.02875183023750	0.974643871530403
75	156	150.350765153910	140.958333333333	1.06663268214420	1.03757370200482
76	159	142.614085218246	141	1.01144741289536	1.11489688943892
77	146	136.194358853572	140.791666666667	0.967346733496809	1.07199741038446
78	154	141.797473802655	140.833333333333	1.00684596782950	1.08605601968853
79	145	NA	NA	0.982687133010857	NA
80	133	NA	NA	0.988226094309306	NA
81	126	NA	NA	0.917055322904584	NA
82	127	NA	NA	0.987886833017397	NA
83	122	NA	NA	0.955414481885438	NA
84	148	NA	NA	1.08500991162078	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Aug/20/t1282258698fg7bm9dw7ofwmg4/1r7fq1282258666.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/20/t1282258698fg7bm9dw7ofwmg4/1r7fq1282258666.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/20/t1282258698fg7bm9dw7ofwmg4/2khws1282258666.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/20/t1282258698fg7bm9dw7ofwmg4/2khws1282258666.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/20/t1282258698fg7bm9dw7ofwmg4/3khws1282258666.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/20/t1282258698fg7bm9dw7ofwmg4/3khws1282258666.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/20/t1282258698fg7bm9dw7ofwmg4/4vqdv1282258666.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/20/t1282258698fg7bm9dw7ofwmg4/4vqdv1282258666.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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