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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: Sat, 11 Dec 2010 18:40:27 +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/11/t1292092982fpiecd25t9rgcnr.htm/, Retrieved Sat, 11 Dec 2010 19:43:07 +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/11/t1292092982fpiecd25t9rgcnr.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 «

132 131,4 132,7 130,9 126 109,7 68,3 70,6 75,3 74,1 74,9 74 74,2 76 76,2 74,9 74,1 76,5 57,8 59,2 57,3 57,5 60,4 59,9 59,9 60 60,2 65,4 62,4 78,8 65,6 64,4 67,4 65,3 66,7 66,8 69,4 71,7 77,1 81,1 82,1 92,1 77,1 78,2 77,7 77,3 78,5 78,8 78,7 79,8 82,2 84 81,7 77,6 64,3 72,6 73,8 73,8 70,1 70 72,3 72,1 73,3 79,1 77 76,1 66,4 72,7 73,2 70,7 73,6 74,2 72,6 73,6 79,1 79,6 78 85,4 82 91,9 89,4 92,1 93,8 93,6 95,6 99,9 103,7 99,2 93,7 93,5 80,7 91,8 105,8 111,3 110,3 109,4 111,4 111,6 111,8 106,6 104,3 105,5 98,5 108,5 106 101,8 101,3 92,4 88,9 84,9 86,4 90,7 86,8 90,6 88,3 95,4 93,6 91,3 91,3 89,3

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	7 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	132	NA	NA	0.997041713452915	NA
2	131.4	NA	NA	1.00422688480432	NA
3	132.7	NA	NA	1.03029730272513	NA
4	130.9	NA	NA	1.04802055534001	NA
5	126	NA	NA	1.01874576779311	NA
6	109.7	NA	NA	1.07223392131078	NA
7	68.3	87.7599443661319	97.5833333333333	0.899333332530814	0.778259381239514
8	70.6	89.5891598582649	92.8666666666667	0.964707392587203	0.788041768799855
9	75.3	87.0665610277353	88.2041666666667	0.987102586171121	0.864855566949670
10	74.1	82.3988337508077	83.5166666666667	0.986615451017454	0.89928457269304
11	74.9	79.0457806751163	79.0208333333333	1.00031570588072	0.947552157247258
12	74	74.8228496875147	75.475	0.991359386386415	0.98900269515327
13	74.2	73.4362765362799	73.6541666666667	0.997041713452915	1.01039981191507
14	76	73.0491373121411	72.7416666666667	1.00422688480432	1.04039558571718
15	76.2	73.6834287665589	71.5166666666667	1.03029730272513	1.03415382909791
16	74.9	73.4400404154511	70.075	1.04802055534001	1.01987961303248
17	74.1	70.0684849540038	68.7791666666667	1.01874576779311	1.05753678060319
18	76.5	72.4696101565924	67.5875	1.07223392131078	1.05561489615714
19	57.8	59.7194805022649	66.4041666666667	0.899333332530814	0.96785838580441
20	59.2	62.8426473987847	65.1416666666667	0.964707392587203	0.942035424197372
21	57.3	62.9853708526023	63.8083333333333	0.987102586171121	0.90973505790881
22	57.5	61.9060086536327	62.7458333333333	0.986615451017454	0.928827447456926
23	60.4	61.8820303550462	61.8625	1.00031570588072	0.97605071542509
24	59.9	60.9396876139949	61.4708333333333	0.991359386386415	0.982939072143256
25	59.9	61.70857338179	61.8916666666667	0.997041713452915	0.970691699991177
26	60	62.6972318412832	62.4333333333333	1.00422688480432	0.956980049005813
27	60.2	64.9817094639596	63.0708333333333	1.03029730272513	0.926414532590719
28	65.4	66.8811784399482	63.8166666666667	1.04802055534001	0.977853583407204
29	62.4	65.611472219909	64.4041666666667	1.01874576779311	0.951053190680662
30	78.8	69.646060830474	64.9541666666667	1.07223392131078	1.13143513158350
31	65.6	59.0299916139913	65.6375	0.899333332530814	1.11129949719409
32	64.4	64.1731396777279	66.5208333333333	0.964707392587203	1.00353512892483
33	67.4	66.8391838661121	67.7125	0.987102586171121	1.00839052934894
34	65.3	68.146351381318	69.0708333333333	0.986615451017454	0.958231786095325
35	66.7	70.5681050677771	70.5458333333333	1.00031570588072	0.94518621317574
36	66.8	71.2993932017329	71.9208333333333	0.991359386386415	0.936894368946416
37	69.4	72.7383473368629	72.9541666666667	0.997041713452915	0.954104712863458
38	71.7	74.3211580328932	74.0083333333333	1.00422688480432	0.964732007650727
39	77.1	77.2851764206688	75.0125	1.03029730272513	0.997603985275768
40	81.1	79.5884276734458	75.9416666666667	1.04802055534001	1.01899236322090
41	82.1	78.3755077355501	76.9333333333333	1.01874576779311	1.04752112454591
42	92.1	83.5538283181426	77.925	1.07223392131078	1.10228342439698
43	77.1	70.8787082700848	78.8125	0.899333332530814	1.08777377412422
44	78.2	76.7304142379046	79.5375	0.964707392587203	1.01915258475653
45	77.7	79.0545783699797	80.0875	0.987102586171121	0.982865276143272
46	77.3	79.3444367503662	80.4208333333333	0.986615451017454	0.974233395130167
47	78.5	80.5504222160452	80.525	1.00031570588072	0.974544860726543
48	78.8	79.2137456363845	79.9041666666667	0.991359386386415	0.99477684544443
49	78.7	78.533652296308	78.7666666666667	0.997041713452915	1.00211817098566
50	79.8	78.3296970147372	78	1.00422688480432	1.01877069669995
51	82.2	79.9553635968981	77.6041666666667	1.03029730272513	1.02807361885587
52	84	81.0076221754688	77.2958333333333	1.04802055534001	1.03693946006771
53	81.7	78.239674966511	76.8	1.01874576779311	1.04422724193282
54	77.6	81.5791308463953	76.0833333333333	1.07223392131078	0.951223667068879
55	64.3	67.8546999394499	75.45	0.899333332530814	0.947613062284235
56	72.6	72.2204071775595	74.8625	0.964707392587203	1.00525603270980
57	73.8	73.2142214018005	74.1708333333333	0.987102586171121	1.00800088544253
58	73.8	72.610786297172	73.5958333333333	0.986615451017454	1.01637792073978
59	70.1	73.218941688361	73.1958333333333	1.00031570588072	0.95740252977657
60	70	72.3072752445591	72.9375	0.991359386386415	0.968090690227845
61	72.3	72.7466560178083	72.9625	0.997041713452915	0.99386011615848
62	72.1	73.3629582136425	73.0541666666667	1.00422688480432	0.982784797063873
63	73.3	75.2460463423587	73.0333333333333	1.03029730272513	0.974137560218055
64	79.1	76.378864722717	72.8791666666667	1.04802055534001	1.03562680968304
65	77	74.2623216980854	72.8958333333333	1.01874576779311	1.03686497054381
66	76.1	78.5053936053044	73.2166666666667	1.07223392131078	0.969360148458107
67	66.4	66.0148138299806	73.4041666666667	0.899333332530814	1.00583484444888
68	72.7	70.8858952844805	73.4791666666667	0.964707392587203	1.02559189960484
69	73.2	72.8317191496592	73.7833333333333	0.987102586171121	1.00505659971563
70	70.7	73.0547632501299	74.0458333333333	0.986615451017454	0.967767149664595
71	73.6	74.1317297699772	74.1083333333333	1.00031570588072	0.992827231043615
72	74.2	73.8934502627774	74.5375	0.991359386386415	1.00414853733494
73	72.6	75.3514274942041	75.575	0.997041713452915	0.963485396551834
74	73.6	77.350575802053	77.025	1.00422688480432	0.95151198600446
75	79.1	80.8783382639227	78.5	1.03029730272513	0.978012180985722
76	79.6	83.9115124642234	80.0666666666667	1.04802055534001	0.948618344043535
77	78	83.3334038054766	81.8	1.01874576779311	0.935999208457556
78	85.4	89.4779207333847	83.45	1.07223392131078	0.954425396791063
79	82	76.6381888205009	85.2166666666667	0.899333332530814	1.06996265519867
80	91.9	84.1908180739124	87.2708333333333	0.964707392587203	1.09156796551519
81	89.4	88.2387453488135	89.3916666666666	0.987102586171121	1.01316037129263
82	92.1	90.0122163144924	91.2333333333333	0.986615451017454	1.02319444816483
83	93.8	92.7334339172508	92.7041666666667	1.00031570588072	1.01150141904268
84	93.6	92.8862438402971	93.6958333333333	0.991359386386415	1.00768419660644
85	95.6	93.7011493622104	93.9791666666667	0.997041713452915	1.02026496633941
86	99.9	94.3178258765593	93.9208333333333	1.00422688480432	1.05918472008405
87	103.7	97.4661248377973	94.6	1.03029730272513	1.0639594030498
88	99.2	100.697308358919	96.0833333333333	1.04802055534001	0.985130601966219
89	93.7	99.3998735183805	97.5708333333333	1.01874576779311	0.942657135098602
90	93.5	106.061805382991	98.9166666666667	1.07223392131078	0.881561459965437
91	80.7	90.1431776973386	100.233333333333	0.899333332530814	0.8952424582918
92	91.8	97.8012315376635	101.379166666667	0.964707392587203	0.938638487028127
93	105.8	100.885997234131	102.204166666667	0.987102586171121	1.04870847194447
94	111.3	101.473399137145	102.85	0.986615451017454	1.09683918097169
95	110.3	103.632707129243	103.6	1.00031570588072	1.06433579760145
96	109.4	103.638362518480	104.541666666667	0.991359386386415	1.05559367536797
97	111.4	105.470395921428	105.783333333333	0.997041713452915	1.05622055389827
98	111.6	107.674043444457	107.220833333333	1.00422688480432	1.03646149461795
99	111.8	111.194836396610	107.925	1.03029730272513	1.00544237145358
100	106.6	112.701510469876	107.5375	1.04802055534001	0.945861324800016
101	104.3	108.768089808045	106.766666666667	1.01874576779311	0.958920949922629
102	105.5	113.317254917194	105.683333333333	1.07223392131078	0.931014434448604
103	98.5	93.5643915831745	104.0375	0.899333332530814	1.05275092728453
104	108.5	98.3880952014873	101.9875	0.964707392587203	1.10277569433380
105	106	98.5292898096474	99.8166666666667	0.987102586171121	1.07582222712440
106	101.8	96.7828648470997	98.0958333333333	0.986615451017454	1.051839084954
107	101.3	96.7346967407737	96.7041666666667	1.00031570588072	1.04719406183140
108	92.4	94.5302481560546	95.3541666666667	0.991359386386415	0.977464904645782
109	88.9	94.0293422595553	94.3083333333333	0.997041713452915	0.94544955716699
110	84.9	93.7320268604234	93.3375	1.00422688480432	0.905773649026333
111	86.4	95.0706836089614	92.275	1.03029730272513	0.908797504342926
112	90.7	95.7061104641124	91.3208333333333	1.04802055534001	0.947692885649244
113	86.8	92.1625337930169	90.4666666666667	1.01874576779311	0.941814384085182
114	90.6	96.4161677325332	89.9208333333333	1.07223392131078	0.939676427000627
115	88.3	NA	NA	0.899333332530814	NA
116	95.4	NA	NA	0.964707392587203	NA
117	93.6	NA	NA	0.987102586171121	NA
118	91.3	NA	NA	0.986615451017454	NA
119	91.3	NA	NA	1.00031570588072	NA
120	89.3	NA	NA	0.991359386386415	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292092982fpiecd25t9rgcnr/1kxug1292092820.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292092982fpiecd25t9rgcnr/1kxug1292092820.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292092982fpiecd25t9rgcnr/2dptj1292092820.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292092982fpiecd25t9rgcnr/2dptj1292092820.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292092982fpiecd25t9rgcnr/3dptj1292092820.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292092982fpiecd25t9rgcnr/3dptj1292092820.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292092982fpiecd25t9rgcnr/4ogam1292092820.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292092982fpiecd25t9rgcnr/4ogam1292092820.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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