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datareeks-cinemaprijs-seda hovhannesian

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Mon, 01 Jun 2009 10:18:44 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Jun/01/t1243873161mytqrlfy65pyqka.htm/, Retrieved Mon, 01 Jun 2009 18:19:21 +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/2009/Jun/01/t1243873161mytqrlfy65pyqka.htm/},
    year = {2009},
}
@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 = {2009},
    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:

Dataseries X:

» Textbox « » Textfile « » CSV «

5.44 5.44 5.44 5.44 5.44 5.49 5.49 5.49 5.49 5.49 5.49 5.60 5.60 5.60 5.60 5.60 5.60 5.60 5.67 5.67 5.67 5.67 5.67 5.67 5.67 5.67 5.67 5.82 5.82 5.95 5.95 5.95 5.95 5.95 5.95 6.02 6.02 6.05 6.05 6.05 6.12 6.12 6.12 6.12 6.12 6.12 6.12 6.12 6.17 6.17 6.17 6.17 6.17 6.28 6.27 6.28 6.28 6.27 6.27 6.28 6.59 6.59 6.59 6.59 6.59 6.63 6.63 6.63 6.63 6.63 6.63 6.63 6.63 6.63 6.63 6.63 6.63 6.63 6.63 6.63 6.63 6.63 6.79 6.79 6.79 6.81 6.80 6.80 6.85 6.85 6.85 6.85 6.85 6.85 6.86 6.86 6.88 6.88 6.88 6.91 6.91 6.91 6.91 6.99 6.99 6.99 7.02 7.02 7.05 7.05 7.05 7.05 7.10 7.10 7.10 7.10 7.12 7.13 7.18

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	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	5.44	NA	NA	1.00297856480580	NA
2	5.44	NA	NA	1.00149938023597	NA
3	5.44	NA	NA	0.99879899657331	NA
4	5.44	NA	NA	1.00007470109577	NA
5	5.44	NA	NA	0.999905593941477	NA
6	5.49	NA	NA	1.00320931646963	NA
7	5.49	5.49691988103536	5.485	1.00217317794628	0.998741134820024
8	5.49	5.50600963527344	5.49833333333333	1.00139611432679	0.997092334315785
9	5.49	5.50613778018278	5.51166666666667	0.998996875751336	0.997069128883614
10	5.49	5.50517010979867	5.525	0.99641087960157	0.9972443885482
11	5.49	5.52550744167327	5.53833333333333	0.997684160398423	0.99357390392682
12	5.6	5.5322255622049	5.54958333333333	0.99687223885365	1.01225084498689
13	5.6	5.57823245126162	5.56166666666667	1.00297856480580	1.00390223048763
14	5.6	5.58502821044926	5.57666666666667	1.00149938023597	1.00268070079265
15	5.6	5.58495105583909	5.59166666666667	0.99879899657331	1.00269455255927
16	5.6	5.60708549081026	5.60666666666667	1.00007470109577	0.998736332659476
17	5.6	5.62113594727434	5.62166666666667	0.999905593941477	0.996239915299578
18	5.6	5.65015847113332	5.63208333333333	1.00320931646963	0.99112264348167
19	5.67	5.65016886282964	5.63791666666667	1.00217317794628	1.00350983088325
20	5.67	5.6516293202318	5.64375	1.00139611432679	1.00325051037980
21	5.67	5.64391609929682	5.64958333333333	0.998996875751336	1.00462159611239
22	5.67	5.64134626334422	5.66166666666667	0.99641087960157	1.0050792373519
23	5.67	5.66684603106304	5.68	0.997684160398423	1.00055656513688
24	5.67	5.6859100323615	5.70375	0.99687223885365	0.997201849436423
25	5.67	5.74706717633726	5.73	1.00297856480580	0.986590173044335
26	5.67	5.76195976762428	5.75333333333333	1.00149938023597	0.984040192689128
27	5.67	5.76972887020515	5.77666666666667	0.99879899657331	0.982715154828132
28	5.82	5.80043326635545	5.8	1.00007470109577	1.00337332277539
29	5.82	5.82278357538587	5.82333333333333	0.999905593941477	0.99952195108236
30	5.95	5.86835649746546	5.84958333333333	1.00320931646963	1.01391249876687
31	5.95	5.89152556985171	5.87875	1.00217317794628	1.00992517633251
32	5.95	5.91741653890936	5.90916666666667	1.00139611432679	1.00550636597515
33	5.95	5.9348739393594	5.94083333333333	0.998996875751336	1.00254867429286
34	5.95	5.94483641042287	5.96625	0.99641087960157	1.00086858396441
35	5.95	5.97446531385256	5.98833333333333	0.997684160398423	0.99590502035457
36	6.02	5.98912533834615	6.00791666666667	0.99687223885365	1.00515512030716
37	6.02	6.04002049880762	6.02208333333333	1.00297856480580	0.9966853591289
38	6.05	6.04530063394937	6.03625	1.00149938023597	1.00077735853602
39	6.05	6.0431500955171	6.05041666666667	0.99879899657331	1.00113349898226
40	6.05	6.0650363643537	6.06458333333333	1.00007470109577	0.997520812168238
41	6.12	6.07817612917175	6.07875	0.999905593941477	1.00688099027396
42	6.12	6.10954473730003	6.09	1.00320931646963	1.00171129980212
43	6.12	6.1136739576298	6.10041666666667	1.00217317794628	1.00103473662712
44	6.12	6.12019925206054	6.11166666666667	1.00139611432679	0.999967443533727
45	6.12	6.1155258743911	6.12166666666667	0.998996875751336	1.00073160112487
46	6.12	6.10965937675696	6.13166666666667	0.99641087960157	1.00169250405062
47	6.12	6.12453363964582	6.13875	0.997684160398423	0.999259757572973
48	6.12	6.12827208835281	6.1475	0.99687223885365	0.998650176063734
49	6.17	6.17876586693909	6.16041666666667	1.00297856480580	0.998581291615856
50	6.17	6.18258950732338	6.17333333333333	1.00149938023597	0.997963716124373
51	6.17	6.17923645880021	6.18666666666667	0.99879899657331	0.99850524270081
52	6.17	6.20004644900163	6.19958333333333	1.00007470109577	0.995153834854501
53	6.17	6.21149687503062	6.21208333333333	0.999905593941477	0.993319343812692
54	6.28	6.24497799502343	6.225	1.00320931646963	1.00560802696254
55	6.27	6.2627472178493	6.24916666666666	1.00217317794628	1.00115808316996
56	6.28	6.2929400817819	6.28416666666666	1.00139611432679	0.997943714446072
57	6.28	6.31282775735199	6.31916666666667	0.998996875751336	0.9947998331946
58	6.27	6.3313607974683	6.35416666666667	0.99641087960157	0.990308434563887
59	6.27	6.37437038147892	6.38916666666667	0.997684160398423	0.983626558352778
60	6.28	6.401165863739	6.42125	0.99687223885365	0.981071281963592
61	6.59	6.47004755846811	6.45083333333333	1.00297856480580	1.01853965375801
62	6.59	6.49013327533751	6.48041666666667	1.00149938023597	1.01538746901269
63	6.59	6.50176530144367	6.50958333333333	0.99879899657331	1.01357088336251
64	6.59	6.53965514958206	6.53916666666666	1.00007470109577	1.00769839529247
65	6.59	6.56854649753389	6.56916666666667	0.999905593941477	1.00326609585152
66	6.63	6.61992747705395	6.59875	1.00320931646963	1.00152154581466
67	6.63	6.62937557211465	6.615	1.00217317794628	1.00009419105594
68	6.63	6.62757328331944	6.61833333333333	1.00139611432679	1.00036615463561
69	6.63	6.61502431226677	6.62166666666667	0.998996875751336	1.00226389005184
70	6.63	6.6012220773604	6.625	0.99641087960157	1.00435948409285
71	6.63	6.61298317650755	6.62833333333333	0.997684160398423	1.00257324463684
72	6.63	6.6092629435997	6.63	0.99687223885365	1.00313757473069
73	6.63	6.64974788466249	6.63	1.00297856480580	0.997030280695598
74	6.63	6.63994089096448	6.63	1.00149938023597	0.99850286453935
75	6.63	6.62203734728104	6.63	0.99879899657331	1.00120244757034
76	6.63	6.63049526826493	6.63	1.00007470109577	0.99992530448407
77	6.63	6.63604012512494	6.63666666666667	0.999905593941477	0.999089799788571
78	6.63	6.67134195452303	6.65	1.00320931646963	0.993803052698416
79	6.63	6.67781394238206	6.66333333333333	1.00217317794628	0.992839881015762
80	6.63	6.68682255341711	6.6775	1.00139611432679	0.991502308762766
81	6.63	6.68537034226759	6.69208333333333	0.998996875751336	0.991717685119474
82	6.63	6.68218046132803	6.70625	0.99641087960157	0.99219110264531
83	6.79	6.7069317682784	6.7225	0.997684160398423	1.01238542967061
84	6.79	6.7197496167393	6.74083333333333	0.99687223885365	1.01045431560213
85	6.79	6.77929928261657	6.75916666666667	1.00297856480580	1.00157844003301
86	6.81	6.78766204954928	6.7775	1.00149938023597	1.00329096385289
87	6.8	6.78767151421278	6.79583333333333	0.99879899657331	1.00181630560074
88	6.8	6.8146756923834	6.81416666666667	1.00007470109577	0.997846457697202
89	6.85	6.82560556064301	6.82625	0.999905593941477	1.00357395972273
90	6.85	6.85400965089687	6.83208333333333	1.00320931646963	0.999414991938866
91	6.85	6.85361182068014	6.83875	1.00217317794628	0.99947300477841
92	6.85	6.85497365094781	6.84541666666667	1.00139611432679	0.999274446379947
93	6.85	6.84479359368957	6.85166666666666	0.998996875751336	1.00076063744497
94	6.85	6.83496346286693	6.85958333333333	0.99641087960157	1.0021999440399
95	6.86	6.85076456806917	6.86666666666667	0.997684160398423	1.00134808777021
96	6.86	6.85017373465599	6.87166666666666	0.99687223885365	1.00143445490941
97	6.88	6.89714926398125	6.87666666666667	1.00297856480580	0.997513572154976
98	6.88	6.89532323292465	6.885	1.00149938023597	0.997777735371203
99	6.88	6.88838374636726	6.89666666666666	0.99879899657331	0.998782915314252
100	6.91	6.90884939340325	6.90833333333333	1.00007470109577	1.00016654098696
101	6.91	6.92017996473664	6.92083333333333	0.999905593941477	0.998528945086903
102	6.91	6.95642060195314	6.93416666666667	1.00320931646963	0.99332694145318
103	6.91	6.96301572593927	6.94791666666667	1.00217317794628	0.992386097055365
104	6.99	6.97180319761927	6.96208333333333	1.00139611432679	1.00261005680524
105	6.99	6.96925195446026	6.97625	0.998996875751336	1.00297708357731
106	6.99	6.9640817060153	6.98916666666667	0.99641087960157	1.00372171020945
107	7.02	6.98669903492346	7.00291666666667	0.997684160398423	1.00476633742345
108	7.02	6.99679702645405	7.01875	0.99687223885365	1.00331622790517
109	7.05	NA	7.03458333333333	NA	NA
110	7.05	NA	7.04708333333333	NA	NA
111	7.05	NA	7.05708333333333	NA	NA
112	7.05	NA	7.06833333333333	NA	NA
113	7.1	NA	7.08083333333333	NA	NA
114	7.1	NA	NA	NA	NA
115	7.1	NA	NA	NA	NA
116	7.1	NA	NA	NA	NA
117	7.12	NA	NA	NA	NA
118	7.13	NA	NA	NA	NA
119	7.18	NA	NA	NA	NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243873161mytqrlfy65pyqka/17tih1243873120.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243873161mytqrlfy65pyqka/17tih1243873120.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243873161mytqrlfy65pyqka/2jfe51243873120.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243873161mytqrlfy65pyqka/2jfe51243873120.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243873161mytqrlfy65pyqka/3owv61243873120.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243873161mytqrlfy65pyqka/3owv61243873120.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243873161mytqrlfy65pyqka/46ztx1243873120.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243873161mytqrlfy65pyqka/46ztx1243873120.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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