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*The author of this computation has been verified*

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

Title produced by software: Classical Decomposition

Date of computation: Sat, 27 Nov 2010 15:01:19 +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/Nov/27/t1290869976pints3z9588s6l5.htm/, Retrieved Sat, 27 Nov 2010 15:59:41 +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/Nov/27/t1290869976pints3z9588s6l5.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:

Dataseries X:

» Textbox « » Textfile « » CSV «

47.54 45.31 46.9 47.16 48.24 52.7 51.72 51.5 52.45 53 48.36 46.63 45.92 45.53 42.17 43.66 45.32 47.43 47.76 49.49 50.69 49.8 52.13 53.94 60.75 59.19 57.58 59.16 64.74 67.04 75.53 78.91 78.4 70.07 66.8 61.02 52.38 42.37 39.83 38.79 37.33 39.4 39.45 43.24 42.33 45.5 43.44 43.88 45.61 45.12 47.56 47.04 51.07 54.72 55.37 55.39 53.13 53.71 54.59 54.61

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	47.54	NA	NA	0.766981818181818	NA
2	45.31	NA	NA	-0.420836363636364	NA
3	46.9	46.4633454545455	47.03	-0.566654545454544	0.436654545454537
4	47.16	48.0851636363636	48.062	0.0231636363636357	-0.92516363636365
5	48.24	49.5413454545455	49.344	0.197345454545454	-1.30134545454546
6	52.7	51.0309818181818	50.264	0.766981818181818	1.66901818181817
7	51.72	50.9011636363636	51.322	-0.420836363636364	0.81883636363635
8	51.5	51.7073454545455	52.274	-0.566654545454544	-0.207345454545461
9	52.45	51.4291636363636	51.406	0.0231636363636357	1.02083636363636
10	53	50.5853454545455	50.388	0.197345454545454	2.41465454545454
11	48.36	50.0389818181818	49.272	0.766981818181818	-1.67898181818182
12	46.63	47.4671636363636	47.888	-0.420836363636364	-0.837163636363634
13	45.92	45.1553454545454	45.722	-0.566654545454544	0.764654545454555
14	45.53	44.8051636363636	44.782	0.0231636363636357	0.724836363636364
15	42.17	44.7173454545455	44.52	0.197345454545454	-2.54734545454545
16	43.66	45.5889818181818	44.822	0.766981818181818	-1.92898181818182
17	45.32	44.8471636363636	45.268	-0.420836363636364	0.472836363636361
18	47.43	46.1653454545455	46.732	-0.566654545454544	1.26465454545455
19	47.76	48.1611636363636	48.138	0.0231636363636357	-0.401163636363634
20	49.49	49.2313454545455	49.034	0.197345454545454	0.25865454545454
21	50.69	50.7409818181818	49.974	0.766981818181818	-0.0509818181818247
22	49.8	50.7891636363636	51.21	-0.420836363636364	-0.98916363636365
23	52.13	52.8953454545455	53.462	-0.566654545454544	-0.765345454545454
24	53.94	55.1851636363636	55.162	0.0231636363636357	-1.24516363636364
25	60.75	56.9153454545455	56.718	0.197345454545454	3.83465454545454
26	59.19	58.8909818181818	58.124	0.766981818181818	0.299018181818184
27	57.58	59.8631636363636	60.284	-0.420836363636364	-2.28316363636364
28	59.16	60.9753454545455	61.542	-0.566654545454544	-1.81534545454546
29	64.74	64.8331636363636	64.81	0.0231636363636357	-0.0931636363636414
30	67.04	69.2733454545455	69.076	0.197345454545454	-2.23334545454546
31	75.53	73.6909818181818	72.924	0.766981818181818	1.83901818181818
32	78.91	73.5691636363636	73.99	-0.420836363636364	5.34083636363636
33	78.4	73.3753454545455	73.942	-0.566654545454544	5.02465454545454
34	70.07	71.0631636363636	71.04	0.0231636363636357	-0.993163636363647
35	66.8	65.9313454545455	65.734	0.197345454545454	0.868654545454532
36	61.02	59.2949818181818	58.528	0.766981818181818	1.72501818181818
37	52.38	52.0591636363636	52.48	-0.420836363636364	0.32083636363636
38	42.37	46.3113454545455	46.878	-0.566654545454544	-3.94134545454546
39	39.83	42.1631636363636	42.14	0.0231636363636357	-2.33316363636364
40	38.79	39.7413454545455	39.544	0.197345454545454	-0.951345454545454
41	37.33	39.7269818181818	38.96	0.766981818181818	-2.39698181818182
42	39.4	39.2211636363636	39.642	-0.420836363636364	0.178836363636357
43	39.45	39.7833454545455	40.35	-0.566654545454544	-0.333345454545452
44	43.24	42.0071636363636	41.984	0.0231636363636357	1.23283636363637
45	42.33	42.9893454545455	42.792	0.197345454545454	-0.659345454545459
46	45.5	44.4449818181818	43.678	0.766981818181818	1.05501818181818
47	43.44	43.7311636363636	44.152	-0.420836363636364	-0.291163636363649
48	43.88	44.1433454545455	44.71	-0.566654545454544	-0.263345454545458
49	45.61	45.1451636363636	45.122	0.0231636363636357	0.464836363636358
50	45.12	46.0393454545455	45.842	0.197345454545454	-0.919345454545464
51	47.56	48.0469818181818	47.28	0.766981818181818	-0.486981818181818
52	47.04	48.6811636363636	49.102	-0.420836363636364	-1.64116363636364
53	51.07	50.5853454545455	51.152	-0.566654545454544	0.484654545454546
54	54.72	52.7411636363636	52.718	0.0231636363636357	1.97883636363636
55	55.37	54.1333454545455	53.936	0.197345454545454	1.23665454545454
56	55.39	55.2309818181818	54.464	0.766981818181818	0.159018181818176
57	53.13	54.0171636363636	54.438	-0.420836363636364	-0.887163636363638
58	53.71	53.7193454545455	54.286	-0.566654545454544	-0.00934545454546765
59	54.59	NA	NA	0.0231636363636357	NA
60	54.61	NA	NA	0.197345454545454	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290869976pints3z9588s6l5/1dn6m1290870076.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290869976pints3z9588s6l5/1dn6m1290870076.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290869976pints3z9588s6l5/26e571290870076.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290869976pints3z9588s6l5/26e571290870076.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290869976pints3z9588s6l5/36e571290870076.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290869976pints3z9588s6l5/36e571290870076.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290869976pints3z9588s6l5/4znna1290870076.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290869976pints3z9588s6l5/4znna1290870076.ps (open in new window)

Parameters (Session):

par1 = additive ; par2 = 5 ;

Parameters (R input):

par1 = additive ; par2 = 5 ;

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])

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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