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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: Wed, 29 Dec 2010 21:05:24 +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/29/t1293656605ldqb1axrss3uysn.htm/, Retrieved Wed, 29 Dec 2010 22:03:28 +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/29/t1293656605ldqb1axrss3uysn.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 «

16 17 23 24 27 31 40 47 43 60 64 65 65 55 57 57 57 65 69 70 71 71 73 68 65 57 41 21 21 17 9 11 6 -2 0 5 3 7 4 8 9 14 12 12 7 15 14 19 39 12 11 17 16 25 24 28 25 31 24 24

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	1 seconds
R Server	'Gwilym Jenkins' @ www.wessa.org

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	16	NA	NA	9.5546875	NA
2	17	NA	NA	-0.330729166666666	NA
3	23	NA	NA	-4.4453125	NA
4	24	NA	NA	-6.45572916666667	NA
5	27	NA	NA	-5.73697916666667	NA
6	31	NA	NA	-0.393229166666667	NA
7	40	38.3984375	40.125	-1.7265625	1.60156250000001
8	47	44.3359375	43.75	0.585937500000001	2.6640625
9	43	44.2630208333333	46.75	-2.48697916666667	-1.26302083333334
10	60	51.5026041666667	49.5416666666667	1.9609375	8.49739583333333
11	64	56.0651041666667	52.1666666666667	3.8984375	7.93489583333333
12	65	60.4088541666667	54.8333333333333	5.57552083333333	4.59114583333334
13	65	67.0130208333333	57.4583333333333	9.5546875	-2.01302083333333
14	55	59.2942708333333	59.625	-0.330729166666666	-4.29427083333334
15	57	57.3046875	61.75	-4.4453125	-0.304687499999993
16	57	56.9192708333333	63.375	-6.45572916666667	0.0807291666666714
17	57	58.4713541666667	64.2083333333333	-5.73697916666667	-1.47135416666666
18	65	64.3151041666667	64.7083333333333	-0.393229166666667	0.684895833333343
19	69	63.1067708333333	64.8333333333333	-1.7265625	5.89322916666667
20	70	65.5026041666667	64.9166666666667	0.585937500000001	4.49739583333334
21	71	61.8463541666667	64.3333333333333	-2.48697916666667	9.15364583333334
22	71	64.1276041666667	62.1666666666667	1.9609375	6.87239583333334
23	73	63.0651041666667	59.1666666666667	3.8984375	9.93489583333334
24	68	61.2421875	55.6666666666667	5.57552083333333	6.7578125
25	65	60.7213541666667	51.1666666666667	9.5546875	4.27864583333334
26	57	45.8776041666667	46.2083333333333	-0.330729166666666	11.1223958333333
27	41	36.5963541666667	41.0416666666667	-4.4453125	4.40364583333334
28	21	28.8359375	35.2916666666667	-6.45572916666667	-7.8359375
29	21	23.4713541666667	29.2083333333333	-5.73697916666667	-2.47135416666667
30	17	23.1484375	23.5416666666667	-0.393229166666667	-6.1484375
31	9	16.6067708333333	18.3333333333333	-1.7265625	-7.60677083333333
32	11	14.2526041666667	13.6666666666667	0.585937500000001	-3.25260416666667
33	6	7.5546875	10.0416666666667	-2.48697916666667	-1.5546875
34	-2	9.91927083333333	7.95833333333333	1.9609375	-11.9192708333333
35	0	10.8151041666667	6.91666666666667	3.8984375	-10.8151041666667
36	5	11.8671875	6.29166666666667	5.57552083333333	-6.8671875
37	3	15.8463541666667	6.29166666666667	9.5546875	-12.8463541666667
38	7	6.12760416666667	6.45833333333333	-0.330729166666666	0.872395833333334
39	4	2.09635416666667	6.54166666666667	-4.4453125	1.90364583333333
40	8	0.835937499999998	7.29166666666667	-6.45572916666667	7.1640625
41	9	2.84635416666666	8.58333333333333	-5.73697916666667	6.15364583333334
42	14	9.35677083333333	9.75	-0.393229166666667	4.64322916666667
43	12	10.1067708333333	11.8333333333333	-1.7265625	1.89322916666667
44	12	14.1276041666667	13.5416666666667	0.585937500000001	-2.12760416666667
45	7	11.5546875	14.0416666666667	-2.48697916666667	-4.5546875
46	15	16.6692708333333	14.7083333333333	1.9609375	-1.66927083333333
47	14	19.2734375	15.375	3.8984375	-5.2734375
48	19	21.7005208333333	16.125	5.57552083333333	-2.70052083333333
49	39	26.6380208333333	17.0833333333333	9.5546875	12.3619791666667
50	12	17.9192708333333	18.25	-0.330729166666666	-5.91927083333333
51	11	15.2213541666667	19.6666666666667	-4.4453125	-4.22135416666667
52	17	14.6276041666667	21.0833333333333	-6.45572916666667	2.37239583333334
53	16	16.4296875	22.1666666666667	-5.73697916666667	-0.4296875
54	25	22.3984375	22.7916666666667	-0.393229166666667	2.6015625
55	24	NA	NA	-1.7265625	NA
56	28	NA	NA	0.585937500000001	NA
57	25	NA	NA	-2.48697916666667	NA
58	31	NA	NA	1.9609375	NA
59	24	NA	NA	3.8984375	NA
60	24	NA	NA	5.57552083333333	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293656605ldqb1axrss3uysn/1byto1293656723.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293656605ldqb1axrss3uysn/1byto1293656723.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293656605ldqb1axrss3uysn/2byto1293656723.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293656605ldqb1axrss3uysn/2byto1293656723.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293656605ldqb1axrss3uysn/337s81293656723.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293656605ldqb1axrss3uysn/337s81293656723.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293656605ldqb1axrss3uysn/437s81293656723.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293656605ldqb1axrss3uysn/437s81293656723.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 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])

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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