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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: Fri, 04 Dec 2009 05:29:37 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/04/t1259929841v98mjmzzuj5dwcc.htm/, Retrieved Fri, 04 Dec 2009 13:30:46 +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/2009/Dec/04/t1259929841v98mjmzzuj5dwcc.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 «

89.1 82.6 102.7 91.8 94.1 103.1 93.2 91 94.3 99.4 115.7 116.8 99.8 96 115.9 109.1 117.3 109.8 112.8 110.7 100 113.3 122.4 112.5 104.2 92.5 117.2 109.3 106.1 118.8 105.3 106 102 112.9 116.5 114.8 100.5 85.4 114.6 109.9 100.7 115.5 100.7 99 102.3 108.8 105.9 113.2 95.7 80.9 113.9 98.1 102.8 104.7 95.9 94.6 101.6 103.9 110.3 114.1

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	5 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	89.1	NA	NA	-6.05234375	NA
2	82.6	NA	NA	-17.46796875	NA
3	102.7	NA	NA	9.11848958333334	NA
4	91.8	NA	NA	0.195572916666671	NA
5	94.1	NA	NA	0.329947916666666	NA
6	103.1	NA	NA	5.88932291666667	NA
7	93.2	95.80078125	98.2625	-2.46171875000001	-2.60078125000001
8	91	95.42890625	99.2666666666667	-3.83776041666667	-4.42890625
9	94.3	94.41328125	100.375	-5.96171875	-0.11328125
10	99.4	104.451822916667	101.645833333333	2.80598958333333	-5.05182291666665
11	115.7	112.508072916667	103.333333333333	9.17473958333334	3.19192708333334
12	116.8	112.846614583333	104.579166666667	8.26744791666667	3.95338541666668
13	99.8	99.62265625	105.675	-6.05234375	0.177343749999991
14	96	89.84453125	107.3125	-17.46796875	6.15546875
15	115.9	117.489322916667	108.370833333333	9.11848958333334	-1.58932291666665
16	109.1	109.383072916667	109.1875	0.195572916666671	-0.283072916666669
17	117.3	110.37578125	110.045833333333	0.329947916666666	6.92421875000001
18	109.8	116.03515625	110.145833333333	5.88932291666667	-6.23515624999999
19	112.8	107.68828125	110.15	-2.46171875000001	5.11171874999999
20	110.7	106.349739583333	110.1875	-3.83776041666667	4.35026041666667
21	100	104.134114583333	110.095833333333	-5.96171875	-4.13411458333331
22	113.3	112.964322916667	110.158333333333	2.80598958333333	0.335677083333351
23	122.4	118.874739583333	109.7	9.17473958333334	3.52526041666667
24	112.5	117.87578125	109.608333333333	8.26744791666667	-5.37578125
25	104.2	103.618489583333	109.670833333333	-6.05234375	0.581510416666674
26	92.5	91.69453125	109.1625	-17.46796875	0.805468750000017
27	117.2	118.168489583333	109.05	9.11848958333334	-0.968489583333337
28	109.3	109.312239583333	109.116666666667	0.195572916666671	-0.0122395833333400
29	106.1	109.184114583333	108.854166666667	0.329947916666666	-3.08411458333333
30	118.8	114.593489583333	108.704166666667	5.88932291666667	4.20651041666666
31	105.3	106.184114583333	108.645833333333	-2.46171875000001	-0.884114583333329
32	106	104.358072916667	108.195833333333	-3.83776041666667	1.64192708333333
33	102	101.829947916667	107.791666666667	-5.96171875	0.170052083333317
34	112.9	110.514322916667	107.708333333333	2.80598958333333	2.38567708333335
35	116.5	116.683072916667	107.508333333333	9.17473958333334	-0.183072916666646
36	114.8	115.41328125	107.145833333333	8.26744791666667	-0.61328125
37	100.5	100.764322916667	106.816666666667	-6.05234375	-0.264322916666657
38	85.4	88.8653645833333	106.333333333333	-17.46796875	-3.46536458333331
39	114.6	115.17265625	106.054166666667	9.11848958333334	-0.572656250000009
40	109.9	106.09140625	105.895833333333	0.195572916666671	3.80859375000001
41	100.7	105.61328125	105.283333333333	0.329947916666666	-4.91328124999998
42	115.5	110.664322916667	104.775	5.88932291666667	4.83567708333335
43	100.7	102.046614583333	104.508333333333	-2.46171875000001	-1.34661458333332
44	99	100.283072916667	104.120833333333	-3.83776041666667	-1.28307291666667
45	102.3	97.9424479166667	103.904166666667	-5.96171875	4.35755208333335
46	108.8	106.189322916667	103.383333333333	2.80598958333333	2.61067708333333
47	105.9	112.15390625	102.979166666667	9.17473958333334	-6.25390624999999
48	113.2	110.884114583333	102.616666666667	8.26744791666667	2.31588541666667
49	95.7	95.9143229166667	101.966666666667	-6.05234375	-0.214322916666674
50	80.9	84.1153645833333	101.583333333333	-17.46796875	-3.21536458333334
51	113.9	110.489322916667	101.370833333333	9.11848958333334	3.41067708333333
52	98.1	101.333072916667	101.1375	0.195572916666671	-3.23307291666667
53	102.8	101.446614583333	101.116666666667	0.329947916666666	1.35338541666665
54	104.7	107.226822916667	101.3375	5.88932291666667	-2.52682291666667
55	95.9	NA	NA	-2.46171875000001	NA
56	94.6	NA	NA	-3.83776041666667	NA
57	101.6	NA	NA	-5.96171875	NA
58	103.9	NA	NA	2.80598958333333	NA
59	110.3	NA	NA	9.17473958333334	NA
60	114.1	NA	NA	8.26744791666667	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259929841v98mjmzzuj5dwcc/117wm1259929771.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259929841v98mjmzzuj5dwcc/117wm1259929771.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259929841v98mjmzzuj5dwcc/2lqm51259929771.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259929841v98mjmzzuj5dwcc/2lqm51259929771.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259929841v98mjmzzuj5dwcc/372001259929771.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259929841v98mjmzzuj5dwcc/372001259929771.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259929841v98mjmzzuj5dwcc/491fc1259929771.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259929841v98mjmzzuj5dwcc/491fc1259929771.ps (open in new window)

Parameters (Session):

par1 = additive ; par2 = 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])

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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