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opgave 9.1

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Tue, 18 Aug 2009 12:29:56 -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/Aug/18/t1250620423xu3iaw8r7dwg8uc.htm/, Retrieved Tue, 18 Aug 2009 20:33:48 +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/Aug/18/t1250620423xu3iaw8r7dwg8uc.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 «

108,87 106,38 104,77 105,38 106,74 110 110,73 115,7 115,44 113,66 118,4 116,71 119,7 114,17 110,52 111,27 111,41 111,62 113,91 118,54 122,26 120,44 121,37 121,49 125 117,24 117,18 115,15 115,27 114,6 117,48 120,8 118,62 116,79 115,46 112,83 115,56 106,66 103,39 102,65 103,22 104,1 104,32

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	108.87	NA	NA	5.48772569444444	NA
2	106.38	NA	NA	-1.40414930555555	NA
3	104.77	NA	NA	-3.43164930555555	NA
4	105.38	NA	NA	-4.20310763888889	NA
5	106.74	NA	NA	-4.07706597222223	NA
6	110	NA	NA	-4.16498263888889	NA
7	110.73	110.103350694444	111.51625	-1.41289930555555	0.62664930555556
8	115.7	115.096892361111	112.292083333333	2.80480902777779	0.603107638888872
9	115.44	116.736059027778	112.85625	3.87980902777778	-1.29605902777776
10	113.66	115.724184027778	113.34125	2.38293402777778	-2.06418402777777
11	118.4	116.314392361111	113.78125	2.5331423611111	2.08560763888887
12	116.71	115.648767361111	114.043333333333	1.60543402777776	1.06123263888890
13	119.7	119.731059027778	114.243333333333	5.48772569444444	-0.0310590277777862
14	114.17	113.090017361111	114.494166666667	-1.40414930555555	1.07998263888891
15	110.52	111.465017361111	114.896666666667	-3.43164930555555	-0.945017361111098
16	111.27	111.260225694444	115.463333333333	-4.20310763888889	0.0097743055555668
17	111.41	111.792517361111	115.869583333333	-4.07706597222223	-0.382517361111084
18	111.62	112.027517361111	116.1925	-4.16498263888889	-0.40751736111109
19	113.91	115.199600694444	116.6125	-1.41289930555555	-1.28960069444443
20	118.54	119.766059027778	116.96125	2.80480902777779	-1.22605902777775
21	122.26	121.246475694444	117.366666666667	3.87980902777778	1.01352430555556
22	120.44	120.188767361111	117.805833333333	2.38293402777778	0.251232638888908
23	121.37	120.661475694444	118.128333333333	2.5331423611111	0.70852430555557
24	121.49	120.018767361111	118.413333333333	1.60543402777776	1.47123263888891
25	125	124.173975694444	118.68625	5.48772569444444	0.826024305555578
26	117.24	117.525017361111	118.929166666667	-1.40414930555555	-0.285017361111116
27	117.18	115.440017361111	118.871666666667	-3.43164930555555	1.73998263888890
28	115.15	114.364809027778	118.567916666667	-4.20310763888889	0.785190972222239
29	115.27	114.092517361111	118.169583333333	-4.07706597222223	1.17748263888889
30	114.6	113.397517361111	117.5625	-4.16498263888889	1.20248263888890
31	117.48	115.395434027778	116.808333333333	-1.41289930555555	2.08456597222222
32	120.8	118.778975694444	115.974166666667	2.80480902777779	2.02102430555554
33	118.62	118.838559027778	114.95875	3.87980902777778	-0.218559027777758
34	116.79	116.246267361111	113.863333333333	2.38293402777778	0.543732638888898
35	115.46	115.373559027778	112.840416666667	2.5331423611111	0.0864409722222206
36	112.83	113.506267361111	111.900833333333	1.60543402777776	-0.676267361111101
37	115.56	NA	110.915	NA	NA
38	106.66	NA	NA	NA	NA
39	103.39	NA	NA	NA	NA
40	102.65	NA	NA	NA	NA
41	103.22	NA	NA	NA	NA
42	104.1	NA	NA	NA	NA
43	104.32	NA	NA	NA	NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Aug/18/t1250620423xu3iaw8r7dwg8uc/1ovbs1250620194.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Aug/18/t1250620423xu3iaw8r7dwg8uc/1ovbs1250620194.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Aug/18/t1250620423xu3iaw8r7dwg8uc/2tko51250620194.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Aug/18/t1250620423xu3iaw8r7dwg8uc/2tko51250620194.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Aug/18/t1250620423xu3iaw8r7dwg8uc/3d4891250620194.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Aug/18/t1250620423xu3iaw8r7dwg8uc/3d4891250620194.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Aug/18/t1250620423xu3iaw8r7dwg8uc/4za421250620194.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Aug/18/t1250620423xu3iaw8r7dwg8uc/4za421250620194.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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