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Paper; Classical Decomposition Coffee

*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: Tue, 21 Dec 2010 12:03:05 +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/21/t129293297560wcfuq4vue7gs3.htm/, Retrieved Tue, 21 Dec 2010 13:03:13 +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/21/t129293297560wcfuq4vue7gs3.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 «

108,35 109,87 111,30 115,50 116,22 116,63 116,84 116,63 117,03 117,00 117,14 116,64 117,24 117,52 117,83 119,79 120,86 120,75 120,63 120,89 120,23 121,19 120,79 120,09 120,86 121,10 121,47 122,01 123,94 125,78 125,31 125,79 126,12 125,57 125,44 126,12 126,01 126,50 126,13 126,66 126,33 126,61 126,36 126,83 125,90 126,29 126,37 125,11

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	4 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	108.35	NA	NA	-0.71075231481482	NA
2	109.87	NA	NA	-0.647974537037033	NA
3	111.3	NA	NA	-0.809502314814815	NA
4	115.5	NA	NA	-0.0517245370370397	NA
5	116.22	NA	NA	0.581053240740726	NA
6	116.63	NA	NA	1.00521990740740	NA
7	116.84	116.264942129630	115.299583333333	0.965358796296297	0.575057870370372
8	116.63	116.654525462963	115.98875	0.665775462962966	-0.0245254629629699
9	117.03	116.831747685185	116.579583333333	0.252164351851857	0.198252314814809
10	117	117.048275462963	117.030416666667	0.0178587962962966	-0.0482754629629625
11	117.14	116.994942129630	117.4025	-0.407557870370362	0.145057870370380
12	116.64	116.907581018519	117.7675	-0.859918981481479	-0.267581018518513
13	117.24	117.386331018519	118.097083333333	-0.71075231481482	-0.146331018518509
14	117.52	117.784525462963	118.4325	-0.647974537037033	-0.264525462962965
15	117.83	117.933831018519	118.743333333333	-0.809502314814815	-0.103831018518505
16	119.79	118.999525462963	119.05125	-0.0517245370370397	0.79047453703707
17	120.86	119.958969907407	119.377916666667	0.581053240740726	0.90103009259262
18	120.75	120.678969907407	119.67375	1.00521990740740	0.0710300925926077
19	120.63	120.933692129630	119.968333333333	0.965358796296297	-0.303692129629624
20	120.89	120.934108796296	120.268333333333	0.665775462962966	-0.0441087962962854
21	120.23	120.821331018519	120.569166666667	0.252164351851857	-0.591331018518503
22	121.19	120.831192129630	120.813333333333	0.0178587962962966	0.358807870370384
23	120.79	120.626608796296	121.034166666667	-0.407557870370362	0.163391203703725
24	120.09	120.512164351852	121.372083333333	-0.859918981481479	-0.422164351851833
25	120.86	121.065914351852	121.776666666667	-0.71075231481482	-0.205914351851845
26	121.1	121.527858796296	122.175833333333	-0.647974537037033	-0.427858796296277
27	121.47	121.815914351852	122.625416666667	-0.809502314814815	-0.345914351851832
28	122.01	123.001608796296	123.053333333333	-0.0517245370370397	-0.991608796296276
29	123.94	124.010636574074	123.429583333333	0.581053240740726	-0.0706365740740722
30	125.78	124.879803240741	123.874583333333	1.00521990740740	0.90019675925926
31	125.31	125.305775462963	124.340416666667	0.965358796296297	0.00422453703703241
32	125.79	125.445775462963	124.78	0.665775462962966	0.344224537037036
33	126.12	125.451331018519	125.199166666667	0.252164351851857	0.668668981481474
34	125.57	125.604942129630	125.587083333333	0.0178587962962966	-0.0349421296296271
35	125.44	125.472858796296	125.880416666667	-0.407557870370362	-0.0328587962962956
36	126.12	125.154664351852	126.014583333333	-0.859918981481479	0.965335648148155
37	126.01	125.382164351852	126.092916666667	-0.71075231481482	0.627835648148164
38	126.5	125.532025462963	126.18	-0.647974537037033	0.967974537037023
39	126.13	125.404664351852	126.214166666667	-0.809502314814815	0.725335648148146
40	126.66	126.183275462963	126.235	-0.0517245370370397	0.476724537037015
41	126.33	126.884803240741	126.30375	0.581053240740726	-0.554803240740767
42	126.61	127.305636574074	126.300416666667	1.00521990740740	-0.695636574074086
43	126.36	NA	NA	0.965358796296297	NA
44	126.83	NA	NA	0.665775462962966	NA
45	125.9	NA	NA	0.252164351851857	NA
46	126.29	NA	NA	0.0178587962962966	NA
47	126.37	NA	NA	-0.407557870370362	NA
48	125.11	NA	NA	-0.859918981481479	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t129293297560wcfuq4vue7gs3/1wc8g1292932980.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t129293297560wcfuq4vue7gs3/1wc8g1292932980.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t129293297560wcfuq4vue7gs3/2wc8g1292932980.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t129293297560wcfuq4vue7gs3/2wc8g1292932980.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t129293297560wcfuq4vue7gs3/3plp11292932980.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t129293297560wcfuq4vue7gs3/3plp11292932980.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t129293297560wcfuq4vue7gs3/4plp11292932980.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t129293297560wcfuq4vue7gs3/4plp11292932980.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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