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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, 02 Dec 2009 12:57:25 -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/02/t1259783985xjn2ie2boh01dhi.htm/, Retrieved Wed, 02 Dec 2009 20:59:56 +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/02/t1259783985xjn2ie2boh01dhi.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 «

17409 11514 31514 27071 29462 26105 22397 23843 21705 18089 20764 25316 17704 15548 28029 29383 36438 32034 22679 24319 18004 17537 20366 22782 19169 13807 29743 25591 29096 26482 22405 27044 17970 18730 19684 19785 18479 10698 31956 29506 34506 27165 26736 23691 18157 17328 18205 20995 17382 9367

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' @ 72.249.127.135

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	17409	NA	NA	0.783190023431188	NA
2	11514	NA	NA	0.563728989535431	NA
3	31514	NA	NA	1.26846791210841	NA
4	27071	NA	NA	1.19484632820052	NA
5	29462	NA	NA	1.41675541593637	NA
6	26105	NA	NA	1.21836120137747	NA
7	22397	23541.4826449259	22944.7083333333	1.02600923502373	0.951384427982384
8	23843	24926.7251793737	23125.0833333333	1.07790855583397	0.956523563702206
9	21705	19402.8873858639	23147.9583333333	0.838211608404511	1.11864793978103
10	18089	19838.7963612771	23099.0833333333	0.858856434906599	0.911799267989237
11	20764	20718.9781605356	23486.0833333333	0.8821810715084	1.00217297586375
12	25316	20936.3314079664	24023.7916666667	0.871483223733405	1.20918987699856
13	17704	19017.8770098031	24282.5833333333	0.783190023431188	0.9309135815146
14	15548	13706.6006063961	24314.1666666667	0.563728989535431	1.13434398845361
15	28029	30671.2898506330	24179.7916666667	1.26846791210841	0.913851361859876
16	29383	28679.3985631603	24002.5833333333	1.19484632820052	1.02453334003118
17	36438	33949.7100320831	23963	1.41675541593637	1.07329340856123
18	32034	29046.74634184	23840.8333333333	1.21836120137747	1.10284296984606
19	22679	24415.2150093181	23796.2916666667	1.02600923502373	0.92888799018745
20	24319	25637.8304362286	23784.7916666667	1.07790855583397	0.948559202795686
21	18004	19935.7454904234	23783.6666666667	0.838211608404511	0.903101416932144
22	17537	20352.3925093512	23697.0833333333	0.858856434906599	0.861667737193174
23	20366	20495.8598645332	23233.1666666667	0.8821810715084	0.993664092875755
24	22782	19779.1106222514	22695.9166666667	0.871483223733405	1.15182125400373
25	19169	17585.0961277710	22453.1666666667	0.783190023431188	1.09007081114146
26	13807	12715.0717799269	22555.2916666667	0.563728989535431	1.08587668547785
27	29743	28752.8906920581	22667.4166666667	1.26846791210841	1.03443512231678
28	25591	27141.7806945573	22715.7083333333	1.19484632820052	0.942863708464483
29	29096	32212.7678921451	22737	1.41675541593637	0.903244331484314
30	26482	27515.1140165584	22583.7083333333	1.21836120137747	0.962452853514013
31	22405	23013.4726423518	22430.0833333333	1.02600923502373	0.973560155313893
32	27044	24006.9547912518	22271.7916666667	1.07790855583397	1.12650689082211
33	17970	18637.1810815864	22234.4583333333	0.838211608404511	0.964201609746358
34	18730	19315.5022926255	22489.7916666667	0.858856434906599	0.969687441529853
35	19684	20182.8326143263	22878.3333333333	0.8821810715084	0.975284310985553
36	19785	20159.3314904061	23132.2083333333	0.871483223733405	0.98143135398194
37	18479	18280.5362356603	23341.125	0.783190023431188	1.01085656141490
38	10698	13181.0407671938	23381.875	0.563728989535431	0.811620280139502
39	31956	29491.8261036909	23249.9583333333	1.26846791210841	1.08355446989431
40	29506	27719.6382500333	23199.3333333333	1.19484632820052	1.06444390557530
41	34506	32697.711464725	23079.2916666667	1.41675541593637	1.05530321402542
42	27165	28105.2577234756	23068.0833333333	1.21836120137747	0.966545130710892
43	26736	23672.8973277785	23072.7916666667	1.02600923502373	1.12939280856962
44	23691	24761.3111289096	22971.625	1.07790855583397	0.956774860453169
45	18157	NA	NA	0.838211608404511	NA
46	17328	NA	NA	0.858856434906599	NA
47	18205	NA	NA	0.8821810715084	NA
48	20995	NA	NA	0.871483223733405	NA
49	17382	NA	NA	NA	NA
50	9367	NA	NA	NA	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/02/t1259783985xjn2ie2boh01dhi/18h6q1259783844.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/02/t1259783985xjn2ie2boh01dhi/18h6q1259783844.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/02/t1259783985xjn2ie2boh01dhi/2zj241259783844.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/02/t1259783985xjn2ie2boh01dhi/2zj241259783844.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/02/t1259783985xjn2ie2boh01dhi/3h2gp1259783844.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/02/t1259783985xjn2ie2boh01dhi/3h2gp1259783844.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/02/t1259783985xjn2ie2boh01dhi/4ybhn1259783844.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/02/t1259783985xjn2ie2boh01dhi/4ybhn1259783844.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

par1 = multiplicative ; par2 = 12 ; par3 = 1 ; par4 = 1 ;

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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