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Shwws9_v2

*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, 11 Dec 2009 09:13:41 -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/11/t12605480834rxzxmbpyz6u6eb.htm/, Retrieved Fri, 11 Dec 2009 17:14:48 +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/11/t12605480834rxzxmbpyz6u6eb.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 «

102.1 102.86 102.99 103.73 105.02 104.43 104.63 104.93 105.87 105.66 106.76 106 107.22 107.33 107.11 108.86 107.72 107.88 108.38 107.72 108.41 109.9 111.45 112.18 113.34 113.46 114.06 115.54 116.39 115.94 116.97 115.94 115.91 116.43 116.26 116.35 117.9 117.7 117.53 117.86 117.65 116.51 115.93 115.31 115

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	102.1	NA	NA	1.00519037227915	NA
2	102.86	NA	NA	1.00184469355228	NA
3	102.99	NA	NA	0.999537013489676	NA
4	103.73	NA	NA	1.01026528475282	NA
5	105.02	NA	NA	1.00486594356418	NA
6	104.43	NA	NA	0.999928785216081	NA
7	104.63	105.083572462831	104.795	1.00275368541277	0.995683697725526
8	104.93	104.288573508554	105.194583333333	0.991387295846708	1.00615049635704
9	105.87	104.558093138201	105.5525	0.990579030702262	1.01254715749326
10	105.66	105.512595737331	105.937916666667	0.995985186959322	1.00139703000991
11	106.76	106.129334173471	106.264166666667	0.998731157478337	1.00594242705319
12	106	106.406605006988	106.520416666667	0.998931550746416	0.996178761581943
13	107.22	107.374854396181	106.820416666667	1.00519037227915	0.998557815076431
14	107.33	107.290470279536	107.092916666667	1.00184469355228	1.00036843645443
15	107.11	107.265314602645	107.315	0.999537013489676	0.998552051954354
16	108.86	108.702018976192	107.5975	1.01026528475282	1.00145334029024
17	107.72	108.494957232481	107.969583333333	1.00486594356418	0.992857205051284
18	107.88	108.414778715091	108.4225	0.999928785216081	0.995067289520593
19	108.38	109.23497272044	108.935	1.00275368541277	0.992173086154119
20	107.72	108.502795671983	109.445416666667	0.991387295846708	0.992785479239177
21	108.41	108.954200328205	109.990416666667	0.990579030702262	0.995005237736909
22	109.9	110.114462294911	110.558333333333	0.995985186959322	0.998052369412324
23	111.45	111.056824021680	111.197916666667	0.998731157478337	1.00354031354475
24	112.18	111.775445870770	111.895	0.998931550746416	1.00361934704065
25	113.34	113.173127526944	112.58875	1.00519037227915	1.00147448848240
26	113.46	113.498150461960	113.289166666667	1.00184469355228	0.999663867104404
27	114.06	113.891412054570	113.944166666667	0.999537013489676	1.00148025160448
28	115.54	115.704420231135	114.52875	1.01026528475282	0.998578963268594
29	116.39	115.560839592310	115.00125	1.00486594356418	1.00717509850755
30	115.94	115.367200231299	115.375416666667	0.999928785216081	1.00496501403824
31	116.97	116.057875921603	115.739166666667	1.00275368541277	1.00785921740471
32	115.94	115.105848140362	116.105833333333	0.991387295846708	1.00724682431966
33	115.91	115.330227355825	116.427083333333	0.990579030702262	1.00502706582192
34	116.43	116.199931787232	116.668333333333	0.995985186959322	1.00197993414651
35	116.26	116.669276988726	116.8175	0.998731157478337	0.996491990014088
36	116.35	116.768854960064	116.89375	0.998931550746416	0.996412956518182
37	117.9	NA	116.874166666667	NA	NA
38	117.7	NA	116.804583333333	NA	NA
39	117.53	NA	116.740416666667	NA	NA
40	117.86	NA	NA	NA	NA
41	117.65	NA	NA	NA	NA
42	116.51	NA	NA	NA	NA
43	115.93	NA	NA	NA	NA
44	115.31	NA	NA	NA	NA
45	115	NA	NA	NA	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/11/t12605480834rxzxmbpyz6u6eb/1ps941260548018.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t12605480834rxzxmbpyz6u6eb/1ps941260548018.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t12605480834rxzxmbpyz6u6eb/2j41f1260548018.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t12605480834rxzxmbpyz6u6eb/2j41f1260548018.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t12605480834rxzxmbpyz6u6eb/34q4m1260548018.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t12605480834rxzxmbpyz6u6eb/34q4m1260548018.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t12605480834rxzxmbpyz6u6eb/4fxki1260548018.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t12605480834rxzxmbpyz6u6eb/4fxki1260548018.ps (open in new window)

Parameters (Session):

par1 = multiplicative ; par2 = 12 ;

Parameters (R input):

par1 = multiplicative ; 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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