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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 12:49:12 -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/t1259956186egg2wzb4zm0r0zj.htm/, Retrieved Fri, 04 Dec 2009 20:49:51 +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/t1259956186egg2wzb4zm0r0zj.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 «

5560 3922 3759 4138 4634 3996 4308 4143 4429 5219 4929 5755 5592 4163 4962 5208 4755 4491 5732 5731 5040 6102 4904 5369 5578 4619 4731 5011 5299 4146 4625 4736 4219 5116 4205 4121 5103 4300 4578 3809 5526 4247 3830 4394 4826 4409 4569 4106 4794 3914 3793 4405 4022 4100 4788 3163 3585 3903 4178 3863 4187

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	5560	NA	NA	1.11501954360955	NA
2	3922	NA	NA	0.902031790995203	NA
3	3759	NA	NA	0.959668441272228	NA
4	4138	NA	NA	0.984943407225544	NA
5	4634	NA	NA	1.05574264506044	NA
6	3996	NA	NA	0.922140070528212	NA
7	4308	4721.98821377813	4567.33333333333	1.03386108898952	0.912327563086632
8	4143	4632.07144663663	4578.70833333333	1.01165462165712	0.894416255821843
9	4429	4495.04264165324	4638.875	0.968994129320847	0.985307671824676
10	5219	5026.31980500137	4733.58333333333	1.06184246712351	1.03833424900797
11	4929	4802.9872845854	4783.20833333333	1.00413508044678	1.02623632084537
12	5755	4712.53743068577	4808.875	0.979966713771052	1.22121045925836
13	5592	5451.14471211649	4888.83333333333	1.11501954360955	1.02583957963369
14	4163	4523.08807731362	5014.33333333333	0.902031790995203	0.920388886716643
15	4962	4900.02707495095	5105.95833333333	0.959668441272228	1.01264746584072
16	5208	5090.39272508478	5168.20833333333	0.984943407225544	1.02310377239376
17	4755	5494.04073561763	5203.95833333333	1.05574264506044	0.865483207864394
18	4491	4782.98685581808	5186.83333333333	0.922140070528212	0.93895302984935
19	5732	5345.23414025731	5170.16666666667	1.03386108898952	1.07235714088365
20	5731	5249.05430901976	5188.58333333333	1.01165462165712	1.09181571814795
21	5040	5036.79110945437	5197.95833333333	0.968994129320847	1.00063709025764
22	6102	5500.47671000815	5180.125	1.06184246712351	1.10935839231850
23	4904	5216.06335330419	5194.58333333333	1.00413508044678	0.940172629784776
24	5369	5098.64431591157	5202.875	0.979966713771052	1.05302501357954
25	5578	5733.84862556916	5142.375	1.11501954360955	0.972819543077197
26	4619	4559.58278019096	5054.79166666667	0.902031790995203	1.01303128436820
27	4731	4778.30912764958	4979.125	0.959668441272228	0.990099190658086
28	5011	4829.99831179953	4903.83333333333	0.984943407225544	1.03747448270495
29	5299	5103.06404273025	4833.625	1.05574264506044	1.03839574726656
30	4146	4382.47068518533	4752.5	0.922140070528212	0.946041696072332
31	4625	4839.20221474232	4680.70833333333	1.03386108898952	0.9557360479606
32	4736	4701.79131097916	4647.625	1.01165462165712	1.00727567149587
33	4219	4484.46445574149	4627.95833333333	0.968994129320847	0.940803532202911
34	5116	4854.2128384551	4571.5	1.06184246712351	1.05392988940885
35	4205	4549.61053261932	4530.875	1.00413508044678	0.924254937835102
36	4121	4453.49956267895	4544.54166666667	0.979966713771052	0.925339711388914
37	5103	5035.01012661187	4515.625	1.11501954360955	1.01350342336528
38	4300	4030.50355011432	4468.25	0.902031790995203	1.06686421349959
39	4578	4298.63485175368	4479.29166666667	0.959668441272228	1.06498927168293
40	3809	4407.74486526021	4475.125	0.984943407225544	0.864160725367922
41	5526	4709.49198250709	4460.83333333333	1.05574264506044	1.17337496709321
42	4247	4126.9226181402	4475.375	0.922140070528212	1.02909610694710
43	3830	4612.95894643511	4461.875	1.03386108898952	0.83026969120543
44	4394	4484.5806332542	4432.91666666667	1.01165462165712	0.979801760596627
45	4826	4248.19138720876	4384.125	0.968994129320847	1.13601284879279
46	4409	4646.88809674924	4376.25	1.06184246712351	0.94880700981036
47	4569	4356.356368595	4338.41666666667	1.00413508044678	1.04881226727408
48	4106	4184.09038028473	4269.625	0.979966713771052	0.981336354335774
49	4794	4798.39368762840	4303.41666666667	1.11501954360955	0.999084341987252
50	3914	3871.55803160937	4292.04166666667	0.902031790995203	1.01096250347899
51	3793	4020.09108667442	4189.04166666667	0.959668441272228	0.943510959881689
52	4405	4054.27329999215	4116.25	0.984943407225544	1.08650790759679
53	4022	4306.24228137088	4078.875	1.05574264506044	0.933992965839257
54	4100	3736.93421331264	4052.45833333333	0.922140070528212	1.09715605519464
55	4788	4153.06307201627	4017.04166666667	1.03386108898952	1.15288400801375
56	3163	NA	NA	1.01165462165712	NA
57	3585	NA	NA	0.968994129320847	NA
58	3903	NA	NA	1.06184246712351	NA
59	4178	NA	NA	1.00413508044678	NA
60	3863	NA	NA	0.979966713771052	NA
61	4187	NA	NA	NA	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956186egg2wzb4zm0r0zj/12v201259956149.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956186egg2wzb4zm0r0zj/12v201259956149.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956186egg2wzb4zm0r0zj/27ki51259956149.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956186egg2wzb4zm0r0zj/27ki51259956149.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956186egg2wzb4zm0r0zj/3dot11259956149.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956186egg2wzb4zm0r0zj/3dot11259956149.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956186egg2wzb4zm0r0zj/4p77v1259956149.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956186egg2wzb4zm0r0zj/4p77v1259956149.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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