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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: Tue, 01 Dec 2009 05:52:55 -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/01/t1259672020551q5vof446pouu.htm/, Retrieved Tue, 01 Dec 2009 13:53:46 +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/01/t1259672020551q5vof446pouu.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 «

149 139 135 130 127 122 117 112 113 149 157 157 147 137 132 125 123 117 114 111 112 144 150 149 134 123 116 117 111 105 102 95 93 124 130 124 115 106 105 105 101 95 93 84 87 116 120 117 109 105 107 109 109 108 107 99 103 131 137 135

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	149	NA	NA	1.07698743664865	NA
2	139	NA	NA	1.00707323925438	NA
3	135	NA	NA	0.987244752463944	NA
4	130	NA	NA	0.9828075799108	NA
5	127	NA	NA	0.959805789945503	NA
6	122	NA	NA	0.922007956896285	NA
7	117	119.064110375558	133.833333333333	0.889644660340411	0.982663874369467
8	112	112.649777197437	133.666666666667	0.842766412948407	0.99423188208976
9	113	113.972156529908	133.458333333333	0.853990557826348	0.991470227821364
10	149	150.394033581143	133.125	1.12972044004614	0.990730791987232
11	157	157.168466713945	132.75	1.18394325208245	0.99892811377837
12	157	154.085548626655	132.375	1.16400792163668	1.01891450171234
13	147	142.207216114149	132.041666666667	1.07698743664865	1.0337028177389
14	137	132.807783426672	131.875	1.00707323925438	1.03156604579312
15	132	130.110631335144	131.791666666667	0.987244752463944	1.01452124738362
16	125	129.2801470741	131.541666666667	0.9828075799108	0.966892464380887
17	123	125.774550390775	131.041666666667	0.959805789945503	0.977940287743786
18	117	120.245204378557	130.416666666667	0.922007956896285	0.973011777098897
19	114	115.246052041597	129.541666666667	0.889644660340411	0.989187898244465
20	111	108.225253529458	128.416666666667	0.842766412948407	1.02563862296508
21	112	108.599132603584	127.166666666667	0.853990557826348	1.03131578784179
22	144	142.533062185822	126.166666666667	1.12972044004614	1.01029191256879
23	150	148.387554261001	125.333333333333	1.18394325208245	1.01086644865218
24	149	144.724984923493	124.333333333333	1.16400792163668	1.02953888769632
25	134	132.82845052	123.333333333333	1.07698743664865	1.00882001917070
26	123	123.030780728910	122.166666666667	1.00707323925438	0.99974981278077
27	116	119.168668662002	120.708333333333	0.987244752463944	0.973410220172978
28	117	117.036002641045	119.083333333333	0.9828075799108	0.999692379778597
29	111	112.697196502768	117.416666666667	0.959805789945503	0.984940206540754
30	105	106.530336019725	115.541666666667	0.922007956896285	0.98563473957839
31	102	101.160011586208	113.708333333333	0.889644660340411	1.0083035618583
32	95	94.5654145862525	112.208333333333	0.842766412948407	1.00459560628639
33	93	94.828534858634	111.041666666667	0.853990557826348	0.98071746166531
34	124	124.363391775080	110.083333333333	1.12972044004614	0.997077984365874
35	130	129.247138352334	109.166666666667	1.18394325208245	1.00582497730521
36	124	126.100858177307	108.333333333333	1.16400792163668	0.98333985820816
37	115	115.821023916257	107.541666666667	1.07698743664865	0.992911270436961
38	106	107.463106905436	106.708333333333	1.00707323925438	0.986385030662442
39	105	104.647943761178	106	0.987244752463944	1.00336419643013
40	105	103.604299048930	105.416666666667	0.9828075799108	1.01347145788237
41	101	100.459672680963	104.666666666667	0.959805789945503	1.00537854946784
42	95	95.8504105190097	103.958333333333	0.922007956896285	0.991127732115023
43	93	92.0040852902042	103.416666666667	0.889644660340411	1.01082467921565
44	84	86.9102863353045	103.125	0.842766412948407	0.966513902346651
45	87	88.1033592157516	103.166666666667	0.853990557826348	0.987476536359418
46	116	116.831922174772	103.416666666667	1.12972044004614	0.992879324765987
47	120	123.031436278901	103.916666666667	1.18394325208245	0.975360473952125
48	117	121.97833012151	104.791666666667	1.16400792163668	0.959186766071066
49	109	114.070919331703	105.916666666667	1.07698743664865	0.955545906341326
50	105	107.882720755126	107.125	1.00707323925438	0.973279124451553
51	107	107.033785246299	108.416666666667	0.987244752463944	0.999684349701157
52	109	107.822181579381	109.708333333333	0.9828075799108	1.01092371164603
53	109	106.578434591865	111.041666666667	0.959805789945503	1.02272096993550
54	108	103.725895150832	112.5	0.922007956896285	1.04120576489557
55	107	NA	NA	0.889644660340411	NA
56	99	NA	NA	0.842766412948407	NA
57	103	NA	NA	0.853990557826348	NA
58	131	NA	NA	1.12972044004614	NA
59	137	NA	NA	1.18394325208245	NA
60	135	NA	NA	1.16400792163668	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/01/t1259672020551q5vof446pouu/1i3nb1259671973.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/01/t1259672020551q5vof446pouu/1i3nb1259671973.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/01/t1259672020551q5vof446pouu/2w9nl1259671973.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/01/t1259672020551q5vof446pouu/2w9nl1259671973.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/01/t1259672020551q5vof446pouu/3b8l81259671973.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/01/t1259672020551q5vof446pouu/3b8l81259671973.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/01/t1259672020551q5vof446pouu/4gy251259671973.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/01/t1259672020551q5vof446pouu/4gy251259671973.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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