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R Software Module: /rwasp_decompose.wasp (opens new window with default values)

Title produced by software: Classical Decomposition

Date of computation: Tue, 12 Jan 2010 08:11:41 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Jan/12/t1263309188o5yu30x89hd3tjz.htm/, Retrieved Tue, 12 Jan 2010 16:13:16 +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/Jan/12/t1263309188o5yu30x89hd3tjz.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:

KDGP2W52

Dataseries X:

» Textbox « » Textfile « » CSV «

100,7 105,9 115,4 113,9 121,5 119,5 115,8 116,3 113,5 110,7 116,9 141,1 101,8 102,9 119 112,8 120,9 123,1 121,9 119,4 110,9 116,8 120,6 143,3 106,4 106,9 125,6 110,9 127 124,3 121,3 124,4 113,2 120,2 122,6 143,3 106,5 105,9 114 121,6 119,7 122,5 126,5 118,2 115,5 120,1 115,3 146,5 107,7 106,3 121,8 115,8 115,4 124,3 121,7 118,7 113,5 113,4 115,1 144,2 100,9 103,2 121,3 111,9 117,3 124,2 122 119,6 114,9 112,2 115,3 143 104 105,3 124,3 114,1 124,8 131,9 125,8 125,2 119,8 116,2 120,2 148,6 109,4 109,6 135,2 115,2 129,1 138,8 126 130,7 120,5 126,5 128 151,7 114,8 118,9 131,5 124,8 137 137,1 137 131,3 126 129,7 125,1 157,8

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	100.7	NA	NA	0.882039447959413	NA
2	105.9	NA	NA	0.888255135787721	NA
3	115.4	NA	NA	1.02545098625144	NA
4	113.9	NA	NA	0.956832102834369	NA
5	121.5	NA	NA	1.02100544232997	NA
6	119.5	NA	NA	1.05614625476188	NA
7	115.8	118.827281261172	115.979166666667	1.02455712242434	0.974523684889174
8	116.3	117.532281109365	115.9	1.01408352984784	0.989515381665928
9	113.5	111.331553653311	115.925	0.960375705441542	1.01947737434296
10	110.7	112.988467347364	116.029166666667	0.97379366407036	0.979746009472557
11	116.9	114.971084110623	115.958333333333	0.991486172711087	1.01677740019848
12	141.1	139.993532396916	116.083333333333	1.20597443558004	1.00790370515080
13	101.8	102.746570194172	116.4875	0.882039447959413	0.990787330493044
14	102.9	103.811117932124	116.870833333333	0.888255135787721	0.99122331066004
15	119	119.866674867908	116.891666666667	1.02545098625144	0.992769676235177
16	112.8	111.985237235477	117.0375	0.956832102834369	1.00727562654361
17	120.9	119.912835012312	117.445833333333	1.02100544232997	1.00823235467318
18	123.1	124.299612966684	117.691666666667	1.05614625476188	0.990349020901574
19	121.9	120.872126518011	117.975	1.02455712242434	1.00850380903852
20	119.4	119.999884365328	118.333333333333	1.01408352984784	0.995000958805082
21	110.9	114.068624413819	118.775	0.960375705441542	0.972221770621833
22	116.8	115.853043709171	118.970833333333	0.97379366407036	1.00817377136164
23	120.6	118.131446286140	119.145833333333	0.991486172711087	1.0208966688504
24	143.3	144.053646330035	119.45	1.20597443558004	0.994768293970784
25	106.4	105.381663044951	119.475	0.882039447959413	1.00966332211530
26	106.9	106.287129123132	119.658333333333	0.888255135787721	1.00576618149275
27	125.6	123.015663938188	119.9625	1.02545098625144	1.02100818691765
28	110.9	115.011218760691	120.2	0.956832102834369	0.96425375885073
29	127	122.954580392587	120.425	1.02100544232997	1.03290173976843
30	124.3	127.274424917597	120.508333333333	1.05614625476188	0.97662983023084
31	121.3	123.471940216163	120.5125	1.02455712242434	0.982409442887507
32	124.4	122.171713258418	120.475	1.01408352984784	1.0182389743268
33	113.2	115.197065867713	119.95	0.960375705441542	0.98266391723895
34	120.2	116.770032742837	119.9125	0.97379366407036	1.02937369440254
35	122.6	119.032046226352	120.054166666667	0.991486172711087	1.02997473274435
36	143.3	144.324990578041	119.675	1.20597443558004	0.99289803814339
37	106.5	105.683026523004	119.816666666667	0.882039447959413	1.00773041333008
38	105.9	106.390758888974	119.775	0.888255135787721	0.995387203793833
39	114	122.656756093000	119.6125	1.02545098625144	0.929422916692526
40	121.6	114.536789509702	119.704166666667	0.956832102834369	1.06166761370327
41	119.7	121.903795624856	119.395833333333	1.02100544232997	0.981921845718096
42	122.5	125.919037223985	119.225	1.05614625476188	0.972847336674727
43	126.5	122.340658393486	119.408333333333	1.02455712242434	1.03399803189825
44	118.2	121.157629728571	119.475	1.01408352984784	0.97558858046995
45	115.5	115.069015773654	119.816666666667	0.960375705441542	1.00374544114633
46	120.1	116.757860322036	119.9	0.97379366407036	1.02862453687268
47	115.3	118.461941677043	119.479166666667	0.991486172711087	0.973308375396516
48	146.5	143.963198247367	119.375	1.20597443558004	1.01762118224322
49	107.7	105.18320416916	119.25	0.882039447959413	1.02392773495274
50	106.3	105.765279230857	119.070833333333	0.888255135787721	1.00505573069945
51	121.8	122.037212788807	119.008333333333	1.02545098625144	0.998056225774206
52	115.8	113.524142200869	118.645833333333	0.956832102834369	1.02004734636183
53	115.4	120.844502478439	118.358333333333	1.02100544232997	0.954946212969763
54	124.3	124.893695234987	118.254166666667	1.05614625476188	0.995246395473603
55	121.7	120.769670805769	117.875	1.02455712242434	1.0077033346868
56	118.7	119.116786624752	117.4625	1.01408352984784	0.996501025283155
57	113.5	112.664074944611	117.3125	0.960375705441542	1.00741962383129
58	113.4	114.059640377841	117.129166666667	0.97379366407036	0.994216706490955
59	115.1	116.049325323446	117.045833333333	0.991486172711087	0.991819639443826
60	144.2	141.244730873830	117.120833333333	1.20597443558004	1.02092303980394
61	100.9	103.312545506613	117.129166666667	0.882039447959413	0.976648087656902
62	103.2	104.084996598992	117.179166666667	0.888255135787721	0.991497366307253
63	121.3	120.259764412637	117.275	1.02545098625144	1.00864990541469
64	111.9	112.220458460758	117.283333333333	0.956832102834369	0.997144384676796
65	117.3	119.704379734503	117.241666666667	1.02100544232997	0.979914020357182
66	124.2	123.780341058093	117.2	1.05614625476188	1.00339035212151
67	122	120.159205520324	117.279166666667	1.02455712242434	1.01531962925108
68	119.6	119.15058940908	117.495833333333	1.01408352984784	1.00377178655304
69	114.9	113.044223661348	117.708333333333	0.960375705441542	1.01641637474738
70	112.2	114.834617835497	117.925	0.97379366407036	0.97705728564124
71	115.3	117.321732578426	118.329166666667	0.991486172711087	0.982767620849153
72	143	143.465733792690	118.9625	1.20597443558004	0.99675369316158
73	104	105.352261730019	119.441666666667	0.882039447959413	0.9871643787441
74	105.3	106.442573771895	119.833333333333	0.888255135787721	0.989265819761707
75	124.3	123.331844658949	120.270833333333	1.02545098625144	1.00785000292283
76	114.1	115.433819606110	120.641666666667	0.956832102834369	0.988445157487979
77	124.8	123.554421089956	121.0125	1.02100544232997	1.01008121683592
78	131.9	128.268962640831	121.45	1.05614625476188	1.02830799660660
79	125.8	124.902051199547	121.908333333333	1.02455712242434	1.00718922381041
80	125.2	124.035091744514	122.3125	1.01408352984784	1.00939176356547
81	119.8	118.074191418598	122.945833333333	0.960375705441542	1.01461630658374
82	116.2	120.210770355886	123.445833333333	0.97379366407036	0.966635515736138
83	120.2	122.617921217657	123.670833333333	0.991486172711087	0.980280849702505
84	148.6	149.706651496817	124.1375	1.20597443558004	0.99260786687998
85	109.4	109.755108641083	124.433333333333	0.882039447959413	0.996764536562537
86	109.6	110.739507991268	124.670833333333	0.888255135787721	0.989710013960345
87	135.2	128.108737169904	124.929166666667	1.02545098625144	1.05535346758349
88	115.2	119.974785294144	125.3875	0.956832102834369	0.960201760041179
89	129.1	128.791328171240	126.141666666667	1.02100544232997	1.00239668177309
90	138.8	133.703715243459	126.595833333333	1.05614625476188	1.03811625389213
91	126	130.067526691769	126.95	1.02455712242434	0.968727577165294
92	130.7	129.359030276215	127.5625	1.01408352984784	1.01036626295761
93	120.5	122.732013589990	127.795833333333	0.960375705441542	0.98181392511455
94	126.5	124.686163737009	128.041666666667	0.97379366407036	1.01454721364928
95	128	127.674500698484	128.770833333333	0.991486172711087	1.00254944644181
96	151.7	155.605876444196	129.029166666667	1.20597443558004	0.974898914273352
97	114.8	114.150605223414	129.416666666667	0.882039447959413	1.00568892977234
98	118.9	115.384342138825	129.9	0.888255135787721	1.03046910695166
99	131.5	133.466718573067	130.154166666667	1.02545098625144	0.985264352086466
100	124.8	124.882536621599	130.516666666667	0.956832102834369	0.999339085961642
101	137	133.270989549463	130.529166666667	1.02100544232997	1.02798066153139
102	137.1	137.998710012824	130.6625	1.05614625476188	0.993487547726055
103	137	NA	NA	1.02455712242434	NA
104	131.3	NA	NA	1.01408352984784	NA
105	126	NA	NA	0.960375705441542	NA
106	129.7	NA	NA	0.97379366407036	NA
107	125.1	NA	NA	0.991486172711087	NA
108	157.8	NA	NA	1.20597443558004	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jan/12/t1263309188o5yu30x89hd3tjz/1de0y1263309098.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/12/t1263309188o5yu30x89hd3tjz/1de0y1263309098.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/12/t1263309188o5yu30x89hd3tjz/21bi31263309098.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/12/t1263309188o5yu30x89hd3tjz/21bi31263309098.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/12/t1263309188o5yu30x89hd3tjz/3lta01263309098.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/12/t1263309188o5yu30x89hd3tjz/3lta01263309098.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/12/t1263309188o5yu30x89hd3tjz/4bd1i1263309098.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/12/t1263309188o5yu30x89hd3tjz/4bd1i1263309098.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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