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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, 14 Dec 2010 16:04:07 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342623fd8i4vstpvd93gs.htm/, Retrieved Tue, 14 Dec 2010 17:03:44 +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/Dec/14/t1292342623fd8i4vstpvd93gs.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:

KDGP2W92

Dataseries X:

» Textbox « » Textfile « » CSV «

97 100,7 101,4 101,5 101,8 101,5 102,2 101,8 98,5 98,4 97,5 97,7 98,3 99,6 99,4 96,7 96,9 96,1 97,9 99,2 97,8 94,9 93,3 91,5 89,1 92,3 91,8 92,1 94,4 92,8 92,6 92,3 92,1 89,8 87,4 87,7 86,3 89,1 90,4 87,1 86,7 84,4 88,4 88,9 88,5 87,2 86,2 83,4 87,5 85,7 87,4 86,8 87,9 85,9 87,7 87 86,8 86,2 86,1 87,5 85,7 88,9 89,8 91,4 95,2 94,1 96,8 96,1 96,6 94,2 93,9 96,5 93,4 95 95,2 94 97 96,9 96,3 96,3 97,3 95,7 96,4 95,1 94,6 95,9 96,2 94,3 98,3 95,9 92,1 94,6 94,7 96,7 97,5 96,2 97,1 95,9 94,5 99,4 101,3 101,4 100,9 101,4 103,1 102,4 101,1 102 103,9 101,7 101,2 101,9 101,1 103,1 103,3 101,4 102,8 103 102,6 102,2

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	3 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	97	NA	NA	0.986147503752455	NA
2	100.7	NA	NA	0.996202677715282	NA
3	101.4	NA	NA	0.998625060712167	NA
4	101.5	NA	NA	0.995219215413027	NA
5	101.8	NA	NA	1.01267669230203	NA
6	101.5	NA	NA	1.00190558733225	NA
7	102.2	101.030275125606	100.054166666667	1.00975580019762	1.01157796386222
8	101.8	101.310655960317	100.0625	1.01247376350098	1.00483013395821
9	98.5	100.923485556894	99.9333333333333	1.00990812765404	0.975986901923561
10	98.4	99.4550568043595	99.65	0.998043721067331	0.989391622324092
11	97.5	98.3260135486921	99.2458333333333	0.990731905272518	0.991599236876587
12	97.7	97.6614944063518	98.8166666666667	0.988309945080302	1.00039427610526
13	98.3	97.0492412130385	98.4125	0.986147503752455	1.01288787806404
14	99.6	97.752387750812	98.125	0.996202677715282	1.01890094238821
15	99.4	97.8527731365335	97.9875	0.998625060712167	1.01581178349752
16	96.7	97.3448795075867	97.8125	0.995219215413027	0.99337531146118
17	96.9	98.7275385270116	97.4916666666667	1.01267669230203	0.981489070280916
18	96.1	97.2432864638225	97.0583333333333	1.00190558733225	0.988243029360718
19	97.9	97.3572884023871	96.4166666666667	1.00975580019762	1.00557443214082
20	99.2	96.923269651813	95.7291666666667	1.01247376350098	1.02349002831174
21	97.8	96.050678840963	95.1083333333333	1.00990812765404	1.01821248095428
22	94.9	94.4149360129695	94.6	0.998043721067331	1.00513757682327
23	93.3	93.4301467168037	94.3041666666666	0.990731905272518	0.998607015814733
24	91.5	92.9629042091158	94.0625	0.988309945080302	0.98426357027503
25	89.1	92.4061300495373	93.7041666666667	0.986147503752455	0.964221745378094
26	92.3	92.8419387185738	93.1958333333333	0.996202677715282	0.994162781108907
27	91.8	92.5434165637471	92.6708333333333	0.998625060712167	0.991966834688505
28	92.1	91.7799453947356	92.2208333333333	0.995219215413027	1.00348719542039
29	94.4	92.9257449773646	91.7625	1.01267669230203	1.01586487171014
30	92.8	91.5324246160287	91.3583333333333	1.00190558733225	1.01384837547228
31	92.6	91.9719241346665	91.0833333333333	1.00975580019762	1.00682899560103
32	92.3	91.9663668513393	90.8333333333333	1.01247376350098	1.00362777350115
33	92.1	91.539755870775	90.6416666666667	1.00990812765404	1.00612022747817
34	89.8	90.19820129146	90.375	0.998043721067331	0.995585263500174
35	87.4	89.0131336391304	89.8458333333333	0.990731905272518	0.981877577238543
36	87.7	88.1325393525359	89.175	0.988309945080302	0.995092171907067
37	86.3	87.4219762076551	88.65	0.986147503752455	0.987165970659482
38	89.1	87.9979031981832	88.3333333333333	0.996202677715282	1.01252412570939
39	90.4	87.9206147202004	88.0416666666667	0.998625060712167	1.02820027234443
40	87.1	87.3636601263402	87.7833333333333	0.995219215413027	0.99698203891688
41	86.7	88.735795162965	87.625	1.01267669230203	0.977057790948667
42	84.4	87.5623737262246	87.3958333333333	1.00190558733225	0.96388433077303
43	88.4	88.118022830579	87.2666666666667	1.00975580019762	1.00319999428452
44	88.9	88.2624003331982	87.175	1.01247376350098	1.00722391034455
45	88.5	87.7694321941999	86.9083333333333	1.00990812765404	1.00832371575771
46	87.2	86.6010853801132	86.7708333333333	0.998043721067331	1.00691578653152
47	86.2	86.0037854768651	86.8083333333333	0.990731905272518	1.00228146379891
48	83.4	85.9047240180007	86.9208333333333	0.988309945080302	0.970843000235053
49	87.5	85.7496343992083	86.9541666666667	0.986147503752455	1.02041251386149
50	85.7	86.5160517150817	86.8458333333333	0.996202677715282	0.990567626481971
51	87.4	86.576631825992	86.6958333333333	0.998625060712167	1.00951028189296
52	86.8	86.1693970678446	86.5833333333333	0.995219215413027	1.00731817737635
53	87.9	87.6345092600865	86.5375	1.01267669230203	1.00302952275485
54	85.9	86.8693890283198	86.7041666666667	1.00190558733225	0.988840844408336
55	87.7	87.6468034571534	86.8	1.00975580019762	1.00060694219011
56	87	87.941783641423	86.8583333333333	1.01247376350098	0.989290828518296
57	86.8	87.9545820176031	87.0916666666667	1.00990812765404	0.986872974765862
58	86.2	87.212387159267	87.3833333333333	0.998043721067331	0.988391704524518
59	86.1	87.0646942254278	87.8791666666667	0.990731905272518	0.988919799994588
60	87.5	87.4901378882337	88.525	0.988309945080302	1.0001127225537
61	85.7	88.0095557619743	89.2458333333333	0.986147503752455	0.973757897742143
62	88.9	89.6623918388658	90.0041666666667	0.996202677715282	0.99149708341223
63	89.8	90.6668336371588	90.7916666666667	0.998625060712167	0.990439352491035
64	91.4	91.0957321841391	91.5333333333333	0.995219215413027	1.00334008859214
65	95.2	93.360352057811	92.1916666666667	1.01267669230203	1.01970480939328
66	94.1	93.0686798499381	92.8916666666667	1.00190558733225	1.01108128053094
67	96.8	94.5005209509947	93.5875	1.00975580019762	1.02433297748906
68	96.1	95.3370607556614	94.1625	1.01247376350098	1.00800254631611
69	96.6	95.5793883813911	94.6416666666667	1.00990812765404	1.01067815599046
70	94.2	94.7892024083698	94.975	0.998043721067331	0.993784076736595
71	93.9	94.2763968858907	95.1583333333333	0.990731905272518	0.996007517275546
72	96.5	94.2353532634067	95.35	0.988309945080302	1.02403181670326
73	93.4	94.1236702852395	95.4458333333333	0.986147503752455	0.992311495258882
74	95	95.0709422099617	95.4333333333333	0.996202677715282	0.999253797129674
75	95.2	95.3395667337412	95.4708333333333	0.998625060712167	0.998536108999415
76	94	95.1056362729074	95.5625	0.995219215413027	0.98837465037577
77	97	96.9426958568293	95.7291666666667	1.01267669230203	1.00059111357142
78	96.9	95.9575076267461	95.775	1.00190558733225	1.00982197637854
79	96.3	96.7009471322587	95.7666666666667	1.00975580019762	0.99585374141465
80	96.3	97.0498288722505	95.8541666666667	1.01247376350098	0.992273774400596
81	97.3	96.8838530462776	95.9333333333333	1.00990812765404	1.00429531795689
82	95.7	95.7997216759505	95.9875	0.998043721067331	0.99895906090116
83	96.4	95.1639275510306	96.0541666666667	0.990731905272518	1.01298887594048
84	95.1	94.943642057381	96.0666666666667	0.988309945080302	1.00164685006
85	94.6	94.5222382346728	95.85	0.986147503752455	1.00082268222568
86	95.9	95.2411268340714	95.6041666666667	0.996202677715282	1.0069179480318
87	96.2	95.2937964184585	95.425	0.998625060712167	1.00950957581291
88	94.3	94.902445683094	95.3583333333333	0.995219215413027	0.993651947757957
89	98.3	96.6557707940104	95.4458333333333	1.01267669230203	1.01701118507961
90	95.9	95.7195550497547	95.5375	1.00190558733225	1.00188514196657
91	92.1	96.6210081314097	95.6875	1.00975580019762	0.953208849515823
92	94.6	96.9865492620317	95.7916666666667	1.01247376350098	0.975392987169964
93	94.7	96.669247569151	95.7208333333333	1.00990812765404	0.979629017307263
94	96.7	95.674966210817	95.8625	0.998043721067331	1.01071370944542
95	97.5	95.3084092872162	96.2	0.990731905272518	1.02299472553549
96	96.2	95.4254431556076	96.5541666666667	0.988309945080302	1.00811687972074
97	97.1	95.804229989551	97.15	0.986147503752455	1.01352518579389
98	95.9	97.4286218805545	97.8	0.996202677715282	0.984310340728943
99	94.5	98.297993476101	98.4333333333333	0.998625060712167	0.961362451645319
100	99.4	98.547436059544	99.0208333333333	0.995219215413027	1.00865130514345
101	101.3	100.668502187257	99.4083333333333	1.01267669230203	1.00627304269977
102	101.4	99.9901776157584	99.8	1.00190558733225	1.01409960876017
103	100.9	101.303750654826	100.325	1.00975580019762	0.996014455020507
104	101.4	102.107979049074	100.85	1.01247376350098	0.993066368998118
105	103.1	102.375228490396	101.370833333333	1.00990812765404	1.00707955938454
106	102.4	101.555107134105	101.754166666667	0.998043721067331	1.00831955073199
107	101.1	100.906044552006	101.85	0.990731905272518	1.00192213904385
108	102	100.721137277996	101.9125	0.988309945080302	1.01269706395862
109	103.9	100.669224341396	102.083333333333	0.986147503752455	1.03209298253503
110	101.7	101.79531028454	102.183333333333	0.996202677715282	0.999063706527605
111	101.2	102.030354640513	102.170833333333	0.998625060712167	0.99186169014664
112	101.9	101.694816828288	102.183333333333	0.995219215413027	1.00201763647462
113	101.1	103.567289218972	102.270833333333	1.01267669230203	0.97617694508007
114	103.1	102.536687650228	102.341666666667	1.00190558733225	1.00549376386815
115	103.3	NA	NA	1.00975580019762	NA
116	101.4	NA	NA	1.01247376350098	NA
117	102.8	NA	NA	1.00990812765404	NA
118	103	NA	NA	0.998043721067331	NA
119	102.6	NA	NA	0.990731905272518	NA
120	102.2	NA	NA	0.988309945080302	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342623fd8i4vstpvd93gs/1gtk71292342643.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342623fd8i4vstpvd93gs/1gtk71292342643.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342623fd8i4vstpvd93gs/2gtk71292342643.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342623fd8i4vstpvd93gs/2gtk71292342643.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342623fd8i4vstpvd93gs/3qk1s1292342643.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342623fd8i4vstpvd93gs/3qk1s1292342643.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342623fd8i4vstpvd93gs/4qk1s1292342643.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342623fd8i4vstpvd93gs/4qk1s1292342643.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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