Home » date » 2010 » Dec » 14 »

9.2

*Unverified author*

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 21:25:25 +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/t1292361807ubwy8nck2cx4d3u.htm/, Retrieved Tue, 14 Dec 2010 22:23:27 +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/t1292361807ubwy8nck2cx4d3u.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 «

89,3 88,1 93,6 79,7 83,8 62,3 62,3 77,6 80,3 97 94 75,1 74 77,6 75,1 85 75,4 63,2 64,7 77 82,6 97,6 99 75,3 71,6 76,8 83,9 79,7 77,5 73,1 65,6 85,2 98,3 98 100,6 84,1 76,7 82,4 95,5 79,9 82,4 83,6 73,1 91,1 118,6 102,9 111,8 93,9 91,6 92 91,1 97,5 94,7 96,7 78,7 103,5 113,8 106,1 120,3 114,2 106,3 98,8 113,1 97,7 116,3 107,2 94,5 123,5 126,6 126,5 141,4 124,3 124,9 108,9 126,7 107,7 121,8 118,3 122,8 149,5 147 139,3 162,1 142,2 141,4 124,7 114 126,6 121,9 125,1 122,1 135,9 148,4 137,5 145,3 139,9 128,2 115,4 124,7 111,5 121,1 122,5 127,4 143,7 157,8 148,8 162,9 153,9

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	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	89.3	NA	NA	0.970572002616154	NA
2	88.1	NA	NA	0.934106720975305	NA
3	93.6	NA	NA	0.984777066169892	NA
4	79.7	NA	NA	0.937098356954485	NA
5	83.8	NA	NA	0.952258735620644	NA
6	62.3	NA	NA	0.914458967391912	NA
7	62.3	67.6659251318622	81.2875	0.832427189074116	0.920699744791703
8	77.6	82.4240294466349	80.2125	1.02757088292517	0.941473020925794
9	80.3	88.4910827167168	79.0041666666667	1.12008121154011	0.907436066265133
10	97	87.7357070325585	78.4541666666667	1.11830525719975	1.10559318754910
11	94	93.1525231907188	78.325	1.18930766920803	1.00909773326855
12	75.1	79.4975412945603	78.0125	1.01903594032444	0.944683304377097
13	74	75.8502020044525	78.15	0.970572002616154	0.975607157851157
14	77.6	73.0704982482932	78.225	0.934106720975305	1.06198810546379
15	75.1	77.1039410433268	78.2958333333333	0.984777066169892	0.974009875290282
16	85	73.4841294911809	78.4166666666667	0.937098356954485	1.15671234848337
17	75.4	74.8951495565636	78.65	0.952258735620644	1.00674076287217
18	63.2	72.1203305616421	78.8666666666667	0.914458967391912	0.876313232452286
19	64.7	65.5744518193135	78.775	0.832427189074116	0.986664748311995
20	77	80.8098868513733	78.6416666666667	1.02757088292517	0.952853703923871
21	82.6	88.4584136813802	78.975	1.12008121154011	0.933772114629122
22	97.6	88.4812438706917	79.1208333333333	1.11830525719975	1.10305863401553
23	99	93.940439521569	78.9875	1.18930766920803	1.05385923787667
24	75.3	81.0006193065389	79.4875	1.01903594032444	0.929622521959178
25	71.6	77.5850994591288	79.9375	0.970572002616154	0.922857616979898
26	76.8	75.0243381396666	80.3166666666666	0.934106720975305	1.02366781106456
27	83.9	80.0746851929393	81.3125	0.984777066169892	1.04777183697749
28	79.7	76.8264469643186	81.9833333333333	0.937098356954485	1.03740317493813
29	77.5	78.1487002366008	82.0666666666667	0.952258735620644	0.991699155140945
30	73.1	75.4428648098327	82.5	0.914458967391912	0.968945177045724
31	65.6	69.1573571789533	83.0791666666667	0.832427189074116	0.948561406565201
32	85.2	85.8278579963245	83.525	1.02757088292517	0.992684682910863
33	98.3	94.3575080621581	84.2416666666667	1.12008121154011	1.04178249318798
34	98	94.7577321267252	84.7333333333333	1.11830525719975	1.03421639374968
35	100.6	101.026731050600	84.9458333333333	1.18930766920803	0.995776058017887
36	84.1	87.216738542518	85.5875	1.01903594032444	0.96426444516727
37	76.7	83.7967602758722	86.3375	0.970572002616154	0.915309849062081
38	82.4	81.1699819414166	86.8958333333333	0.934106720975305	1.01515360764120
39	95.5	86.6480721096234	87.9875	0.984777066169892	1.10215954809909
40	79.9	83.436894957335	89.0375	0.937098356954485	0.957609940313053
41	82.4	85.4255440746353	89.7083333333333	0.952258735620644	0.964582677144065
42	83.6	82.8347414629173	90.5833333333333	0.914458967391912	1.00923837659861
43	73.1	76.2607358590525	91.6125	0.832427189074116	0.95855356202051
44	91.1	95.1873161216346	92.6333333333333	1.02757088292517	0.957060286095138
45	118.6	103.999540491499	92.85	1.12008121154011	1.14038965402635
46	102.9	104.449711022456	93.4	1.11830525719975	0.985163089420868
47	111.8	112.563015441918	94.6458333333333	1.18930766920803	0.993221437441751
48	93.9	97.5259854721336	95.7041666666667	1.01903594032444	0.962820314456914
49	91.6	93.6440220524153	96.4833333333333	0.970572002616154	0.978172423528848
50	92	90.8263101694989	97.2333333333333	0.934106720975305	1.01292235507873
51	91.1	96.065002804873	97.55	0.984777066169892	0.948316216521038
52	97.5	91.351471497113	97.4833333333333	0.937098356954485	1.06730628858104
53	94.7	93.2935818777008	97.9708333333333	0.952258735620644	1.01507518624532
54	96.7	90.6876578453954	99.1708333333333	0.914458967391912	1.06629724813110
55	78.7	83.7664543472041	100.629166666667	0.832427189074116	0.939516905822418
56	103.5	104.324133888978	101.525	1.02757088292517	0.99210025659207
57	113.8	115.060342455458	102.725	1.12008121154011	0.989046248007251
58	106.1	115.912339908754	103.65	1.11830525719975	0.915346891310467
59	120.3	124.352027712943	104.558333333333	1.18930766920803	0.96741486417659
60	114.2	107.911660097273	105.895833333333	1.01903594032444	1.05827303460125
61	106.3	103.843116179907	106.991666666667	0.970572002616154	1.02365957331092
62	98.8	101.335010780471	108.483333333333	0.934106720975305	0.97498386035639
63	113.1	108.177760718763	109.85	0.984777066169892	1.04550139740860
64	97.7	104.236573905237	111.233333333333	0.937098356954485	0.937290975131438
65	116.3	107.569527422547	112.9625	0.952258735620644	1.08116120602779
66	107.2	104.488367761618	114.2625	0.914458967391912	1.02595152260937
67	94.5	96.110655871849	115.458333333333	0.832427189074116	0.983241651435647
68	123.5	119.870425038566	116.654166666667	1.02757088292517	1.03027915317949
69	126.6	131.768220527598	117.641666666667	1.12008121154011	0.960777943976899
70	126.5	132.65896113532	118.625	1.11830525719975	0.9535729732646
71	141.4	141.849716796166	119.270833333333	1.18930766920803	0.996829624998039
72	124.3	122.246098991171	119.9625	1.01903594032444	1.01680136238112
73	124.9	118.025599568135	121.604166666667	0.970572002616154	1.05824499478942
74	108.9	115.704685838141	123.866666666667	0.934106720975305	0.941189193947943
75	126.7	123.884954924172	125.8	0.984777066169892	1.02272305848237
76	107.7	119.183292698661	127.183333333333	0.937098356954485	0.903650147276135
77	121.8	122.440634677156	128.579166666667	0.952258735620644	0.994767793560976
78	118.3	119.051126817335	130.1875	0.914458967391912	0.993690720638982
79	122.8	109.564760315259	131.620833333333	0.832427189074116	1.12079832645695
80	149.5	136.632675066283	132.966666666667	1.02757088292517	1.09417458106178
81	147	149.078142250941	133.095833333333	1.12008121154011	0.986060047304303
82	139.3	149.130665652825	133.354166666667	1.11830525719975	0.93408018659482
83	162.1	159.540668375635	134.145833333333	1.18930766920803	1.01604187603338
84	142.2	136.992398244282	134.433333333333	1.01903594032444	1.03801380092954
85	141.4	130.723916602363	134.6875	0.970572002616154	1.08166893767505
86	124.7	125.255927060114	134.091666666667	0.934106720975305	0.995561670627795
87	114	131.549803089195	133.583333333333	0.984777066169892	0.86659194710238
88	126.6	125.165103877221	133.566666666667	0.937098356954485	1.01146402693986
89	121.9	126.452024600958	132.791666666667	0.952258735620644	0.964001963469366
90	125.1	120.704773450035	131.995833333333	0.914458967391912	1.03641303010924
91	122.1	109.339311284885	131.35	0.832427189074116	1.11670723516693
92	135.9	134.008087769478	130.4125	1.02757088292517	1.01411789588235
93	148.4	146.137929070648	130.470833333333	1.12008121154011	1.01547901317432
94	137.5	145.701196197412	130.2875	1.11830525719975	0.943712224666295
95	145.3	154.164006621091	129.625	1.18930766920803	0.94250274875849
96	139.9	131.948170339676	129.483333333333	1.01903594032444	1.06026479669899
97	128.2	125.782087489043	129.595833333333	0.970572002616154	1.01922302737397
98	115.4	121.566205512261	130.141666666667	0.934106720975305	0.949276976390949
99	124.7	128.866285583882	130.858333333333	0.984777066169892	0.967669700690101
100	111.5	123.435376493342	131.720833333333	0.937098356954485	0.903306678908327
101	121.1	126.578992432374	132.925	0.952258735620644	0.95671483611073
102	122.5	122.758495880969	134.241666666667	0.914458967391912	0.997894272986043
103	127.4	NA	NA	0.832427189074116	NA
104	143.7	NA	NA	1.02757088292517	NA
105	157.8	NA	NA	1.12008121154011	NA
106	148.8	NA	NA	1.11830525719975	NA
107	162.9	NA	NA	1.18930766920803	NA
108	153.9	NA	NA	1.01903594032444	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292361807ubwy8nck2cx4d3u/1nc851292361921.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292361807ubwy8nck2cx4d3u/1nc851292361921.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292361807ubwy8nck2cx4d3u/2nc851292361921.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292361807ubwy8nck2cx4d3u/2nc851292361921.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292361807ubwy8nck2cx4d3u/3xlpq1292361921.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292361807ubwy8nck2cx4d3u/3xlpq1292361921.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292361807ubwy8nck2cx4d3u/4xlpq1292361921.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292361807ubwy8nck2cx4d3u/4xlpq1292361921.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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