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Omzet Product X

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Sat, 24 Jul 2010 10:08:07 +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/Jul/24/t12799660840p52lvwyboghj0h.htm/, Retrieved Sat, 24 Jul 2010 12:08:10 +0200

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/Jul/24/t12799660840p52lvwyboghj0h.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:

Febiri Lordina

Dataseries X:

» Textbox « » Textfile « » CSV «

213 212 211 209 229 228 213 203 204 204 205 207 205 208 201 201 220 218 203 185 179 182 182 185 183 192 177 172 188 182 162 150 141 135 139 148 142 156 143 134 146 142 117 106 104 99 105 106 96 104 96 85 91 98 73 70 62 60 70 82 72 73 68 53 61 73 46 50 52 45 58 73 58 49 44 35 46 61 29 33 37 31 44 57 42 34 27 22 30 47 12 13 18 11 26 41 21 24 30 34 48 64 35 44 55 53 73 94 73 78 87 87 91 104 73 84 103 111 131 155

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	213	NA	NA	0.653163580246913	NA
2	212	NA	NA	4.74112654320987	NA
3	211	NA	NA	0.759645061728397	NA
4	209	NA	NA	-3.89776234567901	NA
5	229	NA	NA	7.76427469135803	NA
6	228	NA	NA	15.9031635802469	NA
7	213	204.356867283951	211.166666666667	-6.80979938271605	8.6431327160494
8	203	201.125385802469	210.666666666667	-9.54128086419753	1.87461419753086
9	204	201.514274691358	210.083333333333	-8.56905864197531	2.48572530864200
10	204	198.347608024691	209.333333333333	-10.9857253086420	5.65239197530863
11	205	207.954089506173	208.625	-0.670910493827154	-2.95408950617281
12	207	218.486496913580	207.833333333333	10.6531635802469	-11.4864969135802
13	205	207.653163580247	207	0.653163580246913	-2.65316358024691
14	208	210.574459876543	205.833333333333	4.74112654320987	-2.57445987654322
15	201	204.801311728395	204.041666666667	0.759645061728397	-3.80131172839506
16	201	198.185570987654	202.083333333333	-3.89776234567901	2.81442901234567
17	220	207.972608024691	200.208333333333	7.76427469135803	12.0273919753086
18	218	214.236496913580	198.333333333333	15.9031635802469	3.76350308641977
19	203	189.690200617284	196.5	-6.80979938271605	13.3097993827161
20	185	185.375385802469	194.916666666667	-9.54128086419753	-0.375385802469111
21	179	184.680941358025	193.25	-8.56905864197531	-5.68094135802468
22	182	180.055941358025	191.041666666667	-10.9857253086420	1.94405864197532
23	182	187.829089506173	188.5	-0.670910493827154	-5.82908950617281
24	185	196.319830246914	185.666666666667	10.6531635802469	-11.3198302469136
25	183	183.111496913580	182.458333333333	0.653163580246913	-0.111496913580226
26	192	184.032793209877	179.291666666667	4.74112654320987	7.96720679012347
27	177	177.009645061728	176.25	0.759645061728397	-0.00964506172837787
28	172	168.810570987654	172.708333333333	-3.89776234567901	3.1894290123457
29	188	176.722608024691	168.958333333333	7.76427469135803	11.2773919753087
30	182	181.528163580247	165.625	15.9031635802469	0.47183641975306
31	162	155.565200617284	162.375	-6.80979938271605	6.43479938271605
32	150	149.625385802469	159.166666666667	-9.54128086419753	0.37461419753086
33	141	147.680941358025	156.25	-8.56905864197531	-6.68094135802468
34	135	142.264274691358	153.25	-10.9857253086420	-7.264274691358
35	139	149.245756172839	149.916666666667	-0.670910493827154	-10.2457561728395
36	148	157.153163580247	146.5	10.6531635802469	-9.15316358024691
37	142	143.611496913580	142.958333333333	0.653163580246913	-1.61149691358023
38	156	143.99112654321	139.25	4.74112654320987	12.0088734567901
39	143	136.634645061728	135.875	0.759645061728397	6.3653549382716
40	134	128.935570987654	132.833333333333	-3.89776234567901	5.0644290123457
41	146	137.680941358025	129.916666666667	7.76427469135803	8.31905864197532
42	142	142.653163580247	126.75	15.9031635802469	-0.653163580246911
43	117	116.273533950617	123.083333333333	-6.80979938271605	0.726466049382708
44	106	109.458719135802	119	-9.54128086419753	-3.45871913580247
45	104	106.305941358025	114.875	-8.56905864197531	-2.30594135802470
46	99	99.889274691358	110.875	-10.9857253086420	-0.889274691358011
47	105	105.870756172840	106.541666666667	-0.670910493827154	-0.870756172839506
48	106	113.069830246914	102.416666666667	10.6531635802469	-7.06983024691358
49	96	99.4031635802469	98.75	0.653163580246913	-3.40316358024691
50	104	100.157793209877	95.4166666666667	4.74112654320987	3.84220679012346
51	96	92.926311728395	92.1666666666667	0.759645061728397	3.07368827160495
52	85	84.8939043209877	88.7916666666667	-3.89776234567901	0.106095679012341
53	91	93.4726080246914	85.7083333333333	7.76427469135803	-2.47260802469135
54	98	99.1531635802469	83.25	15.9031635802469	-1.15316358024691
55	73	74.440200617284	81.25	-6.80979938271605	-1.44020061728395
56	70	69.4170524691358	78.9583333333333	-9.54128086419753	0.582947530864203
57	62	67.9309413580247	76.5	-8.56905864197531	-5.93094135802468
58	60	63.014274691358	74	-10.9857253086420	-3.01427469135803
59	70	70.7457561728395	71.4166666666667	-0.670910493827154	-0.74575617283952
60	82	79.778163580247	69.125	10.6531635802469	2.22183641975309
61	72	67.6114969135803	66.9583333333333	0.653163580246913	4.38850308641975
62	73	69.7411265432099	65	4.74112654320987	3.25887345679013
63	68	64.5096450617284	63.75	0.759645061728397	3.49035493827161
64	53	58.8105709876543	62.7083333333333	-3.89776234567901	-5.81057098765432
65	61	69.3476080246914	61.5833333333333	7.76427469135803	-8.34760802469135
66	73	76.6114969135802	60.7083333333333	15.9031635802469	-3.61149691358024
67	46	52.9402006172839	59.75	-6.80979938271605	-6.94020061728394
68	50	48.6253858024691	58.1666666666667	-9.54128086419753	1.37461419753087
69	52	47.5976080246914	56.1666666666667	-8.56905864197531	4.40239197530864
70	45	43.4309413580247	54.4166666666667	-10.9857253086420	1.56905864197530
71	58	52.3707561728395	53.0416666666667	-0.670910493827154	5.62924382716049
72	73	62.5698302469136	51.9166666666667	10.6531635802469	10.4301697530864
73	58	51.3614969135802	50.7083333333333	0.653163580246913	6.63850308641976
74	49	54.0327932098765	49.2916666666667	4.74112654320987	-5.03279320987654
75	44	48.7179783950617	47.9583333333333	0.759645061728397	-4.71797839506173
76	35	42.852237654321	46.75	-3.89776234567901	-7.852237654321
77	46	53.3476080246914	45.5833333333333	7.76427469135803	-7.34760802469136
78	61	60.2364969135802	44.3333333333333	15.9031635802469	0.763503086419753
79	29	36.1902006172839	43	-6.80979938271605	-7.19020061728394
80	33	32.1670524691358	41.7083333333333	-9.54128086419753	0.83294753086421
81	37	31.8059413580247	40.375	-8.56905864197531	5.19405864197531
82	31	28.1392746913580	39.125	-10.9857253086420	2.86072530864197
83	44	37.2457561728395	37.9166666666667	-0.670910493827154	6.75424382716049
84	57	47.3198302469136	36.6666666666667	10.6531635802469	9.68016975308642
85	42	36.0281635802469	35.375	0.653163580246913	5.97183641975309
86	34	38.5744598765432	33.8333333333333	4.74112654320987	-4.57445987654321
87	27	32.9679783950617	32.2083333333333	0.759645061728397	-5.96797839506173
88	22	26.6855709876543	30.5833333333333	-3.89776234567901	-4.68557098765432
89	30	36.764274691358	29	7.76427469135803	-6.76427469135803
90	47	43.4864969135802	27.5833333333333	15.9031635802469	3.51350308641976
91	12	19.2318672839506	26.0416666666667	-6.80979938271605	-7.23186728395061
92	13	15.2087191358025	24.75	-9.54128086419753	-2.20871913580247
93	18	15.8892746913580	24.4583333333333	-8.56905864197531	2.11072530864198
94	11	14.0976080246914	25.0833333333333	-10.9857253086420	-3.09760802469136
95	26	25.6624228395062	26.3333333333333	-0.670910493827154	0.337577160493822
96	41	38.4448302469136	27.7916666666667	10.6531635802469	2.55516975308642
97	21	30.1114969135803	29.4583333333333	0.653163580246913	-9.11149691358025
98	24	36.4494598765432	31.7083333333333	4.74112654320987	-12.4494598765432
99	30	35.3013117283951	34.5416666666667	0.759645061728397	-5.30131172839506
100	34	33.9355709876543	37.8333333333333	-3.89776234567901	0.0644290123456841
101	48	49.3059413580247	41.5416666666667	7.76427469135803	-1.30594135802468
102	64	61.6114969135803	45.7083333333333	15.9031635802469	2.38850308641975
103	35	43.2735339506173	50.0833333333333	-6.80979938271605	-8.27353395061728
104	44	44.9587191358025	54.5	-9.54128086419753	-0.958719135802468
105	55	50.5559413580247	59.125	-8.56905864197531	4.44405864197531
106	53	52.7226080246914	63.7083333333333	-10.9857253086420	0.277391975308646
107	73	67.0374228395062	67.7083333333333	-0.670910493827154	5.96257716049382
108	94	81.8198302469136	71.1666666666667	10.6531635802469	12.1801697530864
109	73	75.0698302469136	74.4166666666667	0.653163580246913	-2.06983024691358
110	78	82.4077932098765	77.6666666666667	4.74112654320987	-4.40779320987653
111	87	82.0929783950617	81.3333333333333	0.759645061728397	4.90702160493827
112	87	81.852237654321	85.75	-3.89776234567901	5.14776234567903
113	91	98.3476080246914	90.5833333333333	7.76427469135803	-7.34760802469135
114	104	111.444830246914	95.5416666666667	15.9031635802469	-7.44483024691357
115	73	NA	NA	-6.80979938271605	NA
116	84	NA	NA	-9.54128086419753	NA
117	103	NA	NA	-8.56905864197531	NA
118	111	NA	NA	-10.9857253086420	NA
119	131	NA	NA	-0.670910493827154	NA
120	155	NA	NA	10.6531635802469	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jul/24/t12799660840p52lvwyboghj0h/1rko01279966085.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/24/t12799660840p52lvwyboghj0h/1rko01279966085.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/24/t12799660840p52lvwyboghj0h/2rko01279966085.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/24/t12799660840p52lvwyboghj0h/2rko01279966085.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/24/t12799660840p52lvwyboghj0h/32bn31279966085.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/24/t12799660840p52lvwyboghj0h/32bn31279966085.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/24/t12799660840p52lvwyboghj0h/4uk4o1279966085.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/24/t12799660840p52lvwyboghj0h/4uk4o1279966085.ps (open in new window)

Parameters (Session):

par1 = 48 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;

Parameters (R input):

par1 = additive ; par2 = 12 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;

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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