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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: Thu, 05 Aug 2010 14:11:00 +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/Aug/05/t12810179409v3wyw1xi2wb7wy.htm/, Retrieved Thu, 05 Aug 2010 16:19:06 +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/Aug/05/t12810179409v3wyw1xi2wb7wy.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:

Philippe De Vocht

Dataseries X:

» Textbox « » Textfile « » CSV «

73 72 71 69 89 88 73 63 64 64 65 67 69 71 70 72 88 83 76 70 75 71 75 81 87 90 80 85 105 104 98 94 107 112 121 118 120 122 109 112 132 127 116 113 123 125 137 127 123 128 114 120 143 135 119 117 132 139 158 141 139 150 142 149 166 150 139 140 158 169 186 177 175 187 176 185 204 188 171 171 182 185 200 192 185 195 190 195 213 194 171 171 186 182 193 185 172 185 179 182 193 173 155 164 188 186 200 185 173 190 190 193 195 178 163 165 188 182 200 177

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	'George Udny Yule' @ 72.249.76.132

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	73	NA	NA	-2.59413580246913	NA
2	72	NA	NA	4.85030864197531	NA
3	71	NA	NA	-3.75154320987654	NA
4	69	NA	NA	-0.0941358024691316	NA
5	89	NA	NA	14.9567901234568	NA
6	88	NA	NA	1.93364197530864	NA
7	73	60.8873456790123	71.3333333333333	-10.4459876543210	12.1126543209877
8	63	58.0030864197531	71.125	-13.1219135802469	4.99691358024691
9	64	69.266975308642	71.0416666666667	-1.77469135802469	-5.26697530864196
10	64	70.2253086419753	71.125	-0.899691358024698	-6.22530864197532
11	65	80.5771604938272	71.2083333333333	9.36882716049383	-15.5771604938272
12	67	72.5308641975309	70.9583333333333	1.57253086419754	-5.53086419753087
13	69	68.2808641975309	70.875	-2.59413580246913	0.719135802469111
14	71	76.141975308642	71.2916666666667	4.85030864197531	-5.14197530864197
15	70	68.2901234567901	72.0416666666667	-3.75154320987654	1.70987654320989
16	72	72.6975308641975	72.7916666666667	-0.0941358024691316	-0.697530864197546
17	88	88.4567901234568	73.5	14.9567901234568	-0.456790123456784
18	83	76.4336419753086	74.5	1.93364197530864	6.56635802469137
19	76	65.3873456790124	75.8333333333333	-10.4459876543210	10.6126543209876
20	70	64.2530864197531	77.375	-13.1219135802469	5.74691358024691
21	75	76.8086419753086	78.5833333333333	-1.77469135802469	-1.80864197530865
22	71	78.641975308642	79.5416666666667	-0.899691358024698	-7.64197530864196
23	75	90.1604938271605	80.7916666666667	9.36882716049383	-15.1604938271605
24	81	83.9475308641975	82.375	1.57253086419754	-2.94753086419752
25	87	81.5725308641975	84.1666666666667	-2.59413580246913	5.42746913580245
26	90	90.9336419753087	86.0833333333333	4.85030864197531	-0.93364197530866
27	80	84.6651234567901	88.4166666666667	-3.75154320987654	-4.66512345679013
28	85	91.3641975308642	91.4583333333333	-0.0941358024691316	-6.3641975308642
29	105	110.04012345679	95.0833333333333	14.9567901234568	-5.04012345679011
30	104	100.475308641975	98.5416666666667	1.93364197530864	3.52469135802471
31	98	91.0123456790123	101.458333333333	-10.4459876543210	6.98765432098766
32	94	91.0447530864198	104.166666666667	-13.1219135802469	2.95524691358024
33	107	104.933641975309	106.708333333333	-1.77469135802469	2.06635802469137
34	112	108.141975308642	109.041666666667	-0.899691358024698	3.85802469135803
35	121	120.660493827160	111.291666666667	9.36882716049383	0.339506172839521
36	118	114.947530864198	113.375	1.57253086419754	3.05246913580248
37	120	112.489197530864	115.083333333333	-2.59413580246913	7.51080246913581
38	122	121.475308641975	116.625	4.85030864197531	0.524691358024697
39	109	114.331790123457	118.083333333333	-3.75154320987654	-5.33179012345677
40	112	119.197530864198	119.291666666667	-0.0941358024691316	-7.19753086419753
41	132	135.456790123457	120.5	14.9567901234568	-3.45679012345677
42	127	123.475308641975	121.541666666667	1.93364197530864	3.52469135802471
43	116	111.595679012346	122.041666666667	-10.4459876543210	4.40432098765433
44	113	109.294753086420	122.416666666667	-13.1219135802469	3.70524691358027
45	123	121.100308641975	122.875	-1.77469135802469	1.89969135802470
46	125	122.516975308642	123.416666666667	-0.899691358024698	2.48302469135803
47	137	133.577160493827	124.208333333333	9.36882716049383	3.42283950617283
48	127	126.572530864198	125	1.57253086419754	0.427469135802454
49	123	122.864197530864	125.458333333333	-2.59413580246913	0.135802469135797
50	128	130.600308641975	125.75	4.85030864197531	-2.60030864197532
51	114	122.540123456790	126.291666666667	-3.75154320987654	-8.54012345679013
52	120	127.155864197531	127.25	-0.0941358024691316	-7.15586419753087
53	143	143.66512345679	128.708333333333	14.9567901234568	-0.665123456790127
54	135	132.100308641975	130.166666666667	1.93364197530864	2.89969135802471
55	119	120.970679012346	131.416666666667	-10.4459876543210	-1.9706790123457
56	117	119.878086419753	133	-13.1219135802469	-2.87808641975309
57	132	133.308641975309	135.083333333333	-1.77469135802469	-1.30864197530866
58	139	136.558641975309	137.458333333333	-0.899691358024698	2.44135802469137
59	158	148.993827160494	139.625	9.36882716049383	9.00617283950615
60	141	142.780864197531	141.208333333333	1.57253086419754	-1.78086419753086
61	139	140.072530864198	142.666666666667	-2.59413580246913	-1.07253086419752
62	150	149.308641975309	144.458333333333	4.85030864197531	0.69135802469134
63	142	142.748456790123	146.5	-3.75154320987654	-0.74845679012347
64	149	148.739197530864	148.833333333333	-0.0941358024691316	0.260802469135825
65	166	166.206790123457	151.25	14.9567901234568	-0.206790123456784
66	150	155.850308641975	153.916666666667	1.93364197530864	-5.85030864197529
67	139	146.470679012346	156.916666666667	-10.4459876543210	-7.47067901234567
68	140	146.836419753086	159.958333333333	-13.1219135802469	-6.83641975308643
69	158	161.141975308642	162.916666666667	-1.77469135802469	-3.14197530864197
70	169	164.933641975309	165.833333333333	-0.899691358024698	4.06635802469137
71	186	178.285493827160	168.916666666667	9.36882716049383	7.71450617283952
72	177	173.655864197531	172.083333333333	1.57253086419754	3.34413580246914
73	175	172.405864197531	175	-2.59413580246913	2.59413580246917
74	187	182.475308641975	177.625	4.85030864197531	4.52469135802468
75	176	176.16512345679	179.916666666667	-3.75154320987654	-0.165123456790155
76	185	181.489197530864	181.583333333333	-0.0941358024691316	3.5108024691358
77	204	197.79012345679	182.833333333333	14.9567901234568	6.20987654320987
78	188	185.975308641975	184.041666666667	1.93364197530864	2.02469135802468
79	171	174.637345679012	185.083333333333	-10.4459876543210	-3.63734567901233
80	171	172.711419753086	185.833333333333	-13.1219135802469	-1.71141975308640
81	182	184.975308641975	186.75	-1.77469135802469	-2.97530864197532
82	185	186.850308641975	187.75	-0.899691358024698	-1.85030864197529
83	200	197.910493827160	188.541666666667	9.36882716049383	2.08950617283952
84	192	190.739197530864	189.166666666667	1.57253086419754	1.26080246913580
85	185	186.822530864198	189.416666666667	-2.59413580246913	-1.82253086419752
86	195	194.266975308642	189.416666666667	4.85030864197531	0.733024691358054
87	190	185.831790123457	189.583333333333	-3.75154320987654	4.16820987654322
88	195	189.530864197531	189.625	-0.0941358024691316	5.46913580246917
89	213	204.16512345679	189.208333333333	14.9567901234568	8.83487654320987
90	194	190.558641975309	188.625	1.93364197530864	3.44135802469137
91	171	177.345679012346	187.791666666667	-10.4459876543210	-6.3456790123457
92	171	173.711419753086	186.833333333333	-13.1219135802469	-2.71141975308643
93	186	184.183641975309	185.958333333333	-1.77469135802469	1.81635802469137
94	182	184.058641975309	184.958333333333	-0.899691358024698	-2.05864197530863
95	193	192.952160493827	183.583333333333	9.36882716049383	0.047839506172835
96	185	183.447530864198	181.875	1.57253086419754	1.55246913580245
97	172	177.739197530864	180.333333333333	-2.59413580246913	-5.73919753086417
98	185	184.225308641975	179.375	4.85030864197531	0.774691358024683
99	179	175.41512345679	179.166666666667	-3.75154320987654	3.58487654320987
100	182	179.322530864198	179.416666666667	-0.0941358024691316	2.67746913580245
101	193	194.831790123457	179.875	14.9567901234568	-1.83179012345678
102	173	182.100308641975	180.166666666667	1.93364197530864	-9.10030864197532
103	155	169.762345679012	180.208333333333	-10.4459876543210	-14.7623456790123
104	164	167.336419753086	180.458333333333	-13.1219135802469	-3.33641975308643
105	188	179.350308641975	181.125	-1.77469135802469	8.64969135802468
106	186	181.141975308642	182.041666666667	-0.899691358024698	4.85802469135805
107	200	191.952160493827	182.583333333333	9.36882716049383	8.0478395061729
108	185	184.447530864198	182.875	1.57253086419754	0.552469135802482
109	173	180.822530864198	183.416666666667	-2.59413580246913	-7.82253086419752
110	190	188.641975308642	183.791666666667	4.85030864197531	1.35802469135803
111	190	180.081790123457	183.833333333333	-3.75154320987654	9.91820987654322
112	193	183.572530864197	183.666666666667	-0.0941358024691316	9.4274691358025
113	195	198.456790123457	183.5	14.9567901234568	-3.45679012345678
114	178	185.100308641975	183.166666666667	1.93364197530864	-7.10030864197532
115	163	NA	NA	-10.4459876543210	NA
116	165	NA	NA	-13.1219135802469	NA
117	188	NA	NA	-1.77469135802469	NA
118	182	NA	NA	-0.899691358024698	NA
119	200	NA	NA	9.36882716049383	NA
120	177	NA	NA	1.57253086419754	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Aug/05/t12810179409v3wyw1xi2wb7wy/1dknc1281017457.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/05/t12810179409v3wyw1xi2wb7wy/1dknc1281017457.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/05/t12810179409v3wyw1xi2wb7wy/2dknc1281017457.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/05/t12810179409v3wyw1xi2wb7wy/2dknc1281017457.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/05/t12810179409v3wyw1xi2wb7wy/3obmf1281017457.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/05/t12810179409v3wyw1xi2wb7wy/3obmf1281017457.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/05/t12810179409v3wyw1xi2wb7wy/4obmf1281017457.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/05/t12810179409v3wyw1xi2wb7wy/4obmf1281017457.ps (open in new window)

Parameters (Session):

par1 = additive ; par2 = 12 ;

Parameters (R input):

par1 = additive ; 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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