Home » date » 2010 » Dec » 26 »

Retail sale of wines and spirits in specialised stores

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Sun, 26 Dec 2010 14:57:29 +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/26/t1293375364m9ph4jgamwwuf26.htm/, Retrieved Sun, 26 Dec 2010 15:56:08 +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/26/t1293375364m9ph4jgamwwuf26.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 «

57,7 63,6 78 77,4 74,1 85,9 82 78,4 68,1 70,9 85,2 149,6 57,9 63,7 85 66,1 80,2 83,4 85,7 81,8 69,4 76,4 90,3 157,3 65,3 68,4 72,7 86,6 82,6 84,8 93,4 82,2 75,2 83,9 85,4 166,3 70,4 73,9 82,4 92,3 82,7 95,8 105,8 84,2 82,7 88,4 90,2 176,6 69,5 77,3 98,6 86,4 90,8 101,5 112,2 93,6 93,8 90,8 98,1 187,6 75 83,7 99,7 104,9 98,9 117,3 115,7 102,2 101,9 96,6 110 203,7 82,3 93,3 121,9 100,9 107,7 130 123,2 116,1 105,3 107,7 123,9 205,2 90,3 106,9 122,4 111,3 122,6 124,8 139,5 118,8 111 121,2 120,6 219,1 101,3 105 113,4 133,6 123,9 136,2 151,7 121,9 120,2 132,2 125,2 233,8

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	3 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	57.7	NA	NA	0.742642680514456	NA
2	63.6	NA	NA	0.809533496184677	NA
3	78	NA	NA	0.957061403008848	NA
4	77.4	NA	NA	0.932479236330016	NA
5	74.1	NA	NA	0.938635699865698	NA
6	85.9	NA	NA	1.03062188643898	NA
7	82	86.664842469689	80.9166666666667	1.07103821795702	0.94617376162288
8	78.4	76.398164009466	80.9291666666667	0.94401273553374	1.02620267144490
9	68.1	71.0649583788139	81.225	0.874914846153449	0.958278194394921
10	70.9	73.6147199735074	81.0458333333333	0.908309742102811	0.96312259321934
11	85.2	80.067908740653	80.8291666666667	0.990581890703476	1.06409673163777
12	149.6	145.776117878311	80.9791666666667	1.80016816520683	1.02623119738228
13	57.9	60.1757175331859	81.0291666666667	0.742642680514456	0.962182128830771
14	63.7	65.8353115772189	81.325	0.809533496184677	0.967565862056955
15	85	78.0204431244504	81.5208333333333	0.957061403008848	1.08945805222378
16	66.1	76.2806868619467	81.8041666666667	0.932479236330016	0.866536507722173
17	80.2	77.1988753318709	82.2458333333333	0.938635699865698	1.03887523821076
18	83.4	85.3140209078466	82.7791666666667	1.03062188643898	0.977564990051119
19	85.7	89.3335126960987	83.4083333333333	1.07103821795702	0.959326432080877
20	81.8	79.214468670475	83.9125	0.94401273553374	1.03263963481571
21	69.4	73.1392356599027	83.5958333333333	0.874914846153449	0.94887510614289
22	76.4	76.2412489777547	83.9375	0.908309742102811	1.00208221959076
23	90.3	84.0921476716359	84.8916666666667	0.990581890703476	1.07382202144015
24	157.3	153.104302450841	85.05	1.80016816520683	1.02740417794925
25	65.3	63.4433453274495	85.4291666666667	0.742642680514456	1.02926476627246
26	68.4	69.4309895227725	85.7666666666667	0.809533496184677	0.985150873840933
27	72.7	82.3312071938361	86.025	0.957061403008848	0.88301875410182
28	86.6	80.7332752154226	86.5791666666667	0.932479236330016	1.07266798936279
29	82.6	81.3679822321077	86.6875	0.938635699865698	1.01514130907631
30	84.8	89.5180993529456	86.8583333333333	1.03062188643898	0.947294464616106
31	93.4	93.6578295011001	87.4458333333333	1.07103821795702	0.997247112147767
32	82.2	82.9669192942216	87.8875	0.94401273553374	0.99075632431883
33	75.2	77.4481912772084	88.5208333333333	0.874914846153449	0.970971674868926
34	83.9	80.9871673802419	89.1625	0.908309742102811	1.03596659463446
35	85.4	88.5621484534353	89.4041666666667	0.990581890703476	0.964294582858975
36	166.3	161.775112446587	89.8666666666667	1.80016816520683	1.02797023278168
37	70.4	67.462898835734	90.8416666666667	0.742642680514456	1.04353653956403
38	73.9	74.0250921136205	91.4416666666667	0.809533496184677	0.998310139034633
39	82.4	87.8941265988251	91.8375	0.957061403008848	0.937491538838518
40	92.3	86.1028014846229	92.3375	0.932479236330016	1.07197441208093
41	82.7	87.0349952700469	92.725	0.938635699865698	0.950192502951295
42	95.8	96.2128473569388	93.3541666666667	1.03062188643898	0.995709020486555
43	105.8	100.405370274229	93.7458333333333	1.07103821795702	1.05372849789844
44	84.2	88.5955952298415	93.85	0.94401273553374	0.950385849110916
45	82.7	82.8252721025265	94.6666666666667	0.874914846153449	0.998487513540898
46	88.4	86.3764718500519	95.0958333333333	0.908309742102811	1.02342684421588
47	90.2	94.291013721337	95.1875	0.990581890703476	0.956612899152538
48	176.6	172.388603920619	95.7625	1.80016816520683	1.02442966636774
49	69.5	71.4917353775249	96.2666666666667	0.742642680514456	0.972140340879863
50	77.3	78.4640341176998	96.925	0.809533496184677	0.98516474291962
51	98.6	93.580666435036	97.7791666666667	0.957061403008848	1.05363643748411
52	86.4	91.7015622327544	98.3416666666667	0.932479236330016	0.942186783914345
53	90.8	92.7098302721516	98.7708333333333	0.938635699865698	0.979399916205809
54	101.5	102.606997310721	99.5583333333333	1.03062188643898	0.98921128831625
55	112.2	107.36711869095	100.245833333333	1.07103821795702	1.04501267583571
56	93.6	95.1014163322283	100.741666666667	0.94401273553374	0.984212471379152
57	93.8	88.4137906823316	101.054166666667	0.874914846153449	1.06092046586964
58	90.8	92.5302703527984	101.870833333333	0.908309742102811	0.981300493922677
59	98.1	102.009297619735	102.979166666667	0.990581890703476	0.961677046005082
60	187.6	187.17248497738	103.975	1.80016816520683	1.00228406981225
61	75	77.8134811954042	104.779166666667	0.742642680514456	0.96384326787361
62	83.7	85.2303849233101	105.283333333333	0.809533496184677	0.98204413925049
63	99.7	101.428569939708	105.979166666667	0.957061403008848	0.982957760907642
64	104.9	99.363433291266	106.558333333333	0.932479236330016	1.05572036437695
65	98.9	100.711699613507	107.295833333333	0.938635699865698	0.982011031285747
66	117.3	111.783826357888	108.4625	1.03062188643898	1.04934679570237
67	115.7	117.211744977672	109.4375	1.07103821795702	0.987102444571919
68	102.2	103.975136046245	110.141666666667	0.94401273553374	0.98292730249032
69	101.9	97.5238415179044	111.466666666667	0.874914846153449	1.04487270408941
70	96.6	101.935060807488	112.225	0.908309742102811	0.947662160936327
71	110	111.366169062338	112.425	0.990581890703476	0.987732638431932
72	203.7	203.996556621375	113.320833333333	1.80016816520683	0.998546266533676
73	82.3	84.7819450142315	114.1625	0.742642680514456	0.970725547593713
74	93.3	93.1402017922812	115.054166666667	0.809533496184677	1.00171567384055
75	121.9	110.803783933349	115.775	0.957061403008848	1.10014293440850
76	100.9	108.521156458057	116.379166666667	0.932479236330016	0.929772620318485
77	107.7	110.215386074647	117.420833333333	0.938635699865698	0.977177541500937
78	130	121.677796467702	118.0625	1.03062188643898	1.06839541620486
79	123.2	126.873402235492	118.458333333333	1.07103821795702	0.971046711361346
80	116.1	112.675786758748	119.358333333333	0.94401273553374	1.03038996522459
81	105.3	104.942390317581	119.945833333333	0.874914846153449	1.00340767616725
82	107.7	109.360492949178	120.4	0.908309742102811	0.98481633628014
83	123.9	120.310298050482	121.454166666667	0.990581890703476	1.02983702981113
84	205.2	219.365492331829	121.858333333333	1.80016816520683	0.93542515652188
85	90.3	90.8406715494286	122.320833333333	0.742642680514456	0.994048133504446
86	106.9	99.663692549036	123.1125	0.809533496184677	1.07260725812867
87	122.4	118.161193468980	123.4625	0.957061403008848	1.03587308494927
88	111.3	115.872201104459	124.2625	0.932479236330016	0.960541000681114
89	122.6	117.036138827004	124.6875	0.938635699865698	1.04753968499610
90	124.8	128.960857798537	125.129166666667	1.03062188643898	0.967735498432885
91	139.5	135.129321832244	126.166666666667	1.07103821795702	1.03234440984749
92	118.8	119.460878295397	126.545833333333	0.94401273553374	0.994467826582
93	111	110.319471142899	126.091666666667	0.874914846153449	1.00616871029249
94	121.2	115.033644213396	126.645833333333	0.908309742102811	1.05360480256685
95	120.6	126.427141225576	127.629166666667	0.990581890703476	0.953909096028845
96	219.1	230.706551772632	128.158333333333	1.80016816520683	0.949691278017669
97	101.3	95.9061134994376	129.141666666667	0.742642680514456	1.05624132084754
98	105	105.060582523601	129.779166666667	0.809533496184677	0.999423356294575
99	113.4	124.697125300361	130.291666666667	0.957061403008848	0.909403482452788
100	133.6	122.279110524076	131.133333333333	0.932479236330016	1.09258236690964
101	123.9	123.696541313968	131.783333333333	0.938635699865698	1.00164482113947
102	136.2	136.647579368228	132.5875	1.03062188643898	0.99672457155628
103	151.7	NA	NA	1.07103821795702	NA
104	121.9	NA	NA	0.94401273553374	NA
105	120.2	NA	NA	0.874914846153449	NA
106	132.2	NA	NA	0.908309742102811	NA
107	125.2	NA	NA	0.990581890703476	NA
108	233.8	NA	NA	1.80016816520683	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293375364m9ph4jgamwwuf26/1nf6m1293375446.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293375364m9ph4jgamwwuf26/1nf6m1293375446.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293375364m9ph4jgamwwuf26/2yon61293375446.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293375364m9ph4jgamwwuf26/2yon61293375446.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293375364m9ph4jgamwwuf26/3yon61293375446.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293375364m9ph4jgamwwuf26/3yon61293375446.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293375364m9ph4jgamwwuf26/4rxns1293375446.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293375364m9ph4jgamwwuf26/4rxns1293375446.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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