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Retail sale of wines and spirits in specialised stores

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

Title produced by software: (Partial) Autocorrelation Function

Date of computation: Sat, 20 Nov 2010 16:36:46 +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/Nov/20/t12902709390cn19wnlu57tuig.htm/, Retrieved Sat, 20 Nov 2010 17:35:39 +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/Nov/20/t12902709390cn19wnlu57tuig.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:

KDGP2W12

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	2 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Autocorrelation Function
Time lag k	ACF(k)	T-STAT	P-value
1	0.202179	2.1011	0.018979
2	0.15415	1.602	0.056041
3	0.229	2.3798	0.009537
4	0.251364	2.6123	0.005138
5	0.327039	3.3987	0.000475
6	0.324176	3.3689	0.000523
7	0.300239	3.1202	0.001159
8	0.229163	2.3815	0.009495
9	0.188044	1.9542	0.026631
10	0.082208	0.8543	0.197406
11	0.116528	1.211	0.114271
12	0.815807	8.4781	0
13	0.132268	1.3746	0.086055
14	0.07798	0.8104	0.209749
15	0.131965	1.3714	0.086543
16	0.171296	1.7802	0.038931
17	0.219217	2.2782	0.012342
18	0.215614	2.2407	0.013545
19	0.205195	2.1324	0.017618
20	0.12352	1.2837	0.101004
21	0.103617	1.0768	0.141979
22	0.010358	0.1076	0.45724
23	0.020819	0.2164	0.414557
24	0.638961	6.6403	0
25	0.054757	0.5691	0.285251
26	-0.012876	-0.1338	0.4469
27	0.040991	0.426	0.335481
28	0.076002	0.7898	0.215678
29	0.102597	1.0662	0.144352
30	0.115586	1.2012	0.11615
31	0.100057	1.0398	0.150371
32	0.029796	0.3096	0.378714
33	0.007709	0.0801	0.468148
34	-0.076206	-0.792	0.21506
35	-0.065502	-0.6807	0.248754
36	0.476272	4.9496	1e-06
37	-0.042391	-0.4405	0.330214
38	-0.091349	-0.9493	0.172287
39	-0.04296	-0.4465	0.32808
40	-0.024561	-0.2552	0.39951
41	0.006508	0.0676	0.4731
42	0.00991	0.103	0.45908
43	0.006415	0.0667	0.473484
44	-0.048343	-0.5024	0.308205
45	-0.080686	-0.8385	0.201798
46	-0.143241	-1.4886	0.069753
47	-0.132154	-1.3734	0.086239
48	0.315173	3.2754	0.000709

Partial Autocorrelation Function
Time lag k	PACF(k)	T-STAT	P-value
1	0.202179	2.1011	0.018979
2	0.118101	1.2273	0.111181
3	0.187828	1.952	0.026766
4	0.180402	1.8748	0.031761
5	0.2485	2.5825	0.005574
6	0.223887	2.3267	0.010923
7	0.194419	2.0205	0.022905
8	0.089284	0.9279	0.177773
9	0.006808	0.0708	0.471863
10	-0.183265	-1.9045	0.02975
11	-0.187143	-1.9448	0.027197
12	0.777957	8.0848	0
13	-0.282612	-2.937	0.002026
14	-0.198525	-2.0631	0.020749
15	-0.137138	-1.4252	0.078495
16	0.010376	0.1078	0.457164
17	-0.164981	-1.7145	0.04465
18	-0.071216	-0.7401	0.230423
19	0.052207	0.5426	0.294277
20	-0.071312	-0.7411	0.230123
21	0.026261	0.2729	0.39272
22	0.146398	1.5214	0.065539
23	0.023943	0.2488	0.401986
24	-0.040158	-0.4173	0.338631
25	-0.012837	-0.1334	0.447059
26	-0.053519	-0.5562	0.289619
27	-0.045884	-0.4768	0.317219
28	-0.102577	-1.066	0.144397
29	-0.053168	-0.5525	0.290859
30	0.007831	0.0814	0.467645
31	-0.034881	-0.3625	0.358847
32	0.067922	0.7059	0.240897
33	-0.028771	-0.299	0.382757
34	-0.015304	-0.159	0.436964
35	0.031122	0.3234	0.373499
36	0.011418	0.1187	0.452884
37	-0.12366	-1.2851	0.100751
38	0.010078	0.1047	0.458389
39	0.011981	0.1245	0.450571
40	-0.097286	-1.011	0.157132
41	0.011045	0.1148	0.454415
42	-0.014485	-0.1505	0.440313
43	0.000585	0.0061	0.497579
44	-0.016736	-0.1739	0.431125
45	0.032388	0.3366	0.368541
46	0.013691	0.1423	0.443562
47	-0.020008	-0.2079	0.417839
48	-0.121081	-1.2583	0.105496

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/20/t12902709390cn19wnlu57tuig/1hhwj1290271002.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/20/t12902709390cn19wnlu57tuig/1hhwj1290271002.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/20/t12902709390cn19wnlu57tuig/2hhwj1290271002.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/20/t12902709390cn19wnlu57tuig/2hhwj1290271002.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

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

R code (references can be found in the software module):

if (par1 == 'Default') {

par1 = 10*log10(length(x))

} else {

par1 <- as.numeric(par1)

}

par2 <- as.numeric(par2)

par3 <- as.numeric(par3)

par4 <- as.numeric(par4)

par5 <- as.numeric(par5)

if (par6 == 'White Noise') par6 <- 'white' else par6 <- 'ma'

par7 <- as.numeric(par7)

if (par2 == 0) {

x <- log(x)

} else {

x <- (x ^ par2 - 1) / par2

}

if (par3 > 0) x <- diff(x,lag=1,difference=par3)

if (par4 > 0) x <- diff(x,lag=par5,difference=par4)

bitmap(file='pic1.png')

racf <- acf(x, par1, main='Autocorrelation', xlab='time lag', ylab='ACF', ci.type=par6, ci=par7, sub=paste('(lambda=',par2,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep=''))

dev.off()

bitmap(file='pic2.png')

rpacf <- pacf(x,par1,main='Partial Autocorrelation',xlab='lags',ylab='PACF')

dev.off()

(myacf <- c(racf$acf))

(mypacf <- c(rpacf$acf))

lengthx <- length(x)

sqrtn <- sqrt(lengthx)

load(file='createtable')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Autocorrelation Function',4,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Time lag k',header=TRUE)

a<-table.element(a,hyperlink('http://www.xycoon.com/basics.htm','ACF(k)','click here for more information about the Autocorrelation Function'),header=TRUE)

a<-table.element(a,'T-STAT',header=TRUE)

a<-table.element(a,'P-value',header=TRUE)

a<-table.row.end(a)

for (i in 2:(par1+1)) {

a<-table.row.start(a)

a<-table.element(a,i-1,header=TRUE)

a<-table.element(a,round(myacf[i],6))

mytstat <- myacf[i]*sqrtn

a<-table.element(a,round(mytstat,4))

a<-table.element(a,round(1-pt(abs(mytstat),lengthx),6))

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Partial Autocorrelation Function',4,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Time lag k',header=TRUE)

a<-table.element(a,hyperlink('http://www.xycoon.com/basics.htm','PACF(k)','click here for more information about the Partial Autocorrelation Function'),header=TRUE)

a<-table.element(a,'T-STAT',header=TRUE)

a<-table.element(a,'P-value',header=TRUE)

a<-table.row.end(a)

for (i in 1:par1) {

a<-table.row.start(a)

a<-table.element(a,i,header=TRUE)

a<-table.element(a,round(mypacf[i],6))

mytstat <- mypacf[i]*sqrtn

a<-table.element(a,round(mytstat,4))

a<-table.element(a,round(1-pt(abs(mytstat),lengthx),6))

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable1.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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