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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, 15 Jan 2011 11:58:20 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2011/Jan/15/t129509255670zzrrww9qd1c8r.htm/, Retrieved Sat, 15 Jan 2011 12:55:56 +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/2011/Jan/15/t129509255670zzrrww9qd1c8r.htm/},
    year = {2011},
}
@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 = {2011},
    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	-0.439496	-4.5462	7e-06
2	-0.087729	-0.9075	0.183097
3	0.045102	0.4665	0.320889
4	-0.039874	-0.4125	0.340413
5	0.034581	0.3577	0.360633
6	0.024815	0.2567	0.398957
7	0.040712	0.4211	0.337253
8	-0.031762	-0.3285	0.37157
9	0.060675	0.6276	0.265793
10	-0.087905	-0.9093	0.182617
11	-0.443501	-4.5876	6e-06
12	0.848788	8.7799	0
13	-0.363267	-3.7577	0.00014
14	-0.070858	-0.733	0.232591
15	0.01226	0.1268	0.449663
16	-0.008778	-0.0908	0.463911
17	0.020262	0.2096	0.417193
18	0.015872	0.1642	0.43495
19	0.049637	0.5134	0.304348
20	-0.038525	-0.3985	0.345525
21	0.043827	0.4533	0.325608
22	-0.057368	-0.5934	0.277077
23	-0.393071	-4.066	4.6e-05
24	0.725055	7.5	0
25	-0.298637	-3.0891	0.001279
26	-0.072036	-0.7451	0.228909
27	0.018076	0.187	0.426017
28	-0.004163	-0.0431	0.482868
29	0.003858	0.0399	0.484122
30	0.016194	0.1675	0.433641
31	0.051389	0.5316	0.298062
32	-0.031054	-0.3212	0.374335
33	0.028608	0.2959	0.38393
34	-0.044484	-0.4601	0.323173
35	-0.34021	-3.5192	0.000319
36	0.619978	6.4131	0
37	-0.256429	-2.6525	0.004602
38	-0.056186	-0.5812	0.281165
39	0.018823	0.1947	0.422995
40	-0.012443	-0.1287	0.448914
41	0.011699	0.121	0.451951
42	0.005489	0.0568	0.477414
43	0.043148	0.4463	0.328133
44	-0.021498	-0.2224	0.41222
45	0.025723	0.2661	0.395345
46	-0.039394	-0.4075	0.342228
47	-0.274372	-2.8381	0.002715
48	0.49	5.0686	1e-06

Partial Autocorrelation Function
Time lag k	PACF(k)	T-STAT	P-value
1	-0.439496	-4.5462	7e-06
2	-0.34813	-3.6011	0.000241
3	-0.225481	-2.3324	0.010776
4	-0.224321	-2.3204	0.011108
5	-0.155153	-1.6049	0.05573
6	-0.082527	-0.8537	0.197598
7	0.040698	0.421	0.337304
8	0.064943	0.6718	0.251587
9	0.190958	1.9753	0.025406
10	0.108309	1.1204	0.132534
11	-0.692879	-7.1672	0
12	0.511143	5.2873	0
13	0.067237	0.6955	0.244124
14	0.002262	0.0234	0.490689
15	-0.131809	-1.3634	0.087802
16	0.052124	0.5392	0.295444
17	0.018593	0.1923	0.423927
18	0.014536	0.1504	0.44038
19	0.038587	0.3991	0.345291
20	-0.004216	-0.0436	0.482647
21	-0.164828	-1.705	0.045549
22	-0.079446	-0.8218	0.206509
23	0.089358	0.9243	0.178699
24	0.066763	0.6906	0.245655
25	-0.008184	-0.0847	0.466346
26	-0.093189	-0.964	0.168621
27	0.114769	1.1872	0.118892
28	0.011971	0.1238	0.450843
29	-0.010316	-0.1067	0.457609
30	-0.001313	-0.0136	0.494595
31	-0.021605	-0.2235	0.411791
32	-0.043958	-0.4547	0.325123
33	-0.027903	-0.2886	0.386709
34	-0.004424	-0.0458	0.481792
35	0.024964	0.2582	0.398363
36	-0.022245	-0.2301	0.409224
37	-0.045113	-0.4667	0.320847
38	0.082435	0.8527	0.19786
39	0.040765	0.4217	0.337055
40	-0.045293	-0.4685	0.320186
41	0.021403	0.2214	0.412604
42	0.020169	0.2086	0.417567
43	-0.050963	-0.5272	0.299583
44	-0.069953	-0.7236	0.235444
45	0.033399	0.3455	0.365207
46	0.001848	0.0191	0.492394
47	0.057281	0.5925	0.277378
48	-0.137244	-1.4197	0.079307

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/Jan/15/t129509255670zzrrww9qd1c8r/1yvby1295092696.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Jan/15/t129509255670zzrrww9qd1c8r/1yvby1295092696.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Jan/15/t129509255670zzrrww9qd1c8r/2d0hw1295092696.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Jan/15/t129509255670zzrrww9qd1c8r/2d0hw1295092696.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Jan/15/t129509255670zzrrww9qd1c8r/3gzf81295092696.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Jan/15/t129509255670zzrrww9qd1c8r/3gzf81295092696.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 = 48 ; par2 = 1 ; par3 = 1 ; 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 (par8 != '') par8 <- as.numeric(par8)

ox <- x

if (par8 == '') {

if (par2 == 0) {

x <- log(x)

} else {

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

}

} else {

x <- log(x,base=par8)

}

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

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

bitmap(file='picts.png')

op <- par(mfrow=c(2,1))

plot(ox,type='l',main='Original Time Series',xlab='time',ylab='value')

if (par8=='') {

mytitle <- paste('Working Time Series (lambda=',par2,', d=',par3,', D=',par4,')',sep='')

mysub <- paste('(lambda=',par2,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='')

} else {

mytitle <- paste('Working Time Series (base=',par8,', d=',par3,', D=',par4,')',sep='')

mysub <- paste('(base=',par8,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='')

}

plot(x,type='l', main=mytitle,xlab='time',ylab='value')

par(op)

dev.off()

bitmap(file='pic1.png')

racf <- acf(x, par1, main='Autocorrelation', xlab='time lag', ylab='ACF', ci.type=par6, ci=par7, sub=mysub)

dev.off()

bitmap(file='pic2.png')

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

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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