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*The author of this computation has been verified*

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

Title produced by software: (Partial) Autocorrelation Function

Date of computation: Mon, 27 Dec 2010 18:56:38 +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/27/t1293476336kij4uk7qvysq7ql.htm/, Retrieved Mon, 27 Dec 2010 19:59:00 +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/27/t1293476336kij4uk7qvysq7ql.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:

Partial Autocorrelation Function d=1 & D=0 - Gemiddelde bouwgrondprijzen België (1995-2009)

Dataseries X:

» Textbox « » Textfile « » CSV «

26 26 27 28 27 29 27 30 27 30 32 30 32 33 34 32 34 37 37 36 34 38 41 41 44 42 45 45 49 54 52 53 51 55 60 60 63 60 64 65 75 70 72 69 75 74 74 75 79 79 85 78 84 85 85 82 91 90 98 98

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

Autocorrelation Function
Time lag k	ACF(k)	T-STAT	P-value
1	-0.511599	-3.9297	0.000113
2	0.278825	2.1417	0.018179
3	-0.384231	-2.9513	0.002267
4	0.26893	2.0657	0.021629
5	-0.099955	-0.7678	0.222844
6	0.22652	1.7399	0.043543
7	-0.221804	-1.7037	0.046849
8	0.165329	1.2699	0.10455
9	-0.175952	-1.3515	0.090845
10	0.18587	1.4277	0.079325
11	-0.172221	-1.3229	0.095494
12	0.293973	2.258	0.013828
13	-0.252165	-1.9369	0.028774
14	0.217051	1.6672	0.050388
15	-0.321289	-2.4679	0.008256
16	0.237943	1.8277	0.036328
17	-0.136959	-1.052	0.148544
18	0.12087	0.9284	0.178486
19	0.024058	0.1848	0.427014
20	-0.067844	-0.5211	0.302118
21	-0.058909	-0.4525	0.326289
22	0.044657	0.343	0.366403
23	0.003025	0.0232	0.490769
24	0.095096	0.7304	0.234004
25	-0.129266	-0.9929	0.162403
26	0.025594	0.1966	0.422411
27	-0.008806	-0.0676	0.473151
28	0.019773	0.1519	0.439899
29	-0.031437	-0.2415	0.405012
30	0.017325	0.1331	0.447293
31	0.036163	0.2778	0.391079
32	-0.024411	-0.1875	0.425955
33	-0.030154	-0.2316	0.408817
34	-0.052007	-0.3995	0.345493
35	0.104122	0.7998	0.213525
36	-0.047386	-0.364	0.358588
37	0.036752	0.2823	0.389353
38	-0.074605	-0.573	0.284395
39	-0.005173	-0.0397	0.484219
40	0.047799	0.3671	0.357411
41	-0.018765	-0.1441	0.442941
42	-0.041131	-0.3159	0.376585
43	0.047243	0.3629	0.358995
44	-0.052618	-0.4042	0.343775
45	0.015599	0.1198	0.452517
46	-0.026221	-0.2014	0.420535
47	0.037382	0.2871	0.387507
48	-0.063181	-0.4853	0.314629

Partial Autocorrelation Function
Time lag k	PACF(k)	T-STAT	P-value
1	-0.511599	-3.9297	0.000113
2	0.023151	0.1778	0.429734
3	-0.315831	-2.4259	0.009173
4	-0.0667	-0.5123	0.305166
5	0.069614	0.5347	0.297427
6	0.185199	1.4225	0.080068
7	0.007709	0.0592	0.476492
8	0.100749	0.7739	0.221049
9	0.010615	0.0815	0.467645
10	0.025751	0.1978	0.421941
11	-0.071409	-0.5485	0.292707
12	0.208713	1.6032	0.05712
13	0.027691	0.2127	0.416148
14	0.067945	0.5219	0.301848
15	-0.13209	-1.0146	0.15722
16	-0.089382	-0.6866	0.247527
17	-0.042422	-0.3258	0.372846
18	-0.148709	-1.1423	0.128981
19	0.22165	1.7025	0.04696
20	-0.001927	-0.0148	0.494119
21	-0.060011	-0.461	0.323264
22	-0.007599	-0.0584	0.476826
23	0.00954	0.0733	0.470917
24	0.00662	0.0508	0.479809
25	-0.053309	-0.4095	0.341837
26	-0.023567	-0.181	0.428486
27	0.132488	1.0177	0.156498
28	-0.078234	-0.6009	0.275095
29	-0.071965	-0.5528	0.291254
30	-0.024712	-0.1898	0.425051
31	0.013603	0.1045	0.45857
32	0.03775	0.29	0.38643
33	-0.056374	-0.433	0.33329
34	0.035138	0.2699	0.394089
35	0.02769	0.2127	0.416151
36	-0.067563	-0.519	0.302865
37	0.029016	0.2229	0.4122
38	-0.029291	-0.225	0.411383
39	-0.057813	-0.4441	0.329308
40	0.060748	0.4666	0.321246
41	-0.036374	-0.2794	0.390459
42	-0.081321	-0.6246	0.267309
43	-0.046728	-0.3589	0.360468
44	0.016755	0.1287	0.449018
45	-0.035212	-0.2705	0.393871
46	-0.053829	-0.4135	0.340381
47	0.062613	0.4809	0.31617
48	-0.04878	-0.3747	0.354618

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/27/t1293476336kij4uk7qvysq7ql/1v66k1293476195.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/27/t1293476336kij4uk7qvysq7ql/1v66k1293476195.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/27/t1293476336kij4uk7qvysq7ql/26f5n1293476195.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/27/t1293476336kij4uk7qvysq7ql/26f5n1293476195.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/27/t1293476336kij4uk7qvysq7ql/36f5n1293476195.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/27/t1293476336kij4uk7qvysq7ql/36f5n1293476195.ps (open in new window)

Parameters (Session):

par1 = 48 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 4 ; par6 = White Noise ; par7 = 0.95 ;

Parameters (R input):

par1 = 48 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 4 ; 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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