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Opgave 6bis-Stap 1-Inschrijvingen nieuwe personenwagens-Silke Van Lishout

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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, 16 Apr 2011 12:06:48 +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/Apr/16/t1302955404ju7c17egaazh5fp.htm/, Retrieved Fri, 24 May 2013 10:19:11 +0000

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

System-generated keywords (parent):

(pk = 0)

Estimated Impact

Dataseries X:

» Textfile « » CSV « » Stem and Leaf « » Histogram « » Kernel Density « » Harrell-Davis Quantiles « » Central Tendency « » Variability «

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	1 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Autocorrelation Function
Time lag k	ACF(k)	T-STAT	P-value
1	0.499538	4.2387	3.3e-05
2	0.294086	2.4954	0.007437
3	0.186006	1.5783	0.059439
4	-0.025916	-0.2199	0.413283
5	-0.117689	-0.9986	0.16066
6	-0.219519	-1.8627	0.033294
7	-0.186776	-1.5848	0.058692
8	-0.100033	-0.8488	0.199402
9	0.028785	0.2442	0.403868
10	0.073049	0.6198	0.26866
11	0.234924	1.9934	0.025004
12	0.643267	5.4583	0
13	0.276641	2.3474	0.010829
14	0.133165	1.1299	0.131125
15	0.078298	0.6644	0.254283
16	-0.092697	-0.7866	0.217061
17	-0.124197	-1.0538	0.147738
18	-0.205911	-1.7472	0.042432
19	-0.200215	-1.6989	0.04683
20	-0.093638	-0.7945	0.214744
21	-0.010596	-0.0899	0.464304
22	0.05393	0.4576	0.324306
23	0.23488	1.993	0.025025
24	0.540306	4.5846	9e-06
25	0.25674	2.1785	0.016322
26	0.123012	1.0438	0.150037
27	0.021149	0.1795	0.429042
28	-0.115572	-0.9807	0.165021
29	-0.138651	-1.1765	0.121637
30	-0.231081	-1.9608	0.026887
31	-0.210914	-1.7897	0.038857
32	-0.114261	-0.9695	0.167761
33	-0.071502	-0.6067	0.272975
34	-0.041658	-0.3535	0.362382
35	0.074101	0.6288	0.265746
36	0.302874	2.57	0.00612
37	0.102432	0.8692	0.193823
38	0.026195	0.2223	0.412367
39	-0.054585	-0.4632	0.32232
40	-0.14598	-1.2387	0.109743
41	-0.145596	-1.2354	0.110345
42	-0.237249	-2.0131	0.023921
43	-0.205485	-1.7436	0.042748
44	-0.145312	-1.233	0.110791
45	-0.108272	-0.9187	0.180654
46	-0.045215	-0.3837	0.351179
47	0.037691	0.3198	0.375017
48	0.224949	1.9088	0.03014

Partial Autocorrelation Function
Time lag k	PACF(k)	T-STAT	P-value
1	0.499538	4.2387	3.3e-05
2	0.059361	0.5037	0.308008
3	0.024293	0.2061	0.418634
4	-0.186365	-1.5814	0.059089
5	-0.077256	-0.6555	0.257105
6	-0.143315	-1.2161	0.113966
7	0.030159	0.2559	0.399376
8	0.056864	0.4825	0.315454
9	0.139127	1.1805	0.120837
10	-0.008644	-0.0733	0.470867
11	0.194655	1.6517	0.051475
12	0.596492	5.0614	2e-06
13	-0.512861	-4.3518	2.2e-05
14	-0.129969	-1.1028	0.136887
15	0.036015	0.3056	0.380397
16	0.042795	0.3631	0.358787
17	0.009244	0.0784	0.468848
18	0.051144	0.434	0.332805
19	-0.040168	-0.3408	0.36711
20	0.008134	0.069	0.472584
21	0.005467	0.0464	0.481566
22	0.188805	1.6021	0.05676
23	0.141315	1.1991	0.117211
24	-0.110135	-0.9345	0.176578
25	-0.068624	-0.5823	0.281095
26	-0.029649	-0.2516	0.40104
27	-0.15843	-1.3443	0.091532
28	0.123001	1.0437	0.150058
29	0.030432	0.2582	0.398484
30	-0.079591	-0.6754	0.250807
31	0.05977	0.5072	0.306794
32	-0.044999	-0.3818	0.351856
33	-0.018395	-0.1561	0.4382
34	-0.117067	-0.9933	0.161934
35	-0.217087	-1.842	0.034793
36	0.042814	0.3633	0.358726
37	0.024792	0.2104	0.416987
38	0.043274	0.3672	0.357278
39	0.091835	0.7792	0.219194
40	-0.149853	-1.2715	0.103814
41	-0.107575	-0.9128	0.182196
42	0.039475	0.335	0.369316
43	-0.016756	-0.1422	0.443668
44	-0.065404	-0.555	0.290318
45	0.010698	0.0908	0.463961
46	0.005801	0.0492	0.48044
47	-0.003448	-0.0293	0.488372
48	-0.035739	-0.3033	0.381285

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/Apr/16/t1302955404ju7c17egaazh5fp/18js81302955604.png (opens in new window)

http://www.freestatistics.org/blog/date/2011/Apr/16/t1302955404ju7c17egaazh5fp/18js81302955604.ps (opens in new window)

Click here to open pdf file.

http://www.freestatistics.org/blog/date/2011/Apr/16/t1302955404ju7c17egaazh5fp/2gx971302955604.png (opens in new window)

http://www.freestatistics.org/blog/date/2011/Apr/16/t1302955404ju7c17egaazh5fp/2gx971302955604.ps (opens in new window)

Click here to open pdf file.

http://www.freestatistics.org/blog/date/2011/Apr/16/t1302955404ju7c17egaazh5fp/34h481302955604.png (opens in new window)

http://www.freestatistics.org/blog/date/2011/Apr/16/t1302955404ju7c17egaazh5fp/34h481302955604.ps (opens in new window)

Click here to open pdf file.

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 (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')