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ACF zonder trend

*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: Fri, 24 Dec 2010 11:44:46 +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/Dec/24/t1293191133e5a3zu1wma40gwp.htm/, Retrieved Fri, 24 Dec 2010 12:45:33 +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/24/t1293191133e5a3zu1wma40gwp.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:

Dataseries X:

» Textbox « » Textfile « » CSV «

12008 9169 8788 8417 8247 8197 8236 8253 7733 8366 8626 8863 10102 8463 9114 8563 8872 8301 8301 8278 7736 7973 8268 9476 11100 8962 9173 8738 8459 8078 8411 8291 7810 8616 8312 9692 9911 8915 9452 9112 8472 8230 8384 8625 8221 8649 8625 10443 10357 8586 8892 8329 8101 7922 8120 7838 7735 8406 8209 9451 10041 9411 10405 8467 8464 8102 7627 7513 7510 8291 8064 9383

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	'RServer@AstonUniversity' @ vre.aston.ac.uk

Autocorrelation Function
Time lag k	ACF(k)	T-STAT	P-value
1	-0.057067	-0.4383	0.331369
2	-0.150962	-1.1596	0.12545
3	-0.175895	-1.3511	0.090915
4	0.069776	0.536	0.297
5	-0.098314	-0.7552	0.226578
6	-0.016096	-0.1236	0.451011
7	-0.078043	-0.5995	0.27558
8	0.092275	0.7088	0.240626
9	0.109994	0.8449	0.200794
10	0.162903	1.2513	0.107885
11	-0.104317	-0.8013	0.213093
12	-0.309945	-2.3807	0.010263
13	-0.064057	-0.492	0.312262
14	0.186105	1.4295	0.079068
15	0.12975	0.9966	0.161507
16	-0.114016	-0.8758	0.192353
17	0.062714	0.4817	0.315896
18	-0.057601	-0.4424	0.329893
19	0.137159	1.0535	0.148195
20	-0.120851	-0.9283	0.178523
21	0.006155	0.0473	0.481226
22	-0.172283	-1.3233	0.095416
23	0.181168	1.3916	0.084638
24	0.16905	1.2985	0.099584
25	-0.050761	-0.3899	0.349008
26	-0.271227	-2.0833	0.020781
27	0.099221	0.7621	0.224509
28	-0.035467	-0.2724	0.393121
29	0.05713	0.4388	0.331196
30	0.075093	0.5768	0.283134
31	0.009079	0.0697	0.47232
32	-0.001702	-0.0131	0.494808
33	-0.021183	-0.1627	0.435651
34	0.023893	0.1835	0.427508
35	-0.116826	-0.8974	0.186587
36	-0.164745	-1.2654	0.105346
37	0.047408	0.3641	0.358524
38	0.183592	1.4102	0.081866
39	-0.008768	-0.0673	0.473266
40	-0.068326	-0.5248	0.300837
41	-0.021861	-0.1679	0.43361
42	-0.014532	-0.1116	0.45575
43	-0.05522	-0.4242	0.336498
44	0.0075	0.0576	0.477128
45	-0.021029	-0.1615	0.436115
46	0.047314	0.3634	0.358794
47	0.05916	0.4544	0.325597
48	0.151563	1.1642	0.12452
49	-0.023022	-0.1768	0.430121
50	-0.110382	-0.8479	0.199972
51	0.01094	0.084	0.466658
52	-0.060317	-0.4633	0.322426
53	-0.038455	-0.2954	0.384372
54	0.009663	0.0742	0.470541
55	0.011682	0.0897	0.464402
56	0.001289	0.0099	0.496067
57	-0.00155	-0.0119	0.495269
58	0.003246	0.0249	0.490095
59	NA	NA	NA
60	NA	NA	NA

Partial Autocorrelation Function
Time lag k	PACF(k)	T-STAT	P-value
1	-0.057067	-0.4383	0.331369
2	-0.154722	-1.1884	0.11971
3	-0.200099	-1.537	0.064821
4	0.017103	0.1314	0.447966
5	-0.16045	-1.2324	0.111337
6	-0.065605	-0.5039	0.308097
7	-0.124826	-0.9588	0.170785
8	0.007692	0.0591	0.476544
9	0.086024	0.6608	0.255668
10	0.169471	1.3017	0.099034
11	-0.012629	-0.097	0.461524
12	-0.284021	-2.1816	0.016567
13	-0.104958	-0.8062	0.211683
14	0.075981	0.5836	0.280849
15	0.126334	0.9704	0.167907
16	-0.05906	-0.4536	0.325873
17	0.05795	0.4451	0.32893
18	-0.158523	-1.2176	0.114105
19	0.087603	0.6729	0.251821
20	-0.035305	-0.2712	0.393599
21	0.113834	0.8744	0.192729
22	-0.079915	-0.6138	0.270841
23	0.060384	0.4638	0.322242
24	0.092187	0.7081	0.240836
25	-0.129192	-0.9923	0.162541
26	-0.112488	-0.864	0.195534
27	0.106364	0.817	0.208608
28	-0.102425	-0.7867	0.217291
29	0.013207	0.1014	0.459771
30	0.17436	1.3393	0.092807
31	-0.011302	-0.0868	0.465558
32	0.014781	0.1135	0.454995
33	-0.090634	-0.6962	0.244528
34	-0.021542	-0.1655	0.434572
35	0.002602	0.02	0.49206
36	-0.094513	-0.726	0.235364
37	0.019913	0.153	0.439477
38	-0.105024	-0.8067	0.211539
39	-0.000324	-0.0025	0.499011
40	-0.104114	-0.7997	0.213542
41	0.002346	0.018	0.492843
42	-0.038006	-0.2919	0.385683
43	0.008472	0.0651	0.474167
44	-0.076052	-0.5842	0.280668
45	-0.010212	-0.0784	0.468873
46	-0.006495	-0.0499	0.480189
47	0.003764	0.0289	0.488517
48	0.080157	0.6157	0.270231
49	0.086172	0.6619	0.255307
50	0.048902	0.3756	0.354271
51	0.005381	0.0413	0.483584
52	-0.082424	-0.6331	0.264554
53	-0.086308	-0.6629	0.254975
54	-0.059039	-0.4535	0.32593
55	0.035807	0.275	0.392124
56	-0.017006	-0.1306	0.448258
57	-0.063644	-0.4889	0.313377
58	-0.071184	-0.5468	0.293299
59	NA	NA	NA
60	NA	NA	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293191133e5a3zu1wma40gwp/1nt3n1293191082.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293191133e5a3zu1wma40gwp/1nt3n1293191082.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293191133e5a3zu1wma40gwp/2nt3n1293191082.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293191133e5a3zu1wma40gwp/2nt3n1293191082.ps (open in new window)

Parameters (Session):

par1 = 60 ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;

Parameters (R input):

par1 = 60 ; par2 = 1 ; par3 = 1 ; par4 = 1 ; 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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