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Autocorrelatie bloemen en planten noorwegen

*Unverified author*

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

Title produced by software: (Partial) Autocorrelation Function

Date of computation: Thu, 17 Dec 2009 06:10:56 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/17/t1261055506iv3bd1jqs12s2fr.htm/, Retrieved Thu, 17 Dec 2009 14:11:49 +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/2009/Dec/17/t1261055506iv3bd1jqs12s2fr.htm/},
    year = {2009},
}
@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 = {2009},
    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:

KDGP2W21

Dataseries X:

» Textbox « » Textfile « » CSV «

75,9 76,9 77,9 78,9 79,9 80,9 81,9 82,9 83,9 84,9 85,9 86,9 87,9 88,9 89,9 90,9 91,9 92,9 93,9 94,9 95,9 96,9 97,9 98,9 99,9 100,9 101,9 102,9 103,9 104,9 105,9 106,9 107,9 108,9 109,9 110,9 111,9 112,9 113,9 114,9 115,9 116,9 117,9 118,9 119,9 120,9 121,9 122,9 123,9 124,9 125,9 126,9 127,9 128,9 129,9 130,9 131,9 132,9 133,9 134,9 135,9 136,9 137,9 138,9 139,9 140,9 141,9 142,9 143,9 144,9 145,9 146,9 147,9 148,9 149,9 150,9 151,9 152,9 153,9 154,9 155,9 156,9 157,9 158,9 159,9 160,9 161,9 162,9 163,9 164,9 165,9 166,9 167,9 168,9 169,9 170,9 171,9 172,9 173,9 174,9 175,9 176,9 177,9 178,9 179,9 180,9 181,9 182,9

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	'Gwilym Jenkins' @ 72.249.127.135

Autocorrelation Function
Time lag k	ACF(k)	T-STAT	P-value
1	0.972222	10.1036	0
2	0.944454	9.8151	0
3	0.916705	9.5267	0
4	0.888984	9.2386	0
5	0.861302	8.9509	0
6	0.833667	8.6637	0
7	0.806089	8.3771	0
8	0.778578	8.0912	0
9	0.751143	7.8061	0
10	0.723794	7.5219	0
11	0.69654	7.2387	0
12	0.669391	6.9565	0
13	0.642357	6.6756	0
14	0.615446	6.3959	0
15	0.588668	6.1176	0
16	0.562034	5.8408	0
17	0.535552	5.5656	0
18	0.509231	5.2921	0
19	0.483083	5.0203	1e-06
20	0.457115	4.7505	3e-06
21	0.431338	4.4826	9e-06
22	0.405761	4.2168	2.6e-05
23	0.380393	3.9532	6.9e-05
24	0.355245	3.6918	0.000175
25	0.330325	3.4328	0.000424
26	0.305644	3.1763	0.000972
27	0.28121	2.9224	0.002115
28	0.257033	2.6712	0.004364
29	0.233123	2.4227	0.008535
30	0.20949	2.1771	0.015826
31	0.186142	1.9344	0.027837
32	0.163089	1.6949	0.04649
33	0.140342	1.4585	0.073806
34	0.117908	1.2253	0.111556
35	0.095799	0.9956	0.160841
36	0.074023	0.7693	0.221705
37	0.05259	0.5465	0.292913
38	0.03151	0.3275	0.371977
39	0.010791	0.1121	0.455457
40	-0.009555	-0.0993	0.460541
41	-0.029521	-0.3068	0.379796
42	-0.049096	-0.5102	0.305468
43	-0.068271	-0.7095	0.239772
44	-0.087037	-0.9045	0.183868
45	-0.105383	-1.0952	0.137938
46	-0.123301	-1.2814	0.101403
47	-0.14078	-1.463	0.073182
48	-0.157812	-1.64	0.051955

Partial Autocorrelation Function
Time lag k	PACF(k)	T-STAT	P-value
1	0.972222	10.1036	0
2	-0.013911	-0.1446	0.442663
3	-0.013933	-0.1448	0.442572
4	-0.013956	-0.145	0.442479
5	-0.013979	-0.1453	0.442384
6	-0.014002	-0.1455	0.442289
7	-0.014026	-0.1458	0.442192
8	-0.014049	-0.146	0.442095
9	-0.014073	-0.1463	0.441998
10	-0.014097	-0.1465	0.441901
11	-0.01412	-0.1467	0.441805
12	-0.014144	-0.147	0.441709
13	-0.014167	-0.1472	0.441615
14	-0.014189	-0.1475	0.441523
15	-0.014211	-0.1477	0.441433
16	-0.014232	-0.1479	0.441346
17	-0.014253	-0.1481	0.441262
18	-0.014273	-0.1483	0.441181
19	-0.014291	-0.1485	0.441105
20	-0.014309	-0.1487	0.441034
21	-0.014325	-0.1489	0.440968
22	-0.014339	-0.149	0.440908
23	-0.014352	-0.1492	0.440855
24	-0.014364	-0.1493	0.440809
25	-0.014373	-0.1494	0.440771
26	-0.01438	-0.1494	0.440742
27	-0.014385	-0.1495	0.440722
28	-0.014387	-0.1495	0.440713
29	-0.014387	-0.1495	0.440715
30	-0.014383	-0.1495	0.440728
31	-0.014377	-0.1494	0.440755
32	-0.014367	-0.1493	0.440795
33	-0.014353	-0.1492	0.440851
34	-0.014336	-0.149	0.440922
35	-0.014314	-0.1488	0.44101
36	-0.014288	-0.1485	0.441117
37	-0.014258	-0.1482	0.441243
38	-0.014222	-0.1478	0.441389
39	-0.014181	-0.1474	0.441558
40	-0.014134	-0.1469	0.44175
41	-0.014081	-0.1463	0.441967
42	-0.014021	-0.1457	0.442211
43	-0.013955	-0.145	0.442482
44	-0.013881	-0.1443	0.442783
45	-0.0138	-0.1434	0.443115
46	-0.013711	-0.1425	0.443481
47	-0.013613	-0.1415	0.443882
48	-0.013506	-0.1404	0.44432

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261055506iv3bd1jqs12s2fr/1hzm11261055454.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261055506iv3bd1jqs12s2fr/1hzm11261055454.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261055506iv3bd1jqs12s2fr/298j71261055454.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261055506iv3bd1jqs12s2fr/298j71261055454.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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