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ACFbis_uitvoerbelgie

*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: Wed, 22 Dec 2010 14:38:38 +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/22/t12930286061z6mq1zfyyzw1yg.htm/, Retrieved Wed, 22 Dec 2010 15:36:49 +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/2010/Dec/22/t12930286061z6mq1zfyyzw1yg.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 «
15 14.4 13 13.7 13.6 15.2 12.9 14 14.1 13.2 11.3 13.3 14.4 13.3 11.6 13.2 13.1 14.6 14 14.3 13.8 13.7 11 14.4 15.6 13.7 12.6 13.2 13.3 14.3 14 13.4 13.9 13.7 10.5 14.5 15 13.5 13.5 13.2 13.8 16.2 14.7 13.9 16 14.4 12.3 15.9 15.9 15.5 15.1 14.5 15.1 17.4 16.2 15.6 17.2 14.9 13.8 17.5 16.2 17.5 16.6 16.2 16.6 19.6 15.9 18 18.3 16.3 14.9 18.2 18.4 18.5 16 17.4 17.2 19.6 17.2 18.3 19.3 18.1 16.2 18.4 20.5 19 16.5 18.7 19 19.2 20.5 19.3 20.6 20.1 16.1 20.4 19.7 15.6 14.4 13.7 14.1 15 14.2 13.6 15.4 14.8 12.5 16.2 16.1 16 15.8 15.2 15.7 18.9 17.4 17 19.8 17.7 16 19.6 19.7
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'George Udny Yule' @ 72.249.76.132


Autocorrelation Function
Time lag kACF(k)T-STATP-value
1-0.438076-4.55267e-06
20.0302420.31430.376956
30.4045974.20472.7e-05
4-0.274521-2.85290.002597
50.1245921.29480.099076
60.1770551.840.034257
7-0.320724-3.33310.000589
80.1708741.77580.039293
9-0.011469-0.11920.452672
10-0.201362-2.09260.019363
110.1008011.04760.148591
12-0.082721-0.85970.19594
13-0.172-1.78750.038333
140.0910780.94650.173001
150.0143080.14870.441035
16-0.192638-2.0020.023898
170.1187891.23450.10985
18-0.00345-0.03590.485734
19-0.124613-1.2950.099038
200.1489911.54840.06223
210.0018850.01960.492203
22-0.250735-2.60570.005231
230.4676454.85992e-06
24-0.368036-3.82470.00011
250.0492930.51230.304753
260.2444562.54050.006246
27-0.28448-2.95640.001911
280.1151251.19640.117078
290.1249791.29880.098386
30-0.215802-2.24270.01348
310.1555861.61690.054409
320.0202760.21070.416755
33-0.14157-1.47120.072067
340.1294891.34570.090611
35-0.018736-0.19470.422991
36-0.14632-1.52060.065641
370.1318091.36980.086795
38-0.021617-0.22470.411337
39-0.080773-0.83940.201543
400.059630.61970.268384
410.0144870.15050.440306
42-0.069544-0.72270.235706
430.0458840.47680.31722
440.0580960.60380.273637
45-0.173595-1.8040.037006
460.2088432.17040.016085
47-0.090536-0.94090.174435
48-0.072141-0.74970.22753
490.1798711.86930.032147
50-0.16452-1.70970.045092
510.0302910.31480.376764
520.1222411.27040.103342
53-0.200257-2.08110.019893
540.0977131.01550.156077
550.0584450.60740.272439
56-0.180557-1.87640.031649
570.1276981.32710.093641
58-0.011112-0.11550.454139
59-0.155596-1.6170.054397
600.1688791.7550.041044


Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.438076-4.55267e-06
2-0.200062-2.07910.019988
30.4290754.45911e-05
40.143221.48840.069782
50.0419130.43560.332009
60.0893190.92820.177679
7-0.221013-2.29680.011778
8-0.181518-1.88640.030964
9-0.107979-1.12220.132143
10-0.059088-0.61410.270235
11-0.060424-0.62790.265682
12-0.031387-0.32620.372459
13-0.127193-1.32180.09451
14-0.122229-1.27020.103363
150.1668081.73350.042929
160.0345320.35890.360199
17-0.028652-0.29780.383228
18-0.000568-0.00590.497652
19-0.072214-0.75050.227301
20-0.065055-0.67610.250221
210.076690.7970.213605
22-0.247753-2.57470.005693
230.2552492.65260.004595
24-0.153715-1.59740.056544
25-0.085667-0.89030.187648
26-0.066734-0.69350.244738
270.0517270.53760.295993
280.0301650.31350.377257
29-0.008405-0.08740.465277
300.1048891.090.13906
31-0.05352-0.55620.289614
320.0064920.06750.473168
33-0.046315-0.48130.315633
34-0.08707-0.90490.183778
350.0172420.17920.429065
36-0.133963-1.39220.083363
37-0.080389-0.83540.20266
38-0.026646-0.27690.391188
390.1906521.98130.025049
40-0.022544-0.23430.407603
410.0626720.65130.258116
420.0179920.1870.426013
43-0.072824-0.75680.225405
44-0.013587-0.14120.443987
45-0.040527-0.42120.337235
46-0.089416-0.92920.177419
470.1019231.05920.145933
48-0.082001-0.85220.197999
49-0.06849-0.71180.23907
50-0.04672-0.48550.314143
510.0768560.79870.213107
520.0364390.37870.352834
53-0.083593-0.86870.193463
540.0176880.18380.42725
550.0089480.0930.46304
56-0.046848-0.48690.313671
57-0.010182-0.10580.457965
58-0.081798-0.85010.198582
590.0004940.00510.497958
60-0.096965-1.00770.157928
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/22/t12930286061z6mq1zfyyzw1yg/1m6pp1293028715.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t12930286061z6mq1zfyyzw1yg/1m6pp1293028715.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t12930286061z6mq1zfyyzw1yg/2xf6s1293028715.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t12930286061z6mq1zfyyzw1yg/2xf6s1293028715.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t12930286061z6mq1zfyyzw1yg/3xf6s1293028715.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t12930286061z6mq1zfyyzw1yg/3xf6s1293028715.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 (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')
 





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