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ACF aanvoerwaarde

*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: Thu, 16 Dec 2010 11:01:16 +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/16/t1292497148ko76p6r9ves7g0x.htm/, Retrieved Thu, 16 Dec 2010 11:59:11 +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/16/t1292497148ko76p6r9ves7g0x.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 «
6 6.81 9.75 6.96 3.94 5 4.9 5.7 6.5 7.1 7.5 7.8 7 7.4 8.55 7.43 4.7 4.7 5.3 6.2 7.4 7.5 7.32 8.15 7.24 7.4 9.4 8.9 4.5 4.9 5.6 6.4 6 6.9 6.7 5.4 5.6 6.9 6.9 7 4 3.7 4.9 5 5.7 6.1 5.3 5.5 5.7 5.21 5.4 4.5 3.7 4.1 4.8 4.1 5 5.2 5.5 5.9
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'George Udny Yule' @ 72.249.76.132


Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.6206264.80735e-06
20.2168821.680.049082
30.1272470.98570.164131
40.0855350.66260.255076
50.0744590.57680.28313
60.1142510.8850.189848
70.0733040.56780.286142
80.0477110.36960.356503
90.0447230.34640.365119
100.1375221.06520.145518
110.3848672.98120.002072
120.5687984.40592.2e-05
130.3350742.59550.005929
140.0002530.0020.49922
15-0.091447-0.70830.240737
16-0.090432-0.70050.243167
17-0.096778-0.74960.2282
18-0.074481-0.57690.283074
19-0.10586-0.820.207735
20-0.149834-1.16060.1252
21-0.124709-0.9660.168963
22-0.067415-0.52220.30173
230.0737730.57140.284917
240.2227861.72570.044775
250.0717090.55550.290324
26-0.196918-1.52530.066217
27-0.236466-1.83170.035984
28-0.224751-1.74090.043412
29-0.233987-1.81250.03746
30-0.220267-1.70620.046573
31-0.187491-1.45230.075814
32-0.196455-1.52170.066665
33-0.179999-1.39430.084189
34-0.089166-0.69070.246216
350.0076860.05950.47636
360.0739270.57260.284514
370.020970.16240.435754
38-0.127298-0.9860.164035
39-0.152989-1.1850.120336
40-0.145687-1.12850.131804
41-0.156578-1.21290.11497
42-0.121213-0.93890.175771
43-0.101244-0.78420.217994
44-0.103895-0.80480.212066
45-0.055168-0.42730.335335
46-0.018589-0.1440.442994
470.0032750.02540.489923
480.0211820.16410.435113


Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.6206264.80735e-06
2-0.273728-2.12030.019063
30.2209771.71170.04606
4-0.115794-0.89690.186669
50.1231210.95370.172032
60.0336610.26070.397593
7-0.06631-0.51360.304698
80.086980.67370.251531
9-0.056243-0.43570.332325
100.247671.91840.029908
110.3278792.53970.006851
120.2543281.970.026728
13-0.317358-2.45820.008433
14-0.164212-1.2720.104144
15-0.054455-0.42180.337336
16-0.071431-0.55330.291056
17-0.040017-0.310.378829
18-0.09425-0.73010.234099
19-0.070546-0.54640.293393
20-0.059513-0.4610.323239
210.0249040.19290.423842
22-0.14894-1.15370.126602
23-0.027536-0.21330.41591
240.0638620.49470.311319
25-0.095598-0.74050.230942
26-0.011251-0.08710.465421
270.0243510.18860.425512
28-0.108696-0.8420.201578
290.0023440.01820.492788
30-0.090885-0.7040.242081
310.1386521.0740.143563
32-0.080248-0.62160.268281
330.0398730.30890.37925
340.0798540.61850.269278
35-0.098916-0.76620.223281
360.0488590.37850.353212
370.0075860.05880.47667
380.103790.8040.212299
390.0001470.00110.499547
40-0.047503-0.3680.357099
410.0623590.4830.315417
42-0.024866-0.19260.423958
43-0.035781-0.27720.391307
44-0.007124-0.05520.478089
450.0093280.07230.471319
46-0.115084-0.89140.188127
47-0.042413-0.32850.371828
48-0.120663-0.93470.176857
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/16/t1292497148ko76p6r9ves7g0x/1komb1292497271.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/16/t1292497148ko76p6r9ves7g0x/1komb1292497271.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/16/t1292497148ko76p6r9ves7g0x/2dfmw1292497271.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/16/t1292497148ko76p6r9ves7g0x/2dfmw1292497271.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/16/t1292497148ko76p6r9ves7g0x/3dfmw1292497271.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/16/t1292497148ko76p6r9ves7g0x/3dfmw1292497271.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 (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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