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*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: Tue, 28 Dec 2010 16:35:17 +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/28/t1293553979o52nftsjylaznf0.htm/, Retrieved Tue, 28 Dec 2010 17:33:02 +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/28/t1293553979o52nftsjylaznf0.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 «
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 9706 8579 9474 8318 8213 8059 9111 7708 7680 8014 8007 8718 9486 9113 9025 8476 7952 7759 7835 7600 7651 8319 8812 8630
 
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
10.4931394.18444e-05
20.2993282.53990.006624
30.0423910.35970.360064
4-0.270287-2.29350.012371
5-0.396917-3.36790.000609
6-0.442957-3.75860.000172
7-0.362315-3.07430.00149
8-0.306288-2.59890.005668
90.023690.2010.420626
100.182651.54980.062783
110.367713.12010.0013
120.6300885.34651e-06
130.3647023.09460.001403
140.2813322.38720.009803
150.0805970.68390.248121
16-0.171921-1.45880.074485
17-0.284638-2.41520.009133
18-0.371098-3.14890.001193
19-0.326441-2.76990.003564
20-0.293631-2.49150.007512
21-0.027175-0.23060.409143
220.158631.3460.091259
230.3812823.23530.000918
240.5428474.60629e-06
250.2898592.45950.008157
260.2141421.81710.036685
270.0176430.14970.440708
28-0.21217-1.80030.037999
29-0.285259-2.42050.009011
30-0.318965-2.70650.004242
31-0.286276-2.42910.008816
32-0.264649-2.24560.013899
33-0.082574-0.70070.242884
340.0319340.2710.393595
350.2003031.69960.046759
360.3465572.94060.002201
370.2341951.98720.025352
380.2311381.96130.026858
390.0484130.41080.341221
40-0.087849-0.74540.229221
41-0.168618-1.43080.078411
42-0.22063-1.87210.032626
43-0.220457-1.87060.032729
44-0.225133-1.91030.030038
45-0.084314-0.71540.238328
46-0.012761-0.10830.457036
470.118991.00970.158019
480.1707111.44850.075904
490.0651540.55290.291038
500.0807210.68490.247791
51-0.020174-0.17120.432279
52-0.082227-0.69770.2438
53-0.128713-1.09220.139201
54-0.078031-0.66210.255006
55-0.138934-1.17890.121161
56-0.112139-0.95150.17226
57-0.045593-0.38690.349997
580.0083490.07080.471859
590.0662770.56240.287801
600.1014140.86050.196178


Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.4931394.18444e-05
20.0741820.62950.265522
3-0.173855-1.47520.072258
4-0.343084-2.91120.002395
5-0.189253-1.60590.05634
6-0.127689-1.08350.141106
7-0.046211-0.39210.348065
8-0.187948-1.59480.05757
90.1862111.58010.059239
100.0597980.50740.306713
110.1464661.24280.108985
120.3987293.38330.000581
13-0.177133-1.5030.068603
140.0562610.47740.317266
150.0746610.63350.2642
16-0.03261-0.27670.391399
170.091780.77880.219332
18-0.077008-0.65340.257778
190.0058750.04980.48019
20-0.003698-0.03140.487526
21-0.017656-0.14980.440665
220.1713351.45380.07517
230.1605821.36260.08863
240.0582480.49430.311316
25-0.174624-1.48170.071387
26-0.080462-0.68270.248479
270.0423570.35940.36017
28-0.06632-0.56270.28768
290.0139610.11850.453015
300.0479920.40720.342524
31-0.032733-0.27770.391001
32-0.08028-0.68120.248966
33-0.182911-1.55210.062517
34-0.116178-0.98580.163766
35-0.086044-0.73010.233848
36-0.042151-0.35770.36082
370.0390210.33110.370765
380.007240.06140.475594
39-0.1061-0.90030.185483
400.048990.41570.339436
410.0014010.01190.495274
420.0601080.510.305794
430.0561750.47670.317523
44-0.006761-0.05740.477204
450.0169310.14370.443084
46-0.074335-0.63080.265099
47-0.045549-0.38650.350134
48-0.036743-0.31180.378055
49-0.142668-1.21060.115009
50-0.013048-0.11070.456076
510.0454430.38560.350467
52-0.03136-0.26610.395461
53-0.119185-1.01130.157626
540.0604540.5130.304771
55-0.089766-0.76170.224366
560.0941810.79920.213414
57-0.004384-0.03720.485216
580.1280861.08680.140364
59-0.037847-0.32110.374517
60-0.068924-0.58480.280243
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293553979o52nftsjylaznf0/1uwur1293554112.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293553979o52nftsjylaznf0/1uwur1293554112.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t1293553979o52nftsjylaznf0/2ffbe1293554112.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293553979o52nftsjylaznf0/2ffbe1293554112.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t1293553979o52nftsjylaznf0/3ffbe1293554112.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293553979o52nftsjylaznf0/3ffbe1293554112.ps (open in new window)


 
Parameters (Session):
par1 = 60 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;
 
Parameters (R input):
par1 = 60 ; 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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Software written by Ed van Stee & Patrick Wessa


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