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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: Sun, 19 Dec 2010 19:28:58 +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/19/t1292786811xd97p4bqnbzdzte.htm/, Retrieved Sun, 19 Dec 2010 20:26:51 +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/19/t1292786811xd97p4bqnbzdzte.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 «
4.031636 3.702076 3.056176 3.280707 2.984728 3.693712 3.226317 2.190349 2.599515 3.080288 2.929672 2.922548 3.234943 2.983081 3.284389 3.806511 3.784579 2.645654 3.092081 3.204859 3.107225 3.466909 2.984404 3.218072 2.82731 3.182049 2.236319 2.033218 1.644804 1.627971 1.677559 2.330828 2.493615 2.257172 2.655517 2.298655 2.600402 3.04523 2.790583 3.227052 2.967479 2.938817 3.277961 3.423985 3.072646 2.754253 2.910431 3.174369 3.068387 3.089543 2.906654 2.931161 3.02566 2.939551 2.691019 3.19812 3.07639 2.863873 3.013802 3.053364 2.864753 3.057062 2.959365 3.252258 3.602988 3.497704 3.296867 3.602417 3.3001 3.40193 3.502591 3.402348 3.498551 3.199823 2.700064 2.801034 2.898628 2.800854 2.399942 2.402724 2.202331 2.102594 1.798293 1.202484 1.400201 1.200832 1.298083 1.099742 1.001377 0.8361743
 
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'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.8039417.62690
20.6739836.3940
30.5960625.65470
40.5078164.81763e-06
50.4191023.9767.1e-05
60.3055892.89910.002351
70.1595521.51360.06681
80.0793120.75240.22688
90.0611040.57970.281787
10-0.009155-0.08680.465492
11-0.080281-0.76160.224141
12-0.146893-1.39360.083442
13-0.187019-1.77420.039705
14-0.209324-1.98580.025048
15-0.195477-1.85450.033474
16-0.211873-2.010.023713
17-0.242827-2.30370.011772
18-0.198215-1.88040.031641
19-0.185892-1.76350.040603
20-0.15534-1.47370.072028
21-0.12655-1.20060.116536
22-0.123806-1.17450.121642
23-0.115599-1.09670.137856
24-0.115221-1.09310.138637
25-0.110949-1.05260.147681
26-0.106459-1.010.157612
27-0.093563-0.88760.188556
28-0.09721-0.92220.179441
29-0.115193-1.09280.138696
30-0.120702-1.14510.127606
31-0.095556-0.90650.183539
32-0.101542-0.96330.168987
33-0.103835-0.98510.163616
34-0.110362-1.0470.148955
35-0.142992-1.35650.08916
36-0.145837-1.38350.084961
37-0.130544-1.23840.109385
38-0.123906-1.17550.121453
39-0.123657-1.17310.121923
40-0.132446-1.25650.106095
41-0.122048-1.15780.124995
42-0.089107-0.84530.200081
43-0.057344-0.5440.29389
44-0.053957-0.51190.304996
45-0.044603-0.42310.336601
46-0.000866-0.00820.496732
470.0597330.56670.286172
480.1004030.95250.171697
490.1344071.27510.102778
500.1669561.58390.058364
510.2071271.9650.02625
520.2365432.2440.013641
530.2636882.50160.007086
540.2815952.67140.004482
550.2876082.72850.003826
560.2807742.66370.004579
570.2476982.34990.010483
580.2327752.20830.014882
590.2049271.94410.027503
600.1414041.34150.09157


Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.8039417.62690
20.0782120.7420.230015
30.0959260.910.182619
4-0.032596-0.30920.378931
5-0.040089-0.38030.352303
6-0.132718-1.25910.105631
7-0.201543-1.9120.029528
80.0198040.18790.425699
90.1267131.20210.116238
10-0.088991-0.84420.200387
11-0.056046-0.53170.298121
12-0.083936-0.79630.21398
13-0.020984-0.19910.421327
14-0.048119-0.45650.324568
150.0809780.76820.222183
16-0.009021-0.08560.465994
17-0.075853-0.71960.236817
180.1029960.97710.165569
19-0.079762-0.75670.225605
200.0379680.36020.359774
21-0.004422-0.0420.483315
22-0.037518-0.35590.361364
23-0.021715-0.2060.418627
24-0.126913-1.2040.115873
250.0085750.08130.467673
260.0031690.03010.488043
270.010780.10230.459384
28-0.009935-0.09430.462559
29-0.094537-0.89690.186093
30-0.020186-0.19150.424282
310.036860.34970.363695
32-0.041275-0.39160.34815
330.0043580.04130.483557
34-0.03904-0.37040.35599
35-0.088442-0.8390.201836
36-0.067487-0.64020.261822
370.035190.33380.369638
380.029430.27920.390368
39-0.013164-0.12490.450448
40-0.065331-0.61980.268483
41-0.021474-0.20370.419518
420.0180530.17130.432198
430.0069940.06640.473621
44-0.014743-0.13990.444541
450.0263720.25020.401506
460.0453440.43020.334051
470.066450.63040.265016
480.0068770.06520.474064
490.0199150.18890.425286
500.0675760.64110.26155
510.0323670.30710.379752
52-0.050002-0.47440.318195
530.0455980.43260.333176
540.0490920.46570.321267
550.0394040.37380.35471
56-0.016706-0.15850.437215
57-0.090508-0.85860.196411
580.0422990.40130.344582
59-0.028309-0.26860.394442
60-0.090463-0.85820.19653
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292786811xd97p4bqnbzdzte/1kx7i1292786935.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292786811xd97p4bqnbzdzte/1kx7i1292786935.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/19/t1292786811xd97p4bqnbzdzte/2kx7i1292786935.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292786811xd97p4bqnbzdzte/2kx7i1292786935.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 (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')
 





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