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paper/11

*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, 30 Dec 2009 08:58:59 -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/30/t1262188857wi1rh2apr963xhf.htm/, Retrieved Wed, 30 Dec 2009 17:00:59 +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/30/t1262188857wi1rh2apr963xhf.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:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
10519.20 10414.90 12476.80 12384.60 12266.70 12919.90 11497.30 12142.00 13919.40 12656.80 12034.10 13199.70 10881.30 11301.20 13643.90 12517.00 13981.10 14275.70 13425.00 13565.70 16216.30 12970.00 14079.90 14235.00 12213.40 12581.00 14130.40 14210.80 14378.50 13142.80 13714.70 13621.90 15379.80 13306.30 14391.20 14909.90 14025.40 12951.20 14344.30 16093.40 15413.60 14705.70 15972.80 16241.40 16626.40 17136.20 15622.90 18003.90 16136.10 14423.70 16789.40 16782.20 14133.80 12607.00 12004.50 12175.40 13268.00 12299.30 11800.60 13873.30 12315.00
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Autocorrelation Function
Time lag kACF(k)T-STATP-value
1-0.332441-2.57510.006253
2-0.269029-2.08390.020718
30.317752.46130.008369
4-0.099855-0.77350.22114
5-0.108045-0.83690.202981
60.2709512.09880.020027
7-0.251439-1.94760.028071
80.0324790.25160.401112
90.2452391.89960.031147
10-0.380371-2.94630.002286
11-0.082853-0.64180.26173
120.4972023.85130.000144
13-0.206086-1.59630.057834
14-0.170625-1.32170.09565
150.1213740.94020.175452
160.0076010.05890.476623
17-0.048471-0.37550.354325
180.0534280.41380.34023
19-0.021525-0.16670.43407
200.0102330.07930.468543
21-0.005438-0.04210.483271
22-0.084954-0.65810.25651
23-0.074014-0.57330.284288
240.1711111.32540.095028
250.1436331.11260.135163
26-0.299823-2.32240.01181
270.1007470.78040.219116
280.0965770.74810.228667
29-0.11956-0.92610.17905
300.037370.28950.386612
310.1567071.21390.114781
32-0.159253-1.23360.111087
330.0292710.22670.410702
340.0683570.52950.299208
35-0.216907-1.68020.049064
360.1661821.28720.101477
370.088030.68190.248971
38-0.262742-2.03520.023128
390.1350021.04570.149943
400.0503480.390.348961
41-0.135792-1.05180.148546
420.1269420.98330.164707
430.0715050.55390.290862
44-0.162938-1.26210.105897
450.1163670.90140.185496
46-0.053963-0.4180.338722
47-0.08655-0.67040.252584
480.1313711.01760.156479
49-0.034921-0.27050.393855
50-0.085639-0.66340.254821
510.0409540.31720.376085
52-0.023051-0.17850.429447
53-0.034111-0.26420.396257
540.0519990.40280.344269
55-0.029854-0.23120.408956
56-0.008386-0.0650.474212
570.0387820.30040.382453
58-0.030752-0.23820.406267
590.0018720.01450.494238
60NANANA


Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.332441-2.57510.006253
2-0.426704-3.30520.000802
30.0663850.51420.304496
4-0.058544-0.45350.325919
5-0.038629-0.29920.382905
60.1948961.50970.06819
7-0.142303-1.10230.137372
80.0457340.35430.362194
90.1264160.97920.165704
10-0.247424-1.91650.030032
11-0.300965-2.33130.011558
120.2092381.62070.055158
130.1835661.42190.080117
14-0.007141-0.05530.478036
15-0.199775-1.54740.063507
160.0954920.73970.231191
170.0006720.00520.497932
18-0.154835-1.19930.117555
190.1450611.12360.132822
200.0401020.31060.378579
21-0.259596-2.01080.024422
22-0.004451-0.03450.486305
230.0752110.58260.281179
24-0.13304-1.03050.153451
250.1595981.23620.110594
260.035340.27370.392611
270.110660.85720.197381
28-0.200447-1.55270.062883
290.0584730.45290.326116
300.1081880.8380.202674
31-0.041746-0.32340.373773
32-0.023068-0.17870.429393
330.0780910.60490.273766
340.0512410.39690.346422
35-0.166461-1.28940.101103
360.0104640.08110.467833
37-0.063428-0.49130.3125
38-0.091105-0.70570.241554
391.7e-051e-040.499949
400.1471211.13960.129493
410.0217120.16820.433502
42-0.004633-0.03590.485747
430.0773640.59930.275627
44-0.010036-0.07770.469147
45-0.024939-0.19320.423738
46-0.102093-0.79080.216085
470.0301070.23320.408196
48-0.029119-0.22560.411158
49-0.087398-0.6770.25051
50-0.019796-0.15330.439323
510.0012420.00960.496178
52-0.045498-0.35240.362877
530.0014370.01110.495577
54-0.058606-0.4540.325747
55-0.059186-0.45850.32414
56-0.104748-0.81140.210179
570.0496850.38490.350853
580.0817320.63310.26454
59-0.088605-0.68630.247573
60NANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2009/Dec/30/t1262188857wi1rh2apr963xhf/1hbog1262188737.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/30/t1262188857wi1rh2apr963xhf/1hbog1262188737.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/30/t1262188857wi1rh2apr963xhf/230hy1262188737.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/30/t1262188857wi1rh2apr963xhf/230hy1262188737.ps (open in new window)


 
Parameters (Session):
par1 = 60 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;
 
Parameters (R input):
par1 = 60 ; par2 = 1 ; par3 = 1 ; 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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