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Paper Statistiek

*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: Mon, 27 Dec 2010 18:47:15 +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/27/t1293475558ivkyvgqknd4eltg.htm/, Retrieved Mon, 27 Dec 2010 19:45:58 +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/27/t1293475558ivkyvgqknd4eltg.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:
Partial Autocorrelation Function - Gemiddelde bouwgrondprijzen België (1995-2009)
 
Dataseries X:
» Textbox « » Textfile « » CSV «
26 26 27 28 27 29 27 30 27 30 32 30 32 33 34 32 34 37 37 36 34 38 41 41 44 42 45 45 49 54 52 53 51 55 60 60 63 60 64 65 75 70 72 69 75 74 74 75 79 79 85 78 84 85 85 82 91 90 98 98
 
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
10.9404527.28470
20.8889546.88580
30.8417636.52030
40.7995786.19350
50.761725.90030
60.7207965.58330
70.6702155.19151e-06
80.6262724.85115e-06
90.5823574.51091.5e-05
100.5317554.1195.9e-05
110.4856813.76210.000192
120.4387653.39870.000604
130.3910423.0290.001808
140.3489052.70260.004467
150.3005962.32840.011639
160.253221.96140.027237
170.2094341.62230.054995
180.1646351.27530.103567
190.1179180.91340.182347
200.0594550.46050.323399
210.0133940.10380.458856
22-0.02814-0.2180.414095
23-0.063572-0.49240.312109
24-0.105756-0.81920.207962
25-0.144436-1.11880.133843
26-0.184305-1.42760.079293
27-0.215958-1.67280.049787
28-0.242979-1.88210.032337
29-0.271187-2.10060.019943
30-0.29372-2.27510.013242
31-0.323264-2.5040.007509
32-0.34644-2.68350.004701
33-0.366347-2.83770.003095
34-0.382973-2.96650.00216
35-0.390012-3.0210.001849
36-0.403496-3.12550.001368
37-0.40938-3.1710.001197
38-0.420102-3.25410.000935
39-0.421588-3.26560.000904
40-0.416598-3.2270.001014
41-0.406347-3.14760.001282
42-0.402812-3.12020.001389
43-0.397117-3.07610.001579
44-0.391553-3.0330.001787
45-0.376393-2.91550.002493
46-0.366225-2.83680.003103
47-0.354604-2.74680.003967
48-0.341908-2.64840.00516


Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.9404527.28470
20.038990.3020.381842
30.0144970.11230.455484
40.022850.1770.430053
50.0214060.16580.434431
6-0.039459-0.30560.380465
7-0.105528-0.81740.208461
80.0189060.14640.442031
9-0.026353-0.20410.419472
10-0.091754-0.71070.240005
11-0.002215-0.01720.493185
12-0.030581-0.23690.40678
13-0.039506-0.3060.380328
140.004590.03560.485878
15-0.073655-0.57050.285225
16-0.024926-0.19310.423776
17-0.014895-0.11540.454267
18-0.038946-0.30170.381971
19-0.054992-0.4260.335827
20-0.15546-1.20420.116622
210.0577120.4470.328229
22-0.00712-0.05520.4781
230.002820.02180.491323
24-0.078524-0.60820.272662
25-0.003237-0.02510.490038
26-0.038868-0.30110.382202
270.004460.03450.486278
280.0046460.0360.485706
29-0.029107-0.22550.411194
300.0107170.0830.467059
31-0.097784-0.75740.225877
320.0106440.08240.467282
33-0.019946-0.15450.438867
340.004030.03120.4876
350.0511220.3960.34676
36-0.090567-0.70150.242842
370.0467030.36180.359403
38-0.067311-0.52140.302007
390.0525330.40690.342757
400.0182120.14110.444144
410.0197580.1530.439437
42-0.062269-0.48230.315661
430.009270.07180.471498
44-0.040169-0.31110.378385
450.0718970.55690.28983
46-0.094213-0.72980.234186
470.007760.06010.476134
480.0044390.03440.486343
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/27/t1293475558ivkyvgqknd4eltg/1yz0n1293475631.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/27/t1293475558ivkyvgqknd4eltg/1yz0n1293475631.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/27/t1293475558ivkyvgqknd4eltg/2rqiq1293475631.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/27/t1293475558ivkyvgqknd4eltg/2rqiq1293475631.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/27/t1293475558ivkyvgqknd4eltg/3rqiq1293475631.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/27/t1293475558ivkyvgqknd4eltg/3rqiq1293475631.ps (open in new window)


 
Parameters (Session):
par1 = 48 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 4 ; par6 = White Noise ; par7 = 0.95 ;
 
Parameters (R input):
par1 = 48 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 4 ; 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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