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Workshop 9

*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, 10 Dec 2009 15:51:31 -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/10/t1260485533af9199kf0aqu88z.htm/, Retrieved Thu, 10 Dec 2009 23:52:15 +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/10/t1260485533af9199kf0aqu88z.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:
verbetering 4
 
Dataseries X:
» Textbox « » Textfile « » CSV «
1.43 1.43 1.43 1.43 1.43 1.43 1.44 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.57 1.58 1.58 1.58 1.58 1.59 1.6 1.6 1.61 1.61 1.61 1.62 1.63 1.63 1.64 1.64 1.64 1.64 1.64 1.65 1.65 1.65 1.65
 
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
1-0.428571-3.26390.000923
2-0.076923-0.58580.280132
30.0109890.08370.466796
4-0.06044-0.46030.323513
50.0549450.41840.338582
60.0494510.37660.353921
7-0.093407-0.71140.239855
80.0989010.75320.227186
9-0.054945-0.41840.338582
10-0.054945-0.41840.338582
110.0659340.50210.308735
120.0274730.20920.417503
13-0.082418-0.62770.266341
140.0989010.75320.227186
15-0.054945-0.41840.338582
16000.5
17-0.005495-0.04180.483383
18-0.038462-0.29290.385317
190.0934070.71140.239855
20-0.049451-0.37660.353921
21000.5
22000.5
23000.5
24000.5
25000.5
26000.5
27000.5
28000.5
29-0.197802-1.50640.068694
300.3241762.46880.008261
31-0.06044-0.46030.323513
32-0.06044-0.46030.323513
33-0.005495-0.04180.483383
34-0.021978-0.16740.433827
350.0164840.12550.450267
360.0274730.20920.417503
37-0.038462-0.29290.385317
380.0329670.25110.401324
39-0.010989-0.08370.466796
40-0.027473-0.20920.417503
410.0164840.12550.450267
420.0274730.20920.417503
43-0.038462-0.29290.385317
440.0329670.25110.401324
45-0.010989-0.08370.466796
46-0.005495-0.04180.483383
47000.5
48-0.021978-0.16740.433827
490.0384620.29290.385317
50-0.010989-0.08370.466796
51-0.005495-0.04180.483383
52000.5
53000.5
54000.5
55000.5
56000.5
57000.5
58NANANA
59NANANA
60NANANA


Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.428571-3.26390.000923
2-0.319231-2.43120.00908
3-0.230947-1.75880.04194
4-0.269431-2.05190.02235
5-0.198321-1.51040.068189
6-0.095687-0.72870.23455
7-0.176395-1.34340.09219
8-0.044556-0.33930.367793
9-0.064261-0.48940.313204
10-0.139123-1.05950.146875
11-0.098559-0.75060.227963
12-0.023037-0.17540.43067
13-0.119234-0.90810.183802
14-0.010854-0.08270.467204
15-0.011629-0.08860.464865
16-0.009952-0.07580.469922
17-0.038561-0.29370.385028
18-0.087236-0.66440.254544
190.0116430.08870.464824
20-0.046992-0.35790.360865
21-0.008318-0.06330.474855
22-0.028423-0.21650.414694
23-0.016189-0.12330.45115
24-0.013642-0.10390.458805
25-0.02567-0.19550.422843
26-0.023733-0.18070.428599
27-0.035755-0.27230.39318
28-0.032056-0.24410.403997
29-0.377638-2.8760.002813
30-0.050805-0.38690.350117
31-0.012796-0.09740.461353
320.0043270.0330.486913
33-0.021389-0.16290.435584
340.0193320.14720.441732
350.014880.11330.455082
36-0.012407-0.09450.462522
370.024520.18670.426258
38-0.0221-0.16830.433463
39-0.006029-0.04590.481767
40-0.000419-0.00320.498732
410.0137470.10470.45849
42-0.017908-0.13640.445995
430.0160530.12230.451558
44-0.010751-0.08190.467513
45-0.031172-0.23740.406591
46-0.017942-0.13660.445893
47-0.023872-0.18180.428186
48-0.004286-0.03260.487037
49-0.024653-0.18780.425862
50-0.003329-0.02540.489929
51-0.032376-0.24660.403056
52-0.01152-0.08770.465196
53-0.023377-0.1780.429657
54-0.031216-0.23770.406462
55-0.01655-0.1260.450067
56-0.011591-0.08830.464982
57-0.003859-0.02940.488327
58NANANA
59NANANA
60NANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2009/Dec/10/t1260485533af9199kf0aqu88z/13nwe1260485489.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/10/t1260485533af9199kf0aqu88z/13nwe1260485489.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/10/t1260485533af9199kf0aqu88z/2za6g1260485489.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/10/t1260485533af9199kf0aqu88z/2za6g1260485489.ps (open in new window)


 
Parameters (Session):
par1 = 60 ; par2 = 1 ; par3 = 2 ; par4 = 0 ; par5 = 12 ; par6 = MA ; par7 = 0.95 ;
 
Parameters (R input):
par1 = 60 ; par2 = 1 ; par3 = 2 ; par4 = 0 ; par5 = 12 ; par6 = MA ; 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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