Home » date » 2011 » May » 02 »

Autocorrelation niet werkende werkzoekende Sebastiaan Vandesompele

*Unverified author*
R Software Module: /rwasp_autocorrelation.wasp (opens new window with default values)
Title produced by software: (Partial) Autocorrelation Function
Date of computation: Mon, 02 May 2011 08:50:06 +0000
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2011/May/02/t13043260348j1da83ldstxqn2.htm/, Retrieved Mon, 02 May 2011 10:47:14 +0200
 
Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
KDGP2W12
 
Dataseries X:
» Textbox « » Textfile « » CSV «
89507 87562 85209 82360 79054 79069 107551 115759 115585 110260 103444 102303 101397 97994 94044 91159 87239 89235 118647 125620 125154 117529 109459 108483 107137 104699 100804 96066 91971 93228 120144 127233 127166 118194 109940 106683 102834 99882 96666 92540 88744 89321 115870 122401 122030 113802 105791 103076 98658 96945 92497 90687 88796 90015 113228 118711 117460 106556 97347 92657 93118 89037 83570 81693 75956 73993 97088 102394 96549 89727 82336 82653 82303 79596 74472 73562 66618 69029 89899 93774 90305 83799 80320 82497
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk


Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.3294293.00120.001775
2-0.129373-1.17860.120954
3-0.322111-2.93460.002159
4-0.288366-2.62710.005127
5-0.063899-0.58210.281024
60.0007690.0070.497215
7-0.062089-0.56570.286577
8-0.256579-2.33750.010909
9-0.270498-2.46440.007895
10-0.098951-0.90150.18497
110.3156152.87540.002563
120.8517587.75990
130.2675362.43740.008465
14-0.116882-1.06480.145016
15-0.299557-2.72910.003876
16-0.252583-2.30110.011946
17-0.052726-0.48040.316118
18-0.010783-0.09820.460989
19-0.053518-0.48760.313568
20-0.215195-1.96050.026645
21-0.222852-2.03030.022765
22-0.063923-0.58240.280948
230.2596282.36530.010173
240.6942486.32490
250.2211582.01480.023579
26-0.102629-0.9350.176251
27-0.26236-2.39020.009551
28-0.204553-1.86360.032959
29-0.048859-0.44510.328695
30-0.018961-0.17270.431638
31-0.036395-0.33160.370522
32-0.164129-1.49530.069316
33-0.154146-1.40430.081975
34-0.048209-0.43920.330828
350.2039121.85770.033377
360.5471994.98522e-06
370.1632341.48710.070384
38-0.107512-0.97950.165094
39-0.219392-1.99880.024454
40-0.157445-1.43440.077609
41-0.030381-0.27680.391317
420.003660.03330.48674
43-0.022088-0.20120.420505
44-0.126565-1.15310.126098
45-0.104298-0.95020.172385
46-0.040778-0.37150.355605
470.1460581.33070.093474
480.3956453.60450.000266


Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.3294293.00120.001775
2-0.266857-2.43120.008601
3-0.217645-1.98280.025346
4-0.156849-1.4290.078384
5-0.006413-0.05840.476775
6-0.140155-1.27690.102604
7-0.183148-1.66860.049486
8-0.364302-3.31890.000672
9-0.333239-3.0360.001601
10-0.379967-3.46170.000425
11-0.077946-0.71010.239808
120.6978426.35760
13-0.273455-2.49130.00736
140.1374651.25240.106978
150.0728150.66340.254464
160.1034710.94270.174294
17-0.0185-0.16850.433282
180.0006190.00560.497757
190.0792420.72190.236183
200.0390210.35550.361559
210.0025020.02280.490933
220.0660410.60170.274519
23-0.161408-1.47050.072604
24-0.018907-0.17220.43183
250.007220.06580.473856
26-0.078213-0.71260.239059
27-0.017536-0.15980.436728
28-0.002506-0.02280.490919
29-0.056412-0.51390.304332
30-0.010325-0.09410.462641
31-0.000179-0.00160.499351
320.051610.47020.319726
330.0675570.61550.269964
34-0.061762-0.56270.287586
350.0925890.84350.200679
36-0.065525-0.5970.27608
37-0.022413-0.20420.419352
38-0.076615-0.6980.243565
390.0174270.15880.43712
40-0.045397-0.41360.340123
410.0145190.13230.447544
420.0490540.44690.328055
43-0.034325-0.31270.37764
44-0.029601-0.26970.394039
450.0384960.35070.363344
46-0.00926-0.08440.466484
47-0.063263-0.57640.282968
48-0.112859-1.02820.153422
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2011/May/02/t13043260348j1da83ldstxqn2/1vw5o1304326201.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/02/t13043260348j1da83ldstxqn2/1vw5o1304326201.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/May/02/t13043260348j1da83ldstxqn2/2ad941304326201.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/02/t13043260348j1da83ldstxqn2/2ad941304326201.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/May/02/t13043260348j1da83ldstxqn2/3lxg21304326201.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/02/t13043260348j1da83ldstxqn2/3lxg21304326201.ps (open in new window)


 
Parameters (Session):
par1 = 48 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;
 
Parameters (R input):
par1 = 48 ; 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 (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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