Meel, griesmeel en bakmeel d=1 & D=0
| Summary of compuational transaction | |
| Raw Input | view raw input (R code) |
| Raw Output | view raw output of R engine |
| Computing time | 1 seconds |
| R Server | 'Gwilym Jenkins' @ 72.249.127.135 |
| Autocorrelation Function | |||
| Time lag k | ACF(k) | T-STAT | P-value |
| 0 | 1 | 7.6811 | 0 |
| 1 | -0.165767 | -1.2733 | 0.896044 |
| 2 | -0.093262 | -0.7164 | 0.761703 |
| 3 | 0.270601 | 2.0785 | 0.02101 |
| 4 | -0.166771 | -1.281 | 0.897396 |
| 5 | -0.162168 | -1.2456 | 0.891089 |
| 6 | -0.016824 | -0.1292 | 0.55119 |
| 7 | -0.089998 | -0.6913 | 0.753952 |
| 8 | 0.060285 | 0.4631 | 0.322514 |
| 9 | 0.122913 | 0.9441 | 0.174482 |
| 10 | -0.24162 | -1.8559 | 0.965769 |
| 11 | -0.023436 | -0.18 | 0.571121 |
| 12 | 0.049069 | 0.3769 | 0.353798 |
| 13 | -0.242624 | -1.8636 | 0.966324 |
| 14 | -0.092341 | -0.7093 | 0.75953 |
| 15 | 0.198682 | 1.5261 | 0.066164 |
| 16 | -0.020172 | -0.1549 | 0.561302 |
| 17 | 0.052333 | 0.402 | 0.344576 |
| 18 | 0.124838 | 0.9589 | 0.170762 |
| 19 | 0.056602 | 0.4348 | 0.332659 |
| 20 | -0.089412 | -0.6868 | 0.752546 |
| 21 | 0.055932 | 0.4296 | 0.334517 |
| 22 | -0.007366 | -0.0566 | 0.522463 |
| 23 | -0.002762 | -0.0212 | 0.508428 |
| 24 | 0.142582 | 1.0952 | 0.138941 |
| 25 | -0.076271 | -0.5859 | 0.719895 |
| 26 | -0.003766 | -0.0289 | 0.511491 |
| 27 | 0.068738 | 0.528 | 0.299744 |
| 28 | -0.155053 | -1.191 | 0.880786 |
| 29 | -0.155388 | -1.1936 | 0.881286 |
| 30 | 0.135635 | 1.0418 | 0.15087 |
| 31 | -0.010379 | -0.0797 | 0.531635 |
| 32 | -0.088491 | -0.6797 | 0.750328 |
| 33 | 0.202532 | 1.5557 | 0.062567 |
| 34 | -0.089161 | -0.6849 | 0.751942 |
| 35 | -0.016656 | -0.1279 | 0.550684 |
| 36 | 0.055849 | 0.429 | 0.33475 |
| 37 | -0.158067 | -1.2141 | 0.885233 |
| 38 | -0.002846 | -0.0219 | 0.508683 |
| 39 | 0.069659 | 0.5351 | 0.29731 |
| 40 | -0.003515 | -0.027 | 0.510725 |
| 41 | 0.001088 | 0.0084 | 0.49668 |
| 42 | 0.146432 | 1.1248 | 0.132621 |
| 43 | -0.072421 | -0.5563 | 0.709937 |
| 44 | 8.4e-05 | 6e-04 | 0.499745 |
| 45 | 0.072588 | 0.5576 | 0.289626 |
| 46 | -0.141327 | -1.0856 | 0.858953 |
| 47 | 0.004018 | 0.0309 | 0.487743 |
| 48 | 0.149362 | 1.1473 | 0.127951 |
| 49 | -0.142331 | -1.0933 | 0.860639 |
| 50 | 0.003013 | 0.0231 | 0.490806 |
| 51 | 0.065641 | 0.5042 | 0.307999 |
| 52 | -0.075434 | -0.5794 | 0.717744 |
| 53 | -0.002929 | -0.0225 | 0.508938 |
| 54 | -0.003264 | -0.0251 | 0.509959 |
| 55 | 0.001339 | 0.0103 | 0.495914 |
| 56 | 0.001004 | 0.0077 | 0.496935 |
| 57 | 0.00067 | 0.0051 | 0.497957 |
| 58 | 0.000335 | 0.0026 | 0.498978 |
| 59 | NA | NA | NA |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | -0.165767 | -1.2733 | 0.896044 |
| 1 | -0.124152 | -0.9536 | 0.82792 |
| 2 | 0.242959 | 1.8662 | 0.033493 |
| 3 | -0.101652 | -0.7808 | 0.780981 |
| 4 | -0.175232 | -1.346 | 0.908271 |
| 5 | -0.169956 | -1.3055 | 0.901597 |
| 6 | -0.087834 | -0.6747 | 0.748738 |
| 7 | 0.102313 | 0.7859 | 0.21754 |
| 8 | 0.165292 | 1.2696 | 0.1046 |
| 9 | -0.255521 | -1.9627 | 0.972799 |
| 10 | -0.253743 | -1.949 | 0.971975 |
| 11 | -0.127379 | -0.9784 | 0.834068 |
| 12 | -0.101826 | -0.7821 | 0.78137 |
| 13 | -0.129026 | -0.9911 | 0.837149 |
| 14 | 0.071437 | 0.5487 | 0.292635 |
| 15 | -0.015338 | -0.1178 | 0.546691 |
| 16 | -0.032984 | -0.2534 | 0.599564 |
| 17 | -0.059479 | -0.4569 | 0.675277 |
| 18 | 0.091603 | 0.7036 | 0.24222 |
| 19 | -0.133179 | -1.023 | 0.844751 |
| 20 | 0.02403 | 0.1846 | 0.427097 |
| 21 | 0.045506 | 0.3495 | 0.363965 |
| 22 | 0.043012 | 0.3304 | 0.371142 |
| 23 | 0.05312 | 0.408 | 0.342368 |
| 24 | -0.038679 | -0.2971 | 0.616281 |
| 25 | -0.07926 | -0.6088 | 0.727505 |
| 26 | -0.010664 | -0.0819 | 0.532503 |
| 27 | -0.006077 | -0.0467 | 0.518536 |
| 28 | -0.060546 | -0.4651 | 0.678201 |
| 29 | 0.057934 | 0.445 | 0.328975 |
| 30 | 0.119297 | 0.9163 | 0.181611 |
| 31 | -0.019193 | -0.1474 | 0.558351 |
| 32 | 0.085807 | 0.6591 | 0.2562 |
| 33 | -0.128733 | -0.9888 | 0.836605 |
| 34 | 0.020455 | 0.1571 | 0.437844 |
| 35 | 0.019086 | 0.1466 | 0.441971 |
| 36 | 0.02255 | 0.1732 | 0.431538 |
| 37 | -0.074382 | -0.5713 | 0.715031 |
| 38 | -0.111882 | -0.8594 | 0.803197 |
| 39 | 0.02801 | 0.2151 | 0.415197 |
| 40 | -0.008595 | -0.066 | 0.526207 |
| 41 | 0.066587 | 0.5115 | 0.305468 |
| 42 | -0.029138 | -0.2238 | 0.588162 |
| 43 | 0.042953 | 0.3299 | 0.371313 |
| 44 | 0.049078 | 0.377 | 0.353774 |
| 45 | -0.041804 | -0.3211 | 0.625366 |
| 46 | -0.036382 | -0.2795 | 0.609563 |
| 47 | 0.05979 | 0.4593 | 0.323868 |
| 48 | -0.079603 | -0.6114 | 0.728371 |
| 49 | -0.059847 | -0.4597 | 0.676288 |
| 50 | -0.051232 | -0.3935 | 0.652324 |
| 51 | 0.008914 | 0.0685 | 0.472822 |
| 52 | 0.061606 | 0.4732 | 0.318907 |
| 53 | -0.021676 | -0.1665 | 0.565832 |
| 54 | 0.025676 | 0.1972 | 0.422165 |
| 55 | -0.044306 | -0.3403 | 0.632589 |
| 56 | -0.115031 | -0.8836 | 0.809743 |
| 57 | 0.112346 | 0.8629 | 0.195831 |
| 58 | NA | NA | NA |
| 59 | NA | NA | NA |
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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 (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='lags',ylab='ACF')
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 1:par1) {
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(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-1,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(mytstat,lengthx),6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
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