Meel, griesmeel en bakmeel d=0 & D=0
| Summary of compuational transaction | |
| Raw Input | view raw input (R code) |
| Raw Output | view raw output of R engine |
| Computing time | 2 seconds |
| R Server | 'Gwilym Jenkins' @ 72.249.127.135 |
| Autocorrelation Function | |||
| Time lag k | ACF(k) | T-STAT | P-value |
| 0 | 1 | 7.746 | 0 |
| 1 | 0.915049 | 7.0879 | 0 |
| 2 | 0.844915 | 6.5447 | 0 |
| 3 | 0.782189 | 6.0588 | 0 |
| 4 | 0.68983 | 5.3434 | 1e-06 |
| 5 | 0.622412 | 4.8212 | 5e-06 |
| 6 | 0.579565 | 4.4893 | 1.7e-05 |
| 7 | 0.536719 | 4.1574 | 5.2e-05 |
| 8 | 0.511405 | 3.9613 | 1e-04 |
| 9 | 0.488437 | 3.7834 | 0.00018 |
| 10 | 0.421019 | 3.2612 | 0.000916 |
| 11 | 0.375826 | 2.9111 | 0.002524 |
| 12 | 0.330633 | 2.5611 | 0.006484 |
| 13 | 0.278032 | 2.1536 | 0.017649 |
| 14 | 0.247656 | 1.9183 | 0.029915 |
| 15 | 0.234443 | 1.816 | 0.037185 |
| 16 | 0.199004 | 1.5415 | 0.064229 |
| 17 | 0.163565 | 1.267 | 0.105031 |
| 18 | 0.120719 | 0.9351 | 0.176747 |
| 19 | 0.063055 | 0.4884 | 0.313515 |
| 20 | 0.017862 | 0.1384 | 0.445209 |
| 21 | -0.019922 | -0.1543 | 0.561061 |
| 22 | -0.065115 | -0.5044 | 0.692079 |
| 23 | -0.080675 | -0.6249 | 0.732798 |
| 24 | -0.08648 | -0.6699 | 0.747245 |
| 25 | -0.107102 | -0.8296 | 0.794975 |
| 26 | -0.120315 | -0.932 | 0.822453 |
| 27 | -0.133529 | -1.0343 | 0.847429 |
| 28 | -0.154151 | -1.194 | 0.88142 |
| 29 | -0.16971 | -1.3146 | 0.903171 |
| 30 | -0.170453 | -1.3203 | 0.904129 |
| 31 | -0.186013 | -1.4408 | 0.922587 |
| 32 | -0.201572 | -1.5614 | 0.938152 |
| 33 | -0.229602 | -1.7785 | 0.959805 |
| 34 | -0.279858 | -2.1678 | 0.982923 |
| 35 | -0.322704 | -2.4997 | 0.992408 |
| 36 | -0.365551 | -2.8315 | 0.996852 |
| 37 | -0.415806 | -3.2208 | 0.998967 |
| 38 | -0.431366 | -3.3413 | 0.999281 |
| 39 | -0.417292 | -3.2323 | 0.999002 |
| 40 | -0.410627 | -3.1807 | 0.998837 |
| 41 | -0.403961 | -3.1291 | 0.998647 |
| 42 | -0.387542 | -3.0019 | 0.998047 |
| 43 | -0.385939 | -2.9895 | 0.997976 |
| 44 | -0.376927 | -2.9197 | 0.997536 |
| 45 | -0.367916 | -2.8499 | 0.997007 |
| 46 | -0.366313 | -2.8374 | 0.996903 |
| 47 | -0.330014 | -2.5563 | 0.993435 |
| 48 | -0.293715 | -2.2751 | 0.986757 |
| 49 | -0.272233 | -2.1087 | 0.980423 |
| 50 | -0.235934 | -1.8275 | 0.963704 |
| 51 | -0.199636 | -1.5464 | 0.936363 |
| 52 | -0.200379 | -1.5521 | 0.937054 |
| 53 | -0.173834 | -1.3465 | 0.908399 |
| 54 | -0.157414 | -1.2193 | 0.886254 |
| 55 | -0.140995 | -1.0921 | 0.860431 |
| 56 | -0.104696 | -0.811 | 0.789707 |
| 57 | -0.078522 | -0.6082 | 0.727334 |
| 58 | -0.052348 | -0.4055 | 0.656719 |
| 59 | -0.026174 | -0.2027 | 0.579989 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.915049 | 7.0879 | 0 |
| 1 | 0.046716 | 0.3619 | 0.359364 |
| 2 | 0.014913 | 0.1155 | 0.45421 |
| 3 | -0.209842 | -1.6254 | 0.945343 |
| 4 | 0.077198 | 0.598 | 0.276054 |
| 5 | 0.122481 | 0.9487 | 0.17328 |
| 6 | 0.034761 | 0.2693 | 0.394329 |
| 7 | 0.051735 | 0.4007 | 0.345018 |
| 8 | -0.024468 | -0.1895 | 0.574841 |
| 9 | -0.27737 | -2.1485 | 0.98214 |
| 10 | 0.063581 | 0.4925 | 0.312082 |
| 11 | 0.01044 | 0.0809 | 0.467909 |
| 12 | 0.049744 | 0.3853 | 0.350685 |
| 13 | 0.025973 | 0.2012 | 0.420618 |
| 14 | 0.055158 | 0.4273 | 0.335363 |
| 15 | -0.163663 | -1.2677 | 0.895104 |
| 16 | -0.110396 | -0.8551 | 0.802058 |
| 17 | -0.089912 | -0.6965 | 0.755584 |
| 18 | 0.024972 | 0.1934 | 0.423637 |
| 19 | 0.022203 | 0.172 | 0.432014 |
| 20 | 0.046169 | 0.3576 | 0.36094 |
| 21 | -0.117084 | -0.9069 | 0.815963 |
| 22 | 0.037201 | 0.2882 | 0.387109 |
| 23 | -0.016611 | -0.1287 | 0.550973 |
| 24 | 0.009041 | 0.07 | 0.472201 |
| 25 | -0.016893 | -0.1309 | 0.551835 |
| 26 | 0.033941 | 0.2629 | 0.396761 |
| 27 | -0.016394 | -0.127 | 0.550311 |
| 28 | -0.028401 | -0.22 | 0.586688 |
| 29 | 0.034965 | 0.2708 | 0.393723 |
| 30 | -0.067098 | -0.5197 | 0.69742 |
| 31 | -0.122783 | -0.9511 | 0.827308 |
| 32 | -0.131281 | -1.0169 | 0.843358 |
| 33 | -0.133456 | -1.0337 | 0.847299 |
| 34 | -0.005688 | -0.0441 | 0.517499 |
| 35 | -0.022679 | -0.1757 | 0.569427 |
| 36 | -0.054341 | -0.4209 | 0.662341 |
| 37 | 0.071481 | 0.5537 | 0.290924 |
| 38 | 0.092296 | 0.7149 | 0.238715 |
| 39 | -0.019825 | -0.1536 | 0.560766 |
| 40 | -0.11862 | -0.9188 | 0.819068 |
| 41 | 0.051416 | 0.3983 | 0.345922 |
| 42 | 0.024578 | 0.1904 | 0.424826 |
| 43 | 0.083336 | 0.6455 | 0.260527 |
| 44 | -0.014327 | -0.111 | 0.543999 |
| 45 | -0.040585 | -0.3144 | 0.622836 |
| 46 | 0.082091 | 0.6359 | 0.263637 |
| 47 | 0.005816 | 0.045 | 0.48211 |
| 48 | -0.002579 | -0.02 | 0.507935 |
| 49 | 0.09012 | 0.6981 | 0.243916 |
| 50 | 0.043719 | 0.3386 | 0.368029 |
| 51 | -0.13144 | -1.0181 | 0.843649 |
| 52 | 0.079873 | 0.6187 | 0.26923 |
| 53 | -0.102762 | -0.796 | 0.785413 |
| 54 | 0.038005 | 0.2944 | 0.38474 |
| 55 | -0.036241 | -0.2807 | 0.610054 |
| 56 | 0.006241 | 0.0483 | 0.480802 |
| 57 | 0.017013 | 0.1318 | 0.447798 |
| 58 | -0.088675 | -0.6869 | 0.752595 |
| 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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