methode2 op tijdreeks1
| 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 | 7.6811 | 0 |
| 1 | 0.281674 | 2.1636 | 0.017279 |
| 2 | 0.04152 | 0.3189 | 0.375457 |
| 3 | -0.068811 | -0.5285 | 0.700449 |
| 4 | -0.037998 | -0.2919 | 0.614294 |
| 5 | 0.082344 | 0.6325 | 0.264753 |
| 6 | 0.1244 | 0.9555 | 0.171603 |
| 7 | 0.211771 | 1.6266 | 0.054571 |
| 8 | 0.192916 | 1.4818 | 0.071855 |
| 9 | 0.186701 | 1.4341 | 0.078415 |
| 10 | 0.191034 | 1.4674 | 0.073794 |
| 11 | 0.171926 | 1.3206 | 0.09587 |
| 12 | -0.026059 | -0.2002 | 0.578979 |
| 13 | -0.059345 | -0.4558 | 0.674909 |
| 14 | 0.013311 | 0.1022 | 0.459454 |
| 15 | 0.073177 | 0.5621 | 0.288094 |
| 16 | 0.06251 | 0.4802 | 0.316447 |
| 17 | 0.266405 | 2.0463 | 0.022596 |
| 18 | 0.182806 | 1.4042 | 0.082757 |
| 19 | 0.01141 | 0.0876 | 0.465229 |
| 20 | -0.109716 | -0.8427 | 0.798612 |
| 21 | -0.076113 | -0.5846 | 0.71949 |
| 22 | -0.116634 | -0.8959 | 0.813022 |
| 23 | -0.081789 | -0.6282 | 0.733862 |
| 24 | 0.085073 | 0.6535 | 0.258 |
| 25 | 0.02793 | 0.2145 | 0.415436 |
| 26 | -0.023855 | -0.1832 | 0.57238 |
| 27 | -0.060023 | -0.461 | 0.676769 |
| 28 | 0.007321 | 0.0562 | 0.477672 |
| 29 | -0.025701 | -0.1974 | 0.577909 |
| 30 | -0.178391 | -1.3702 | 0.912099 |
| 31 | -0.148413 | -1.14 | 0.87055 |
| 32 | -0.150256 | -1.1541 | 0.873452 |
| 33 | -0.020831 | -0.16 | 0.56329 |
| 34 | -0.002168 | -0.0167 | 0.506615 |
| 35 | 0.13904 | 1.068 | 0.144939 |
| 36 | -0.082659 | -0.6349 | 0.736031 |
| 37 | -0.193787 | -1.4885 | 0.929028 |
| 38 | -0.196893 | -1.5124 | 0.93211 |
| 39 | -0.132431 | -1.0172 | 0.843399 |
| 40 | -0.048962 | -0.3761 | 0.645897 |
| 41 | -0.089513 | -0.6876 | 0.752788 |
| 42 | -0.013096 | -0.1006 | 0.539893 |
| 43 | -0.08354 | -0.6417 | 0.738219 |
| 44 | -0.073529 | -0.5648 | 0.712819 |
| 45 | -0.038484 | -0.2956 | 0.615715 |
| 46 | -0.141838 | -1.0895 | 0.859814 |
| 47 | -0.149663 | -1.1496 | 0.872523 |
| 48 | -0.062022 | -0.4764 | 0.682226 |
| 49 | -0.079568 | -0.6112 | 0.728284 |
| 50 | 0.009724 | 0.0747 | 0.470357 |
| 51 | -0.083261 | -0.6395 | 0.737527 |
| 52 | 0.031744 | 0.2438 | 0.404103 |
| 53 | 0.032163 | 0.247 | 0.402865 |
| 54 | -0.038928 | -0.299 | 0.617008 |
| 55 | -0.056681 | -0.4354 | 0.667562 |
| 56 | -0.072147 | -0.5542 | 0.709221 |
| 57 | -0.046612 | -0.358 | 0.639202 |
| 58 | -0.057183 | -0.4392 | 0.668951 |
| 59 | NA | NA | NA |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.281674 | 2.1636 | 0.017279 |
| 1 | -0.04108 | -0.3155 | 0.623268 |
| 2 | -0.075525 | -0.5801 | 0.717978 |
| 3 | 0.004406 | 0.0338 | 0.486557 |
| 4 | 0.102897 | 0.7904 | 0.216239 |
| 5 | 0.074129 | 0.5694 | 0.285624 |
| 6 | 0.165475 | 1.271 | 0.104351 |
| 7 | 0.115872 | 0.89 | 0.188532 |
| 8 | 0.140576 | 1.0798 | 0.142316 |
| 9 | 0.160903 | 1.2359 | 0.110693 |
| 10 | 0.142162 | 1.092 | 0.139643 |
| 11 | -0.098334 | -0.7553 | 0.773469 |
| 12 | -0.044461 | -0.3415 | 0.633035 |
| 13 | -0.001599 | -0.0123 | 0.504878 |
| 14 | -0.033865 | -0.2601 | 0.602163 |
| 15 | -0.102543 | -0.7877 | 0.782973 |
| 16 | 0.201468 | 1.5475 | 0.063544 |
| 17 | 0.002163 | 0.0166 | 0.493399 |
| 18 | -0.095218 | -0.7314 | 0.76628 |
| 19 | -0.105696 | -0.8119 | 0.789934 |
| 20 | -0.013071 | -0.1004 | 0.539817 |
| 21 | -0.190592 | -1.464 | 0.925744 |
| 22 | -0.093659 | -0.7194 | 0.762636 |
| 23 | 0.045808 | 0.3519 | 0.3631 |
| 24 | -0.13312 | -1.0225 | 0.844644 |
| 25 | -0.119738 | -0.9197 | 0.81927 |
| 26 | -0.023219 | -0.1783 | 0.57047 |
| 27 | 0.007102 | 0.0546 | 0.47834 |
| 28 | 0.011089 | 0.0852 | 0.466204 |
| 29 | -0.070992 | -0.5453 | 0.706199 |
| 30 | -0.001363 | -0.0105 | 0.504159 |
| 31 | -0.119076 | -0.9146 | 0.817948 |
| 32 | 0.112659 | 0.8653 | 0.195176 |
| 33 | -0.035521 | -0.2728 | 0.607036 |
| 34 | 0.16253 | 1.2484 | 0.108405 |
| 35 | -0.086721 | -0.6661 | 0.746034 |
| 36 | 0.045029 | 0.3459 | 0.365335 |
| 37 | -0.081367 | -0.625 | 0.732806 |
| 38 | 0.044731 | 0.3436 | 0.36619 |
| 39 | 0.062119 | 0.4771 | 0.317512 |
| 40 | -0.057694 | -0.4432 | 0.670364 |
| 41 | 0.002383 | 0.0183 | 0.492729 |
| 42 | -0.039521 | -0.3036 | 0.618736 |
| 43 | -0.009541 | -0.0733 | 0.529087 |
| 44 | -0.002021 | -0.0155 | 0.506167 |
| 45 | -0.177006 | -1.3596 | 0.910436 |
| 46 | 0.051987 | 0.3993 | 0.345551 |
| 47 | 0.039999 | 0.3072 | 0.379872 |
| 48 | -0.027335 | -0.21 | 0.582789 |
| 49 | -0.015589 | -0.1197 | 0.547454 |
| 50 | -0.00913 | -0.0701 | 0.527836 |
| 51 | 0.016209 | 0.1245 | 0.450669 |
| 52 | 0.063981 | 0.4914 | 0.312468 |
| 53 | 0.055356 | 0.4252 | 0.336119 |
| 54 | 0.089069 | 0.6842 | 0.248278 |
| 55 | 0.012015 | 0.0923 | 0.46339 |
| 56 | 0.007985 | 0.0613 | 0.475651 |
| 57 | -0.031218 | -0.2398 | 0.594339 |
| 58 | NA | NA | NA |
| 59 | NA | NA | NA |
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| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/28/t1196241151nhaa7syg4q2muq7/1pgdi1196241707.png (open in new window) |
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| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/28/t1196241151nhaa7syg4q2muq7/23ffd1196241707.png (open in new window) |
| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/28/t1196241151nhaa7syg4q2muq7/23ffd1196241707.ps (open in new window) |
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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