Description Usage Arguments Details Value See Also Examples
RFloglikelihood
returns the log likelihood for Gaussian
random fields. In case NAs are given that refer to linear modeling, the
ML of the linear model is returned.
1 2 3 |
model,params |
\argModel |
x |
\argX |
y,z |
\argYz |
T |
\argT |
grid |
\argGrid |
distances,dim |
\argDistances |
data |
\argData |
likelihood |
Not programmed yet. Character.
Choice of kind of likelihood ("full", "composite", etc.),
see also |
estimate_variance |
logical or |
... |
\argDots |
The function calculates the likelihood for data of a Gaussian process
with given covariance structure.
The covariance structure may not have NA
values in the
parameters except for a global variance. In this case the variance
is returned that maximizes the likelihood.
Additional to the covariance structure the model may include a
trend. The latter may contain unknown linear parameters.
In this case again, the unknown parameters are estimated, and returned.
RFloglikelihood
returns a list
containing the likelihood, the log likelihood, and
the global variance (if estimated – see details).
Bayesian,
RMmodel
,
RFfit
,
RFsimulate
,
RFlinearpart
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 | RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
## RFoptions(seed=NA) to make them all random again
requireNamespace("mvtnorm")
pts <- 4
repet <- 3
model <- RMexp()
x <- runif(n=pts, min=-1, max=1)
y <- runif(n=pts, min=-1, max=1)
dta <- as.matrix(RFsimulate(model, x=x, y=y, n=repet, spC = FALSE))
print(cbind(x, y, dta))
print(system.time(likeli <- RFlikelihood(model, x, y, data=dta)))
str(likeli, digits=8)
L <- 0
C <- RFcovmatrix(model, x, y)
for (i in 1:ncol(dta)) {
print(system.time(dn <- mvtnorm::dmvnorm(dta[,i], mean=rep(0, nrow(dta)),
sigma=C, log=TRUE)))
L <- L + dn
}
print(L)
stopifnot(all.equal(likeli$log, L))
pts <- 4
repet <- 1
trend <- 2 * sin(R.p(new="isotropic")) + 3
#trend <- RMtrend(mean=0)
model <- 2 * RMexp() + trend
x <- seq(0, pi, len=pts)
dta <- as.matrix(RFsimulate(model, x=x, n=repet, spC = FALSE))
print(cbind(x, dta))
print(system.time(likeli <- RFlikelihood(model, x, data=dta)))
str(likeli, digits=8)
L <- 0
tr <- RFfctn(trend, x=x, spC = FALSE)
C <- RFcovmatrix(model, x)
for (i in 1:ncol(dta)) {
print(system.time(dn <- mvtnorm::dmvnorm(dta[,i], mean=tr, sigma=C,log=TRUE)))
L <- L + dn
}
print(L)
stopifnot(all.equal(likeli$log, L))
pts <- c(3, 4)
repet <- c(2, 3)
trend <- 2 * sin(R.p(new="isotropic")) + 3
model <- 2 * RMexp() + trend
x <- y <- dta <- list()
for (i in 1:length(pts)) {
x[[i]] <- list(x = runif(n=pts[i], min=-1, max=1),
y = runif(n=pts[i], min=-1, max=1))
dta[[i]] <- as.matrix(RFsimulate(model, x=x[[i]]$x, y=x[[i]]$y,
n=repet[i], spC = FALSE))
}
print(system.time(likeli <- RFlikelihood(model, x, data=dta)))
str(likeli, digits=8)
L <- 0
for (p in 1:length(pts)) {
tr <- RFfctn(trend, x=x[[p]]$x, y=x[[p]]$y,spC = FALSE)
C <- RFcovmatrix(model, x=x[[p]]$x, y=x[[p]]$y)
for (i in 1:ncol(dta[[p]])) {
print(system.time(dn <- mvtnorm::dmvnorm(dta[[p]][,i], mean=tr, sigma=C,
log=TRUE)))
L <- L + dn
}
}
print(L)
stopifnot(all.equal(likeli$log, L))
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