View source: R/NuggetKrigingClass.R
logMargPostFun.NuggetKriging | R Documentation |
Compute the log-marginal posterior of a kriging model, using the prior XXXY.
## S3 method for class 'NuggetKriging'
logMargPostFun(object, theta_alpha, return_grad = FALSE, bench = FALSE, ...)
object |
S3 NuggetKriging object. |
theta_alpha |
Numeric vector of correlation range and variance over variance plus nugget parameters at which the function is to be evaluated. |
return_grad |
Logical. Should the function return the gradient (w.r.t theta_alpha)? |
bench |
Logical. Should the function display benchmarking output |
... |
Not used. |
The value of the log-marginal posterior computed for the
given vector \boldsymbol{theta_alpha}
.
Yann Richet yann.richet@irsn.fr
XXXY A reference describing the model (prior, ...)
rgasp
in the RobustGaSP package.
f <- function(x) 1 - 1 / 2 * (sin(12 * x) / (1 + x) + 2 * cos(7 * x) * x^5 + 0.7)
set.seed(123)
X <- as.matrix(runif(10))
y <- f(X) + 0.1 * rnorm(nrow(X))
k <- NuggetKriging(y, X, "matern3_2", objective="LMP")
print(k)
theta0 = k$theta()
lmp_alpha <- function(alpha) k$logMargPostFun(cbind(theta0,alpha))$logMargPost
a <- seq(from = 0.9, to = 1.0, length.out = 101)
plot(a, Vectorize(lmp_alpha)(a), type = "l",xlim=c(0.9,1))
abline(v = k$sigma2()/(k$sigma2()+k$nugget()), col = "blue")
alpha0 = k$sigma2()/(k$sigma2()+k$nugget())
lmp_theta <- function(theta) k$logMargPostFun(cbind(theta,alpha0))$logMargPost
t <- seq(from = 0.001, to = 2, length.out = 101)
plot(t, Vectorize(lmp_theta)(t), type = 'l')
abline(v = k$theta(), col = "blue")
lmp <- function(theta_alpha) k$logMargPostFun(theta_alpha)$logMargPost
t <- seq(from = 0.4, to = 0.6, length.out = 51)
a <- seq(from = 0.9, to = 1, length.out = 51)
contour(t,a,matrix(ncol=length(t),lmp(expand.grid(t,a))),
nlevels=50,xlab="theta",ylab="sigma2/(sigma2+nugget)")
points(k$theta(),k$sigma2()/(k$sigma2()+k$nugget()),col='blue')
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