# logLikelihoodFun.NuggetKriging: Compute Log-Likelihood of NuggetKriging Model In rlibkriging: Kriging Models using the 'libKriging' Library

 logLikelihoodFun.NuggetKriging R Documentation

## Compute Log-Likelihood of NuggetKriging Model

### Description

Compute Log-Likelihood of NuggetKriging Model

### Usage

``````## S3 method for class 'NuggetKriging'
logLikelihoodFun(object, theta_alpha, grad = FALSE, bench = FALSE, ...)
``````

### Arguments

 `object` An S3 NuggetKriging object. `theta_alpha` A numeric vector of (positive) range parameters and variance over variance plus nugget at which the log-likelihood will be evaluated. `grad` Logical. Should the function return the gradient? `bench` Logical. Should the function display benchmarking output `...` Not used.

### Value

The log-Likelihood computed for given `\boldsymbol{theta_alpha}`.

### Author(s)

Yann Richet yann.richet@irsn.fr

### Examples

``````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, kernel = "matern3_2")
print(k)

theta0 = k\$theta()
ll_alpha <- function(alpha) logLikelihoodFun(k,cbind(theta0,alpha))\$logLikelihood
a <- seq(from = 0.9, to = 1.0, length.out = 101)
plot(a, Vectorize(ll_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())
ll_theta <- function(theta) logLikelihoodFun(k,cbind(theta,alpha0))\$logLikelihood
t <- seq(from = 0.001, to = 2, length.out = 101)
plot(t, Vectorize(ll_theta)(t), type = 'l')
abline(v = k\$theta(), col = "blue")

ll <- function(theta_alpha) logLikelihoodFun(k,theta_alpha)\$logLikelihood
a <- seq(from = 0.9, to = 1.0, length.out = 31)
t <- seq(from = 0.001, to = 2, length.out = 101)
contour(t,a,matrix(ncol=length(a),ll(expand.grid(t,a))),xlab="theta",ylab="sigma2/(sigma2+nugget)")
points(k\$theta(),k\$sigma2()/(k\$sigma2()+k\$nugget()),col='blue')
``````

rlibkriging documentation built on July 9, 2023, 5:53 p.m.