`Kriging::leaveOneOutVec`

Description

Compute Leave-One-Out (LOO) errors vector for an object "Kriging" representing a kriging model.

Usage

Python

# k = Kriging(...)
k.leaveOneOutVec(theta)

R

# k = Kriging(...)
k$leaveOneOutVec(theta)

Matlab/Octave

% k = Kriging(...)
k.leaveOneOutVec(theta)

Arguments

Argument	Description
`theta`	A numeric vector of range parameters at which the LOO will be evaluated.

Details

The returned value is the mean and standard deviation of \(\hat{y}_{i,(-i)}\), the prediction of \(y_i\) based on the the observations \(y_j\) with \(j \neq i\) .

Value

The leave-One-Out vector (mean and standard deviation) computed for the given vector \(theta\) of correlation ranges.

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)

k <- Kriging(y, X, kernel = "matern3_2", objective = "LOO", optim="BFGS")
print(k)

k$leaveOneOutVec(k$theta())

Kriging::leaveOneOutVec