Kriging::leaveOneOut

Description

Get the Minimized Leave-One-Out Sum of Squares of a Kriging Model

Usage

  • Python

    # k = Kriging(...)
    k.leaveOneOut()
    
  • R

    # k = Kriging(...)
    k$leaveOneOut()
    
  • Matlab/Octave

    % k = Kriging(...)
    k.leaveOneOut()
    

Value

The minimized Leave-One-Out (LOO) sum of squares \(\texttt{SSE}_{\texttt{LOO}}\), corresponding to the estimated value \(\widehat{\theta}\) of the vector of correlation ranges. See leaveOneOutFun.Kriging for more details.

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")
print(k)

k$leaveOneOut()

Results

* data: 10x[0.0455565,0.940467] -> 10x[0.194057,1.00912]
* trend constant (est.): 0.406331
* variance (est.): 0.0471509
* covariance:
  * kernel: matern3_2
  * range (est.): 0.284722
  * fit:
    * objective: LOO
    * optim: BFGS
[1] 0.003159176

Reference