Predict.Kriging {rkriging}

Kriging Prediction

Description

This function gives prediction and uncertainty quantification of the kriging model on a new input.

Usage

Predict.Kriging(kriging, X)

Arguments

kriging

a kriging class object

X

a matrix for the new input (features) to perform predictions

Value

mean

kriging mean computed at the new input

sd

kriging standard computed at the new input

Author(s)

Chaofan Huang and V. Roshan Joseph

References

Joseph, V. R. (2006). Limit kriging. Technometrics, 48(4), 458-466.

Joseph, V. R. (2024). Rational Kriging. Journal of the American Statistical Association.

Rasmussen, C. E. & Williams, C. K. (2006). Gaussian Processes for Machine Learning. The MIT Press.

Santner, T. J., Williams, B. J., Notz, W. I., & Williams, B. J. (2003). The design and analysis of computer experiments (Vol. 1). New York: Springer.

See Also

Fit.Kriging.

Examples

# one dimensional example 
f <- function(x) {
  x <- 0.5 + 2*x
  y <- sin(10*pi*x)/(2*x) + (x-1)^4
  return (y)
}

set.seed(1234)
# train set
n <- 30
p <- 1
X <- matrix(runif(n),ncol=p)
y <- apply(X, 1, f)
newX <- matrix(seq(0,1,length=1001), ncol=p)

kriging <- Fit.Kriging(X, y, interpolation=TRUE, fit=TRUE, model="OK",
                       kernel.parameters=list(type="Gaussian"))
pred <- Predict.Kriging(kriging, newX)
plot(newX, f(newX), "l")
points(X, y, pch=16, col="blue")
lines(newX, pred$mean, col="red", lty=2)
lines(newX, pred$mean-2*pred$sd, col="red", lty=3)
lines(newX, pred$mean+2*pred$sd, col="red", lty=3)