rlibkriging (version 0.9-1)

predict.NuggetKriging: Predict from a NuggetKriging object.

Description

Given "new" input points, the method compute the expectation, variance and (optionnally) the covariance of the corresponding stochastic process, conditional on the values at the input points used when fitting the model.

Usage

# S3 method for NuggetKriging
predict(
  object,
  x,
  return_stdev = TRUE,
  return_cov = FALSE,
  return_deriv = FALSE,
  ...
)

Value

A list containing the element mean and possibly

stdev and cov.

Arguments

object

S3 NuggetKriging object.

x

Input points where the prediction must be computed.

return_stdev

Logical. If TRUE the standard deviation is returned.

return_cov

Logical. If TRUE the covariance matrix of the predictions is returned.

return_deriv

Logical. If TRUE the derivatives of mean and sd of the predictions are returned.

...

Ignored.

Author

Yann Richet

Examples

f <- function(x) 1 - 1 / 2 * (sin(12 * x) / (1 + x) + 2 * cos(7 * x) * x^5 + 0.7)
plot(f)
set.seed(123)
X <- as.matrix(runif(10))
y <- f(X) + 0.1 * rnorm(nrow(X))
points(X, y, col = "blue", pch = 16)

k <- NuggetKriging(y, X, "matern3_2")

## include design points to see interpolation
x <- sort(c(X,seq(from = 0, to = 1, length.out = 101)))
p <- predict(k, x)

lines(x, p$mean, col = "blue")
polygon(c(x, rev(x)), c(p$mean - 2 * p$stdev, rev(p$mean + 2 * p$stdev)),
 border = NA, col = rgb(0, 0, 1, 0.2))