rlibkriging (version 0.9-1)

update_simulate.NoiseKriging: Update previous simulation of a NoiseKriging model object.

Description

This method draws paths of the stochastic process conditional on the values at the input points used in the fit, plus the new input points and their values given as argument (knonw as 'update' points).

Usage

# S3 method for NoiseKriging
update_simulate(object, y_u, noise_u, X_u, ...)

Value

a matrix with nrow(x) rows and nsim

columns containing the simulated paths at the inputs points given in x.

Arguments

object

S3 NoiseKriging object.

y_u

Numeric vector of new responses (output).

noise_u

Numeric vector of new noise variances (output).

X_u

Numeric matrix of new input points.

...

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) + X/10 * rnorm(nrow(X))
points(X, y, col = "blue")

k <- NoiseKriging(y, (X/10)^2, X, "matern3_2")

x <- seq(from = 0, to = 1, length.out = 101)
s <- k$simulate(nsim = 3, x = x, will_update = TRUE)

lines(x, s[ , 1], col = "blue")
lines(x, s[ , 2], col = "blue")
lines(x, s[ , 3], col = "blue")

X_u <- as.matrix(runif(3))
y_u <- f(X_u) + 0.1 * rnorm(nrow(X_u))
points(X_u, y_u, col = "red")

su <- k$update_simulate(y_u, rep(0.1^2,3), X_u)

lines(x, su[ , 1], col = "blue", lty=2)
lines(x, su[ , 2], col = "blue", lty=2)
lines(x, su[ , 3], col = "blue", lty=2)