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

In rlibkriging: Kriging Models using the 'libKriging' Library

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 class 'NoiseKriging'
update_simulate(object, y_u, noise_u, X_u, ...)

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.

Value

a matrix with nrow(x) rows and nsim columns containing the simulated paths at the inputs points given in x.

Author(s)

Yann Richet [e-mail]

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)

rlibkriging documentation built on Oct. 3, 2024, 1:06 a.m.