update_simulate.NoiseKriging {rlibkriging} | R Documentation |
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 yann.richet@asnr.fr
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)
[Package rlibkriging version 0.9-2.1 Index]