simulate.NoiseKriging: Simulation from a 'NoiseKriging' model object.

In rlibkriging: Kriging Models using the 'libKriging' Library

Simulation from a NoiseKriging model object.

Description

This method draws paths of the stochastic process at new input points conditional on the values at the input points used in the fit.

Usage

## S3 method for class 'NoiseKriging'
simulate(
  object,
  nsim = 1,
  seed = 123,
  x,
  with_noise = NULL,
  will_update = FALSE,
  ...
)

Arguments

object	S3 NoiseKriging object.
nsim	Number of simulations to perform.
seed	Random seed used.
x	Points in model input space where to simulate.
with_noise	Set to array of values if wish to add the noise in the simulation.
will_update	Set to TRUE if wish to use update_simulate(...) later.
...	Ignored.

Value

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

Note

The names of the formal arguments differ from those of the simulate methods for the S4 classes "km" and "KM". The formal x corresponds to newdata. These names are chosen Python and Octave interfaces to libKriging.

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, kernel = "matern3_2")

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

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

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