NoiseKriging: Create an object with S3 class '"NoiseKriging"' using the...

In rlibkriging: Kriging Models using the 'libKriging' Library

Create an object with S3 class "NoiseKriging" using the libKriging library.

Description

The hyper-parameters (variance and vector of correlation ranges) are estimated thanks to the optimization of a criterion given by objective, using the method given in optim.

Usage

NoiseKriging(
  y = NULL,
  noise = NULL,
  X = NULL,
  kernel = NULL,
  regmodel = c("constant", "linear", "interactive", "none"),
  normalize = FALSE,
  optim = c("BFGS", "none"),
  objective = c("LL"),
  parameters = NULL
)

Arguments

y	Numeric vector of response values.
noise	Numeric vector of response variances.
X	Numeric matrix of input design.
kernel	Character defining the covariance model: "exp", "gauss", "matern3_2", "matern5_2".
regmodel	Universal NoiseKriging "linear", "interactive", "quadratic".
normalize	Logical. If TRUE both the input matrix X and the response y in normalized to take values in the interval [0, 1].
optim	Character giving the Optimization method used to fit hyper-parameters. Possible values are: "BFGS" and "none", the later simply keeping the values given in parameters. The method "BFGS" uses the gradient of the objective (note that "BGFS10" means 10 multi-start of BFGS).
objective	Character giving the objective function to optimize. Possible values are: "LL" for the Log-Likelihood.
parameters	Initial values for the hyper-parameters. When provided this must be named list with elements "sigma2" and "theta" containing the initial value(s) for the variance and for the range parameters. If theta is a matrix with more than one row, each row is used as a starting point for optimization.

Value

An object with S3 class "NoiseKriging". Should be used with its predict, simulate, update methods.

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)
set.seed(123)
X <- as.matrix(runif(10))
y <- f(X) + X/10 * rnorm(nrow(X)) # add noise dep. on X
## fit and print
k <- NoiseKriging(y, noise=(X/10)^2, X, kernel = "matern3_2")
print(k)

x <- as.matrix(seq(from = 0, to = 1, length.out = 101))
p <- predict(k,x = x, return_stdev = TRUE, return_cov = FALSE)

plot(f)
points(X, y)
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))

s <- simulate(k, nsim = 10, seed = 123, x = x)

matlines(x, s, col = rgb(0, 0, 1, 0.2), type = "l", lty = 1)

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