fit.NuggetKriging: Fit 'NuggetKriging' object on given data.

In rlibkriging: Kriging Models using the 'libKriging' Library

Fit NuggetKriging object on given data.

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

## S3 method for class 'NuggetKriging'
fit(
  object,
  y,
  X,
  regmodel = c("constant", "linear", "interactive", "none"),
  normalize = FALSE,
  optim = c("BFGS", "none"),
  objective = c("LL", "LMP"),
  parameters = NULL,
  ...
)

Arguments

object	S3 NuggetKriging object.
y	Numeric vector of response values.
X	Numeric matrix of input design.
regmodel	Universal NuggetKriging "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 and "LMP" for the Log-Marginal Posterior.
parameters	Initial values for the hyper-parameters. When provided this must be named list with some elements "sigma2", "theta", "nugget" containing the initial value(s) for the variance, range and nugget parameters. If theta is a matrix with more than one row, each row is used as a starting point for optimization.
...	Ignored.

Value

No return value. NuggetKriging object argument is modified.

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) + 0.1 * rnorm(nrow(X))
points(X, y, col = "blue", pch = 16)

k <- NuggetKriging("matern3_2")
print(k)

fit(k,y,X)
print(k)

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