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"),
  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 trend.
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.
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 July 9, 2023, 5:53 p.m.