fit.NoiseKriging {rlibkriging}

Fit NoiseKriging 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 'NoiseKriging'
fit(
  object,
  y,
  noise,
  X,
  regmodel = c("constant", "linear", "interactive"),
  normalize = FALSE,
  optim = c("BFGS", "none"),
  objective = c("LL"),
  parameters = NULL,
  ...
)

Arguments

object

S3 NoiseKriging object.

y

Numeric vector of response values.

noise

Numeric vector of response variances.

X

Numeric matrix of input design.

regmodel

Universal NoiseKriging 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.

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.

...

Ignored.

Value

No return value. NoiseKriging 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) + X/10 * rnorm(nrow(X)) # add noise dep. on X
points(X, y, col = "blue", pch = 16)

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

fit(k,y,noise=(X/10)^2,X)
print(k)