NoiseKriging

Description

Create an object "NoiseKriging" using the libKriging library.

Usage

Just build the model:

NoiseKriging(kernel)
# later, call fit(y,X,...)

or, build and fit at the same time:

NoiseKriging(
  y,
  noise,
  X,
  kernel,
  regmodel = "constant",
  normalize = FALSE,
  optim = "BFGS",
  objective = "LL",
  parameters = NULL
)

Arguments

Argument

Description

y

Numeric vector of response values.

noise

Numeric vector of response variances.

X

Numeric matrix of input design.

kernel

Character defining the covariance model: "gauss" , "exp" , "matern3_2" , "matern5_2".

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.

Details

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 .

Value

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

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")
k

x <- as.matrix(seq(from = 0, to = 1, length.out = 101))
p <- k$predict(x = x, stdev = TRUE, 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 <- k$simulate(nsim = 10, seed = 123, x = x)

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

Results

* data: 10x[0.0455565,0.940467] -> 10x[0.152144,0.957381]
* trend constant (est.): 0.487335
* variance (est.): 0.0635381
* covariance:
  * kernel: matern3_2
  * range (est.): 0.211413
  * noise: 0.000827008, 0.00621425, 0.00167262, 0.0077972, 0.00884479, 2.07539e-05, 0.00278895, 0.00796412, 0.00304081, 0.00208497
  * fit:
    * objective: LL
    * optim: BFGS