`Kriging::fit`

Description

Fit a Kriging Object using Given Observations

Usage

Python

# k = Kriging(kernel=...)
k.fit(y, X,
      regmodel = "constant",
      normalize = False,
      optim = "BFGS",
      objective = "LL",
      parameters = None,
      noise = None)

R

# k = Kriging(kernel=...)
k$fit(y, X,
      regmodel = "constant",
      normalize = FALSE,
      optim = "BFGS",
      objective = "LL",
      parameters = NULL,
      noise = NULL)

Matlab/Octave

% k = Kriging(kernel=...)
k.fit(y, X,
      regmodel = "constant",
      normalize = false,
      optim = "BFGS",
      objective = "LL",
      parameters = [],
      noise = [])

Julia

# k = Kriging("matern5_2")
fit(k, y, X,
    regmodel   = "constant",
    normalize  = false,
    optim      = "BFGS",
    objective  = "LL",
    parameters = nothing,
    noise      = nothing)

Arguments

Argument	Description
`y`	Numeric vector of response values.
`X`	Numeric matrix of input design.
`regmodel`	Universal Kriging linear trend: `"constant"`, `"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"` , `"Newton"` 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). The method `"Newton"` uses both the gradient and the Hessian of the objective.
`objective`	Character giving the objective function to optimize. Possible values are: `"LL"` for the Log-Likelihood, `"LOO"` for the Leave-One-Out sum of squares and `"LMP"` for the Log-Marginal Posterior.
`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.
`noise`	Either a numeric vector of per-observation noise variances, `"nugget"` to estimate a homogeneous nugget, or `NULL` (default) for noise-free interpolation.

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

No return value. The Kriging object is modified in place.

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)

k <- Kriging("matern3_2")
print("before fit")
print(k)

k$fit(y,X)
print("after fit")
print(k)

Results

[1] "before fit"
* covariance:
  * kernel: matern3_2
[1] "after fit"
* data: 10x[0.0455565,0.940467] -> 10x[0.194057,1.00912]
* trend constant (est.): 0.433954
* variance (est.): 0.0873685
* covariance:
  * kernel: matern3_2
  * range (est.): 0.240585
  * fit:
    * objective: LL
    * optim: BFGS

Kriging::fit