KM: Create an 'KM' Object

In rlibkriging: Kriging Models using the 'libKriging' Library

Create an KM Object

Description

Create an object of S4 class "KM" similar to a km object in the DiceKriging package.

Usage

KM(
  formula = ~1,
  design,
  response,
  covtype = c("matern5_2", "gauss", "matern3_2", "exp"),
  coef.trend = NULL,
  coef.cov = NULL,
  coef.var = NULL,
  nugget = NULL,
  nugget.estim = FALSE,
  noise.var = NULL,
  estim.method = c("MLE", "LOO"),
  penalty = NULL,
  optim.method = "BFGS",
  lower = NULL,
  upper = NULL,
  parinit = NULL,
  multistart = 1,
  control = NULL,
  gr = TRUE,
  iso = FALSE,
  scaling = FALSE,
  knots = NULL,
  kernel = NULL,
  ...
)

Arguments

formula	R formula object to setup the linear trend in Universal Kriging. Supports ~ 1, ~. and ~ .^2.
design	Data frame. The design of experiments.
response	Vector of output values.
covtype	Covariance structure. For now all the kernels are tensor product kernels.
coef.trend	Optional value for a fixed vector of trend coefficients. If given, no optimization is done.
coef.cov	Optional value for a fixed correlation range value. If given, no optimization is done.
coef.var	Optional value for a fixed variance. If given, no optimization is done.
nugget, nugget.estim, noise.var	Not implemented yet.
estim.method	Estimation criterion. "MLE" for Maximum-Likelihood or "LOO" for Leave-One-Out cross-validation.
penalty	Not implemented yet.
optim.method	Optimization algorithm used in the optimization of the objective given in estim.method. Supports "BFGS".
lower, upper	Not implemented yet.
parinit	Initial values for the correlation ranges which will be optimized using optim.method.
multistart, control, gr, iso	Not implemented yet.
scaling, knots, kernel	Not implemented yet.
...	Ignored.

Details

The class "KM" extends the "km" class of the DiceKriging package, hence has all slots of "km". It also has an extra slot "Kriging" slot which contains a copy of the original object.

Value

A KM object. See Details.

Author(s)

Yann Richet [e-mail]

See Also

km in the DiceKriging package for more details on the slots.

Examples

# a 16-points factorial design, and the corresponding response
d <- 2; n <- 16
design.fact <- as.matrix(expand.grid(x1 = seq(0, 1, length = 4),
                                     x2 = seq(0, 1, length = 4)))
y <- apply(design.fact, 1, DiceKriging::branin) 

# Using `km` from DiceKriging and a similar `KM` object 
# kriging model 1 : matern5_2 covariance structure, no trend, no nugget effect
km1 <- DiceKriging::km(design = design.fact, response = y, covtype = "gauss",
                       parinit = c(.5, 1), control = list(trace = FALSE))
KM1 <- KM(design = design.fact, response = y, covtype = "gauss",
          parinit = c(.5, 1))

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