rlibkriging (version 0.9-1)

NoiseKM: Create an NoiseKM Object

Description

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

Usage

NoiseKM(
  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,
  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,
  ...
)

Value

A NoiseKM object. See Details.

Arguments

formula

R formula object to setup the linear trend in Universal NoiseKriging. 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

Not implemented.

noise.var

Vector of output values variance.

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.

Author

Yann Richet

Details

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

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) + rnorm(nrow(design.fact))

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