View source: R/NuggetKMClass.R
NuggetKM  R Documentation 
NuggetKM
ObjectCreate an object of S4 class "NuggetKM"
similar to a
km
object in the DiceKriging package.
NuggetKM(
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 = TRUE,
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,
...
)
formula 
R formula object to setup the linear trend in
Universal NuggetKriging. Supports 
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.estim, nugget 
Should nugget be estimated? (defaults TRUE) or given values. 
noise.var 
Not implemented. 
estim.method 
Estimation criterion. 
penalty 
Not implemented yet. 
optim.method 
Optimization algorithm used in the
optimization of the objective given in

lower, upper 
Not implemented yet. 
parinit 
Initial values for the correlation ranges which
will be optimized using 
multistart, control, gr, iso 
Not implemented yet. 
scaling, knots, kernel, 
Not implemented yet. 
... 
Ignored. 
The class "NuggetKM"
extends the "km"
class of the
DiceKriging package, hence has all slots of "km"
. It
also has an extra slot "NuggetKriging"
slot which contains a copy
of the original object.
A NuggetKM object. See Details.
Yann Richet yann.richet@irsn.fr
km
in the DiceKriging
package for more details on the slots.
# a 16points 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 `NuggetKM` object
# kriging model 1 : matern5_2 covariance structure, no trend, no nugget effect
km1 < DiceKriging::km(design = design.fact, response = y, covtype = "gauss",
nugget.estim=TRUE,
parinit = c(.5, 1), control = list(trace = FALSE))
KM1 < NuggetKM(design = design.fact, response = y, covtype = "gauss",
parinit = c(.5, 1))
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