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 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 `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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