km-class {DiceKriging} | R Documentation |
S4 class for kriging models.
To create a km
object, use km
. See also this function for more details.
d
:"integer"
. The spatial dimension. n
:"integer"
. The number of observations. X
:"matrix"
. The design of experiments. y
:"matrix"
. The vector of response values at design points. p
:"integer"
. The number of basis functions of the linear trend. F
:"matrix"
. The experimental matrix corresponding to the evaluation of the linear trend basis functions at the design of experiments.trend.formula
:"formula"
. A formula specifying the trend as a linear model (no response needed). trend.coef
:"numeric"
. Trend coefficients. covariance
:"covTensorProduct"
. See covTensorProduct-class
. noise.flag
:"logical"
. Are the observations noisy? noise.var
:"numeric"
. If the observations are noisy, the vector of noise variances. known.param
:"character"
. Internal use. One of: "None", "All"
or "Trend"
. case
:"character"
. Internal use. One of: "NoNugget", "1Nugget", "Nuggets"
. param.estim
:"logical"
. TRUE
if at least one parameter is estimated, FALSE
otherwise. method
:"character"
. "MLE"
or "PMLE"
depending on penalty
. penalty
:"list"
. For penalized ML estimation. optim.method
:"character"
. To be chosen between "BFGS"
and "gen"
.lower
:"numeric"
. Lower bounds for covariance parameters estimation. upper
:"numeric"
. Upper bounds for covariance parameters estimation. control
:"list"
. Additional control parameters for covariance parameters estimation. gr
:"logical"
. Do you want analytical gradient to be used ? call
:"language"
. User call reminder. parinit
:"numeric"
. Initial values for covariance parameters estimation. logLik
:"numeric"
. Value of the concentrated log-Likelihood at its optimum. T
:"matrix"
. Triangular matrix delivered by the Choleski decomposition of the covariance matrix. z
:"numeric"
. Auxiliary variable: see computeAuxVariables
. M
:"matrix"
. Auxiliary variable: see computeAuxVariables
. signature(x = "km")
: see plot.km
. signature(object = "km")
: see predict.km
. signature(object = "km")
: see show.km
. signature(object = "km")
: see simulate.km
. O. Roustant, D. Ginsbourger
km
for more details about slots and to create a km
object, covStruct.create
to construct a covariance structure, and covTensorProduct-class
for the S4 covariance class defined in this package.