S4 class for kriging models.
To create a km object, use km. See also this function for more details.
d:Object of class "integer". The spatial dimension.
n:Object of class "integer". The number of observations.
X:Object of class "matrix". The design of experiments.
y:Object of class "matrix". The vector of response values at design points.
p:Object of class "integer". The number of basis functions of the linear trend.
F:Object of class "matrix". The experimental matrix corresponding to the evaluation of the linear trend basis functions at the design of experiments.
trend.formula:Object of class "formula". A formula specifying the trend as a linear model (no response needed).
trend.coef:Object of class "numeric". Trend coefficients.
covariance:Object of class "covTensorProduct". See covTensorProduct-class.
noise.flag:Object of class "logical". Are the observations noisy?
noise.var:Object of class "numeric". If the observations are noisy, the vector of noise variances.
known.param:Object of class "character". Internal use. One of: "None", "All", "CovAndVar" or "Trend".
case:Object of class "character". Indicates the likelihood to use in estimation (Internal use). One of: "LLconcentration_beta", "LLconcentration_beta_sigma2", "LLconcentration_beta_v_alpha".
param.estim:Object of class "logical". TRUE if at least one parameter is estimated, FALSE otherwise.
method:Object of class "character". "MLE" or "PMLE" depending on penalty.
penalty:Object of class "list". For penalized ML estimation.
optim.method:Object of class "character". To be chosen between "BFGS" and "gen".
lower:Object of class "numeric". Lower bounds for covariance parameters estimation.
upper:Object of class "numeric". Upper bounds for covariance parameters estimation.
control:Object of class "list". Additional control parameters for covariance parameters estimation.
gr:Object of class "logical". Do you want analytical gradient to be used ?
call:Object of class "language". User call reminder.
parinit:Object of class "numeric". Initial values for covariance parameters estimation.
logLik:Object of class "numeric". Value of the concentrated log-Likelihood at its optimum.
T:Object of class "matrix". Triangular matrix delivered by the Choleski decomposition of the covariance matrix.
z:Object of class "numeric". Auxiliary variable: see computeAuxVariables.
M:Object of class "matrix". Auxiliary variable: see computeAuxVariables.
signature(x = "km") Get the coefficients of the km object.
signature(x = "km"): see plot,km-method.
signature(object = "km"): see predict,km-method.
signature(object = "km"): see show,km-method.
signature(object = "km"): see simulate,km-method.
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.