# km-class

0th

Percentile

##### Kriging models class

S4 class for kriging models.

Keywords
classes
##### Objects from the Class

To create a km object, use km. See also this function for more details.

##### Slots

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.

##### Methods

coef

signature(x = "km") Get the coefficients of the km object.

plot

signature(x = "km"): see plot,km-method.

predict

signature(object = "km"): see predict,km-method.

show

signature(object = "km"): see show,km-method.

simulate

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.