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`

.

- 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.