R: Kriging models class

km-class {DiceKriging} | R Documentation

## Kriging models class

### Description

S4 class for kriging models.

### 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`.

### Author(s)

O. Roustant, D. Ginsbourger

### See Also

`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.

[Package DiceKriging version 1.6.0 Index]