rlibkriging (version 0.9-1)

predict,KM-method: Prediction Method for a KM Object

Description

Compute predictions for the response at new given input points. These conditional mean, the conditional standard deviation and confidence limits at the 95% level. Optionnally the conditional covariance can be returned as well.

Usage

# S4 method for KM
predict(
  object,
  newdata,
  type = "UK",
  se.compute = TRUE,
  cov.compute = FALSE,
  light.return = TRUE,
  bias.correct = FALSE,
  checkNames = FALSE,
  ...
)

Value

A named list. The elements are the conditional mean and standard deviation (mean and sd), the predicted trend (trend) and the confidence limits (lower95

and upper95). Optionnally, the conditional covariance matrix is returned in cov.

Arguments

object

KM object.

newdata

Matrix of "new" input points where to perform prediction.

type

character giving the kriging type. For now only "UK" is possible.

se.compute

Logical. Should the standard error be computed?

cov.compute

Logical. Should the covariance matrix between newdata points be computed?

light.return

Logical. If TRUE, no auxiliary results will be returned (such as the Cholesky root of the correlation matrix).

bias.correct

Logical. If TRUE the UK variance and covariance are .

checkNames

Logical to check the consistency of the column names between the design stored in object@X and the new one given newdata.

...

Ignored.

Author

Yann Richet

Details

Without a dedicated predict method for the class "KM", this method would have been inherited from the "km" class. The dedicated method is expected to run faster. A comparison can be made by coercing a KM object to a km object with as.km before calling predict.

Examples

## a 16-points factorial design, and the corresponding response
d <- 2; n <- 16
design.fact <- expand.grid(x1 = seq(0, 1, length = 4), x2 = seq(0, 1, length = 4))
y <- apply(design.fact, 1, DiceKriging::branin) 

## library(DiceKriging)
## kriging model 1 : matern5_2 covariance structure, no trend, no nugget
## m1 <- km(design = design.fact, response = y, covtype = "gauss",
##          parinit = c(.5, 1), control = list(trace = FALSE))
KM1 <- KM(design = design.fact, response = y, covtype = "gauss",
               parinit = c(.5, 1))
Pred <- predict(KM1, newdata = matrix(.5,ncol = 2), type = "UK",
                checkNames = FALSE, light.return = TRUE)