predict,NoiseKM-method: Prediction Method for a `NoiseKM` 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 NoiseKM
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: NoiseKM 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 yann.richet@irsn.fr

Details

Without a dedicated predict method for the class "NoiseKM", 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 NoiseKM object to a km object with as.km before calling predict.

Examples

Run this code

## 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) + rnorm(nrow(design.fact))

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

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