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

newdata 
Matrix of "new" input points where to perform prediction. 
type 
character giving the kriging type. For now only

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 
bias.correct 
Logical. If 
checkNames 
Logical to check the consistency of the column
names between the design stored in 
... 
Ignored. 
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
.
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
.
Yann Richet yann.richet@irsn.fr
## a 16points 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)
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