predictMobKriging: Predict a Partitioned Kriging Model
In schiffner/mobKriging: Recursive Partitioning of Kriging Models

Description Usage Arguments Details Value See Also Examples

Interface function for predicting a partitioned Kriging model.

predictMobKriging(object, newdata = NULL, type = c("response", "node"),
  km.args = list())

fittedMobKriging(object, type = c("response", "node"), km.args = list())

`object`	[`mob`] A tree with fitted Kriging models in the terminal nodes.
`newdata`	[`data.frame`] The cases to be predicted.
`type`	[`character(1)`] Passed to `type` in `predict.mob`: A character string specifying whether the response or the ID of the associated terminal node should be predicted.
`km.args`	[`list`] Additional arguments to `predict.km`.

predictMobKriging calls predict.mob, but handles name clashes and generates prettier output.

If type = "node" a vector of associated terminal node IDs.

If type = "response" a list containing at least the Kriging mean and trend computed at newdata, depending on km.args.

predict.mob, predict.km, mobKriging.

## currently nonsense example
if (require(DiceKriging)) {
    d <- 2L
    x <- seq(0, 1, length = 10L)
    design <- expand.grid(x1 = x, x2 = x)
    y <- apply(design, 1, branin)
    df <- data.frame(y = y, design)

    ## linear trend (formula = ~ x1 + x2)
    m <- mobKriging(y ~ x1 + x2 | x1 + x2, data = df,
        km.args = list(formula = ~ ., control = list(trace = FALSE)))

    ## predict the response
    pred <- predictMobKriging(m, newdata = design, km.args = list(type = "UK",
        se.compute = TRUE, cov.compute = TRUE))

    ## calculate fitted values, only trend and mean
    pred <- fittedMobKriging(m, km.args = list(type = "UK", se.compute = FALSE,
        light.return = TRUE))

    ## predict nodes
    nodes <- predictMobKriging(m, newdata = design, type = "node")
}