predict.NuggetKriging: Predict from a 'NuggetKriging' object.

In rlibkriging: Kriging Models using the 'libKriging' Library

Predict from a NuggetKriging object.

Description

Given "new" input points, the method compute the expectation, variance and (optionnally) the covariance of the corresponding stochastic process, conditional on the values at the input points used when fitting the model.

Usage

## S3 method for class 'NuggetKriging'
predict(
  object,
  x,
  return_stdev = TRUE,
  return_cov = FALSE,
  return_deriv = FALSE,
  ...
)

Arguments

object	S3 NuggetKriging object.
x	Input points where the prediction must be computed.
return_stdev	Logical. If TRUE the standard deviation is returned.
return_cov	Logical. If TRUE the covariance matrix of the predictions is returned.
return_deriv	Logical. If TRUE the derivatives of mean and sd of the predictions are returned.
...	Ignored.

Value

A list containing the element mean and possibly stdev and cov.

Note

The names of the formal arguments differ from those of the predict methods for the S4 classes "km" and "KM". The formal x corresponds to newdata, stdev corresponds to se.compute and cov to cov.compute. These names are chosen Python and Octave interfaces to libKriging.

Author(s)

Yann Richet [e-mail]

Examples

f <- function(x) 1 - 1 / 2 * (sin(12 * x) / (1 + x) + 2 * cos(7 * x) * x^5 + 0.7)
plot(f)
set.seed(123)
X <- as.matrix(runif(10))
y <- f(X) + 0.1 * rnorm(nrow(X))
points(X, y, col = "blue", pch = 16)

k <- NuggetKriging(y, X, "matern3_2")

## include design points to see interpolation
x <- sort(c(X,seq(from = 0, to = 1, length.out = 101)))
p <- predict(k, x)

lines(x, p$mean, col = "blue")
polygon(c(x, rev(x)), c(p$mean - 2 * p$stdev, rev(p$mean + 2 * p$stdev)),
 border = NA, col = rgb(0, 0, 1, 0.2))

rlibkriging documentation built on Oct. 3, 2024, 1:06 a.m.