predict.NoiseKriging {rlibkriging}

Predict from a NoiseKriging 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 'NoiseKriging'
predict(object, x, stdev = TRUE, cov = FALSE, deriv = FALSE, ...)

Arguments

object

S3 NoiseKriging object.

x

Input points where the prediction must be computed.

stdev

Logical. If TRUE the standard deviation is returned.

cov

Logical. If TRUE the covariance matrix of the predictions is returned.

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) + X/10 * rnorm(nrow(X))
points(X, y, col = "blue", pch = 16)

k <- NoiseKriging(y, (X/10)^2, X, "matern3_2")

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))