View source: R/NuggetKrigingClass.R
predict.NuggetKriging  R Documentation 
NuggetKriging
object.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.
## S3 method for class 'NuggetKriging'
predict(object, x, stdev = TRUE, cov = FALSE, deriv = FALSE, ...)
object 
S3 NuggetKriging object. 
x 
Input points where the prediction must be computed. 
stdev 

cov 

deriv 

... 
Ignored. 
A list containing the element mean
and possibly
stdev
and cov
.
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.
Yann Richet yann.richet@irsn.fr
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))
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