View source: R/NoiseKrigingClass.R
NoiseKriging  R Documentation 
"NoiseKriging"
using
the libKriging library.The hyperparameters (variance and vector of correlation ranges)
are estimated thanks to the optimization of a criterion given by
objective
, using the method given in optim
.
NoiseKriging(
y = NULL,
noise = NULL,
X = NULL,
kernel = NULL,
regmodel = c("constant", "linear", "interactive"),
normalize = FALSE,
optim = c("BFGS", "none"),
objective = c("LL"),
parameters = NULL
)
y 
Numeric vector of response values. 
noise 
Numeric vector of response variances. 
X 
Numeric matrix of input design. 
kernel 
Character defining the covariance model:

regmodel 
Universal NoiseKriging linear trend. 
normalize 
Logical. If 
optim 
Character giving the Optimization method used to fit
hyperparameters. Possible values are: 
objective 
Character giving the objective function to
optimize. Possible values are: 
parameters 
Initial values for the hyperparameters. When
provided this must be named list with elements 
An object with S3 class "NoiseKriging"
. Should be used
with its predict
, simulate
, update
methods.
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)
set.seed(123)
X < as.matrix(runif(10))
y < f(X) + X/10 * rnorm(nrow(X)) # add noise dep. on X
## fit and print
k < NoiseKriging(y, noise=(X/10)^2, X, kernel = "matern3_2")
print(k)
x < as.matrix(seq(from = 0, to = 1, length.out = 101))
p < predict(k,x = x, stdev = TRUE, cov = FALSE)
plot(f)
points(X, y)
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))
s < simulate(k, nsim = 10, seed = 123, x = x)
matlines(x, s, col = rgb(0, 0, 1, 0.2), type = "l", lty = 1)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.