Package {rlibkriging}

Type:	Package
Title:	Kriging Models using the 'libKriging' Library
Version:	1.0-0
Date:	2026-05-13
Maintainer:	Yann Richet <yann.richet@asnr.fr>
Description:	Interface to 'libKriging' 'C++' library https://github.com/libKriging that should provide most standard Kriging / Gaussian process regression features (like in 'DiceKriging', 'kergp' or 'RobustGaSP' packages). 'libKriging' relies on Armadillo linear algebra library (Apache 2 license) by Conrad Sanderson, 'lbfgsb_cpp' is a 'C++' port around by Pascal Have of 'lbfgsb' library (BSD-3 license) by Ciyou Zhu, Richard Byrd, Jorge Nocedal and Jose Luis Morales used for hyperparameters optimization.
License:	Apache License (≥ 2)
Encoding:	UTF-8
LinkingTo:	Rcpp, RcppArmadillo
Depends:	R (≥ 4.2)
Imports:	Rcpp (≥ 1.0.12), methods, DiceKriging
Suggests:	testthat, utils, foreach, roxygen2, RobustGaSP
SystemRequirements:	GNU make, cmake (>= 3.2.0), gcc
URL:	https://github.com/libKriging
RoxygenNote:	7.3.3
NeedsCompilation:	yes
Packaged:	2026-05-13 16:19:39 UTC; richet
Author:	Yann Richet [aut, cre], Pascal Havé [aut], Yves Deville [aut], Conrad Sanderson [ctb], Ciyou Zhu [ctb], Richard Byrd [ctb], Jorge Nocedal [ctb], Jose Luis Morales [ctb], Mike Smith [ctb]
Repository:	CRAN
Date/Publication:	2026-05-13 18:20:02 UTC

Get trend matrix F

Description

Get trend matrix F

Usage

F_(object, ...)

Arguments

Get trend matrix F for an MLPKriging model

Description

Get trend matrix F for an MLPKriging model

Usage

## S3 method for class 'MLPKriging'
F_(object, ...)

Arguments

Get trend matrix F for a WarpKriging model

Description

Get trend matrix F for a WarpKriging model

Usage

## S3 method for class 'WarpKriging'
F_(object, ...)

Arguments

Create an KM Object

Description

Create an object of S4 class "KM" similar to a km object in the DiceKriging package.

Usage

KM(
  formula = ~1,
  design,
  response,
  covtype = c("matern5_2", "gauss", "matern3_2", "exp"),
  coef.trend = NULL,
  coef.cov = NULL,
  coef.var = NULL,
  nugget = NULL,
  nugget.estim = FALSE,
  noise.var = NULL,
  estim.method = c("MLE", "LOO"),
  penalty = NULL,
  optim.method = "BFGS",
  lower = NULL,
  upper = NULL,
  parinit = NULL,
  multistart = 1,
  control = NULL,
  gr = TRUE,
  iso = FALSE,
  scaling = FALSE,
  knots = NULL,
  kernel = NULL,
  ...
)

Arguments

Details

The class "KM" extends the "km" class of the DiceKriging package, hence has all slots of "km". It also has an extra slot "Kriging" slot which contains a copy of the original object.

Value

A KM object. See Details.

Author(s)

Yann Richet yann.richet@asnr.fr

See Also

km in the DiceKriging package for more details on the slots.

Examples

# a 16-points factorial design, and the corresponding response
d <- 2; n <- 16
design.fact <- as.matrix(expand.grid(x1 = seq(0, 1, length = 4),
                                     x2 = seq(0, 1, length = 4)))
y <- apply(design.fact, 1, DiceKriging::branin) 

# Using `km` from DiceKriging and a similar `KM` object 
# kriging model 1 : matern5_2 covariance structure, no trend, no nugget effect
km1 <- DiceKriging::km(design = design.fact, response = y, covtype = "gauss",
                       parinit = c(.5, 1), control = list(trace = FALSE))
KM1 <- KM(design = design.fact, response = y, covtype = "gauss",
          parinit = c(.5, 1))

S4 class for Kriging Models Extending the "km" Class

Description

This class is intended to be used either by using its own dedicated S4 methods or by using the S4 methods inherited from the "km" class of the libKriging package.

Slots

d,n,X,y,p,F

Number of (numeric) inputs, number of observations, design matrix, response vector, number of trend variables, trend matrix.

trend.formula,trend.coef

Formula used for the trend, vector \hat{\boldsymbol{\beta}} of estimated (or fixed) trend coefficients with length p.

covariance

A S4 object with class "covTensorProduct" representing a covariance kernel.

noise.flag,noise.var

Logical flag and numeric value for an optional noise term.

known.param

A character code indicating what parameters are known.

lower,upper

Bounds on the correlation range parameters.

method,penalty,optim.method,control,gr,parinit

Objects defining the estimation criterion, the optimization.

T,M,z

Auxiliary variables (matrices and vectors) that can be used in several computations.

case

The possible concentration (a.k.a. profiling) of the likelihood.

param.estim

Logical. Is an estimation used?

Kriging

A copy of the Kriging object used to create the current KM object.

Author(s)

Yann Richet yann.richet@asnr.fr

See Also

km-class in the DiceKriging package. The creator KM.

Create an object with S3 class "Kriging" using the libKriging library.

Description

The hyper-parameters (variance and vector of correlation ranges) are estimated thanks to the optimization of a criterion given by objective, using the method given in optim.

Usage

Kriging(
  y = NULL,
  X = NULL,
  kernel = NULL,
  noise = NULL,
  regmodel = c("constant", "linear", "interactive", "none"),
  normalize = FALSE,
  optim = c("BFGS", "none"),
  objective = c("LL", "LOO", "LMP"),
  parameters = NULL
)

Arguments

Value

An object with S3 class "Kriging". Should be used with its predict, simulate, update methods.

Author(s)

Yann Richet yann.richet@asnr.fr

Examples

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)
## fit and print
k <- Kriging(y, X, kernel = "matern3_2")
print(k)

x <- as.matrix(seq(from = 0, to = 1, length.out = 101))
p <- predict(k, x = x, return_stdev = TRUE, return_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)

Get whitened trend matrix M

Description

Get whitened trend matrix M

Usage

M(object, ...)

Arguments

Get whitened trend matrix M for an MLPKriging model

Description

Get whitened trend matrix M for an MLPKriging model

Usage

## S3 method for class 'MLPKriging'
M(object, ...)

Arguments

Get whitened trend matrix M for a WarpKriging model

Description

Get whitened trend matrix M for a WarpKriging model

Usage

## S3 method for class 'WarpKriging'
M(object, ...)

Arguments

Create an MLPKriging model (Deep Kernel Learning)

Description

Kriging with a joint multi-layer perceptron applied to all inputs before the GP kernel is evaluated. The MLP weights, GP range parameters, variance and trend are jointly fitted by maximising the concentrated log-likelihood.

Usage

MLPKriging(
  y,
  X,
  hidden_dims,
  d_out = 2,
  activation = "selu",
  kernel = "gauss",
  regmodel = "constant",
  normalize = FALSE,
  optim = "BFGS+Adam",
  objective = "LL",
  parameters = NULL
)

Arguments

Value

An S3 object of class "MLPKriging".

Examples

X <- as.matrix(seq(0.01, 0.99, length.out = 10))
f <- function(x) 1 - 1/2 * (sin(12*x)/(1+x) + 2*cos(7*x)*x^5 + 0.7)
y <- f(X)
k <- MLPKriging(y, X, hidden_dims = c(16, 8), d_out = 2,
                activation = "selu", kernel = "gauss")
print(k)

Create a KM object with heteroscedastic noise (deprecated)

Description

This is a thin compatibility wrapper. Use KM(noise.var = ...) directly instead.

Usage

NoiseKM(..., noise.var)

Arguments

Value

A KM object. See KM.

Create a KM object with nugget effect (deprecated)

Description

This is a thin compatibility wrapper. Use KM(nugget.estim = TRUE) directly instead.

Usage

NuggetKM(...)

Arguments

Value

A KM object. See KM.

Get Cholesky factor T

Description

Get Cholesky factor T

Usage

T_(object, ...)

Arguments

Get Cholesky factor T for an MLPKriging model

Description

Get Cholesky factor T for an MLPKriging model

Usage

## S3 method for class 'MLPKriging'
T_(object, ...)

Arguments

Get Cholesky factor T for a WarpKriging model

Description

Get Cholesky factor T for a WarpKriging model

Usage

## S3 method for class 'WarpKriging'
T_(object, ...)

Arguments

Create a WarpKriging model

Description

Kriging with per-variable input warping. Each input dimension is independently transformed before the GP kernel is evaluated. Supports continuous, categorical, ordinal variables and joint deep kernel learning.

Usage

WarpKriging(
  y,
  X,
  warping,
  kernel = "gauss",
  regmodel = "constant",
  normalize = FALSE,
  optim = "BFGS+Adam",
  objective = "LL",
  parameters = NULL,
  noise = NULL
)

Arguments

Value

An S3 object of class "WarpKriging".

Examples

# Continuous with Kumaraswamy warping
X <- as.matrix(c(0.0, 0.25, 0.5, 0.75, 1.0))
f <- function(x) 1 - 1/2 * (sin(12*x)/(1+x) + 2*cos(7*x)*x^5 + 0.7)
y <- f(X)
k <- WarpKriging(y, X, warping = "kumaraswamy", kernel = "gauss")
print(k)

# Mixed: 1 continuous + 1 categorical (3 levels)
n <- 15
X_mix <- cbind(runif(n), rep(0:2, length.out = n))
y_mix <- sin(2 * pi * X_mix[, 1]) * c(1, 2, 0.5)[X_mix[, 2] + 1]
k2 <- WarpKriging(y_mix, X_mix,
         warping = c("mlp(16:8,2,selu)", "categorical(3,2)"),
         kernel = "matern5_2")

Get training input matrix

Description

Get training input matrix

Usage

X(object, ...)

Arguments

Value

matrix of training inputs

Get training input matrix

Description

Get training input matrix

Usage

## S3 method for class 'MLPKriging'
X(object, ...)

Arguments

Value

matrix of training inputs

Get activation function name

Description

Get activation function name

Usage

activation(object, ...)

Arguments

Get activation function for an MLPKriging model

Description

Get activation function for an MLPKriging model

Usage

## S3 method for class 'MLPKriging'
activation(object, ...)

Arguments

Coerce an Object into a km Object

Description

Coerce an object into an object with S4 class "km" from the DiceKriging package.

Usage

as.km(x, ...)

Arguments

Details

Such a coercion is typically used to compare the performance of the methods implemented in the current rlibkriging package to those which are available in the DiceKriging package.

Value

An object with S4 class "km".

Coerce a Kriging object into the "km" class of the DiceKriging package.

Description

Coerce a Kriging object into the "km" class of the DiceKriging package.

Usage

## S3 method for class 'Kriging'
as.km(x, .call = NULL, ...)

Arguments

Value

An object of having the S4 class "KM" which extends the "km" class of the DiceKriging package and contains an extra Kriging slot.

Author(s)

Yann Richet yann.richet@asnr.fr

Examples

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)

k <- Kriging(y, X, "matern3_2")
print(k)

k_km <- as.km(k)
print(k_km)

Coerce a Kriging Object into a List

Description

Coerce a Kriging Object into a List

Usage

## S3 method for class 'Kriging'
as.list(x, ...)

Arguments

Value

A list with its elements copying the content of the Kriging object fields: kernel, optim, objective, theta (vector of ranges), sigma2 (variance), X, centerX, scaleX, y, centerY, scaleY, regmodel, F, T, M, z, beta.

Author(s)

Yann Richet yann.richet@asnr.fr

Examples

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)

k <- Kriging(y, X, kernel = "matern3_2")

l <- as.list(k)
cat(paste0(names(l), " =" , l, collapse = "\n"))

Get trend coefficients beta

Description

Get trend coefficients beta

Usage

beta(object, ...)

Arguments

Get trend coefficients beta for an MLPKriging model

Description

Get trend coefficients beta for an MLPKriging model

Usage

## S3 method for class 'MLPKriging'
beta(object, ...)

Arguments

Get trend coefficients beta for a WarpKriging model

Description

Get trend coefficients beta for a WarpKriging model

Usage

## S3 method for class 'WarpKriging'
beta(object, ...)

Arguments

Get input centering vector

Description

Get input centering vector

Usage

centerX(object, ...)

Arguments

Get input centering vector for an MLPKriging model

Description

Get input centering vector for an MLPKriging model

Usage

## S3 method for class 'MLPKriging'
centerX(object, ...)

Arguments

Get input centering vector for a WarpKriging model

Description

Get input centering vector for a WarpKriging model

Usage

## S3 method for class 'WarpKriging'
centerX(object, ...)

Arguments

Get output centering value

Description

Get output centering value

Usage

centerY(object, ...)

Arguments

Get output centering value for an MLPKriging model

Description

Get output centering value for an MLPKriging model

Usage

## S3 method for class 'MLPKriging'
centerY(object, ...)

Arguments

Get output centering value for a WarpKriging model

Description

Get output centering value for a WarpKriging model

Usage

## S3 method for class 'WarpKriging'
centerY(object, ...)

Arguments

Shortcut to provide functions to the S3 class "Kriging"

Description

Shortcut to provide functions to the S3 class "Kriging"

Usage

classKriging(nk)

Arguments

Value

An object of class "Kriging" with methods to access and manipulate the data

Shortcut to provide functions to the S3 class "MLPKriging"

Description

Shortcut to provide functions to the S3 class "MLPKriging"

Usage

classMLPKriging(obj)

Arguments

Value

An object of class "MLPKriging" with methods accessible via $

Shortcut to provide functions to the S3 class "WarpKriging"

Description

Shortcut to provide functions to the S3 class "WarpKriging"

Usage

classWarpKriging(obj)

Arguments

Value

An object of class "WarpKriging" with methods accessible via $

Duplicate object.

Description

Duplicate a model given in object.

Usage

copy(object, ...)

Arguments

Value

The copied object.

Duplicate a Kriging Model

Description

Duplicate a Kriging Model

Usage

## S3 method for class 'Kriging'
copy(object, ...)

Arguments

Value

The copy of object.

Author(s)

Yann Richet yann.richet@asnr.fr

Examples

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)

k <- Kriging(y, X, kernel = "matern3_2", objective="LMP")
print(k)

print(copy(k))

Deep copy of MLPKriging model

Description

Deep copy of MLPKriging model

Usage

## S3 method for class 'MLPKriging'
copy(object, ...)

Arguments

Value

a new independent MLPKriging object

Deep copy of WarpKriging model

Description

Deep copy of WarpKriging model

Usage

## S3 method for class 'WarpKriging'
copy(object, ...)

Arguments

Value

a new independent WarpKriging object

covariance function

Description

Compute the covariance matrix of a model given in object, between given set of points.

Usage

covMat(object, ...)

Arguments

Value

The covariance matrix.

Compute Covariance Matrix of Kriging Model

Description

Compute Covariance Matrix of Kriging Model

Usage

## S3 method for class 'Kriging'
covMat(object, x1, x2, ...)

Arguments

Value

A matrix of the covariance matrix of the Kriging model.

Author(s)

Yann Richet yann.richet@asnr.fr

Examples

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)

k <- Kriging(y, X, kernel = "gauss")

x1 = runif(10)
x2 = runif(10)

covMat(k, x1, x2)

Get feature dimensionality (d_out)

Description

Get feature dimensionality (d_out)

Usage

feature_dim(object, ...)

Arguments

Get feature dimensionality for an MLPKriging model

Description

Get feature dimensionality for an MLPKriging model

Usage

## S3 method for class 'MLPKriging'
feature_dim(object, ...)

Arguments

Get feature dimensionality of warped space

Description

Get feature dimensionality of warped space

Usage

## S3 method for class 'WarpKriging'
feature_dim(object, ...)

Arguments

Fit model on data.

Description

Fit a model given in object.

Usage

fit(object, ...)

Arguments

Value

No return value. Kriging object argument is modified.

Fit Kriging object on given data.

Description

The hyper-parameters (variance and vector of correlation ranges) are estimated thanks to the optimization of a criterion given by objective, using the method given in optim.

Usage

## S3 method for class 'Kriging'
fit(
  object,
  y,
  X,
  noise = NULL,
  regmodel = c("constant", "linear", "interactive", "none"),
  normalize = FALSE,
  optim = c("BFGS", "none"),
  objective = c("LL", "LOO", "LMP"),
  parameters = NULL,
  ...
)

Arguments

Value

No return value. Kriging object argument is modified.

Author(s)

Yann Richet yann.richet@asnr.fr

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)
points(X, y, col = "blue", pch = 16)

k <- Kriging("matern3_2")
print(k)

fit(k,y,X)
print(k)

Fit an MLPKriging model to data

Description

(Re-)fit an already-constructed MLPKriging object on new data. The MLP architecture and kernel are kept from construction.

Usage

## S3 method for class 'MLPKriging'
fit(
  object,
  y,
  X,
  regmodel = "constant",
  normalize = FALSE,
  optim = "BFGS+Adam",
  objective = "LL",
  parameters = NULL,
  ...
)

Arguments

Value

No return value. MLPKriging object argument is modified.

Fit a WarpKriging model to data

Description

(Re-)fit an already-constructed WarpKriging object on new data. The warping specification and kernel are kept from construction.

Usage

## S3 method for class 'WarpKriging'
fit(
  object,
  y,
  X,
  regmodel = "constant",
  normalize = FALSE,
  optim = "BFGS+Adam",
  objective = "LL",
  parameters = NULL,
  noise = NULL,
  ...
)

Arguments

Value

No return value. WarpKriging object argument is modified.

Get hidden layer sizes

Description

Get hidden layer sizes

Usage

hidden_dims(object, ...)

Arguments

Get hidden layer sizes for an MLPKriging model

Description

Get hidden layer sizes for an MLPKriging model

Usage

## S3 method for class 'MLPKriging'
hidden_dims(object, ...)

Arguments

Check if the model has been fitted

Description

Check if the model has been fitted

Usage

is_fitted(object, ...)

Arguments

Check whether an MLPKriging model is fitted

Description

Check whether an MLPKriging model is fitted

Usage

## S3 method for class 'MLPKriging'
is_fitted(object, ...)

Arguments

Check whether a WarpKriging model is fitted

Description

Check whether a WarpKriging model is fitted

Usage

## S3 method for class 'WarpKriging'
is_fitted(object, ...)

Arguments

Get kernel name

Description

Get kernel name

Usage

kernel(object, ...)

Arguments

Value

Character scalar naming the covariance kernel.

Get kernel name

Description

Get kernel name

Usage

## S3 method for class 'WarpKriging'
kernel(object, ...)

Arguments

Compute Leave-One-Out

Description

Compute the leave-One-Out error of a model given in object.

Usage

leaveOneOut(object, ...)

Arguments

Value

The Leave-One-Out sum of squares.

Get leaveOneOut of Kriging Model

Description

Get leaveOneOut of Kriging Model

Usage

## S3 method for class 'Kriging'
leaveOneOut(object, ...)

Arguments

Value

The leaveOneOut computed for fitted \boldsymbol{theta}.

Author(s)

Yann Richet yann.richet@asnr.fr

Examples

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)

k <- Kriging(y, X, kernel = "matern3_2", objective="LOO")
print(k)

leaveOneOut(k)

Leave-One-Out function

Description

Compute the leave-One-Out error of a model given in object, at a different value of the parameters.

Usage

leaveOneOutFun(object, ...)

Arguments

Value

The Leave-One-Out sum of squares.

Compute Leave-One-Out (LOO) error for an object with S3 class "Kriging" representing a kriging model.

Description

The returned value is the sum of squares \sum_{i=1}^n [y_i - \hat{y}_{i,(-i)}]^2 where \hat{y}_{i,(-i)} is the prediction of y_i based on the the observations y_j with j \neq i.

Usage

## S3 method for class 'Kriging'
leaveOneOutFun(object, theta, return_grad = FALSE, bench = FALSE, ...)

Arguments

Value

The leave-One-Out value computed for the given vector \boldsymbol{\theta} of correlation ranges.

Author(s)

Yann Richet yann.richet@asnr.fr

Examples

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)

k <- Kriging(y, X, kernel = "matern3_2", objective = "LOO", optim="BFGS")
print(k)

loo <-  function(theta) leaveOneOutFun(k, theta)$leaveOneOut
t <-  seq(from = 0.001, to = 2, length.out = 101)
plot(t, loo(t), type = "l")
abline(v = k$theta(), col = "blue")

Leave-One-Out vector

Description

Compute the leave-One-Out vector error of a model given in object, at a different value of the parameters.

Usage

leaveOneOutVec(object, ...)

Arguments

Value

The Leave-One-Out errors (mean and stdev) for each conditional point.

Compute Leave-One-Out (LOO) vector error for an object with S3 class "Kriging" representing a kriging model.

Description

The returned value is the mean and stdev of \hat{y}_{i,(-i)}, the prediction of y_i based on the the observations y_j with j \neq i.

Usage

## S3 method for class 'Kriging'
leaveOneOutVec(object, theta, ...)

Arguments

Value

The leave-One-Out vector computed for the given vector \boldsymbol{\theta} of correlation ranges.

Author(s)

Yann Richet yann.richet@asnr.fr

Examples

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(c(0.0, 0.25, 0.5, 0.75, 1.0))
y <- f(X)

k <- Kriging(y, X, kernel = "matern3_2")
print(k)

x <- as.matrix(seq(0, 1, , 101))
p <- predict(k, x, TRUE, 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))

# Compute leave-one-out (no range re-estimate) on 2nd point
X_no2 = X[-2,,drop=FALSE]
y_no2 = f(X_no2)
k_no2 = Kriging(y_no2, X_no2, "matern3_2", optim = "none", parameters = list(theta = k$theta()))
print(k_no2)

p_no2 <- predict(k_no2, x, TRUE, FALSE)
lines(x, p_no2$mean, col = 'red')
polygon(c(x, rev(x)), c(p_no2$mean - 2 * p_no2$stdev, rev(p_no2$mean + 2 * p_no2$stdev)), 
        border = NA, col = rgb(1, 0, 0, 0.2))

# Use leaveOneOutVec to get the same
loov = k$leaveOneOutVec(matrix(k$theta()))
points(X[2],loov$mean[2],col='red')
lines(rep(X[2],2),loov$mean[2]+2*c(-loov$stdev[2],loov$stdev[2]),col='red')

Load any Kriging Model from a file storage. Back to base::load if not a Kriging object.

Description

Load any Kriging Model from a file storage. Back to base::load if not a Kriging object.

Usage

load(filename, ...)

Arguments

Value

The loaded "*"Kriging object, or nothing if base::load is used (update parent environment).

Author(s)

Yann Richet yann.richet@asnr.fr

Examples

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)

k <- Kriging(y, X, kernel = "matern3_2", objective="LMP")
print(k)

outfile = tempfile("k.json") 
save(k,outfile)

print(load(outfile))

Load a Kriging Model from a file storage

Description

Load a Kriging Model from a file storage

Usage

load.Kriging(filename, ...)

Arguments

Value

The loaded Kriging object.

Author(s)

Yann Richet yann.richet@asnr.fr

Examples

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)

k <- Kriging(y, X, kernel = "matern3_2", objective="LMP")
print(k)

outfile = tempfile("k.json")
save(k,outfile)

print(load.Kriging(outfile)) 
unlink(outfile)

Load an MLPKriging model from file

Description

Load an MLPKriging model from file

Usage

## S3 method for class 'MLPKriging'
load(filename, ...)

Arguments

Value

MLPKriging object

Load a WarpKriging model from file

Description

Load a WarpKriging model from file

Usage

## S3 method for class 'WarpKriging'
load(filename, ...)

Arguments

Value

WarpKriging object

Compute Log-Likelihood

Description

Compute the log-Likelihood of a model given in object.

Usage

logLikelihood(object, ...)

Arguments

Value

The log-likelihood.

Get Log-Likelihood of Kriging Model

Description

Get Log-Likelihood of Kriging Model

Usage

## S3 method for class 'Kriging'
logLikelihood(object, ...)

Arguments

Value

The log-Likelihood computed for fitted \boldsymbol{theta}.

Author(s)

Yann Richet yann.richet@asnr.fr

Examples

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)

k <- Kriging(y, X, kernel = "matern3_2", objective="LL")
print(k)

logLikelihood(k)

Log-likelihood of the fitted model

Description

Log-likelihood of the fitted model

Usage

## S3 method for class 'WarpKriging'
logLikelihood(object, ...)

Arguments

Log-Likelihood function

Description

Compute the log-Likelihood of a model given in object, at a different value of the parameters.

Usage

logLikelihoodFun(object, ...)

Arguments

Value

The log-likelihood.

Compute Log-Likelihood of Kriging Model

Description

Compute Log-Likelihood of Kriging Model

Usage

## S3 method for class 'Kriging'
logLikelihoodFun(
  object,
  theta,
  return_grad = FALSE,
  return_hess = FALSE,
  bench = FALSE,
  ...
)

Arguments

Value

The log-Likelihood computed for given \boldsymbol{theta}.

Author(s)

Yann Richet yann.richet@asnr.fr

Examples

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)

k <- Kriging(y, X, kernel = "matern3_2")
print(k)

ll <- function(theta) logLikelihoodFun(k, theta)$logLikelihood

t <- seq(from = 0.001, to = 2, length.out = 101)
plot(t, ll(t), type = 'l')
abline(v = k$theta(), col = "blue")

Evaluate log-likelihood at given GP theta

Description

Evaluate log-likelihood at given GP theta

Usage

## S3 method for class 'MLPKriging'
logLikelihoodFun(object, theta, return_grad = FALSE, return_hess = FALSE, ...)

Arguments

Evaluate log-likelihood at given theta

Description

Evaluate log-likelihood at given theta

Usage

## S3 method for class 'WarpKriging'
logLikelihoodFun(object, theta, return_grad = FALSE, return_hess = FALSE, ...)

Arguments

Value

list with logLikelihood and optionally gradient, hessian

Compute log-Marginal Posterior

Description

Compute the log-Marginal Posterior of a model given in object.

Usage

logMargPost(object, ...)

Arguments

Value

The log-marginal posterior.

Get logMargPost of Kriging Model

Description

Get logMargPost of Kriging Model

Usage

## S3 method for class 'Kriging'
logMargPost(object, ...)

Arguments

Value

The logMargPost computed for fitted \boldsymbol{theta}.

Author(s)

Yann Richet yann.richet@asnr.fr

Examples

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)

k <- Kriging(y, X, kernel = "matern3_2", objective="LMP")
print(k)

logMargPost(k)

log-Marginal Posterior function

Description

Compute the log-Marginal Posterior of a model given in object, at a different value of the parameters.

Usage

logMargPostFun(object, ...)

Arguments

Value

The log-marginal posterior.

Compute the log-marginal posterior of a kriging model, using the prior XXXY.

Description

Compute the log-marginal posterior of a kriging model, using the prior XXXY.

Usage

## S3 method for class 'Kriging'
logMargPostFun(object, theta, return_grad = FALSE, bench = FALSE, ...)

Arguments

Value

The value of the log-marginal posterior computed for the given vector theta.

Author(s)

Yann Richet yann.richet@asnr.fr

References

XXXY A reference describing the model (prior, ...)

See Also

rgasp in the RobustGaSP package.

Examples

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)

k <- Kriging(y, X, "matern3_2", objective="LMP")
print(k)

lmp <- function(theta) logMargPostFun(k, theta)$logMargPost

t <- seq(from = 0.01, to = 2, length.out = 101)
plot(t, lmp(t), type = "l")
abline(v = k$theta(), col = "blue")

Get normalize flag

Description

Get normalize flag

Usage

normalize(object, ...)

Arguments

Get normalize flag for an MLPKriging model

Description

Get normalize flag for an MLPKriging model

Usage

## S3 method for class 'MLPKriging'
normalize(object, ...)

Arguments

Get normalize flag for a WarpKriging model

Description

Get normalize flag for a WarpKriging model

Usage

## S3 method for class 'WarpKriging'
normalize(object, ...)

Arguments

Prediction Method for a KM Object

Description

Compute predictions for the response at new given input points. These conditional mean, the conditional standard deviation and confidence limits at the 95% level. Optionnally the conditional covariance can be returned as well.

Usage

## S4 method for signature 'KM'
predict(
  object,
  newdata,
  type = "UK",
  se.compute = TRUE,
  cov.compute = FALSE,
  light.return = TRUE,
  bias.correct = FALSE,
  checkNames = FALSE,
  ...
)

Arguments

Details

Without a dedicated predict method for the class "KM", this method would have been inherited from the "km" class. The dedicated method is expected to run faster. A comparison can be made by coercing a KM object to a km object with as.km before calling predict.

Value

A named list. The elements are the conditional mean and standard deviation (mean and sd), the predicted trend (trend) and the confidence limits (lower95 and upper95). Optionnally, the conditional covariance matrix is returned in cov.

Author(s)

Yann Richet yann.richet@asnr.fr

Examples

## a 16-points factorial design, and the corresponding response
d <- 2; n <- 16
design.fact <- expand.grid(x1 = seq(0, 1, length = 4), x2 = seq(0, 1, length = 4))
y <- apply(design.fact, 1, DiceKriging::branin) 

## library(DiceKriging)
## kriging model 1 : matern5_2 covariance structure, no trend, no nugget
## m1 <- km(design = design.fact, response = y, covtype = "gauss",
##          parinit = c(.5, 1), control = list(trace = FALSE))
KM1 <- KM(design = design.fact, response = y, covtype = "gauss",
               parinit = c(.5, 1))
Pred <- predict(KM1, newdata = matrix(.5,ncol = 2), type = "UK",
                checkNames = FALSE, light.return = TRUE)

Predict from a Kriging 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 'Kriging'
predict(
  object,
  x,
  return_stdev = TRUE,
  return_cov = FALSE,
  return_deriv = FALSE,
  ...
)

Arguments

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 yann.richet@asnr.fr

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)
points(X, y, col = "blue", pch = 16)

k <- Kriging(y, 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))

Predict with an MLPKriging model

Description

Predict with an MLPKriging model

Usage

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

Arguments

Value

list with mean, optionally stdev, cov, mean_deriv, stdev_deriv

Predict with a WarpKriging model

Description

Predict with a WarpKriging model

Usage

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

Arguments

Value

list with mean, optionally stdev, cov, mean_deriv, stdev_deriv

Print the content of a Kriging object.

Description

Print the content of a Kriging object.

Usage

## S3 method for class 'Kriging'
print(x, ...)

Arguments

Value

String of printed object.

Author(s)

Yann Richet yann.richet@asnr.fr

Examples

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)

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

print(k)
## same thing
k

Get regression model type

Description

Get regression model type

Usage

regmodel(object, ...)

Arguments

Get regression model type for an MLPKriging model

Description

Get regression model type for an MLPKriging model

Usage

## S3 method for class 'MLPKriging'
regmodel(object, ...)

Arguments

Get regression model type for a WarpKriging model

Description

Get regression model type for a WarpKriging model

Usage

## S3 method for class 'WarpKriging'
regmodel(object, ...)

Arguments

Save a Kriging Model inside a file. Back to base::save if argument is not a Kriging object.

Description

Save a Kriging Model inside a file. Back to base::save if argument is not a Kriging object.

Usage

save(object = NULL, filename = NULL, ...)

Arguments

Author(s)

Yann Richet yann.richet@asnr.fr

Save a Kriging Model to a file storage

Description

Save a Kriging Model to a file storage

Usage

## S3 method for class 'Kriging'
save(object, filename, ...)

Arguments

Value

The loaded Kriging object.

Author(s)

Yann Richet yann.richet@asnr.fr

Examples

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)

k <- Kriging(y, X, kernel = "matern3_2", objective="LMP")
print(k)

outfile = tempfile("k.json") 
save(k,outfile)
unlink(outfile)

Save an MLPKriging model to file

Description

Save an MLPKriging model to file

Usage

## S3 method for class 'MLPKriging'
save(object, filename, ...)

Arguments

Save a WarpKriging model to file

Description

Save a WarpKriging model to file

Usage

## S3 method for class 'WarpKriging'
save(object, filename, ...)

Arguments

Get input scaling vector

Description

Get input scaling vector

Usage

scaleX(object, ...)

Arguments

Get input scaling vector for an MLPKriging model

Description

Get input scaling vector for an MLPKriging model

Usage

## S3 method for class 'MLPKriging'
scaleX(object, ...)

Arguments

Get input scaling vector for a WarpKriging model

Description

Get input scaling vector for a WarpKriging model

Usage

## S3 method for class 'WarpKriging'
scaleX(object, ...)

Arguments

Get output scaling value

Description

Get output scaling value

Usage

scaleY(object, ...)

Arguments

Get output scaling value for an MLPKriging model

Description

Get output scaling value for an MLPKriging model

Usage

## S3 method for class 'MLPKriging'
scaleY(object, ...)

Arguments

Get output scaling value for a WarpKriging model

Description

Get output scaling value for a WarpKriging model

Usage

## S3 method for class 'WarpKriging'
scaleY(object, ...)

Arguments

Get process variance

Description

Get process variance

Usage

sigma2(object, ...)

Arguments

Value

Numeric process variance estimate.

Get process variance (concentrated MLE)

Description

Get process variance (concentrated MLE)

Usage

## S3 method for class 'WarpKriging'
sigma2(object, ...)

Arguments

Simulation from a KM Object

Description

The simulate method is used to simulate paths from the kriging model described in object.

Usage

## S4 method for signature 'KM'
simulate(
  object,
  nsim = 1,
  seed = NULL,
  newdata,
  cond = TRUE,
  nugget.sim = 0,
  checkNames = FALSE,
  ...
)

Arguments

Details

Without a dedicated simulate method for the class "KM", this method would have been inherited from the "km" class. The dedicated method is expected to run faster. A comparison can be made by coercing a KM object to a km object with as.km before calling simulate.

Value

A numeric matrix with nrow(newdata) rows and nsim columns containing as its columns the simulated paths at the input points given in newdata.

XXX method simulate KM

Author(s)

Yann Richet yann.richet@asnr.fr

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(5))
y <- f(X)
points(X, y, col = 'blue')
k <- KM(design = X, response = y, covtype = "gauss")
x <- seq(from = 0, to = 1, length.out = 101)
s_x <- simulate(k, nsim = 3, newdata = x)
lines(x, s_x[ , 1], col = 'blue')
lines(x, s_x[ , 2], col = 'blue')
lines(x, s_x[ , 3], col = 'blue')

Simulation from a Kriging model object.

Description

This method draws paths of the stochastic process at new input points conditional on the values at the input points used in the fit.

Usage

## S3 method for class 'Kriging'
simulate(
  object,
  nsim = 1,
  seed = 123,
  x,
  with_noise = NULL,
  will_update = FALSE,
  ...
)

Arguments

Value

a matrix with nrow(x) rows and nsim columns containing the simulated paths at the inputs points given in x.

Note

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

Author(s)

Yann Richet yann.richet@asnr.fr

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)
points(X, y, col = "blue")

k <- Kriging(y, X, kernel = "matern3_2")

x <- seq(from = 0, to = 1, length.out = 101)
s <- simulate(k, nsim = 3, x = x)

lines(x, s[ , 1], col = "blue")
lines(x, s[ , 2], col = "blue")
lines(x, s[ , 3], col = "blue")

Simulate from an MLPKriging model

Description

Simulate from an MLPKriging model

Usage

## S3 method for class 'MLPKriging'
simulate(object, nsim = 1, seed = 123, x, will_update = FALSE, ...)

Arguments

Value

matrix (m x nsim)

Simulate from a WarpKriging model

Description

Simulate from a WarpKriging model

Usage

## S3 method for class 'WarpKriging'
simulate(object, nsim = 1, seed = 123, x, will_update = FALSE, ...)

Arguments

Value

matrix (m x nsim)

Get GP range parameters

Description

Get GP range parameters

Usage

theta(object, ...)

Arguments

Value

Numeric vector of range parameters.

Get GP range parameters

Description

Get GP range parameters

Usage

## S3 method for class 'WarpKriging'
theta(object, ...)

Arguments

Update a KM Object with New Points

Description

The update method is used when new observations are added to a fitted kriging model. Rather than fitting the model from scratch with the updated observations added, the results of the fit as stored in object are used to achieve some savings.

Usage

## S4 method for signature 'KM'
update(
  object,
  newX,
  newy,
  newX.alreadyExist = FALSE,
  cov.reestim = TRUE,
  trend.reestim = cov.reestim,
  nugget.reestim = FALSE,
  newnoise.var = NULL,
  kmcontrol = NULL,
  newF = NULL,
  ...
)

Arguments

Details

Without a dedicated update method for the class "KM", this would have been inherited from the class "km". The dedicated method is expected to run faster. A comparison can be made by coercing a KM object to a km object with as.km before calling update.

Value

The updated KM object.

Author(s)

Yann Richet yann.richet@asnr.fr

See Also

as.km to coerce a KM object to the class "km".

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(5))
y <- f(X)
points(X, y, col = "blue")
KMobj <- KM(design = X, response = y,covtype = "gauss")
x <-  seq(from = 0, to = 1, length.out = 101)
p_x <- predict(KMobj, x)
lines(x, p_x$mean, col = "blue")
lines(x, p_x$lower95, col = "blue")
lines(x, p_x$upper95, col = "blue")
newX <- as.matrix(runif(3))
newy <- f(newX)
points(newX, newy, col = "red")

## replace the object by its udated version
KMobj <- update(KMobj, newX=newX, newy=newy)

x <- seq(from = 0, to = 1, length.out = 101)
p2_x <- predict(KMobj, x)
lines(x, p2_x$mean, col = "red")
lines(x, p2_x$lower95, col = "red")
lines(x, p2_x$upper95, col = "red")

Update a Kriging model object with new points

Description

Update a Kriging model object with new points

Usage

## S3 method for class 'Kriging'
update(object, y_u, ..., X_u = NULL, noise_u = NULL, refit = TRUE)

Arguments

Value

No return value. Kriging object argument is modified.

Caution

The method does not return the updated object, but instead changes the content of object. This behaviour is quite unusual in R and differs from the behaviour of update.km in DiceKriging and the update method for class KM.

Author(s)

Yann Richet yann.richet@asnr.fr

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)
points(X, y, col = "blue")

k <- Kriging(y, 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))

X_u <- as.matrix(runif(3))
y_u <- f(X_u)
points(X_u, y_u, col = "red")

## change the content of the object 'k'
update(k, y_u, X_u)

## include design points to see interpolation
x <- sort(c(X,X_u,seq(from = 0, to = 1, length.out = 101)))
p2 <- predict(k, x)
lines(x, p2$mean, col = "red")
polygon(c(x, rev(x)), c(p2$mean - 2 * p2$stdev, rev(p2$mean + 2 * p2$stdev)),
 border = NA, col = rgb(1, 0, 0, 0.2))

Update an MLPKriging model with new observations

Description

Update an MLPKriging model with new observations

Usage

## S3 method for class 'MLPKriging'
update(object, y_u, X_u, refit = TRUE, ...)

Arguments

Update a WarpKriging model with new observations

Description

Update a WarpKriging model with new observations

Usage

## S3 method for class 'WarpKriging'
update(object, y_u, X_u, refit = TRUE, ...)

Arguments

Update simulation of model on data.

Description

Update previous simulate of a model given in object.

Usage

update_simulate(object, ...)

Arguments

Value

Updated simulation of model output.

Update previous simulation of a Kriging model object.

Description

This method draws paths of the stochastic process conditional on the values at the input points used in the fit, plus the new input points and their values given as argument (knonw as 'update' points).

Usage

## S3 method for class 'Kriging'
update_simulate(object, y_u, ..., X_u = NULL, noise_u = NULL)

Arguments

Value

a matrix with nrow(x) rows and nsim columns containing the simulated paths at the inputs points given in x.

Author(s)

Yann Richet yann.richet@asnr.fr

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)
points(X, y, col = "blue")

k <- Kriging(y, X, kernel = "matern3_2")

x <- seq(from = 0, to = 1, length.out = 101)
s <- k$simulate(nsim = 3, x = x, will_update = TRUE)

lines(x, s[ , 1], col = "blue")
lines(x, s[ , 2], col = "blue")
lines(x, s[ , 3], col = "blue")

X_u <- as.matrix(runif(3))
y_u <- f(X_u)
points(X_u, y_u, col = "red")

su <- k$update_simulate(y_u, X_u)

lines(x, su[ , 1], col = "blue", lty=2)
lines(x, su[ , 2], col = "blue", lty=2)
lines(x, su[ , 3], col = "blue", lty=2)

Update simulated paths with new observations (FOXY algorithm)

Description

Update simulated paths with new observations (FOXY algorithm)

Usage

## S3 method for class 'MLPKriging'
update_simulate(object, y_u, X_u, ...)

Arguments

Value

matrix (m x nsim) of updated simulated paths

Update simulated paths with new observations (FOXY algorithm)

Description

Update simulated paths with new observations (FOXY algorithm)

Usage

## S3 method for class 'WarpKriging'
update_simulate(object, y_u, X_u, ...)

Arguments

Value

matrix (m x nsim) of updated simulated paths

Affine warping: w(x) = a*x + b

Description

Affine warping: w(x) = a*x + b

Usage

warp_affine()

Value

string "affine"

Box-Cox warping

Description

Box-Cox warping

Usage

warp_boxcox()

Value

string "boxcox"

Categorical embedding

Description

Categorical embedding

Usage

warp_categorical(n_levels, embed_dim = 2)

Arguments

Value

string e.g. "categorical(5,2)"

Piecewise-linear monotone warping with knots (Xiong et al. 2007)

Description

Implements the non-stationary input warping of Xiong, Chen, Apley & Ding (2007, Int. J. Numer. Meth. Engng). The domain [0,1] is split into K+1 intervals by K interior knots, each carrying a learnable positive slope s_k = \exp(r_k). The warp is:

w(x) = \sum_{j<k} s_j (t_{j+1}-t_j) + s_k (x-t_k)

for x \in [t_k, t_{k+1}), giving a continuous, monotone piecewise-linear function with K+1 free parameters.

This is the same construction as the knots argument in DiceKriging.

Usage

warp_knots(n_knots = 3, knot_positions = NULL)

Arguments

Value

warp specification string, e.g. "knots(3)" or "knots(0.25:0.5:0.75)"

References

Xiong, Y., Chen, W., Apley, D. & Ding, X. (2007). A non-stationary covariance-based Kriging method for metamodelling in engineering design. International Journal for Numerical Methods in Engineering, 71(6), 733–756.

See Also

WarpKriging

Kumaraswamy CDF warping on [0,1]

Description

Kumaraswamy CDF warping on [0,1]

Usage

warp_kumaraswamy()

Value

string "kumaraswamy"

Per-variable MLP warping (unconstrained, multi-dim output)

Description

Per-variable MLP warping (unconstrained, multi-dim output)

Usage

warp_mlp(hidden_dims, d_out = 2, activation = "selu")

Arguments

Value

string e.g. "mlp(16:8,3,selu)"

Monotone neural network warping

Description

Monotone neural network warping

Usage

warp_neural_mono(n_hidden = 8)

Arguments

Value

string e.g. "neural_mono(16)"

No warping (identity)

Description

No warping (identity)

Usage

warp_none()

Value

string "none"

Ordinal warping (learned ordered positions)

Description

Ordinal warping (learned ordered positions)

Usage

warp_ordinal(n_levels)

Arguments

Value

string e.g. "ordinal(4)"

Get warping specifications as strings

Description

Get warping specifications as strings

Usage

warping(object, ...)

Arguments

Get warping specification for a WarpKriging model

Description

Get warping specification for a WarpKriging model

Usage

## S3 method for class 'WarpKriging'
warping(object, ...)

Arguments

Get training output vector

Description

Get training output vector

Usage

y(object, ...)

Arguments

Value

vector of training outputs

Get training output vector

Description

Get training output vector

Usage

## S3 method for class 'MLPKriging'
y(object, ...)

Arguments

Value

vector of training outputs

Get whitened residuals z

Description

Get whitened residuals z

Usage

z(object, ...)

Arguments

Get whitened residuals z for an MLPKriging model

Description

Get whitened residuals z for an MLPKriging model

Usage

## S3 method for class 'MLPKriging'
z(object, ...)

Arguments

Get whitened residuals z for a WarpKriging model

Description

Get whitened residuals z for a WarpKriging model

Usage

## S3 method for class 'WarpKriging'
z(object, ...)

Arguments