| kergp-package {kergp} | R Documentation |
Gaussian Process Laboratory
Description
Laboratory Package for Gaussian Process interpolation, regression and simulation, with an emphasis on user-defined covariance kernels.
Details
| Package: | kergp |
| Type: | Package |
| Title: | Gaussian Process Laboratory |
| Version: | 0.5.7 |
| Date: | 2024-02-05 |
| Author: | Yves Deville, David Ginsbourger, Olivier Roustant. Contributors: Nicolas Durrande. |
| Maintainer: | Olivier Roustant <roustant@insa-toulouse.fr> |
| Description: | Gaussian process regression with an emphasis on kernels. Quantitative and qualitative inputs are accepted. Some pre-defined kernels are available, such as radial or tensor-sum for quantitative inputs, and compound symmetry, low rank, group kernel for qualitative inputs. The user can define new kernels and composite kernels through a formula mechanism. Useful methods include parameter estimation by maximum likelihood, simulation, prediction and leave-one-out validation. |
| License: | GPL-3 |
| Depends: | Rcpp (>= 0.10.5), methods, testthat, nloptr, lattice |
| Suggests: | DiceKriging, DiceDesign, inline, foreach, knitr, ggplot2, reshape2, corrplot |
| Imports: | MASS, numDeriv, stats4, doParallel, doFuture, utils |
| LinkingTo: | Rcpp |
| RoxygenNote: | 6.0.1 |
| Collate: | 'CovFormulas.R' 'allGenerics.R' 'checkGrad.R' 'covComp.R' 'covMan.R' 'covQual.R' 'q1CompSymm.R' 'q1Symm.R' 'q1LowRank.R' 'covQualNested.R' 'covQualOrd.R' 'covRadial.R' 'covTS.R' 'covTP.R' 'covANOVA.R' 'covZZAll.R' 'gp.R' 'kFuns.R' 'kernelNorm.R' 'kernels1d_Call.R' 'logLikFuns.R' 'methodGLS.R' 'methodMLE.R' 'miscUtils.R' 'prinKrige.R' 'q1Diag.R' 'simulate_gp.R' 'warpFuns.R' |
Warning
As a lab, kergp may strongly evolve in its future life. Users interested in stable software for the Analysis of Computer Experiments are encouraged to use other packages such as DiceKriging instead.
Note
This package was developed within the frame of the ReDice Consortium, gathering industrial partners (CEA, EDF, IFPEN, IRSN, Renault) and academic partners (Mines Saint-Étienne, INRIA, and the University of Bern) around advanced methods for Computer Experiments.
Author(s)
Yves Deville (Alpestat), David Ginsbourger (University of Bern), Olivier Roustant (INSA Toulouse), with contributions from Nicolas Durrande (Mines Saint-Étienne).
Maintainer: Olivier Roustant, <roustant@insa-toulouse.fr>
References
Nicolas Durrande, David Ginsbourger, Olivier Roustant (2012). "Additive covariance kernels for high-dimensional gaussian process modeling". Annales de la Faculté des Sciences de Toulouse, 21 (3): 481-499. link
Nicolas Durrande, David Ginsbourger, Olivier Roustant, Laurent Carraro (2013). "ANOVA kernels and RKHS of zero mean functions for model-based sensitivity analysis". Journal of Multivariate Analysis, 115, 57-67. link
David Ginsbourger, Xavier Bay, Olivier Roustant, Laurent Carraro (2012). "Argumentwise invariant kernels for the approximation of invariant functions". Annales de la Faculté des Sciences de Toulouse, 21 (3): 501-527. link
David Ginsbourger, Nicolas Durrande, Olivier Roustant (2013). "Kernels and designs for modelling invariant functions: From group invariance to additivity". mODa 10 - Advances in Model-Oriented Design and Analysis. Contributions to Statistics, 107-115. link
Olivier Roustant, David Ginsbourger, Yves Deville (2012). "DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization". Journal of Statistical Software, 51(1), 1-55. doi:10.18637/jss.v051.i01
Examples
## ------------------------------------------------------------------
## Gaussian process modelling of function with invariance properties,
## by using an argumentwise invariant kernel
## ------------------------------------------------------------------
## -- define manually an argumentwise invariant kernel --
kernFun <- function(x1, x2, par) {
h <- (abs(x1) - abs(x2)) / par[1]
S <- sum(h^2)
d2 <- exp(-S)
K <- par[2] * d2
d1 <- 2 * K * S / par[1]
attr(K, "gradient") <- c(theta = d1, sigma2 = d2)
return(K)
}
## ---------------------------------------------------------------
## quicker: with Rcpp; see also an example with package inline
## in "gp" doc. file. Note that the Rcpp "sugar" fucntions are
## vectorized, so no for loops is required.
## ---------------------------------------------------------------
## Not run:
cppFunction('
NumericVector cppKernFun(NumericVector x1, NumericVector x2,
NumericVector par){
int n1 = x1.size();
double S, d1, d2;
NumericVector K(1), h(n1);
h = (abs(x1) - abs(x2)) / par[0]; // sugar function "abs"
S = sum(h * h); // sugar "*" and "sum"
d2 = exp(-S);
K[0] = par[1] * d2;
d1 = 2 * K[0] * S / par[0];
K.attr("gradient") = NumericVector::create(Named("theta", d1),
Named("sigma2", d2));
return K;
}')
## End(Not run)
## ---------------------------------------------------------------
## Below: with the R-based code for the kernel namely 'kernFun'.
## You can also replace 'kernFun' by 'cppKernFun' for speed.
## ---------------------------------------------------------------
covSymGauss <- covMan(kernel = kernFun,
hasGrad = TRUE,
label = "argumentwise invariant",
d = 2,
parLower = c(theta = 0.0, sigma2 = 0.0),
parUpper = c(theta = Inf, sigma2 = Inf),
parNames = c("theta", "sigma2"),
par = c(theta = 0.5, sigma2 = 2))
covSymGauss
## -- simulate a path from the corresponding GP --
nGrid <- 24; n <- nGrid^2; d <- 2
xGrid <- seq(from = -1, to = 1, length.out = nGrid)
Xgrid <- expand.grid(x1 = xGrid, x2 = xGrid)
Kmat <- covMat(object = covSymGauss, X = Xgrid,
compGrad = FALSE, index = 1L)
library(MASS)
set.seed(1)
ygrid <- mvrnorm(mu = rep(0, n), Sigma = Kmat)
## -- extract a design and the corr. response from the grid --
nDesign <- 25
tab <- subset(cbind(Xgrid, ygrid), x1 > 0 & x2 > 0)
rowIndex <- seq(1, nrow(tab), length = nDesign)
X <- tab[rowIndex, 1:2]
y <- tab[rowIndex, 3]
opar <- par(mfrow = c(1, 3))
contour(x = xGrid, y = xGrid,
z = matrix(ygrid, nrow = nGrid, ncol = nGrid),
nlevels = 15)
abline(h = 0, v = 0, col = "SpringGreen3")
points(x2 ~ x1, data = X, type = "p", pch = 21,
col = "orangered", bg = "yellow", cex = 0.8)
title("GRF Simulation")
## -- Fit the Gaussian process model (trend + covariance parameters) --
covSymGauss
symgp <- gp(formula = y ~ 1, data = data.frame(y, X),
inputs = names(X),
cov = covSymGauss,
parCovIni = c(0.1, 2),
varNoiseIni = 1.0e-8,
varNoiseLower = 0.9e-8, varNoiseUpper = 1.1e-8)
# mind that the noise is not a symmetric kernel
# so varNoiseUpper should be chosen as small as possible.
summary(symgp)
## -- predict and compare --
predSymgp <- predict(object = symgp, newdata = Xgrid, type = "UK")
contour(x = xGrid, y = xGrid,
z = matrix(predSymgp$mean, nrow = nGrid, ncol = nGrid),
nlevels = 15)
abline(h = 0, v = 0, col = "SpringGreen3")
points(x2 ~ x1, data = X, type = "p", pch = 21,
col = "orangered", bg = "yellow", cex = 0.8)
title("Kriging mean")
contour(x = xGrid, y = xGrid,
z = matrix(predSymgp$sd, nrow = nGrid, ncol = nGrid),
nlevels = 15)
abline(h = 0, v = 0, col = "SpringGreen3")
points(x2 ~ x1, data = X, type = "p", pch = 21,
col = "orangered", bg = "yellow", cex = 0.8)
title("Kriging s.d.")
par(opar)