DiceOptim (version 2.0)

kriging.quantile.grad: Analytical gradient of the Kriging quantile of level beta

Description

Computes the gradient of the Kriging quantile of level beta at the current location. Only available for Universal Kriging with constant trend (Ordinary Kriging).

Usage

kriging.quantile.grad(x, model, beta = 0.1, type = "UK", envir = NULL)

Arguments

x

a vector representing the input for which one wishes to calculate kriging.quantile.grad.

model

an object of class km.

beta

A quantile level (between 0 and 1)

type

Kriging type: "SK" or "UK"

envir

environment for inheriting intermediate calculations from "kriging.quantile"

Value

The gradient of the Kriging mean predictor with respect to x. Returns 0 at design points (where the gradient does not exist).

References

O. Roustant, D. Ginsbourger, Y. Deville, DiceKriging, DiceOptim: Two R packages for the analysis of computer experiments by kriging-based metamodeling and optimization, submitted to J. Stat. Soft., 2010. https://hal.archives-ouvertes.fr/hal-00495766/

D. Ginsbourger (2009), Multiples metamodeles pour l'approximation et l'optimisation de fonctions numeriques multivariables, Ph.D. thesis, Ecole Nationale Superieure des Mines de Saint-Etienne, 2009. https://tel.archives-ouvertes.fr/tel-00772384

See Also

EI.grad

Examples

## Not run: 
# ##########################################################################
# ###    KRIGING QUANTILE SURFACE AND ITS GRADIENT FOR                  ####
# ###    THE BRANIN FUNCTION KNOWN AT A 12-POINT LATIN HYPERCUBE DESIGN ####
# ##########################################################################
# set.seed(421)
# 
# # Set test problem parameters
# doe.size <- 12
# dim <- 2
# test.function <- get("branin2")
# lower <- rep(0,1,dim)
# upper <- rep(1,1,dim)
# noise.var <- 0.2
# 
# # Generate DOE and response
# doe <- as.data.frame(matrix(runif(doe.size*dim),doe.size))
# y.tilde <- rep(0, 1, doe.size)
# for (i in 1:doe.size)  {
# y.tilde[i] <- test.function(doe[i,]) + sqrt(noise.var)*rnorm(n=1)
# }
# y.tilde <- as.numeric(y.tilde)
# 
# # Create kriging model
# model <- km(y~1, design=doe, response=data.frame(y=y.tilde),
#         covtype="gauss", noise.var=rep(noise.var,1,doe.size),
# 	lower=rep(.1,dim), upper=rep(1,dim), control=list(trace=FALSE))
# 
# # Compute actual function and criterion on a grid
# n.grid <- 12 # Change to 21 for a nicer picture
# x.grid <- y.grid <- seq(0,1,length=n.grid)
# design.grid <- expand.grid(x.grid, y.grid)
# nt <- nrow(design.grid)
# 
# crit.grid <- apply(design.grid, 1, kriging.quantile, model=model, beta=.1)
# crit.grad <- t(apply(design.grid, 1, kriging.quantile.grad, model=model, beta=.1))
# 
# z.grid <- matrix(crit.grid, n.grid, n.grid)
# contour(x.grid,y.grid, z.grid, 30)
# title("kriging.quantile and its gradient")
# points(model@X[,1],model@X[,2],pch=17,col="blue")
# 
# for (i in 1:nt)
# {
#  x <- design.grid[i,]
#  arrows(x$Var1,x$Var2, x$Var1+crit.grad[i,1]*.01,x$Var2+crit.grad[i,2]*.01,
# length=0.04,code=2,col="orange",lwd=2)
# }
# ## End(Not run)