obliqueProfiles {profExtrema} | R Documentation |
Oblique coordinate profiles starting from a kriging model
Description
The function obliqueProfiles computes the (oblique) profile extrema functions for the posterior mean of a Gaussian process and its confidence bounds
Usage
obliqueProfiles(object, allPsi, threshold, options_full = NULL,
options_approx = NULL, uq_computations = FALSE, plot_level = 0,
plot_options = NULL, CI_const = NULL, return_level = 1, ...)
Arguments
object |
either a km model or a list containing partial results. If |
allPsi |
a list containing the matrices Psi (dim |
threshold |
the threshold of interest |
options_full |
an optional list of options for getProfileExtrema, see getProfileExtrema for details. |
options_approx |
an optional list of options for approxProfileExtrema, see approxProfileExtrema for details. |
uq_computations |
boolean, if TRUE the uq computations for the profile mean are computed. |
plot_level |
an integer to select the plots to return (0=no plots, 1=basic plots, 2= all plots) |
plot_options |
an optional list of parameters for plots. See setPlotOptions for currently available options. |
CI_const |
an optional vector containing the constants for the CI. If not NULL, then profiles extrema for |
return_level |
an integer to select the amount of details returned |
... |
additional parameters to be passed to obliqueProf_UQ. |
Value
If return_level=1 a list containing
profMean_full:
the results ofgetProfileExtrema
for the posterior meanprofMean_approx:
the results ofapproxProfileExtrema
for the posterior meanres_UQ:
the results ofobliqueProf_UQ
for the posterior mean
if return_level=2 the same list as above but also including
abs_err:
the vector of maximum absolute approximation errors for the profile inf /sup on posterior mean for the chosen approximationtimes:
a list containingfull:
computational time for the full computation of profile extremaapprox:
computational time for the approximate computation of profile extrema
Author(s)
Dario Azzimonti
Examples
if (!requireNamespace("DiceKriging", quietly = TRUE)) {
stop("DiceKriging needed for this example to work. Please install it.",
call. = FALSE)
}
# Compute a kriging model from 50 evaluations of the Branin function
# Define the function
g=function(x){
return(-branin(x))
}
gp_des<-lhs::maximinLHS(20,2)
reals<-apply(gp_des,1,g)
kmModel<-km(design = gp_des,response = reals,covtype = "matern3_2")
threshold=-10
# Compute oblique profiles on the posterior mean
# (for theta=0 it is equal to coordinateProfiles)
options_full<-list(multistart=4,heavyReturn=TRUE,discretization=100)
options_approx<- list(multistart=4,heavyReturn=TRUE,initDesign=NULL,fullDesignSize=100)
theta=pi/4
allPsi = list(Psi1=matrix(c(cos(theta),sin(theta)),ncol=2),
Psi2=matrix(c(cos(theta+pi/2),sin(theta+pi/2)),ncol=2))
## Not run:
profMeans<-obliqueProfiles(object = kmModel,allPsi = allPsi,threshold = threshold,
options_full = options_full,options_approx = options_approx,
uq_computations = FALSE,plot_level = 3,plot_options = NULL,
CI_const = NULL,return_level = 2)
# Approximate oblique profiles with UQ
plot_options<-list(save=FALSE, titleProf = "Coordinate profiles",
title2d = "Posterior mean",qq_fill=TRUE)
options_sims<-list(nsim=150)
obProfUQ<-obliqueProfiles(object=profMeans,threshold=threshold,allPsi = allPsi,
options_full=options_full, options_approx=options_approx,
uq_computations=TRUE, plot_level=3,plot_options=NULL,
CI_const=NULL,return_level=2,options_sims=options_sims)
## End(Not run)