print_uncertainty_nd {KrigInv} | R Documentation |
Print a measure of uncertainty for functions with dimension d strictly larger than 2.
Description
This function draws projections on various plans of a given measure of uncertainty.
The function can be used to print relevant outputs after having used the function EGI
or EGIparallel
.
Usage
print_uncertainty_nd(model,T,type="pn",lower=NULL,upper=NULL,
resolution=20, nintegpoints=400,
cex.lab=1,cex.contourlab=1,cex.axis=1,
nlevels=10,levels=NULL,
xdecal=3,ydecal=3, option="mean", pairs=NULL,...)
Arguments
model |
Kriging model of |
T |
Array containing one or several thresholds. |
type |
Type of uncertainty that the user wants to print.
Possible values are |
lower |
Vector containing the lower bounds of the input domain. If nothing is set we use a vector of 0. |
upper |
Vector containing the upper bounds of the input domain. If nothing is set we use a vector of 1. |
resolution |
Number of points to discretize a plan included in the domain. For the moment, we cannot use values higher than 40 do to
computation time, except when the argument |
nintegpoints |
to do |
cex.lab |
Multiplicative factor for the size of titles of the axis. |
cex.contourlab |
Multiplicative factor for the size of labels of the contour plot. |
cex.axis |
Multiplicative factor for the size of the axis graduations. |
nlevels |
Integer corresponding to the number of levels of the contour plot. |
levels |
Array: one can directly set the levels of the contour plot. |
xdecal |
Optional position shifting of the titles of the x axis. |
ydecal |
Optional position shifting of the titles of the y axis. |
option |
Optional argument (a string). The 3 possible values are |
pairs |
Optional argument. When set to codeNULL (default) the function performs the projections on plans spanned by each pair (i,j) of dimension. Otherwise, the argument is an array of size 2 corresponding to the dimensions spanning the (only) plan on which the projection is performed. |
... |
Additional arguments to the |
Value
The integrated uncertainty
Author(s)
Clement Chevalier (University of Neuchatel, Switzerland)
References
Bect J., Ginsbourger D., Li L., Picheny V., Vazquez E. (2012), Sequential design of computer experiments for the estimation of a probability of failure, Statistics and Computing vol. 22(3), pp 773-793
See Also
print_uncertainty_1d
,print_uncertainty_2d
Examples
#print_uncertainty_nd
set.seed(9)
N <- 30 #number of observations
T <- -1 #threshold
testfun <- hartman3
#The hartman3 function is defined over the domain [0,1]^3.
lower <- rep(0,times=3)
upper <- rep(1,times=3)
#a 30 points initial design
design <- data.frame( matrix(runif(3*N),ncol=3) )
response <- apply(design,1,testfun)
#km object with matern3_2 covariance
#params estimated by ML from the observations
model <- km(formula=~., design = design,
response = response,covtype="matern3_2")
## Not run:
print_uncertainty_nd(model=model,T=T,main="average probability of excursion",type="pn",
option="mean")
print_uncertainty_nd(model=model,T=T,main="maximum probability of excursion",type="pn",
option="max")
## End(Not run)