View source: R/print_uncertainty_nd.R
print_uncertainty_nd | R Documentation |
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
.
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,...)
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 |
The integrated uncertainty
Clement Chevalier (University of Neuchatel, Switzerland)
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
print_uncertainty_1d
,print_uncertainty_2d
#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)
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