print_uncertainty: Prints a measure of uncertainty for a function of any dimension.

Description

This function prints the value 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(model, T, type = "pn", ...)

Value

the integrated uncertainty

Arguments

model: Kriging model of km class.
T: Array containing one or several thresholds.
type: Type of uncertainty that the user wants to print. Possible values are "pn" (probability of excursion), or "sur", "imse", "timse", "vorob" if we print a measure of uncertainty corresponding to one criterion.
...: Other arguments of the functions print_uncertainty_1d, 2d or nd.

Author

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

Examples

Run this code

#print_uncertainty

set.seed(9)
N <- 20 #number of observations
T <- c(80,100) #threshold
testfun <- branin
lower <- c(0,0)
upper <- c(1,1)

#a 20 points initial design
design <- data.frame( matrix(runif(2*N),ncol=2) )
response <- testfun(design)

#km object with matern3_2 covariance
#params estimated by ML from the observations
model <- km(formula=~., design = design, 
	response = response,covtype="matern3_2")

#you could do many plots, but only one is run here
print_uncertainty(model=model,T=T,main="probability of excursion",type="pn")
#print_uncertainty(model=model,T=T,main="Vorob'ev uncertainty",type="vorob")
#print_uncertainty(model=model,T=T,main="imse uncertainty",type="imse")
#print_uncertainty(model=model,T=T,main="timse uncertainty",type="timse")
#print_uncertainty(model=model,T=T,main="sur uncertainty",type="sur")
#print_uncertainty(model=model,T=T,main="probability of excursion",type="pn",
#vorobmean=TRUE)

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