print_uncertainty {KrigInv}

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", ...)

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.

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,print_uncertainty_nd

Examples

#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)