View source: R/print_uncertainty.R
print_uncertainty | R Documentation |
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
.
print_uncertainty(model, T, type = "pn", ...)
model |
Kriging model of |
T |
Array containing one or several thresholds. |
type |
Type of uncertainty that the user wants to print.
Possible values are |
... |
Other arguments of the functions |
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
#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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