View source: R/vorob_threshold.R
vorob_threshold | R Documentation |
Evaluation of the Vorob'ev threshold given an excursion probability vector. This threshold is such that the volume of the set (x : pn(x) > threshold) is equal to the integral of pn.
vorob_threshold(pn)
pn |
Input vector of arbitrary size containing the excursion probabilities pn(x). |
In this function, all the points x are supposed to be equaly weighted.
a scalar: the Vorob'ev thresold
Clement Chevalier (University of Neuchatel, Switzerland)
Chevalier C., Ginsbouger D., Bect J., Molchanov I. (2013) Estimating and quantifying uncertainties on level sets using the Vorob'ev expectation and deviation with gaussian process models mODa 10, Advances in Model-Oriented Design and Analysis, Contributions to Statistics, pp 35-43
Chevalier C. (2013) Fast uncertainty reduction strategies relying on Gaussian process models Ph.D Thesis, University of Bern
max_vorob_parallel
, vorob_optim_parallel
#vorob_threshold set.seed(9) N <- 20 #number of observations T <- 80 #threshold testfun <- branin #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") ## Not run: ###we need to compute some additional arguments: #integration points, and current kriging means and variances at these points integcontrol <- list(n.points=50,distrib="sobol") obj <- integration_design(integcontrol=integcontrol, lower=c(0,0),upper=c(1,1),model=model,T=T) integration.points <- obj$integration.points pred <- predict_nobias_km(object=model,newdata=integration.points, type="UK",se.compute=TRUE) pn <- pnorm((pred$mean-T)/pred$sd) vorob_threshold(pn) ## End(Not run)
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