print_uncertainty_1d: Prints a measure of uncertainty for 1d function.

Description

This function draws the value of a given measure of uncertainty over the whole input domain (1D). Possible measures are "pn" (beeing the probability of excursion) and measures specific to a sampling criterion: "sur", "timse" and "imse". This function can be used to print relevant outputs after having used the function EGI.

Usage

print_uncertainty_1d(model, T, type = "pn", 
lower = 0, upper = 1, resolution = 500, 
new.points = 0, xlab = "", ylab = "", main = "", 
xscale = c(0, 1), show.points = TRUE, cex.main = 1, cex.lab = 1, 
cex.points = 1, cex.axis = 1, pch.points.init = 17, pch.points.end = 17, 
col.points.init = "black", col.points.end = "red", xaxislab = NULL, 
yaxislab = NULL, xaxispoint = NULL, yaxispoint = NULL, 
xdecal = 3, ydecal = 3,DiceViewplot=FALSE,vorobmean=FALSE)

Arguments

model

Kriging model of km class.

Target value (scalar).

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.

lower

Lower bound for the input domain.

upper

Upper bound for the input domain.

resolution

Number of points to discretize the interval (lower,upper).

new.points

Number of new observations. These observations are the last new.points observations and can be printed in another color and the initial observations (see argument: col.points.end).

xlab

Label for the x axis.

ylab

Label for the y axis.

main

Title of the graph.

xscale

If one wants to rescale the input domain on another interval it is possible to set this vector of size 2. The new interval will be translated by xscale[1] and expanded by a factor xscale[2] - xscale[1].

show.points

Boolean: should we show the observations on the graph ?

cex.main

Multiplicative factor for the size of the title.

cex.lab

Multiplicative factor for the size of titles of the axis.

cex.points

Multiplicative factor for the size of the points.

cex.axis

Multiplicative factor for the size of the axis graduations.

pch.points.init

Symbol for the n-new.points first observations.

pch.points.end

Symbol for the new.points last observations.

col.points.init

Color for the n-new.points first observations.

col.points.end

Color for the new.points last observations.

xaxislab

Optional new labels that will replace the normal levels on x axis.

yaxislab

Optional new labels that will replace the normal levels on y axis.

xaxispoint

Position of these new labels on x axis.

yaxispoint

Position of these new labels on y axis.

xdecal

Optional position shifting of the titles of the x axis.

ydecal

Optional position shifting of the titles of the y axis.

DiceViewplot

Optional boolean. When it is set to TRUE (default) a second plot is added, generated with the DiceView package. This plot shows the kriging mean and confidence intervals on the whole input domain.

vorobmean

Optional boolean. When it is set to TRUE the Vorob'ev expectation is plotted. It corresponds to the averaged excursion set, using the definition of Vorob'ev. Here, the estimated set is the set above the Vorob'ev threshold (plotted in blue).

Value

the integrated uncertainty

References

Bect J., Ginsbourger D., Li L., Picheny V., Vazquez E. (2010), Sequential design of computer experiments for the estimation of a probability of failure, Statistics and Computing, pp.1-21, 2011, http://arxiv.org/abs/1009.5177

Examples

Run this code

# NOT RUN {
#print_uncertainty_1d

set.seed(8)
N <- 9 #number of observations
T <- 1 #threshold
testfun <- fundet
lower <- c(0)
upper <- c(1)

#a 9 points initial design
design <- data.frame( matrix(runif(N),ncol=1) )
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")

print_uncertainty_1d(DiceViewplot=FALSE,model=model,T=T,
main="probability of excursion",cex.points=1.5,col.points.init="red",vorobmean=TRUE)


# }