R: Prints a measure of uncertainty for 2d function.
print_uncertainty_2d
R Documentation
Prints a measure of uncertainty for 2d function.
Description
This function draws the value of a given measure of uncertainty over the whole input domain (2D).
Possible measures are "pn" (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.
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
Vector containing the lower bounds of the input domain.
upper
Vector containing the upper bounds of the input domain.
resolution
Number of points to discretize the domain. This discretization is used in each dimension, so that the total number of points is resolution^2.
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].
yscale
see: xscale.
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.contourlab
Multiplicative factor for the size of labels of the contour plot.
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.
nlevels
Integer corresponding to the number of levels of the contour plot.
levels
Array: one can directly set the levels of the contour plot.
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.
krigmeanplot
Optional boolean. When it is set to FALSE (default) the contour plot corresponds to the uncertainty selected. When it is set to TRUE the contour plot gives the kriging mean.
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.
Value
the integrated uncertainty
Author(s)
Clement Chevalier (IMSV, Switzerland, and IRSN, France)
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
See Also
EGI
Examples
#print_uncertainty_2d
set.seed(8)
N <- 9 #number of observations
T <- 80 #threshold
testfun <- branin
lower <- c(0,0)
upper <- c(1,1)
#a 9 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")
print_uncertainty_2d(model=model,T=T,main="probability of excursion",
type="pn",krigmeanplot=TRUE)
#print_uncertainty_2d(model=model,T=T,main="vorob uncertainty",
#type="vorob",krigmeanplot=FALSE)
#print_uncertainty_2d(model=model,T=T,main="imse uncertainty",
#type="imse",krigmeanplot=FALSE)
#print_uncertainty_2d(model=model,T=T,main="timse uncertainty",
#type="timse",krigmeanplot=FALSE)
#print_uncertainty_2d(model=model,T=T,main="sur
#uncertainty",type="sur",krigmeanplot=FALSE)
#print_uncertainty_2d(model=model,T=T,main="probability of excursion",
# type="pn",krigmeanplot=TRUE,vorobmean=TRUE)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(KrigInv)
Loading required package: DiceKriging
Loading required package: pbivnorm
Loading required package: rgenoud
## rgenoud (Version 5.7-12.4, Build Date: 2015-07-19)
## See http://sekhon.berkeley.edu/rgenoud for additional documentation.
## Please cite software as:
## Walter Mebane, Jr. and Jasjeet S. Sekhon. 2011.
## ``Genetic Optimization Using Derivatives: The rgenoud package for R.''
## Journal of Statistical Software, 42(11): 1-26.
##
Loading required package: randtoolbox
Loading required package: rngWELL
This is randtoolbox. For overview, type 'help("randtoolbox")'.
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/KrigInv/print_uncertainty_2d.Rd_%03d_medium.png", width=480, height=480)
> ### Name: print_uncertainty_2d
> ### Title: Prints a measure of uncertainty for 2d function.
> ### Aliases: print_uncertainty_2d
>
> ### ** Examples
>
> #print_uncertainty_2d
>
> set.seed(8)
> N <- 9 #number of observations
> T <- 80 #threshold
> testfun <- branin
> lower <- c(0,0)
> upper <- c(1,1)
>
> #a 9 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")
optimisation start
------------------
* estimation method : MLE
* optimisation method : BFGS
* analytical gradient : used
* trend model : ~X1 + X2
* covariance model :
- type : matern3_2
- nugget : NO
- parameters lower bounds : 1e-10 1e-10
- parameters upper bounds : 1.448893 1.853021
- best initial criterion value(s) : -25.38168
N = 2, M = 5 machine precision = 2.22045e-16
At X0, 0 variables are exactly at the bounds
At iterate 0 f= 25.382 |proj g|= 0.19431
At iterate 1 f = 25.027 |proj g|= 0.13259
At iterate 2 f = 25.014 |proj g|= 1.6725
At iterate 3 f = 25.002 |proj g|= 0.15969
At iterate 4 f = 25.001 |proj g|= 0.17792
At iterate 5 f = 24.999 |proj g|= 0.31318
At iterate 6 f = 24.998 |proj g|= 0.14968
At iterate 7 f = 24.998 |proj g|= 0.03446
At iterate 8 f = 24.998 |proj g|= 0.03458
At iterate 9 f = 24.998 |proj g|= 0.0084816
At iterate 10 f = 24.998 |proj g|= 0.038393
At iterate 11 f = 24.997 |proj g|= 1.3196
At iterate 12 f = 24.997 |proj g|= 1.3327
At iterate 13 f = 24.994 |proj g|= 1.8077
At iterate 14 f = 24.991 |proj g|= 1.8106
At iterate 15 f = 24.975 |proj g|= 1.8136
At iterate 16 f = 24.937 |proj g|= 1.8202
At iterate 17 f = 24.816 |proj g|= 1.8136
At iterate 18 f = 24.652 |proj g|= 0.81261
At iterate 19 f = 24.652 |proj g|= 0.25743
At iterate 20 f = 24.651 |proj g|= 0.0033442
At iterate 21 f = 24.651 |proj g|= 1.4045e-05
iterations 21
function evaluations 30
segments explored during Cauchy searches 22
BFGS updates skipped 0
active bounds at final generalized Cauchy point 1
norm of the final projected gradient 1.40447e-05
final function value 24.6515
F = 24.6515
final value 24.651471
converged
>
> print_uncertainty_2d(model=model,T=T,main="probability of excursion",
+ type="pn",krigmeanplot=TRUE)
[1] 0.09380482
>
> #print_uncertainty_2d(model=model,T=T,main="vorob uncertainty",
> #type="vorob",krigmeanplot=FALSE)
>
> #print_uncertainty_2d(model=model,T=T,main="imse uncertainty",
> #type="imse",krigmeanplot=FALSE)
>
> #print_uncertainty_2d(model=model,T=T,main="timse uncertainty",
> #type="timse",krigmeanplot=FALSE)
>
> #print_uncertainty_2d(model=model,T=T,main="sur
> #uncertainty",type="sur",krigmeanplot=FALSE)
>
> #print_uncertainty_2d(model=model,T=T,main="probability of excursion",
> # type="pn",krigmeanplot=TRUE,vorobmean=TRUE)
>
>
>
>
>
>
> dev.off()
null device
1
>