PLIquantile_multivar: Perturbed-Law based sensitivity Indices (PLI) for quantile...

In sensitivity: Global Sensitivity Analysis of Model Outputs and Importance Measures

Perturbed-Law based sensitivity Indices (PLI) for quantile and simultaneous perturbations of 2 inputs

Description

PLIquantile_multivar computes the Perturbed-Law based Indices (PLI) for quantile and simultaneous perturbations of the means of 2 inputs, estimated by a Monte Carlo method.

Usage

PLIquantile_multivar(order,x,y,inputs,deltasvector,InputDistributions,samedelta=TRUE,
            percentage=TRUE,nboot=0,conf=0.95,bootsample=TRUE)

Arguments

order	the order of the quantile to estimate.
x	the matrix of simulation points coordinates, one column per variable.
y	the vector of model outputs.
inputs	the vector of the two inputs' indices for which the indices will be computed.
deltasvector	a vector containing the values of the perturbed means for which the indices will be computed. Warning: if samedelta=FALSE, deltasvector has to be the vector of deltas (mean perturbations)
InputDistributions	a list of list. Each list contains, as a list, the name of the distribution to be used and the parameters. Implemented cases so far (for a mean perturbation): Gaussian, Uniform, Triangle, Left Trucated Gaussian, Left Truncated Gumbel. Using Gumbel requires the package evd.
samedelta	a boolean used with the value "MOY" for type. If it is set at TRUE, the mean perturbation will be the same for all the variables. If not, the mean perturbation will be new_mean = mean+sigma*delta where mean, sigma are parameters defined in InputDistributions and delta is a value of deltasvector.
percentage	a boolean that defines the formula used for the PLI. If it is set at FALSE, the initially proposed formula is used (see Sueur et al., 2017). If not (set as TRUE), the PLI is given in percentage of variation of the quantile (see Iooss et al., 2021).
nboot	the number of bootstrap replicates.
conf	the confidence level for bootstrap confidence intervals.
bootsample	If TRUE, the uncertainty about the original quantile estimation is taken into account in the PLI confidence intervals (see Iooss et al., 2021). If FALSE, standard confidence intervals are computed for the PLI. It mainly changes the CI at small delta values.

Details

This function does not allow perturbations on the variance of the inputs' distributions.

Value

PLIquantile_multivar returns a list of matrices (delta twist of input 1 (in rows) vs. delta twist of input 2 (in columns)) containing the following components:

PLI	the PLI.
PLICIinf	the bootstrap lower confidence interval values of the PLI.
PLICIsup	the bootstrap upper confidence interval values of the PLI.
quantile	the perturbed quantile.
quantileCIinf	the bootstrap lower confidence interval values of the perturbed quantile.
quantileCIsup	the bootstrap upper confidence interval values of the perturbed quantile.

Author(s)

Bertrand Iooss

References

T. Delage, R. Sueur and B. Iooss, 2018, Robustness analysis of epistemic uncertainties propagation studies in LOCA assessment thermal-hydraulic model, ANS Best Estimate Plus Uncertainty International Conference (BEPU 2018), Lucca, Italy, May 13-19, 2018.

B. Iooss, V. Verges and V. Larget, 2022, BEPU robustness analysis via perturbed law-based sensitivity indices, Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 236:855-865.

P. Lemaitre, E. Sergienko, A. Arnaud, N. Bousquet, F. Gamboa and B. Iooss, 2015, Density modification based reliability sensitivity analysis, Journal of Statistical Computation and Simulation, 85:1200-1223.

R. Sueur, N. Bousquet, B. Iooss and J. Bect, 2016, Perturbed-Law based sensitivity Indices for sensitivity analysis in structural reliability, Proceedings of the SAMO 2016 Conference, Reunion Island, France, December 2016.

R. Sueur, B. Iooss and T. Delage, 2017, Sensitivity analysis using perturbed-law based indices for quantiles and application to an industrial case, 10th International Conference on Mathematical Methods in Reliability (MMR 2017), Grenoble, France, July 2017.

See Also

PLI, PLIquantile, PLIsuperquantile, PLIsuperquantile_multivar

Examples

# Model: 3D function 

distribution = list()
for (i in 1:3) distribution[[i]]=list("norm",c(0,1))
N = 5000
X = matrix(0,ncol=3,nrow=N)
for(i in 1:3) X[,i] = rnorm(N,0,1)
Y = 2 * X[,1] + X[,2] + X[,3]/2
alpha <- 0.95
nboot <- 20 # put nboot=200 for consistency

q95 = quantile(Y,alpha)
v_delta = seq(-1,1,1/10) 
toto12 = PLIquantile_multivar(alpha,X,Y,c(1,2),deltasvector=v_delta,
    InputDistributions=distribution,samedelta=TRUE)
toto = PLIquantile(alpha,X,Y,deltasvector=v_delta,InputDistributions=distribution,
    type="MOY",samedelta=TRUE,nboot=0)

par(mar=c(4,5,1,1))
plot(v_delta,diag(toto12$PLI),,ylim=c(-1,1),xlab=expression(delta),
    ylab=expression(hat(PLI[i*delta])),pch=16,cex=1.5,col="blue")
points(v_delta,toto$PLI[,1],col="darkgreen",pch=15,cex=1.5)
points(v_delta,toto$PLI[,2],col="black",pch=19,cex=1.5)
points(v_delta,toto$PLI[,3],col="red",pch=17,cex=1.5)
abline(h=0,lty=2)
legend(-1,1.,legend=c("X1","X2","X3","X1X2"),col=c("darkgreen","black","red","blue"),
    pch=c(15,19,17,16),cex=1.5)

# with bootstrap

v_delta = seq(-1,1,2/10) 

toto12 = PLIquantile_multivar(alpha,X,Y,c(1,2),deltasvector=v_delta,
    InputDistributions=distribution,samedelta=TRUE,nboot=nboot,bootsample=FALSE)
toto = PLIquantile(alpha,X,Y,deltasvector=v_delta,InputDistributions=distribution,
    type="MOY",samedelta=TRUE,nboot=nboot,bootsample=FALSE)

par(mar=c(4,5,1,1))
plot(v_delta,diag(toto12$PLI),ylim=c(-1,1),xlab=expression(delta),
    ylab=expression(hat(PLI[i*delta])),pch=16,cex=1.5,col="blue")
points(v_delta,toto$PLI[,1],col="darkgreen",pch=15,cex=1.5)
points(v_delta,toto$PLI[,2],col="black",pch=19,cex=1.5)
points(v_delta,toto$PLI[,3],col="red",pch=17,cex=1.5)
lines(v_delta,diag(toto12$PLICIinf),col="blue")
lines(v_delta,diag(toto12$PLICIsup),col="blue")
lines(v_delta,toto$PLICIinf[,2],col="black")
lines(v_delta,toto$PLICIsup[,2],col="black")
lines(v_delta,toto$PLICIinf[,1],col="darkgreen")
lines(v_delta,toto$PLICIsup[,1],col="darkgreen")
lines(v_delta,toto$PLICIinf[,3],col="red")
lines(v_delta,toto$PLICIsup[,3],col="red")
abline(h=0,lty=2)
legend(-1,1,legend=c("X1","X2","X3","X1X2"),col=c("darkgreen","black","red","blue"),
    pch=c(15,19,17,16),cex=1.5)

###################################################		
# another visualizations by using the plotrix, 
# viridisLite, lattice and grid packages (from Vanessa Verges)

library(plotrix)

parameters = list(colors=c("darkgreen","black","red"),symbols=c(15,19,17))
par(mar=c(4,5,1,1),xpd=TRUE)
plotCI(v_delta,diag(toto12$PLI),ui=diag(toto12$PLICIsup),li=diag(toto12$PLICIinf),
       xlab=expression(delta),ylab=expression(hat(PLI[i*delta])),
       main=bquote("PLI-quantile (N ="~.(N) ~ ","~alpha~"="~.(alpha)~
       ") on "~X[1]~"and"~X[2]~"of Y="~2*X[1] + X[2] + X[3]/2),
       cex=1.5,col="blue",pch=16)
for (i in 1:3){
  plotCI(v_delta,toto$PLI[,i],ui=toto$PLICIsup[,i],li=toto$PLICIinf[,i],
         cex=1.5,col=parameters$colors[i],pch=parameters$symbols[i],
         add=TRUE)
}
abline(h=0,lty=2)
legend("topleft",legend=c("X1","X2","X3","X1X2"),
        col=c(parameters$colors,"blue"),pch=c(parameters$symbols,16),cex=1.5)

# Visu of all the PLIs (at any paired combinations of deltas)

library(viridisLite)
library(lattice)
library(grid)

colnames(toto12$PLI) = round(v_delta,2)
rownames(toto12$PLI) = round(v_delta,2)
coul = viridis(100)
levelplot(toto12$PLI, col.regions = coul, xlab=bquote(delta[X~.(1)]), 
  ylab=bquote(delta[X~.(2)]), main=bquote(hat(PLI)[quantile[~X[1]~X[2]]]))

sensitivity documentation built on Sept. 11, 2024, 9:09 p.m.