sensitivity (version 1.30.1)

shapleyBlockEstimation: Computation of the Shapley effects in the Gaussian linear framework with an unknown block-diagonal covariance matrix

Description

shapleyBlockEstimation estimates the Shapley effects of a Gaussian linear model when the parameters are unknown and when the number of inputs is large, choosing the most likely block-diagonal structure of the covariance matrix.

Usage

shapleyBlockEstimationS(Beta, S, kappa=0,  M=20, tol=10^(-6))
shapleyBlockEstimationX(X, Y, delta=NULL, M=20, tol=10^(-6))

Value

shapleyBlockEstimationS and shapleyblockEstimationX return a list containing the following compopents:

label

a vector containing the label of the group of each input variable.

S_B

the block-diagonal estimated covariance matrix of the inputs.

Shapley

a vector containing all the estimated Shapley effects.

Arguments

Beta

A vector containing the (estimated) coefficients of the linear model.

S

Empirical covariance matrix of the inputs. Has to be positive semi-definite matrix with same size that Beta.

X

Matrix containing an i.i.d. sample of the inputs.

Y

Vector containing the corresponding i.i.d. sample of the (noisy) output.

kappa

The positive penalization coefficient that promotes block-diagonal matrices. It is advised to choose kappa=0 to get the largest block structure such that the maximal block size is M.

delta

Positive number that fixes the positive penalization coefficient kappa to \(1/(p n^{delta})\). It is advised to choose delta to 2/3 for a positive penalisation or delta=NULL to get the largest block structure such that the maximal block size is M.

M

Maximal size of the estimate of the block-diagonal structure. The computation time grows exponentially with M.

tol

A relative tolerance to detect zero singular values of Sigma.

Author

Baptiste Broto, CEA LIST

Details

If kappa = 0 or if delta = NULL, there is no penalization.

It is advised to choose M smaller or equal than 20. For M larger or equal than 25, the computation is very long.

References

B. Broto, F. Bachoc, L. Clouvel and J-M Martinez, 2022,Block-diagonal covariance estimation and application to the Shapley effects in sensitivity analysis, SIAM/ASA Journal on Uncertainty Quantification, 10, 379--403.

B. Broto, F. Bachoc, M. Depecker, and J-M. Martinez, 2019, Sensitivity indices for independent groups of variables, Mathematics and Computers in Simulation, 163, 19--31.

B. Iooss and C. Prieur, 2019, Shapley effects for sensitivity analysis with correlated inputs: comparisons with Sobol' indices, numerical estimation and applications, International Journal of Uncertainty Quantification, 9, 493--514.

A.B. Owen and C. Prieur, 2016, On Shapley value for measuring importance of dependent inputs, SIAM/ASA Journal of Uncertainty Quantification, 5, 986--1002.

See Also

shapleyLinearGaussian, shapleyPermEx, shapleyPermRand, shapleySubsetMc

Examples

# packages for the plots of the matrices
library(gplots)
library(graphics)


# the following function improves the plots of the matrices
sig=function(x,alpha=0.4)
{
  return(1/(1+exp(-x/alpha)))
}


# 1) we generate the parameters by groups in order

K=4 # number or groups

pk=rep(0,K)
for(k in 1:K)
{
  pk[k]=round(6+4*runif(1))
}
p=sum(pk)
Sigma_ord=matrix(0,nrow=p, ncol=p)
ind_min=0
L=5
for(k in 1:K)
{
  p_k=pk[k]
  ind=ind_min+(1:p_k)
  ind_min=ind_min+p_k
  
  A=2*matrix(runif(p_k*L),nrow=L,ncol=p_k)-1
  Sigma_ord[ind,ind]=t(A)%*%A + 0.2*diag(rep(1,p_k))
}


image((0:p)+0.5,(0:p)+0.5,z=sig(Sigma_ord),col=cm.colors(100), zlim=c(0,1),
      ylim=c(p+0.5,0.5), main=expression(Sigma["order"]), 
      cex.main=3,ylab = "", xlab = "",axes=FALSE)
box()


Beta_ord=3*runif(p)+1
eta_ord=shapleyLinearGaussian(Beta=Beta_ord, Sigma=Sigma_ord)
barplot(eta_ord,main=expression(eta["order"]),cex.axis = 2,cex.main=3)


# 2) We sample the input variables to get an input vector more general

samp=sample(1:p)
Sigma=Sigma_ord[samp,samp]

image((0:p)+0.5,(0:p)+0.5,z=sig(Sigma),col=cm.colors(100), zlim=c(0,1),
      ylim=c(p+0.5,0.5), main=expression(Sigma), 
      cex.main=3,ylab = "",xlab = "",axes=FALSE)
box()


Beta=Beta_ord[samp]
eta=shapleyLinearGaussian(Beta=Beta, Sigma=Sigma)
barplot(eta,main=expression(eta),cex.axis = 2,cex.main=3)




# 3) We generate the observations with these parameters

n=5*p #sample size


C=chol(Sigma)
X0=matrix(rnorm(p*n),ncol=p)
X=X0%*%C

S=var(X) #empirical covariance matrix
image((0:p)+0.5,(0:p)+0.5,z=sig(S),col=cm.colors(100), zlim=c(0,1),
      ylim=c(p+0.5,0.5), main=expression(S), 
      cex.main=3,ylab = "", xlab = "",axes=FALSE)
box()

beta0=rnorm(1)
Y=X%*%as.matrix(Beta)+beta0+0.2*rnorm(p)



# 4) We estimate the block-diagonal covariance matrix 
# and the Shapley effects using the observations
# We assume that we know that the groups are smaller than 15

Estim=shapleyBlockEstimationX(X,Y,delta=3/4, M=15, tol=10^(-6))

eta_hat=Estim$Shapley
S_B=Estim$S_B

image((0:p)+0.5,(0:p)+0.5,z=sig(S_B),col=cm.colors(100), zlim=c(0,1),
      ylim=c(p+0.5,0.5), main=expression(S[hat(B)]), 
      cex.main=3,ylab = "",xlab = "",axes=FALSE)
box()

barplot(eta_hat,main=expression(hat(eta)),cex.axis = 2,cex.main=3)


sum(abs(eta_hat-eta))