sensitivity (version 1.28.0)

shapleyPermRand: Estimation of Shapley effects by random permutations of inputs (Agorithm of Song et al, 2016), in cases of independent or dependent inputs

Description

shapleyPermRand implements the Monte Carlo estimation of the Shapley effects (Owen, 2014) and their standard errors by randomly sampling permutations of inputs (Song et al., 2016). It also estimates full first order and independent total Sobol' indices (Mara et al., 2015), and their standard errors. The function also allows the estimations of all these sensitivity indices in case of dependent inputs. The total cost of this algorithm is \(Nv + m \times (d-1) \times No \times Ni\) model evaluations.

Usage

shapleyPermRand(model = NULL, Xall, Xset, d, Nv, m, No = 1, Ni = 3, colnames = NULL, ...)
# S3 method for shapleyPermRand
tell(x, y = NULL, return.var = NULL, ...)
# S3 method for shapleyPermRand
print(x, ...)
# S3 method for shapleyPermRand
plot(x, ylim = c(0, 1), ...)
# S3 method for shapleyPermRand
ggplot(x, ylim = c(0, 1), title = NULL, ...)

Value

shapleyPermRand returns a list of class "shapleyPermRand", containing all the input arguments detailed before, plus the following components:

call

the matched call.

X

a data.frame containing the design of experiments.

y

the response used.

E

the estimation of the ouput mean.

V

the estimation of the ouput variance.

Shapley

the estimations of the Shapley effects.

SobolS

the estimations of the full first-order Sobol' indices.

SobolT

the estimations of the independent total sensitivity Sobol' indices.

Users can ask more ouput variables with the argument return.var

(for example, the list of permutations perms).

Arguments

model

a function, or a model with a predict method, defining the model to analyze.

Xall

Xall(n) is a function to generate a n-sample of a d-dimensional input vector (following the required joint distribution).

Xset

Xset(n, Sj, Sjc, xjc) is a function to generate a n-sample of a d-dimensional input vector corresponding to the indices in Sj conditional on the input values xjc with the index set Sjc (following the required joint distribution).

d

number of inputs.

Nv

Monte Carlo sample size to estimate the output variance.

m

Number of randomly sampled permutations.

No

Outer Monte Carlo sample size to estimate the expectation of the conditional variance of the model output.

Ni

Inner Monte Carlo sample size to estimate the conditional variance of the model output.

colnames

Optional: A vector containing the names of the inputs.

x

a list of class "shapleyPermRand" storing the state of the sensitivity study (parameters, data, estimates).

y

a vector of model responses.

return.var

a vector of character strings giving further internal variables names to store in the output object x.

ylim

y-coordinate plotting limits.

title

a title of the plot with ggplot() function.

...

any other arguments for model which are passed unchanged each time it is called.

Author

Bertrand Iooss, Eunhye Song, Barry L. Nelson, Jeremy Staum

Details

This function requires R package "gtools".

The default values No = 1 and Ni = 3 are the optimal ones obtained by the theoretical analysis of Song et al., 2016.

The computations of the standard errors do not consider the samples to estimate expectation of conditional variances. They are only made regarding the random permutations and are based on the variance of the Monte carlo estimates divided by m. The confidence intervals at 95% correspond to +- 1.96 standard deviations.

References

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.

T. Mara, S. Tarantola, P. Annoni, 2015, Non-parametric methods for global sensitivity analysis of model output with dependent inputs, Environmental Modeling & Software 72, 173--183.

A.B. Owen, 2014, Sobol' indices and Shapley value, SIAM/ASA Journal of Uncertainty Quantification, 2, 245--251.

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.

E. Song, B.L. Nelson, and J. Staum, 2016, Shapley effects for global sensitivity analysis: Theory and computation, SIAM/ASA Journal of Uncertainty Quantification, 4, 1060--1083.

See Also

shapleyPermEx, shapleyLinearGaussian, shapleySubsetMc, shapleysobol_knn

Examples

# \donttest{

##################################
# Test case : the Ishigami function
# See Iooss and Prieur (2019)

library(gtools)

d <- 3
Xall <- function(n) matrix(runif(d*n,-pi,pi),nc=d)
Xset <- function(n, Sj, Sjc, xjc) matrix(runif(n*length(Sj),-pi,pi),nc=length(Sj))

x <- shapleyPermRand(model = ishigami.fun, Xall=Xall, Xset=Xset, d=d, Nv=1e4, m=1e4, No = 1, Ni = 3)
print(x)
plot(x)

library(ggplot2)
ggplot(x)

##################################
# Test case : Linear model (3 Gaussian inputs including 2 dependent)
# See Iooss and Prieur (2019)

library(ggplot2)
library(gtools)
library(mvtnorm) # Multivariate Gaussian variables
library(condMVNorm) # Conditional multivariate Gaussian variables

modlin <- function(X) apply(X,1,sum)

d <- 3
mu <- rep(0,d)
sig <- c(1,1,2)
ro <- 0.9
Cormat <- matrix(c(1,0,0,0,1,ro,0,ro,1),d,d)
Covmat <- ( sig %*% t(sig) ) * Cormat

Xall <- function(n) mvtnorm::rmvnorm(n,mu,Covmat)

Xset <- function(n, Sj, Sjc, xjc){
  if (is.null(Sjc)){
    if (length(Sj) == 1){ rnorm(n,mu[Sj],sqrt(Covmat[Sj,Sj]))
    } else{ mvtnorm::rmvnorm(n,mu[Sj],Covmat[Sj,Sj])}
  } else{ condMVNorm::rcmvnorm(n, mu, Covmat, dependent.ind=Sj, given.ind=Sjc, X.given=xjc)}}

m <- 1e3 # put m)1e4 for more precised results
x <- shapleyPermRand(model = modlin, Xall=Xall, Xset=Xset, d=d, Nv=1e3, m = m, No = 1, Ni = 3)
print(x)
ggplot(x)

# }