sensitivity (version 1.28.0)

soboltouati: Monte Carlo Estimation of Sobol' Indices (formulas of Martinez (2011) and Touati (2016))

Description

soboltouati implements the Monte Carlo estimation of the Sobol' indices for both first-order and total indices using correlation coefficients-based formulas, at a total cost of \((p+2) \times n\) model evaluations. These are called the Martinez estimators. It also computes their confidence intervals based on asymptotic properties of empirical correlation coefficients.

Usage

soboltouati(model = NULL, X1, X2, conf = 0.95, ...)
# S3 method for soboltouati
tell(x, y = NULL, return.var = NULL, ...)
# S3 method for soboltouati
print(x, ...)
# S3 method for soboltouati
plot(x, ylim = c(0, 1), ...)
# S3 method for soboltouati
ggplot(x, ylim = c(0, 1), ...)

Value

soboltouati returns a list of class "soboltouati", 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

V

the estimations of normalized variances of the Conditional Expectations (VCE) with respect to each factor and also with respect to the complementary set of each factor ("all but \(X_i\)").

S

the estimations of the Sobol' first-order indices.

T

the estimations of the Sobol' total sensitivity indices.

Arguments

model

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

X1

the first random sample.

X2

the second random sample.

conf

the confidence level for confidence intervals, or zero to avoid their computation if they are not needed.

x

a list of class "sobol" 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.

...

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

Author

Taieb Touati, Khalid Boumhaout

Details

This estimator supports missing values (NA or NaN) which can occur during the simulation of the model on the design of experiments (due to code failure) even if Sobol' indices are no more rigorous variance-based sensitivity indices if missing values are present. In this case, a warning is displayed.

References

J-M. Martinez, 2011, Analyse de sensibilite globale par decomposition de la variance, Presentation in the meeting of GdR Ondes and GdR MASCOT-NUM, January, 13th, 2011, Institut Henri Poincare, Paris, France.

T. Touati, 2016, Confidence intervals for Sobol' indices. Proceedings of the SAMO 2016 Conference, Reunion Island, France, December 2016.

T. Touati, 2017, Intervalles de confiance pour les indices de Sobol, 49emes Journees de la SFdS, Avignon, France, Juin 2017.

See Also

sobol, sobol2002, sobolSalt, sobol2007, soboljansen, sobolmartinez

Examples

# Test case : the non-monotonic Sobol g-function

# The method of sobol requires 2 samples
# There are 8 factors, all following the uniform distribution
# on [0,1]

library(boot)
n <- 1000
X1 <- data.frame(matrix(runif(8 * n), nrow = n))
X2 <- data.frame(matrix(runif(8 * n), nrow = n))

# sensitivity analysis

x <- soboltouati(model = sobol.fun, X1, X2)
print(x)
plot(x)

library(ggplot2)
ggplot(x)