
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
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), …)
a function, or a model with a predict
method,
defining the model to analyze.
the first random sample.
the second random sample.
the confidence level for confidence intervals, or zero to avoid their computation if they are not needed.
a list of class "sobol"
storing the state of the
sensitivity study (parameters, data, estimates).
a vector of model responses.
a vector of character strings giving further
internal variables names to store in the output object x
.
y-coordinate plotting limits.
any other arguments for model
which are passed
unchanged each time it is called
soboltouati
returns a list of class "soboltouati"
,
containing all the input arguments detailed before, plus the following
components:
the matched call.
a data.frame
containing the design of experiments.
the response used
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
the estimations of the Sobol' first-order indices.
the estimations of the Sobol' total sensitivity indices.
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.
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.
sobol, sobol2002, sobolSalt, sobol2007, soboljansen, sobolmartinez
# NOT RUN {
# 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)
# }
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