sensiFdiv | R Documentation |
sensiFdiv
conducts a density-based sensitivity
analysis where the impact of an input variable is defined
in terms of dissimilarity between the original output density function
and the output density function when the input variable is fixed.
The dissimilarity between density functions is measured with Csiszar f-divergences.
Estimation is performed through kernel density estimation and
the function kde
of the package ks
.
sensiFdiv(model = NULL, X, fdiv = "TV", nboot = 0, conf = 0.95, ...)
## S3 method for class 'sensiFdiv'
tell(x, y = NULL, ...)
## S3 method for class 'sensiFdiv'
print(x, ...)
## S3 method for class 'sensiFdiv'
plot(x, ylim = c(0, 1), ...)
## S3 method for class 'sensiFdiv'
ggplot(data, mapping = aes(), ylim = c(0, 1), ..., environment
= parent.frame())
model |
a function, or a model with a |
X |
a matrix or |
fdiv |
a string or a list of strings specifying the Csiszar f-divergence to be used. Available choices are "TV" (Total-Variation), "KL" (Kullback-Leibler), "Hellinger" and "Chi2" (Neyman chi-squared). |
nboot |
the number of bootstrap replicates |
conf |
the confidence level for confidence intervals. |
x |
a list of class |
data |
a list of class |
y |
a vector of model responses. |
ylim |
y-coordinate plotting limits. |
mapping |
Default list of aesthetic mappings to use for plot. If not specified, must be supplied in each layer added to the plot. |
environment |
[Deprecated] Used prior to tidy evaluation. |
... |
any other arguments for |
Some of the Csiszar f-divergences produce sensitivity indices that have already been studied in the context of sensitivity analysis. In particular, "TV" leads to the importance measure proposed by Borgonovo (2007) (up to a constant), "KL" corresponds to the mutual information (Krzykacz-Hausmann 2001) and "Chi2" produces the squared-loss mutual information. See Da Veiga (2015) for details.
sensiFdiv
returns a list of class "sensiFdiv"
, containing all
the input arguments detailed before, plus the following components:
call |
the matched call. |
X |
a |
y |
a vector of model responses. |
S |
the estimations of the Csiszar f-divergence sensitivity indices. If several divergences have been selected, Sis a list where each element encompasses the estimations of the sensitivity indices for one of the divergence. |
Sebastien Da Veiga, Snecma
Borgonovo E. (2007), A new uncertainty importance measure, Reliability Engineering and System Safety 92(6), 771–784.
Da Veiga S. (2015), Global sensitivity analysis with dependence measures, Journal of Statistical Computation and Simulation, 85(7), 1283–1305.
Krzykacz-Hausmann B. (2001), Epistemic sensitivity analysis based on the concept of entropy, Proceedings of SAMO2001, 53–57.
kde, sensiHSIC
library(ks)
# Test case : the non-monotonic Sobol g-function
n <- 100
X <- data.frame(matrix(runif(8 * n), nrow = n))
# Density-based sensitivity analysis
# the next lines are put in comment because too long for CRAN tests
#x <- sensiFdiv(model = sobol.fun, X = X, fdiv = c("TV","KL"), nboot=30)
#print(x)
#library(ggplot2)
#ggplot(x)
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