
sobolmara
implements the Monte Carlo estimation of
the first-order Sobol' sensitivity indices using the formula of Mara and Joseph (2008), called the
Mara estimator.
This method allows the estimation of all first-order p indices at a cost of
2N model calls (the random sample size), then independently of p (the number of inputs).
sobolmara(model = NULL, X1, ...)
# S3 method for sobolmara
tell(x, y = NULL, return.var = NULL, ...)
# S3 method for sobolmara
print(x, ...)
# S3 method for sobolmara
plot(x, ylim = c(0, 1), ...)
# S3 method for sobolmara
plotMultOut(x, ylim = c(0, 1), ...)
# S3 method for sobolmara
ggplot(data, mapping = aes(), ylim = c(0, 1), ..., environment
= parent.frame())
sobolmara
returns a list of class "sobolmara"
, containing all
the input arguments detailed before, plus the following components:
the matched call.
a data.frame
containing the design of experiments.
a vector of model responses.
the estimations of the Sobol' sensitivity indices.
a function, or a model with a predict
method,
defining the model to analyze.
the random sample.
a list of class "sobolmara"
storing the state of the
sensitivity study (parameters, data, estimates).
a list of class "sobolmara"
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.
Default list of aesthetic mappings to use for plot. If not specified, must be supplied in each layer added to the plot.
[Deprecated] Used prior to tidy evaluation.
any other arguments for model
which are passed
unchanged each time it is called.
Bertrand Iooss
The estimator used by sobolmara is based on rearragement of a unique matrix via random permutations (see Mara and Joseph, 2008). Bootstrap confidence intervals are not available.
Mara, T. and Joseph, O.R. (2008), Comparison of some efficient methods to evaluate the main effect of computer model factors, Journal of Statistical Computation and Simulation, 78:167--178
sobolroalhs, sobol, sobolMultOut
# Test case : the non-monotonic Sobol g-function
# The method of sobolmara requires 1 sample
# (there are 8 factors, all following the uniform distribution on [0,1])
n <- 1000
X1 <- data.frame(matrix(runif(8 * n), nrow = n))
# sensitivity analysis
x <- sobolmara(model = sobol.fun, X1 = X1)
print(x)
plot(x)
library(ggplot2)
ggplot(x)
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