sobolmara | R Documentation |
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 class 'sobolmara'
tell(x, y = NULL, return.var = NULL, ...)
## S3 method for class 'sobolmara'
print(x, ...)
## S3 method for class 'sobolmara'
plot(x, ylim = c(0, 1), ...)
## S3 method for class 'sobolmara'
plotMultOut(x, ylim = c(0, 1), ...)
## S3 method for class 'sobolmara'
ggplot(data, mapping = aes(), ylim = c(0, 1), ..., environment
= parent.frame())
model |
a function, or a model with a |
X1 |
the random sample. |
x |
a list of class |
data |
a list of class |
y |
a vector of model responses. |
return.var |
a vector of character strings giving further
internal variables names to store in the output object |
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 |
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.
sobolmara
returns a list of class "sobolmara"
, 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 Sobol' sensitivity indices. |
Bertrand Iooss
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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