sobolEff | R Documentation |
sobolEff
implements the Monte Carlo estimation of the Sobol' sensitivity indices using the asymptotically efficient formulas in section 4.2.4.2 of Monod et al. (2006). Either all first-order indices or all total-effect indices are estimated at a cost of N \times (p+1)
model calls or all closed second-order indices are estimated at a cost of N \times p \choose 2)
model calls.
sobolEff(model = NULL, X1, X2, order=1, nboot = 0, conf = 0.95, ...)
## S3 method for class 'sobolEff'
tell(x, y = NULL, ...)
## S3 method for class 'sobolEff'
print(x, ...)
## S3 method for class 'sobolEff'
plot(x, ylim = c(0, 1), ...)
## S3 method for class 'sobolEff'
ggplot(data, mapping = aes(), ylim = c(0, 1), ..., environment
= parent.frame())
model |
a function, or a model with a |
X1 |
the first random sample. |
X2 |
the second random sample. |
order |
an integer specifying the indices to estimate: 0 for total effect indices,1 for first-order indices and 2 for closed second-order indices. |
nboot |
the number of bootstrap replicates, or zero to use asymptotic standard deviation estimates given in Janon et al. (2012). |
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 |
The estimator used by sobolEff is defined in Monod et al. (2006), Section 4.2.4.2 and studied under the name T_N in Janon et al. (2012). This estimator is good for large first-order indices.
sobolEff
returns a list of class "sobolEff"
, 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. |
Alexandre Janon, Laurent Gilquin
Monod, H., Naud, C., Makowski, D. (2006), Uncertainty and sensitivity analysis for crop models in Working with Dynamic Crop Models: Evaluation, Analysis, Parameterization, and Applications, Elsevier.
A. Janon, T. Klein, A. Lagnoux, M. Nodet, C. Prieur (2014), Asymptotic normality and efficiency of two Sobol index estimators, ESAIM: Probability and Statistics, 18:342-364.
sobol, sobol2002, sobolSalt, sobol2007, soboljansen, sobolmartinez,
sobolSmthSpl
# 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])
n <- 1000
X1 <- data.frame(matrix(runif(8 * n), nrow = n))
X2 <- data.frame(matrix(runif(8 * n), nrow = n))
# sensitivity analysis
x <- sobolEff(model = sobol.fun, X1 = X1, X2 = X2, nboot = 0)
print(x)
library(ggplot2)
ggplot(x)
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