sobolowen | R Documentation |
sobolowen
implements the Monte Carlo estimation of
the Sobol' indices for both first-order and total indices at the same
time (alltogether 2p
indices). Take as input 3 independent matrices.
These are called the Owen estimators.
sobolowen(model = NULL, X1, X2, X3, nboot = 0, conf = 0.95, varest = 2, ...)
## S3 method for class 'sobolowen'
tell(x, y = NULL, return.var = NULL, varest = 2, ...)
## S3 method for class 'sobolowen'
print(x, ...)
## S3 method for class 'sobolowen'
plot(x, ylim = c(0, 1), ...)
## S3 method for class 'sobolowen'
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. |
X3 |
the third random sample. |
nboot |
the number of bootstrap replicates. |
conf |
the confidence level for bootstrap confidence intervals. |
varest |
choice for the variance estimator for the denominator of the Sobol' indices. varest=1 is for a classical estimator. varest=2 (default) is for the estimator proposed in Janon et al. (2012). |
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 |
sobolowen
returns a list of class "sobolowen"
, containing all
the input arguments detailed before, plus the following components:
call |
the matched call. |
X |
a |
y |
the response used |
V |
the estimations of Variances of the Conditional Expectations
(VCE) with respect to each factor and also with respect to the
complementary set of each factor ("all but |
S |
the estimations of the Sobol' first-order indices. |
T |
the estimations of the Sobol' total sensitivity indices. |
Users can ask more ouput variables with the argument
return.var
(for example, bootstrap outputs V.boot
,
S.boot
and T.boot
).
Taieb Touati and Bernardo Ramos
A. Owen, 2013, Better estimations of small Sobol' sensitivity indices, ACM Transactions on Modeling and Computer Simulations (TOMACS), 23(2), 11.
Janon, A., Klein T., Lagnoux A., Nodet M., Prieur C. (2012), Asymptotic normality and efficiency of two Sobol index estimators. Accepted in ESAIM: Probability and Statistics.
sobol, sobol2002, sobolSalt, sobol2007, soboljansen, sobolmartinez, sobolEff
# Test case : the non-monotonic Sobol g-function
# The method of sobolowen requires 3 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))
X3 <- data.frame(matrix(runif(8 * n), nrow = n))
# sensitivity analysis
x <- sobolowen(model = sobol.fun, X1, X2, X3, nboot = 10) # put nboot=100
print(x)
plot(x)
library(ggplot2)
ggplot(x)
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