sensitivity (version 1.28.0)

sobolTIIlo: Liu and Owen Estimation of Total Interaction Indices

Description

sobolTIIlo implements the asymptotically efficient formula of Liu and Owen (2006) for the estimation of total interaction indices as described e.g. in Section 3.4 of Fruth et al. (2014). Total interaction indices (TII) are superset indices of pairs of variables, thus give the total influence of each second-order interaction. The total cost of the method is \((1+N+\choose(N,2)) \times n\) where \(N\) is the number of indices to estimate. Asymptotic confidence intervals are provided. Via plotFG (which uses functions of the package igraph), the TIIs can be visualized in a so-called FANOVA graph as described in section 2.2 of Muehlenstaedt et al. (2012).

Usage

sobolTIIlo(model = NULL, X1, X2, conf = 0.95, ...)
# S3 method for sobolTIIlo
tell(x, y = NULL, ...)
# S3 method for sobolTIIlo
print(x, ...)
# S3 method for sobolTIIlo
plot(x, ylim = NULL, ...)
# S3 method for sobolTIIlo
ggplot(x, ylim = NULL, ...)
# S3 method for sobolTIIlo
plotFG(x)

Value

sobolTIIlo returns a list of class "sobolTIIlo", containing all the input arguments detailed before, plus the following components:

call

the matched call.

X

a data.frame containing the design of experiments.

y

a vector of model responses.

V

the estimation of the overall variance.

tii.unscaled

the unscaled estimations of the TIIs.

tii.scaled

the scaled estimations of the TIIs together with asymptotic confidence intervals.

Arguments

model

a function, or a model with a predict method, defining the model to analyze.

X1

the first random sample.

X2

the second random sample.

conf

the confidence level for asymptotic confidence intervals, defaults to 0.95.

x

a list of class "sobolTIIlo" storing the state of the sensitivity study (parameters, data, estimates).

y

a vector of model responses.

...

any other arguments for model which are passed unchanged each time it is called.

ylim

optional, the y limits of the plot.

Author

Jana Fruth

References

R. Liu, A. B. Owen, 2006, Estimating mean dimensionality of analysis of variance decompositions, JASA, 101 (474), 712--721.

J. Fruth, O. Roustant, S. Kuhnt, 2014, Total interaction index: A variance-based sensitivity index for second-order interaction screening, J. Stat. Plan. Inference, 147, 212--223.

T. Muehlenstaedt, O. Roustant, L. Carraro, S. Kuhnt, 2012, Data-driven Kriging models based on FANOVA-decomposition, Stat. Comput., 22 (3), 723--738.

See Also

sobolTIIpf

Examples

# Test case : the Ishigami function

# The method requires 2 samples
n <- 1000
X1 <- data.frame(matrix(runif(3 * n, -pi, pi), nrow = n))
X2 <- data.frame(matrix(runif(3 * n, -pi, pi), nrow = n))

# sensitivity analysis (the true values of the scaled TIIs are 0, 0.244, 0)
x <- sobolTIIlo(model = ishigami.fun, X1 = X1, X2 = X2)
print(x)

# plot of tiis and FANOVA graph
plot(x)

library(ggplot2)
ggplot(x)

# \donttest{
library(igraph)
plotFG(x)
# }