plot.support: Support index functions: Measuring the effect of input...

In sensitivity: Global Sensitivity Analysis of Model Outputs and Importance Measures

Support index functions: Measuring the effect of input variables over their support

Description

Methods to plot the normalized support index functions (Fruth et al., 2016).

Usage

## S3 method for class 'support'
plot(x, i = 1:ncol(x$X),
        xprob = FALSE, p = NULL, p.arg = NULL,
        ylim = NULL, col = 1:3, lty = 1:3, lwd = c(2,2,1), cex = 1, ...)
## S3 method for class 'support'
scatterplot(x, i = 1:ncol(x$X), 
               xprob = FALSE, p = NULL, p.arg = NULL, 
               cex = 1, cex.lab = 1, ...)

Arguments

x	an object of class support.
i	an optional vector of integers indicating the subset of input variables X_i for plotting. Default is the entire set of input variables.
xprob	an optional boolean indicating whether the inputs should be plotted in probability scale.
p	,
p.arg	list of probability names and parameters for the input distribution.
ylim	,
col	,
lty	,
lwd	,
cex	,
cex.lab	usual graphical parameters.
...	additional graphical parameters to be passed to scatterplot method (ggMarginal function).

Details

If xprob = TRUE, the input variable X_i is plotted in probability scale according to the informations provided in the arguments p, p.arg: The x-axis is thus F(x), where F is the cdf of X_i. If these ones are not provided, the empirical distribution is used for rescaling: The x-axis is thus Fn(x), where Fn is the empirical cdf of X_i.

Legend details:

zeta*T : normalized total support index function

zeta* : normalized 1st-order support index function

nu* : normalized DGSM

Notice that the sum of (normalized) DGSM (nu*) over all input variables is equal to 1. Furthermore, the expectation of the total support index function (zeta*T) is equal to the (normalized) DGSM (nu*).

Author(s)

O. Roustant

See Also

Estimation of support index functions: support

sensitivity documentation built on Sept. 11, 2024, 9:09 p.m.