sensitivity (version 1.30.1)

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

Author

O. Roustant

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*).

See Also

Estimation of support index functions: support