R: Support index functions: Measuring the effect of input...

plot.support {sensitivity}

R Documentation

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)